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This means that version 2.1.0 is an official release that is safe for corporate and individual users to use, which has been voted on by the Apache SeaTunnel(Incubating) community and the Apache Incubator.","s":"2.1.0 Released! Apache SeaTunnel(Incubating) First Apache Release Refactors Kernel and Supports Flink Overall","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","p":102109},{"i":102127,"t":"More than a month after the release of Apache SeaTunnel(Incubating) 2.1.2, we have been collecting user and developer feedback to bring you version 2.1.3. The new version introduces the Assert Sink connector, which is an inurgent need in the community, and two Transforms, NullRate and Nulltf. Some usability problems in the previous version have also been fixed, improving stability and efficiency.","s":"SeaTunnel 2.1.3 released! 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If you still not installation","s":"Deployment","u":"/docs/2.2.0-beta/deployment","p":104895},{"i":104908,"t":"Why I should install computing engine like Spark or Flink","s":"FAQ","u":"/docs/2.2.0-beta/faq","p":104908},{"i":104957,"t":"Slack","s":"About Seatunnel","u":"/docs/2.2.0-beta/intro/about","p":104957},{"i":104973,"t":"SeaTunnel was formerly named WaterDrop","s":"History of Seatunnel","u":"/docs/2.2.0-beta/intro/history","p":104973},{"i":104979,"t":"SeaTunnel will do its best to solve the problems that may be encountered in the synchronization of massive data:","s":"Why SeaTunnel","u":"/docs/2.2.0-beta/intro/why","p":104979},{"i":104981,"t":"WIP","s":"Set Up with Docker","u":"/docs/2.2.0-beta/start-v2/docker","p":104981},{"i":104983,"t":"This section provides a quick guide to using SeaTunnel with Kubernetes.","s":"Set Up with Kubernetes","u":"/docs/2.2.0-beta/start-v2/kubernetes","p":104983},{"i":104996,"t":"Let's take an application that randomly generates data in memory, processes it through SQL, and finally outputs it to the console as an example.","s":"Set Up with Locally","u":"/docs/2.2.0-beta/start-v2/local","p":104996},{"i":105012,"t":"WIP","s":"Set Up with Docker","u":"/docs/2.2.0-beta/start/docker","p":105012},{"i":105014,"t":"This section provides a quick guide to using SeaTunnel with Kubernetes.","s":"Set Up with Kubernetes","u":"/docs/2.2.0-beta/start/kubernetes","p":105014},{"i":105027,"t":"Let's take an application that randomly generates data in memory, processes it through SQL, and finally outputs it to the console as an example.","s":"Set Up with Locally","u":"/docs/2.2.0-beta/start/local","p":105027},{"i":105043,"t":"Json transform plugin","s":"Json","u":"/docs/2.2.0-beta/transform/json","p":105043},{"i":105068,"t":"Common parameters of transform plugins","s":"Common Options","u":"/docs/2.2.0-beta/transform/common-options","p":105068},{"i":105080,"t":"NULL rate transform plugin","s":"NullRate","u":"/docs/2.2.0-beta/transform/nullRate","p":105080},{"i":105098,"t":"NULL default value transform plugin","s":"Nulltf","u":"/docs/2.2.0-beta/transform/nulltf","p":105098},{"i":105108,"t":"Replace transform plugin","s":"Replace","u":"/docs/2.2.0-beta/transform/replace","p":105108},{"i":105130,"t":"Split transform plugin","s":"Split","u":"/docs/2.2.0-beta/transform/split","p":105130},{"i":105150,"t":"Sql transform plugin","s":"Sql","u":"/docs/2.2.0-beta/transform/sql","p":105150},{"i":105167,"t":"UDF transform plugin","s":"udf","u":"/docs/2.2.0-beta/transform/udf","p":105167},{"i":105179,"t":"UUID transform plugin","s":"UUID","u":"/docs/2.2.0-beta/transform/uuid","p":105179},{"i":105195,"t":"List all datasource supported Apache SeaTunnel Web for now.","s":"Supported DataSources","u":"/seatunnel_web/1.0.0/category/datasource","p":105195},{"i":105197,"t":"List all sink nodes supported Apache SeaTunnel Web for now.","s":"Supported SinkNodes","u":"/seatunnel_web/1.0.0/category/sink_node","p":105197},{"i":105199,"t":"SeaTunnel Web is a web project that provides visual management of jobs, scheduling, running and monitoring capabilities. It is developed based on the SeaTunnel Connector API and the SeaTunnel Zeta Engine. It is a web project that can be deployed independently. It is also a sub-project of SeaTunnel. For more information, please refer to SeaTunnel Web.","s":"About SeaTunnel Web","u":"/seatunnel_web/1.0.0/about","p":105199},{"i":105201,"t":"List all source nodes supported Apache SeaTunnel Web for now.","s":"Supported SourceNodes","u":"/seatunnel_web/1.0.0/category/source_node","p":105201},{"i":105205,"t":"Apache SeaTunnel offers two methods for submitting tasks:","s":"Deployment of Apache SeaTunnel Web","u":"/seatunnel_web/1.0.0/deploy","p":105205},{"i":105213,"t":"SeaTunnel Web is a web project that provides visual management of jobs, scheduling, running and monitoring capabilities. It is developed based on the SeaTunnel Connector API and the SeaTunnel Zeta Engine. It is a web project that can be deployed independently. It is also a sub-project of SeaTunnel. For more information, please refer to SeaTunnel Web","s":"About SeaTunnel Web","u":"/seatunnel_web/next/about","p":105213},{"i":105215,"t":"Step 1: Click Create","s":"JDBC-MySQL DataSource","u":"/seatunnel_web/next/datasources/jdbc-mysql","p":105215},{"i":105217,"t":"1 Preparing the Apache SeaTunnel environment","s":"Deploy","u":"/seatunnel_web/next/deploy","p":105217},{"i":105225,"t":"In order to continuously improve the quality of Apache SeaTunnel code, we have compiled a code review guide.","s":"Code Review Guidance","u":"/community/contribution_guide/code-review","p":105225},{"i":105227,"t":"SeaTunnel Project Management Committee (PMC) is responsible for assessing the contributions of candidates.","s":"Become Apache SeaTunnel Committer","u":"/community/contribution_guide/committer","p":105227},{"i":105229,"t":"This guide documents the best way to make various types of contribution to Apache SeaTunnel,","s":"Participate in Contributing","u":"/community/contribution_guide/contribute","p":105229},{"i":105231,"t":"Release managers shepherd a release from an initial community consensus to getting the compiled code package to final distribution, and may be involved in publicizing the release to the project's community and the ASF in general.","s":"Become Apache Release Manager","u":"/community/contribution_guide/release","p":105231},{"i":105233,"t":"What is a STIP?","s":"SeaTunnel Improvement Proposal (STIP)","u":"/community/contribution_guide/STIP","p":105233},{"i":105235,"t":"- dev@seatunnel.apache.org (developer holds other general discussions)","s":"Mailing lists","u":"/community/contribution_guide/subscribe","p":105235},{"i":105237,"t":"Good documentation is critical for any type of software. Any contribution that can improve the Seatunnel documentation is welcome.","s":"Documentation Notice","u":"/community/submit_guide/document","p":105237},{"i":105239,"t":"As we know that Seatunnel is an open-source undergoing project at The Apache Software Foundation (ASF), which means that you have to follow the Apache way to become the Seatunnel contributor. Furthermore, Apache has extremely strict rules according to the License. This passage will explain the ASF license and how to avoid License risks at the early stage when you participate in Seatunnel.","s":"License Notice","u":"/community/submit_guide/license","p":105239},{"i":105241,"t":"* First from the remote repository  fork a copy of the code into your own repository","s":"Submit 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Incubator, and after nearly four months of endeavor by the community contributors, we passed the first Apache version control in one go and released it on March 18, 2022. This means that version 2.1.0 is an official release that is safe for corporate and individual users to use, which has been voted on by the Apache SeaTunnel(Incubating) community and the Apache Incubator. Note: A software license is a legal instrument governing the use or redistribution of software. A typical software license grants the licensee, typically an end-user, permission to use one or more copies of the software in ways where such a use would otherwise potentially constitute copyright infringement of the software owner's exclusive rights under copyright. Effectively, a software license is a contract between the software developer and the user that guarantees the user will not be sued within the scope of the license. Before and after entering the incubator, we spent a lot of time sorting through the external dependencies of the entire project to ensure compliance. It is important to note that the choice of License for open source software does not necessarily mean that the project itself is compliant. While the stringent version control process of ASF ensures compliance and legal distribution of the software license maximumly.","s":"2.1.0 Released! Apache SeaTunnel(Incubating) First Apache Release Refactors Kernel and Supports Flink Overall","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"","p":102109},{"i":102112,"t":"We bring the following key featuresto this release: The kernel of the microkernel plug-in architecture is overall optimized, which is mainly in Java. And a lot of improvements are made to command line parameter parsing, plug-in loading, etc. At the same time, the users (or contributors) can choose the language to develop plug-in extensions, which greatly reduces the development threshold of plug-ins. Overall support for Flink, while the users are free to choose the underlying engine. This version also brings a large number of Flink plug-ins and welcomes anyone to contribute more. Provide local development fast startup environment support (example), allow contributors or users quickly and smoothly start without changing any code to facilitate rapid local development debugging. This is certainly exciting news for contributors or users who need to customize their plugins. In fact, we've had a large number of contributors use this approach to quickly test the plugin in our pre-release testing. With Docker container installation provided, users can deploy and install Apache SeaTunnel(Incubating) via Docker extremely fast, and we will iterate around Docker & K8s in the future, any interesting proposal on this is welcomed.","s":"Release Note","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#release-note","p":102109},{"i":102115,"t":"Use JCommander to do command line parameter parsing, making developers focus on the logic itself. Flink is upgraded from 1.9 to 1.13.5, keeping compatibility with older versions and preparing for subsequent CDC. Support for Doris, Hudi, Phoenix, Druid, and other Connector plugins, and you can find complete plugin support here plugins-supported-by-seatunnel. Local development extremely fast starts environment support. It can be achieved by using the example module without modifying any code, which is convenient for local debugging. Support for installing and trying out Apache SeaTunnel(Incubating) via Docker containers. SQL component supports SET statements and configuration variables. Config module refactoring to facilitate understanding for the contributors while ensuring code compliance (License) of the project. Project structure realigned to fit the new Roadmap. CI&CD support, code quality automation control (more plans will be carried out to support CI&CD development).","s":"[Features]","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#features","p":102109},{"i":102117,"t":"Thanks to the following contributors who participated in this version release (GitHub IDs, in no particular order). Al-assad, BenJFan, CalvinKirs, JNSimba, JiangTChen, Rianico, TyrantLucifer, Yves-yuan, ZhangchengHu0923, agendazhang, an-shi-chi-fan, asdf2014, bigdataf, chaozwn, choucmei, dailidong, dongzl, felix-thinkingdata, fengyuceNv, garyelephant, kalencaya, kezhenxu94, legendtkl, leo65535, liujinhui1994, mans2singh, marklightning, mosence, nielifeng, ououtt, ruanwenjun, simon824, totalo, wntp, wolfboys, wuchunfu, xbkaishui, xtr1993, yx91490, zhangbutao, zhaomin1423, zhongjiajie, zhuangchong, zixi0825. Also sincere gratitude to our Mentors: Zhenxu Ke, Willem Jiang, William Guo, LiDong Dai, Ted Liu, Kevin, JB for their help!","s":"Acknowledgments","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#acknowledgments","p":102109},{"i":102119,"t":"CDC support. Support for the monitoring system. UI system support. More Connector and efficient Sink support, such as ClickHouse support will be available in the next release soon. The follow-up Features are decided by the community consensus, and we sincerely appeal to more participation in the community construction. We need your attention and contributions:)","s":"Planning for the next few releases:","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#planning-for-the-next-few-releases","p":102109},{"i":102122,"t":"Since entering the Apache incubator, the contributor group has grown from 13 to 55 and continues to grow, with the average weekly community commits remaining at 20+. Three contributors from different companies (Lei Xie, HuaJie Wang, Chunfu Wu) have been invited to become Committers on account of their contributions to the community. We held two Meetups, where instructors from Bilibili, OPPO, Vipshop, and other companies shared their large-scale production practices based on SeaTunnel in their companies (we will hold one meetup monthly in the future, and welcome SeaTunnel users or contributors to come and share their stories about SeaTunnel).","s":"Recent Development","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#recent-development","p":102109},{"i":102124,"t":"Note: Only registered users are included. Registered users of Apache SeaTunnel(Incubating) are shown below. If you are also using Apache SeaTunnel(Incubating), too, welcome to register on Who is using SeaTunne!","s":"Users of Apache SeaTunnel(Incubating)","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#users-of-apache-seatunnelincubating","p":102109},{"i":102126,"t":"LiFeng Nie, PPMC of Apache SeaTunnel(Incubating), commented on the first Apache version release. From the first day entering Apache Incubating, we have been working hard to learn the Apache Way and various Apache policies. Although the first release took a lot of time (mainly for compliance), we think it was well worth it, and that's one of the reasons we chose to enter Apache. We need to give our users peace of mind, and Apache is certainly the best choice, with its almost demanding license control that allows users to avoid compliance issues as much as possible and ensure that the software is circulating reasonably and legally. In addition, its practice of the Apache Way, such as public service mission, pragmatism, community over code, openness and consensus decision-making, and meritocracy, can drive the Apache SeaTunnel(Incubating) community to become more open, transparent, and diverse.","s":"PPMC's Word","u":"/blog/2.1.0-Released-Apache-SeaTunnel-Incubating-First-Apache-Release-Refactors-Kernel-and-Supports-Flink-Overall","h":"#ppmcs-word","p":102109},{"i":102128,"t":"More than a month after the release of Apache SeaTunnel(Incubating) 2.1.2, we have been collecting user and developer feedback to bring you version 2.1.3. The new version introduces the Assert Sink connector, which is an inurgent need in the community, and two Transforms, NullRate and Nulltf. Some usability problems in the previous version have also been fixed, improving stability and efficiency. This article will introduce the details of the update of Apache SeaTunnel(Incubating) version 2.1.3. Release Note: https://github.com/apache/incubator-seatunnel/blob/2.1.3/release-note.md Download address: https://seatunnel.apache.org/download","s":"SeaTunnel 2.1.3 released! Introducing in Assert Sink connector and NullRate, Nulltf Transform","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"","p":102127},{"i":102131,"t":"Assert Sink connector is introduced in SeaTunnel version 2.1.3to verify data correctness. Special thanks to Lhyundeadsoul for his contribution.","s":"Introduces Assert Sink connector","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#introduces-assert-sink-connector","p":102127},{"i":102133,"t":"In addition, the 2.1.3 version also adds two Transforms, NullRate and Nulltf, which are used to detect data quality and convert null values ​​in the data to generate default values. These two Transforms can effectively improve the availability of data and reduce the frequency of abnormal situations. Special thanks to wsyhj and Interest1-wyt for their contributions. At present, SeaTunnel has supported 9 types of Transforms including Common Options, Json, NullRate, Nulltf, Replace, Split, SQL, UDF, and UUID, and the community is welcome to contribute more Transform types. For details of Transform, please refer to the official documentation: https://seatunnel.apache.org/docs/2.1.3/category/transform","s":"Add two Transforms","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#add-two-transforms","p":102127},{"i":102135,"t":"At the same time, SeaTunnel 2.1.3 version brings Rsync data transfer mode support to ClickhouseFile connector, users can now choose SCP and Rsync data transfer modes. Thanks to Emor-nj for contributing to this feature.","s":"ClickhouseFile connector supports Rsync data transfer method now","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#clickhousefile-connector-supports-rsync-data-transfer-method-now","p":102127},{"i":102137,"t":"Flink Fake data supports BigInteger type https://github.com/apache/incubator-seatunnel/pull/2118 Add Flink Assert Sink connector https://github.com/apache/incubator-seatunnel/pull/2022 Spark ClickhouseFile connector supports Rsync data file transfer method https://github.com/apache/incubator-seatunnel/pull/2074 Add Flink Assert Sink e2e module https://github.com/apache/incubator-seatunnel/pull/2036 Add NullRate Transform for detecting data quality https://github.com/apache/incubator-seatunnel/pull/1978 Add Nulltf Transform for setting defaults https://github.com/apache/incubator-seatunnel/pull/1958","s":"Specific feature updates:","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#specific-feature-updates","p":102127},{"i":102139,"t":"Refactored Spark TiDB-related parameter information Refactor the code to remove redundant code warning information Optimize connector jar package loading logic Add Plugin Discovery module Add documentation for some modules Upgrade common-collection from version 4 to 4.4 Upgrade common-codec version to 1.13","s":"Optimization","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#optimization","p":102127},{"i":102141,"t":"In addition, in response to the feedback from users of version 2.1.2, we also fixed some usability issues, such as the inability to use the same components of Source and Sink, and further improved the stability. Fixed the problem of Hudi Source loading twice Fix the problem that the field TwoPhaseCommit is not recognized after Doris 0.15 Fixed abnormal data output when accessing Hive using Spark JDBC Fix JDBC data loss when partition_column (partition mode) is set Fix KafkaTableStream schema JSON parsing error Fix Shell script getting APP_DIR path error Updated Flink RunMode enumeration to get correct help messages for run modes Fix the same source and sink registered connector cache error Fix command line parameter -t( — check) conflict with Flink deployment target parameter Fix Jackson type conversion error problem Fix the problem of failure to run scripts in paths other than SeaTunnel_Home","s":"Bug Fix","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#bug-fix","p":102127},{"i":102143,"t":"Thanks to all the contributors (GitHub ID, in no particular order,), it is your efforts that fuel the launch of this version, and we look forward to more contributions to the Apache SeaTunnel(Incubating) community! leo65535, CalvinKirs, mans2singh, ashulin, wanghuan2054, lhyundeadsoul, tobezhou33, Hisoka-X, ic4y, wsyhj, Emor-nj, gleiyu, smallhibiscus, Bingz2, kezhenxu94, youyangkou, immustard, Interest1-wyt, superzhang0929, gaaraG, runwenjun","s":"Acknowledgment","u":"/blog/2022/09/12/SeaTunnel-2.1.3-released","h":"#acknowledgment","p":102127},{"i":102145,"t":"As SeaTunnel gets popular around the world, it is attracting more and more contributors from overseas to join the open-source career. Among them, a big data platform engineer at Kakao enterprise corp., Namgung Chan has recently contributed the Neo4j Sink Connector for the SeaTunnel. We have a talk with him to know why SeaTunnel is attractive to him, and how he thinks SeaTunnel should gain popularity in the South Korean market.","s":"Talk With Overseas contributors | Why do I contribute to SeaTunnel?","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"","p":102144},{"i":102147,"t":"Namgung Chan, South Korea, Big Data Platform Engineer at Kakao enterprise corp. Blog (written in Korean): https://getchan.github.io/ GitHub ID: https://github.com/getChan LinkedIn : https://www.linkedin.com/in/namgung-chan-6a06441b6/","s":"Personal Profile","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"#personal-profile","p":102144},{"i":102149,"t":"He writes the Neo4j Sink Connector code for the new SeaTunnel Connector API.","s":"Contributions to the community","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"#contributions-to-the-community","p":102144},{"i":102151,"t":"It’s the first time Namgung Chan to engage in open source. He wants to learn technical skills by contributing, at the same time experience the open-source culture. For him, an open source project which is written by java lang, and made for data engineering, has many issues of ‘help wanted’ or ‘good first issue’ is quite suitable. Then he found SeaTunnel on the Apache Software Foundation project webpage.","s":"How to know SeaTunnel for the first time?","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"#how-to-know-seatunnel-for-the-first-time","p":102144},{"i":102153,"t":"Though it was his first open source experience, he felt it was comfortable and interesting to go to the community. He also felt very welcome, because there are many ‘good first issue, and ‘volunteer wanted’ tagged issues and will get a quick response of code review. With gaining knowledge of Neo4j, he grows much more confident in open source contribution.","s":"The first impression of SeaTunnel Community","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"#the-first-impression-of-seatunnel-community","p":102144},{"i":102155,"t":"Before knowing about SeaTunnel, Namgung Chan used Spring Cloud Data Flow for data integration. While after experiencing SeaTunnel, he thinks the latter is more lightweight than SCDF, because in SCDF, every source, processor, and sink component are individual applications, but SeaTunnel is not. Though hasn’t used SeaTunnel in his working environment yet, Namgung Chan said he would like to use it positively when he is in need, especially for data integration for various data storage.","s":"Research and comparison","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"#research-and-comparison","p":102144},{"i":102157,"t":"The most exciting new features or optimizations for Namgung Chan are: Data Integration for various data storage. Strict data validation. monitoring extension Low computing resource exactly-once data processing In the future, Namgung Chan plans to keep contributing from light issues to heavy ones, and we hope he will have a good time here!","s":"Expectations for SeaTunnel","u":"/blog/2022/09/14/Talk-With-Overseas-contributors-Why-do-I-contribute-to-SeaTunnel","h":"#expectations-for-seatunnel","p":102144},{"i":102159,"t":"At the Apache SeaTunnel&Apache Doris Joint Meetup held on July 24, Liu Li — senior engineer of WhaleOps and contributor to Apache SeaTunnel — mentioned an easy way to develop a connector in SeaTunnel quickly. We’ll divide it into four key parts: ● The definition of a Connector ● How to access data sources and targets ● Code to demonstrate how to implement a Connector ● Sources and targets that are currently supported","s":"Code Demo for SeaTunnel Connector Development Process","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"","p":102158},{"i":102161,"t":"The Connector consists of Source and Sink and is a concrete implementation of accessing data sources. Source: The Source is responsible for reading data from sources such as MySQLSource, DorisSource, HDFSSource, TXTSource, and more. Sink: The Sink is responsible for writing read data to the target, including MySQLSink, ClickHouseSink, HudiSink, and more. Data transfer, and more specifically, data synchronization is completed through the cooperation between the Source and Sink. Of course, different sources and sinks can cooperate with each other. For example, you can use MySQL Source, and Doris Sink to synchronize data from MySQL to Doris, or even read data from MySQL Source and write to HDFS Sink.","s":"Definition of a Connector","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#definition-of-a-connector","p":102158},{"i":102164,"t":"Firstly, let’s take a look at how we can access the Source. To elaborate, let’s dive in and check out how we can implement a source and the core interfaces that need to be implemented to access the Source. The simplest Source is a single concurrent Source. However, if a source does not support state storage and other advanced functions, what interfaces should we implement in these simple single concurrent sources? Firstly, we need to use getBoundedness in the Source to identify whether the Source supports real-time or offline, or both. createReader creates a Reader whose main function is to read the specific implementation of data. A single concurrent source is really simple as we only need to implement one method, pollNext, through which the read data is sent. If concurrent reading is required, what additional interfaces should we implement? For concurrent reading, we’ll introduce a new member, called the Enumerator. We implement createEnumerator in Source, and the main function of this member is to create an Enumerator to split the task into segments and then send it to the Reader. For example, a task can be divided into 4 splits. If it is concurrent twice, it’ll correspond to two Readers. Two of the four splits will be sent to Reader1, and the other two will be sent to Reader2. If the number of concurrencies is more — for example, let’s say there are four concurrences, then you have to create four Readers. You have to use the corresponding four splits for concurrent reading for improved efficiency. A corresponding interface in the Enumerator called the addSplitsBack sends the splits to the corresponding Reader. Through this method, the ID of the Reader can be specified. Similarly, there is an interface called the addSplits in the Reader to receive the splits sent by the Enumerator for data reading. In a nutshell, for concurrent reading, we need an Enumerator to implement task splitting and send the splits to the reader. Also, the reader receives the splits and uses them for reading. In addition, if we need to support resuming and exactly-once semantics, what additional interfaces should we implement? If the goal is to resume the transfer from a breakpoint, we must save the state and restore it. For this, we need to implement a restoreEnumerator in Source. The restoreEnumerator method is used to restore an Enumerator through the state and restore the split. Correspondingly, we need to implement a snapshotState in this enumerator, which is used to save the state of the current Enumerator and perform failure recovery during checkpoints. At the same time, the Reader will also have a snapshotState method to save the split state of the Reader. In the event of a failed restart, the Enumerator can be restored through the saved state. After the split is restored, reading can be continued from the place of failure, including fetching and incoming data. The exact one-time semantics actually requires the source to support data replays, such as Kafka, Pulsar, and others. In addition, the sink must be submitted in two phases, i.e., the precise one-time semantics can be achieved with the cooperation of these two sources and sinks.","s":"How to access Source","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#how-to-access-source","p":102158},{"i":102166,"t":"Now, let’s take a look at how to connect to the Sink. What interfaces does the Sink need to implement? Truth be told, Sink is relatively simple. For concurrent sinks, when state storage and two-phase commit are not supported, the Sink is simple. To elaborate, the Sink does not distinguish between stream synchronization and batch synchronization as the Sink — and the entire SeaTunnel API system — supports Unified Stream and Batch Processing. Firstly, we need to implement createWriter. A Writer is used for data writing. You need to implement a writer method in Writer through which data is written to the target library. As shown in the figure above, if two concurrencies are set, the engine will call the createWriter method twice in order to generate two Writers. The engine will feed data to these two writers, which will write the data to the target through the write method. For a more advanced setup, for example, we need to support two-phase commit and state storage. Here, what additional interfaces should we implement? First, let’s introduce a new member, the Committer, whose main role is for the second-stage commit. Since Sink is stored in state, it is necessary to restore Writer through the state. Hence, restoreWriter should be implemented. Also, since we have introduced a new member, the Committer, we should also implement a createCommitter in the sink. We can then use this method to create a Committer for the second-stage commit or rollback. In this case, what additional interfaces does Writer need to implement? Since it is a two-phase commit, the first-phase commit is done in the Writer through the implementation of the prepareCommit method — which is mainly used for the first-phase commit. In addition, state storage and failure recovery is also supported, meaning we need snapshotState to take snapshots at checkpoints. This saves the state for failure recovery scenarios. The Committer is the core here. It is mainly used for rollback and commit operations in the second phase. For the corresponding process, we need to write data to the database. Here, the engine will trigger the first stage commit during the checkpoint, and then the Writer needs to prepare a commit. At the same time, it will return commitInfo to the engine, and the engine will judge whether the first stage commits of all writers are successful. If they are indeed successful, the engine will use the commit method to actually commit. For MySQL, the first-stage commit just saves a transaction ID and sends it to the commit. The engine determines whether the transaction ID is committed or rolled back.","s":"How to access Sink","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#how-to-access-sink","p":102158},{"i":102168,"t":"We’ve taken a look at Source and Sink; let’s now look at how to access the data source and implement your own Connector. Firstly, we need to build a development environment for the Connector.","s":"How to implement the Connector","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#how-to-implement-the-connector","p":102158},{"i":102170,"t":"Java 1.8\\11, Maven, IntelliJ IDEA Windows users need to additionally download gitbash (https://gitforwindows.org/) Once you have these, you can download the SeaTunnel source code by cloning the git. Download SeaTunnel source code 1, git clone https://github.com/apache/incubator-seatunnel.git2, cd incubator-seatunnel","s":"The necessary environment","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#the-necessary-environment","p":102158},{"i":102172,"t":"We then open it again through the IDE, and see the directory structure as shown in the figure: The directory is divided into several parts: Connector — v2 Specific implementation of the new Connector(Connector — v2) will be placed in this module. connector-v2-dist The translation layer of the new connector translates into specific engine implementation — instead of implementing under corresponding engines such as Spark, Flink, and ST-Engine. ST-Engine is the “important, big project” the community is striving to implement. This project is worth the wait. examples This package provides a single-machine local operation method, which is convenient for debugging while implementing the Connector. e2e The e2e package is for e2e testing of the Connector. Next, let’s check out how a Connector can be created (based on the new Connector). Here is the step-by-step process: Create a new module in the seatunnel-connectors-v2 directory and name it this way: connector-{connector name}. The pom file can refer to the pom file of the existing connector and add the current child model to the parent model’s pom file. Create two new packages corresponding to the packages of Source and Sink, respectively: a. org.apache.seatunnel.connectors.seatunnel.{connector name}.source b. org.apache.seatunnel.connectors.seatunnel.{connector name}.sink Take this mysocket example shown in the figure: To do some implementation, develop the connector. During implementation, you can use the example module for local debugging if you need to debug. That said, this module mainly provides the local running environment of Flink and Spark. As you can see in the image, there are numerous examples under the “Example” module — including seatunnel-flink-connector-v2-example. So how do you use them? Let’s take an example. The debugging steps on Flink are as follows (these actions are under the seatunnel-flink-connector-v2-example module: Add connector dependencies in pom.xml Add the task configuration file under resources/examples Configure the file in the SeaTunnelApiExample main method Run the main method","s":"SeaTunnel Engineering Structure","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#seatunnel-engineering-structure","p":102158},{"i":102174,"t":"This code demonstration is based on DingTalk. Here’s a reference（ 19:35s–37:10s）: https://weixin.qq.com/sph/A1ri7B","s":"Code Demo","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#code-demo","p":102158},{"i":102176,"t":"As of July 14, contributions and statistics for the completed connectors are welcome. You are more than welcome to try them out, and raise issues in our community if you find bugs. The Connector shared below have already been claimed and developed: Also, we have Connectors in the roadmap — the connectors we want to support in the near future. To foster the process, the SeaTunnel Community initiated SeaTunnel Connector Access Incentive Plan, you are more than welcome to contribute to the project. SeaTunnel Connector Access Incentive Plan: https://github.com/apache/incubator-seatunnel/issues/1946 You can claim tasks that haven’t been marked in the comment area, and take a spree home! Here is part of the connectors that need to be accessed as soon as possible: In fact, the implementations of Connectors like Feishu, DingTalk, and Facebook messenger are quite simple as the connectors do not need to carry a large amount of data (just a simple Source and Sink). This is in sharp contrast to Hive and other databases that need to consider transaction consistency or concurrency issues. We welcome everyone to make contributions and join our Apache SeaTunnel family!","s":"New Connectors supported at this stage","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#new-connectors-supported-at-this-stage","p":102158},{"i":102178,"t":"SeaTunnel (formerly Waterdrop) is an easy-to-use, ultra-high-performance distributed data integration platform that supports the real-time synchronization of massive amounts of data and can synchronize hundreds of billions of data per day stably and efficiently.","s":"About SeaTunnel","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#about-seatunnel","p":102158},{"i":102180,"t":"SeaTunnel does everything it can to solve the problems you may encounter in synchronizing massive amounts of data. Data loss and duplication Task buildup and latency Low throughput Long application-to-production cycle time Lack of application status monitoring","s":"Why do we need SeaTunnel?","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#why-do-we-need-seatunnel","p":102158},{"i":102182,"t":"Massive data synchronization Massive data integration ETL of large volumes of data Massive data aggregation Multi-source data processing","s":"SeaTunnel Usage Scenarios","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#seatunnel-usage-scenarios","p":102158},{"i":102184,"t":"Rich components High scalability Easy to use Mature and stable","s":"Features of SeaTunnel","u":"/blog/2022/09/19/Code-Demo-for-SeaTunnel-Connector-Development-Process","h":"#features-of-seatunnel","p":102158},{"i":102186,"t":"cover SeaTunnel Zeta has been officially released with the joint efforts of the community. After comparing the performance of SeaTunnel with DataX and Airbyte, we also compared the performance of SeaTunnel with the popular data synchronization tool AWS GLUE. The results showed that SeaTunnel batch syncs MySQL data to MySQL 420% faster than GLUE. To ensure the accuracy of the test, we took on the test under the same test environment: under the same resource conditions, we tested SeaTunnel and AWS GLUE to synchronize data from MySQL to MySQL in batches and compared the time required for the two tools. We created a table in MySQL containing 31 fields, with the primary key selected as an incrementing ID, and all other fields generated randomly, without setting any indexes. The table creation statement is as follows: create table test.type_source_table ( id int auto_increment primary key, f_binary binary(64) null, f_blob blob null, f_long_varbinary mediumblob null, f_longblob longblob null, f_tinyblob tinyblob null, f_varbinary varbinary(100) null, f_smallint smallint null, f_smallint_unsigned smallint unsigned null, f_mediumint mediumint null, f_mediumint_unsigned mediumint unsigned null, f_int int null, f_int_unsigned int unsigned null, f_integer int null, f_integer_unsigned int unsigned null, f_bigint bigint null, f_bigint_unsigned bigint unsigned null, f_numeric decimal null, f_decimal decimal null, f_float float null, f_double double null, f_double_precision double null, f_longtext longtext null, f_mediumtext mediumtext null, f_text text null, f_tinytext tinytext null, f_varchar varchar(100) null, f_date date null, f_datetime datetime null, f_time time null, f_timestamp timestamp null );","s":"Performance Test Report: SeaTunnel Synchronizes data in batches 420% Faster than GLUE!","u":"/blog/2023/3/29/Performance_Test_Report_SeaTunnel_Synchronizes_Data_in_Batches_420_Percent_Faster_than_GLUE.md","h":"","p":102185},{"i":102188,"t":"In SeaTunnel, we split the data according to the ID field and process it in multiple sub-tasks. Here is the configuration file for SeaTunnel: env { job.mode = \"BATCH\" checkpoint.interval = 300000 } source { Jdbc { url = \"jdbc:mysql://XXX:3306/test\" driver = \"com.mysql.cj.jdbc.Driver\" user = \"root\" password = \"password\" connection_check_timeout_sec = 100 query = \"select id, f_binary, f_blob, f_long_varbinary, f_longblob, f_tinyblob, f_varbinary, f_smallint, f_smallint_unsigned, f_mediumint, f_mediumint_unsigned, f_int, f_int_unsigned, f_integer, f_integer_unsigned, f_bigint, f_bigint_unsigned, f_numeric, f_decimal, f_float, f_double, f_double_precision, f_longtext, f_mediumtext, f_text, f_tinytext, f_varchar, f_date, f_datetime, f_time, f_timestamp from test\" partition_column = \"id\" partition_num = 40 parallelism = 2 } } sink { Jdbc { url = \"jdbc:mysql://XXX:3306/test\" driver = \"com.mysql.cj.jdbc.Driver\" user = \"root\" password = \"password\" query = \"insert into test_1 values (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\" } } Under fixed JVM memory of 4G and parallelism of 2, SeaTunnel completed the synchronization in 1965 seconds. Based on this conclusion, we tested the speed of GLUE under the same memory and concurrency settings.","s":"SeaTunnel Task Configuration","u":"/blog/2023/3/29/Performance_Test_Report_SeaTunnel_Synchronizes_Data_in_Batches_420_Percent_Faster_than_GLUE.md","h":"","p":102185},{"i":102190,"t":"We created a MySQL-to-MySQL job as follows: Configuration source connect with the target: Job configuration: Adjust the memory: job parameters configuration — conf spark.yarn.executor.memory=4g Under this configuration, GLUE took 8191 seconds to complete the synchronization.","s":"GLUE Task Configuration","u":"/blog/2023/3/29/Performance_Test_Report_SeaTunnel_Synchronizes_Data_in_Batches_420_Percent_Faster_than_GLUE.md","h":"","p":102185},{"i":102192,"t":"After comparing the best configurations, we conducted a more in-depth comparison for different memory sizes. The following chart shows the comparison results obtained through repeated testing under the same environment. The unit is seconds. Note: This comparison is based on SeaTunnel: commit ID f57b897, and we welcome to download and test it!","s":"Conclusion","u":"/blog/2023/3/29/Performance_Test_Report_SeaTunnel_Synchronizes_Data_in_Batches_420_Percent_Faster_than_GLUE.md","h":"","p":102185},{"i":102194,"t":"Written by Wang Hailin, Apache SeaTunnel PPMC","s":"SeaTunnel now supports CDC (Capture Change Data) writing by ClickHouse Connector!","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"","p":102193},{"i":102196,"t":"Currently, SeaTunnel supports database change data capture (CDC https://github.com/apache/incubator-seatunnel/issues/3175), to transfer data changes to downstream systems in real time. SeaTunnel categorizes the captured data changes into the following 4 types: INSERT: Data insertion UPDATE_BEFORE: Historical value before data change UPDATE_AFTER: New value after data change DELETE: Data deletion To handle the above data change operations, the Sink Connector needs to support writing behavior. This article will introduce how the ClickHouse Sink Connector supports writing these CDC types of data changes. For CDC scenarios, the primary key is a necessary condition, so first, it needs to support the general requirements of INSERT, UPDATE, DELETE, etc. based on the primary key and ensure that the writing order is consistent with the CDC event order. In addition, considering the complexity of the data source in practice, it also needs to support UPSERT writing. Finally, according to the characteristics of ClickHouse itself, corresponding optimizations need to be made, such as UPDATE and DELETE being heavyweight operations in ClickHouse, which should be optimized based on the corresponding table engine's characteristics.","s":"Preface","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#preface","p":102193},{"i":102198,"t":"The current ClickHouse Sink Connector is based on the JDBC Driver implementation, and a group of JDBC executors can be designed to encapsulate the processing of different types of data, making it convenient to switch or combine implementations based on actual scenarios and encapsulate implementation details. JdbcBatchStatementExecutor is the top-level interface of the executor. public interface JdbcBatchStatementExecutor extends AutoCloseable { void prepareStatements(Connection connection) throws SQLException; void addToBatch(SeaTunnelRow record) throws SQLException; void executeBatch() throws SQLException; void closeStatements() throws SQLException; @Override default void close() throws SQLException { closeStatements(); } } JdbcBatchStatementExecutor has the following implementation classes: SimpleBatchStatementExecutor // implements simple SQL Batch execution logic InsertOrUpdateBatchStatementExecutor // implements INSERT, UPDATE update, also supports UPSERT mode ReduceBufferedBatchStatementExecutor // memory accumulation, when refreshing to the database, the data change type (INSERT, UPDATE, DELETE) is distributed to the specific execution executor","s":"Overall design","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#overall-design","p":102193},{"i":102200,"t":"Currently, in CDC processing, the primary key is a necessary condition. If the Sink Connector is not specified in the primary key column configuration, it uses the append-only mode to write, calling SimpleBatchStatementExecutor directly.","s":"Handling of cases where the primary key is not specified","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#handling-of-cases-where-the-primary-key-is-not-specified","p":102193},{"i":102202,"t":"We divide the execution logic of data processing as follows: different data types enter the corresponding Executor and are finally transformed into their respective SQL statements for execution, and Jdbc Batch batching is used during this process. CDC Event / \\ / \\ / \\ / \\ DELETE Executor INSERT OR UPDATE Executor / \\ / \\ / \\ / \\ INSERT Executor UPDATE Executor","s":"CDC data process","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#cdc-data-process","p":102193},{"i":102204,"t":"CDC events are ordered, and writing must be processed in the order in which the events occur, otherwise data inconsistencies may occur. In the previous logic, data of different types were distributed to their respective Executors and Jdbc Batch was used for batch submission to improve write performance, but categorizing batching can result in the order of submissions not being consistent with the CDC event order. We can add an execution barrier marker, when the processed data row is of the same type as the previous data row, it can be batched, if not, the previous batch is first flushed to the database, ensuring that the data write order is strictly consistent with the CDC event order. Example for InsertOrUpdateBatchStatementExecutor public class InsertOrUpdateBatchStatementExecutor implements JdbcBatchStatementExecutor { @Override public void addToBatch(SeaTunnelRow record) throws SQLException { boolean currentChangeFlag = hasInsert(record); if (currentChangeFlag) { if (preChangeFlag != null && !preChangeFlag) { updateStatement.executeBatch(); updateStatement.clearBatch(); } valueRowConverter.toExternal(record, insertStatement); insertStatement.addBatch(); } else { if (preChangeFlag != null && preChangeFlag) { insertStatement.executeBatch(); insertStatement.clearBatch(); } valueRowConverter.toExternal(record, updateStatement); updateStatement.addBatch(); } preChangeFlag = currentChangeFlag; submitted = false; } @Override public void executeBatch() throws SQLException { if (preChangeFlag != null) { if (preChangeFlag) { insertStatement.executeBatch(); insertStatement.clearBatch(); } else { updateStatement.executeBatch(); updateStatement.clearBatch(); } } submitted = true; } } Of course, this will significantly slow down the batch processing, so we use ReduceBufferedBatchStatementExecutorto add a memory buffer layer, and when executing batch submissions, we distribute submissions to the database. Example for ReduceBufferedBatchStatementExecutor public class ReduceBufferedBatchStatementExecutor implements JdbcBatchStatementExecutor { private final LinkedHashMap<SeaTunnelRow, Pair<Boolean, SeaTunnelRow>> buffer = new LinkedHashMap<>(); @Override public void addToBatch(SeaTunnelRow record) throws SQLException { buffer.put(record, ...); } @Override public void executeBatch() throws SQLException { Boolean preChangeFlag = null; Set<Map.Entry<SeaTunnelRow, Pair<Boolean, SeaTunnelRow>>> entrySet = buffer.entrySet(); for (Map.Entry<SeaTunnelRow, Pair<Boolean, SeaTunnelRow>> entry : entrySet) { Boolean currentChangeFlag = entry.getValue().getKey(); if (currentChangeFlag) { if (preChangeFlag != null && !preChangeFlag) { deleteExecutor.executeBatch(); } insertOrUpdateExecutor.addToBatch(entry.getValue().getValue()); } else { if (preChangeFlag != null && preChangeFlag) { insertOrUpdateExecutor.executeBatch(); } deleteExecutor.addToBatch(entry.getKey()); } preChangeFlag = currentChangeFlag; } if (preChangeFlag != null) { if (preChangeFlag) { insertOrUpdateExecutor.executeBatch(); } else { deleteExecutor.executeBatch(); } } buffer.clear(); } }","s":"Maintaining the Order of CDC Data","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#maintaining-the-order-of-cdc-data","p":102193},{"i":102206,"t":"In InsertOrUpdateBatchStatementExecutor, you can configure to turn on UPSERT, when processing INSERT or UPDATE data types, it will first use the primary key to query the data row to see if it already exists and then decide to use INSERT or UPDATE SQL for writing. Note: This configuration is optional and will slow down the write speed, only opens when certain special scenarios are required. Example for InsertOrUpdateBatchStatementExecutor public class InsertOrUpdateBatchStatementExecutor implements JdbcBatchStatementExecutor { @Override public void addToBatch(SeaTunnelRow record) throws SQLException { boolean currentChangeFlag = hasInsert(record); ... } private boolean hasInsert(SeaTunnelRow record) throws SQLException { if (upsertMode()) { return !exist(keyExtractor.apply(record)); } switch (record.getRowKind()) { case INSERT: return true; case UPDATE_AFTER: return false; default: throw new UnsupportedOperationException(); } } private boolean exist(SeaTunnelRow pk) throws SQLException { keyRowConverter.toExternal(pk, existStatement); try (ResultSet resultSet = existStatement.executeQuery()) { return resultSet.next(); } } }","s":"Implementing a General UPSERT Write","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#implementing-a-general-upsert-write","p":102193},{"i":102208,"t":"The ReplacingMergeTree table engine can configure an ORDER BY field, and when executing the INSERT INTO statement, it covers the records with the same ORDER BY field. We can also utilize this feature to implement UPSERT. When the user writes to the ReplacingMergeTree table engine and the table's ORDER BY field is the same as the primary key field configured in the Sink Connector, both INSERT/UPDATE_AFTER data types are processed as INSERT to implement UPSERT.","s":"Optimizing UPSERT for ReplacingMergeTree Engine","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#optimizing-upsert-for-replacingmergetree-engine","p":102193},{"i":102210,"t":"DELETE and UPDATE are heavyweight operations in ClickHouse, but there is an experimental lightweight deletion (https://clickhouse.com/docs/en/sql-reference/statements/delete) for MergeTree engine, which performs better than the heavyweight deletion. We allow the user to configure the lightweight deletion. When the user writes to the MergeTree table engine and enables the lightweight deletion, we treat both DELETE/UPDATE_BEFORE data types as lightweight deletions, and treat both INSERT/UPDATE_AFTER data types as INSERTs, avoiding the UPDATE operation and using the lightweight deletion.","s":"Optimizing Updates for the MergeTree Engine","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#optimizing-updates-for-the-mergetree-engine","p":102193},{"i":102212,"t":"https://github.com/apache/incubator-seatunnel/pull/3653 Contribution to improving the related functions is welcomed, if you have any questions, please raise an issue on SeaTunnel GitHub (https://www.github.com/apache/incubator-seatunnel), and we will reply as soon as possible.","s":"Related PR","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#related-pr","p":102193},{"i":102214,"t":"https://clickhouse.com/docs/en/sql-reference/statements/delete https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/replacingmergetree","s":"Reference","u":"/blog/2023/02/09/SeaTunnel_Now_Supports_CDC_Writing_by_ClickHouse_Connector.md","h":"#reference","p":102193},{"i":102216,"t":"SeaTunnel version 2.3.1 was released recently. This is a high-profile release with many important function updates and optimizations. At the level of programming user experience, the new version improves the stability of SeaTunnel Zeta and CI/CD; at the level of connectors, the new version implements 7+ new connectors and fixes existing commonly used connectors bugs, and improved security. The community refactored multiple underlying base classes and added an important feature, AI Compatible. With the optimized API, users can use ChatGPT 4.0 to quickly build the SaaS Connector they need. Major Feature update","s":"SeaTunnel 2.3.1 is released! The refactored AI Compatible feature allows ChatGPT to automatically generate Connector code","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102219,"t":"The first version of the data integration engine-SeaTunnel Zeta is introduced in the SeaTunnel 2.3.0 release and has received feedback from numerous community users. In SeaTunnel version 2.3.1, we have fixed all the bugs reported by users, optimized the use of memory and threads, and greatly improved the stability of Zeta. In version 2.3.1, the community also added several new Zeta features, including a dedicated JVM parameter configuration file, client output of job monitoring information, Rest API for Zeta cluster information and job information, etc. At the checkpoint level, version 2.3.1 Zeta supports using OSS as checkpoint storage. It also supports savepoint running jobs and resuming jobs from savepoints. In addition, version 2.3.1 also adds a set of Zeta’s Rest API, which can be used to obtain the list of jobs running on Zeta, the status information of jobs, and the monitoring indicators of Zeta cluster nodes. For specific usage methods, please refer to https:/ /seatunnel.apache.org/docs/seatunnel-engine/rest-api/","s":"01 SeaTunnel Zeta","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"#01-seatunnel-zeta","p":102215},{"i":102221,"t":"In SeaTunnel 2.3.1, the HTTP interface and related APIs are reconstructed, and the SaaS Connector-related API and Connector construction process are simplified according to the existing xGPT level capabilities so that ChatGPT 4.0 can directly generate SaaS Connectors and quickly generate various SaaS Connector interfaces. Under normal circumstances, the results obtained by this method are 95% similar to the code written by open-source contributors (see appendix). Of course, because ChatGPT4.0 will be updated in October 2021, it is necessary to provide some latest vectorized documents for the latest SaaS interface adaptation to have the latest interface adaptation. However, this refactored API and code framework allows users to generate Connectors more quickly and contribute to the open-source community, making the SeaTunnel interface more powerful.","s":"02 AI Compatible","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"#02-ai-compatible","p":102215},{"i":102224,"t":"While fixing the bugs of known connectors and optimizing the connectors, the community has added 7 new connectors including SAP HANA, Persistiq, TDEngine, SelectDB Cloud, Hbase, FieldMapper Transform, and SimpleSQL Transform.","s":"01 7+ new connectors","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"#01-7-new-connectors","p":102215},{"i":102226,"t":"Since the previous SQL Transform connector was defined based on Flink SQL and Spark SQL, SQL Transform cannot adapt to the execution of multiple engines, so we removed the SQL Transform function in version 2.3.0. In version 2.3.1, we reimplemented SQL Transform. SQL Transform is an API that does not depend on a task-specific execution engine and can perfectly run on three different engines: Flink/Spark/Zeta. Special thanks to contributor Ma Chengyuan (GitHub ID: rewerma) for leading and contributing this important Feature. For the functions already supported by SQL Transform, please refer to https://seatunnel.apache.org/docs/2.3.1/transform-v2/sql-functions","s":"02 Reimplement SQL Transform","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"#02-reimplement-sql-transform","p":102215},{"i":102228,"t":"At the CDC connector level, the community has newly added a SQL Server CDC connector, and made a lot of optimizations to MySQL CDC, improving the stability of MySQL CDC.","s":"03 New SQL Server CDC","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"#03-new-sql-server-cdc","p":102215},{"i":102230,"t":"In addition, version 2.3.1 also added the function of the CDC connector to output debezium-json format. Users can use MySQL CDC to read binlog and output data in debezium-json format to Kafka, so that users can create new synchronization tasks to read The data in debezium-json format in Kafka is synchronized to the target data source, or you can directly write other programs to read the data in debezium-json format in Kafka to perform some indicator calculations.","s":"04 Added CDC connector to output debezium-json format function","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"#04-added-cdc-connector-to-output-debezium-json-format-function","p":102215},{"i":102232,"t":"Before version 2.3.1, users need to configure the database username, password, and other information in plain text in the config file, which may cause some security problems. In version 2.3.1, we added the configuration file encryption function, and users can fill in the encrypted database username, password, and other information in the config file. When the job is running, SeaTunnel will decrypt the content in the config file based on the default encryption and decryption algorithm. At the same time, the encryption function provides SPI, by which users can customize the parameter list of encryption and decryption and the algorithm of encryption and decryption based on their own needs. For how to use this function, please refer to https://seatunnel.apache.org/docs/2.3.1/connector-v2/Config-Encryption-Decryption","s":"Safety","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102234,"t":"SeaTunnel version 2.3.1 supports Spark version 3.3, as well as Flink 1.14.6, Flink 1.15, Flink 1.16, and other versions, basically covering the mainstream versions of Spark and Flink.","s":"Third-party engine support","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102236,"t":"The new version introduces an SPI for job configuration. Previously, only hocon json configuration files were supported. Now SPI is opened to the users to customize the format of job configuration files to meet different business system integration requirements.","s":"Client","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102238,"t":"SeaTunnel 2.1.3 version has made many important optimizations, including changes in core components, connector components, CI/CD, Zeta(ST-Engine), and E2E components, involving updating new functions, improving existing functions, and optimizing tests and deployment processes. Some notable changes include adding parallelism and column projection interfaces in Core API, introducing MySQL-CDC source factory in Connector-V2 and supporting only-once semantics for JDBC source connectors, improving CI/CD process and stability for E2E In Zeta (ST-Engine), the logic of restarting the job when all nodes are down is added, and the timeout period for writing data is configurable. For a detailed list, see the Release Note [Improve] section.","s":"Optimization","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102240,"t":"In addition, the new version also has a series of updates to the documentation, including adding transform v2 documentation and some hints, as well as improving the documentation of various connectors. See the Release Note [Docs] section for details. Document address: https://seatunnel.apache.org/versions/","s":"Document","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102242,"t":"https://github.com/apache/incubator-seatunnel/blob/2.3.1/release-note.md Project address: https://seatunnel.apache.org/ Download address: https://seatunnel.apache.org/download","s":"Release Note","u":"/blog/2023/3/31/SeaTunnel_2_3_1_Released_Refactored_AI_Compatible_Feature_Allows_ChatGPT_Automatic_Get","h":"","p":102215},{"i":102246,"t":"During the recent live event of the SeaTunnel Connector Access Plan, Beluga open source engineer Wang Hailin shared the “SeaTunnel Connector Access Plan and Development Guide to Avoiding Pit,” and taught everyone how to develop a connector from scratch, including the whole process — from preparation to testing, and final PR.","s":"SeaTunnel Connector Acess Plan","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#seatunnel-connector-acess-plan","p":102244},{"i":102248,"t":"Wang Hailin Wailin Hailin is an open-source enthusiast, SkyWalking Committer, DolphinScheduler, and SeaTunnel contributor. His current work focuses on performance monitoring, data processing, and more. He likes to study related technical implementations and participate in community exchanges and contributions.","s":"Speaker","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#speaker","p":102244},{"i":102250,"t":"About the connector access incentive program Preparation before claiming/developing connector Small things in development Considerations for writing E2E Tests Preparations to submit a PR","s":"This presentation is divided into 5 parts:","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#this-presentation-is-divided-into-5-parts","p":102244},{"i":102252,"t":"Firstly, let me introduce the SeaTunnel Connector Access Incentive Program, and the steps to develop a connector from start to finish (even for novices). This includes the whole process of preparation for development, testing, and final PR. The SeaTunnel community released a new connector API not long ago, which supports running on various engines, including Flink, Spark, and more. This eliminates the need for repeated development of the old version. After the new API is released, the old connector needs to be migrated, or the new connector should be supported. In order to motivate the community to actively participate in the SeaTunnel Connector Access work and help build SeaTunnel into a more efficient data integration platform, the SeaTunnel community-initiated activities, sponsored by Beluga Open Source. The activities have three modes: simple, medium, and hard for the task of accessing the connector. The threshold is low. You can see which tasks need to be claimed on the activity issue list, as well as segmentation based on difficulty and priority. You can choose the activity you are comfortable with. You can start contributing based on the difficulty level. The ecological construction of SeaTunnel can become more complete and advanced only with the help of your contributions. You are welcome to participate actively. In order to express our gratitude, our event has set up a link where points can be exchanged for physical prizes. The more points you get, the more prized you can win! Presently, we’ve seen many small partners participate in the event and submit their connectors. It’s not too late to join as there is still a significant period of time before the event ends. Based on the difficulty of the activity, the deadline may be relaxed or extended.","s":"1. About the Connector Access Incentive Plan","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#1-about-the-connector-access-incentive-plan","p":102244},{"i":102254,"t":"So, how do you get involved with this amazing activity? By first getting to know the basics of a connector.","s":"2. Preparations Before Claiming/Developing Connectors","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#2-preparations-before-claimingdeveloping-connectors","p":102244},{"i":102256,"t":"A connector is composed of Source and SInk (Source + Sink). In the above figure, the connectors are connected to various data sources at the upper and lower layers. The source is responsible for reading data from external data sources, while the sink is responsible for writing data to external sources. There is also an abstraction layer between the source and the sink. Through this abstraction later, the data types of various data sources can be uniformly converted into the data format of SeaTunnelRow. This allows users to arbitrarily assemble various sources and sinks, so as to realize the integration of heterogeneous data sources, and data synchronization between multiple data sources.","s":"01. What is a connector?","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#01-what-is-a-connector","p":102244},{"i":102258,"t":"After understanding the basic concepts, the next step is to claim the connector. GitHub link: https://github.com/apache/incubator-seatunnel/issues/1946 You can use the above-mentioned GitHub link to see our plans for connecting to the connector. You can make any additions at any time. First, find a connector that has not been claimed. To avoid conflicts, search the entire issue to see if anyone has submitted a PR. After claiming the connector, we suggest that you create an issue of the corresponding feature, synchronize the problems you encountered in the development, and discuss the design of your solution. If you encounter any problems and need help, you can describe them in the issue, and the community can take it up together. Participate in the discussions to help solve the problem. This is also added to the record of the function implementation process, which makes it easy to refer to when maintaining and modifying in the future.","s":"02. How to claim a connector","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#02-how-to-claim-a-connector","p":102244},{"i":102260,"t":"After claiming the connector, it’s time to prepare the development environment. First, fork the SeaTunnel project to the local development environment and compile it. Here’s the compilation reference documentation: https://github.com/apache/incubator-seatunnel/blob/dev/docs/en/contribution/setup.md Run the testcase in the documentation after the compilation is successful. You might encounter some issues/problems during the first contact compilation process, such as the following compilation errors: The solution to the above exceptions: rm {your_maven_dir}/repository/org/apache/seatunnel ./mvnw clean Recompile it","s":"03. Compile the project","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#03-compile-the-project","p":102244},{"i":102262,"t":"The success of project compilation means that the development environment is ready. Next, let’s take a look at the project code structure and API interface structure of the connector. Engineering Code structure​ After the project is compiled, there are three parts related to the connector. The first part is the code implementation and dependency management of the new connector module. seatunnel-connectors-v2 stores the connector submodule seatunnel-connectors-v2-dist manages connectors-v2 maven dependencies The second part is the example. When testing locally, you can build a corresponding case on the example to test the connector. seatunnel-flink-connector-v2-example example running on Flink seatunnel-spark-connector-v2-example example running on Spark The third part is the E2E-testcase: adding targeted test cases on the respective running engines of Spark or Flink, and verifying the functional logic of the connector through automated testing. seatunnel-flink-connector-v2-e2e testcase running on Flink seatunnel-spark-connector-v2-e2e testcase running on Spark Code structure (interfaces, base classes) The public interfaces and base classes used in the development are fully described in our readme. For example, API function usage scenarios. Here’s the link: https://github.com/apache/incubator-seatunnel/blob/dev/seatunnel-connectors-v2/README.en.md","s":"04. Understand Connector related code structure","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#04-understand-connector-related-code-structure","p":102244},{"i":102264,"t":"After going through the above steps, don’t rush to start the work. Instead, first, check out how others do it. We strongly recommend you check out the connector novice development tutorial shared on the community's official account: [SeaTunnel Connector Minimalist Development Process] [New API Connector Development Analysis] [The way of decoupling Apache SeaTunnel (Incubating) and the computing engine — what we’ve done to reconstruct the API] In addition, you can refer to the merged Connector code to see the scope of changes, the public interfaces and dependencies used, and the test cases.","s":"05. See how other people develop connectors","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#05-see-how-other-people-develop-connectors","p":102244},{"i":102266,"t":"Next, you have to officially enter the connector development process. What problems may be encountered during the development process? The connector is divided into the source and sink ends — you can choose either one or both.","s":"3. Small Issues/Tasks During Development","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#3-small-issuestasks-during-development","p":102244},{"i":102268,"t":"The first thing to pay attention to when developing a source is to determine the reading mode of the source: is it streaming or batch? Is support still required? Use the Source#getBoundedness interface to mark the modes supported by the source. For example, Kafka naturally supports streaming reading, but it can also support batch mode reading by obtaining lastOffset in the source. Another question to be aware of: does the source require concurrent reads? If it is single concurrency, after the source is started, a reader will be created to read the data from the data source. If you want to achieve multi-concurrency, you need to implement an enumerator interface through which data blocks are allocated to readers, and the readers each read their allocated data blocks. For example, the Kafka source uses partition sharding, and the jdbc source uses fields for range query sharding. It should be noted here that if it is a concurrent reading method, the stability of the data block distribution rules must be ensured. This is because currently, the connector has a corresponding enumerator on each shard in actual operation, and it is necessary to ensure that the enumerator has data in each shard. Thirdly, does the source need to support resumable transfer/state restoration? If you want to support this, you need to implement: Source#restoreEnumerator: restore state Enumerator#snapshotState: storage shard allocation Reader#snapshotState: stores the read position","s":"01. Source-related development","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#01-source-related-development","p":102244},{"i":102270,"t":"If the sink is a common sink implementation, use Sink#createWriter to write our data according to the concurrency of the source. If you need to support failure recovery, you need to implement: Sink#restoreWriter: restore state Writer#snapshotState: snapshot state If you want to support a two-phase commit, you need to implement the following interfaces: Sink#createCommitter Writer#prepareCommit: pre-commit Committer#commit: abort Phase 2 commit","s":"02. Sink-related development","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#02-sink-related-development","p":102244},{"i":102272,"t":"Next, let’s take a look at some of the general problems, especially when the first contribution is made with different styles for each environment, there are often various problems. Therefore, it is recommended that you import tools/checkstyle/checkStyle.xml from the project during development, and use a unified coding format. Whether it is a source or a sink, it will involve defining the data format. The community is pushing for a unified data format definition. To define Schema, please refer to PR: https://github.com/apache/incubator-seatunnel/pull/2392 To define the Format, please refer to PR: https://github.com/apache/incubator-seatunnel/pull/2435 If you feel that the compilation speed is slow, you can temporarily annotate the old version of the connector-related module in order to speed up both development and debugging.","s":"03. Connector related","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#03-connector-related","p":102244},{"i":102274,"t":"When you encounter problems during development and need help, you can: Describe the problem in your Issue and call active contributors Discuss on mailing lists and Slack Communicate through the WeChat group (if you have not joined, please follow the SeaTunnel official account to join the group, and add a small assistant WeChat seatunnel1) There may be a community docking group for docking third-party components (allowing you to do more with less)","s":"04. How to seek help","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#04-how-to-seek-help","p":102244},{"i":102276,"t":"E2E testing is very important. It is often called the gatekeeper of connector quality. This is because, if the connector you wrote is not tested, it could be difficult for the community to judge whether there are problems with the implementation of the static code. Therefore, E2E testing is not only functional verification but also a process of checking data logic, which can reduce the pressure on the community to review code and ensure basic functional correctness. In E2E testing, these are some of the problems that may be encountered:","s":"4. Notes on Writing E2E Tests","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#4-notes-on-writing-e2e-tests","p":102244},{"i":102278,"t":"Because the E2E network deployment structure has the following characteristics: External components that Spark, Flink, and e2e-test case depend on (for example, MySQL), use the container networkAliases(host) as the access address e2e-test case on both sides of Spark and Flink may run in parallel under the same host External components that e2e-test case depends on, need to map ports to hosts for e2e-test case to access Therefore, E2E has to pay attention to: The external components e2e-test case depends on the ports mapped to the external networkAliases, and so cannot be the same in the test cases on both sides of Spark and Flink e2e-test case uses localhost, the above-mapped port, to access external components e2e’s configuration file uses networkAliases(host), the external components that depend on port access in the container Here’s the E2E Testcase reference PR: https://github.com/apache/incubator-seatunnel/pull/2429","s":"01. E2E Failed — Test Case Network Address Conflict","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#01-e2e-failed--test-case-network-address-conflict","p":102244},{"i":102280,"t":"Spark uses the parent first-class loader by default, which may conflict with the package referenced by the connector. For this, the userClassPathFirst classloader can be configured in the Connector environment. However, the current packaging structure of SeaTunnel will cause userClassPathFirst to not work properly, so we created an issue, https://github.com/apache/incubator-seatunnel/pull/2474, to track this issue. Everyone is welcome to contribute solutions. Currently, this can only be resolved by replacing conflicting packages in the spark jars directory with the documentation.","s":"02. E2E failure — Spark jar package conflict","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#02-e2e-failure--spark-jar-package-conflict","p":102244},{"i":102282,"t":"Both the old and new versions of Connector are dependent on the E2E project and cause conflicts. PR https://github.com/apache/incubator-seatunnel/pull/2414 has resolved this issue. Version conflict between Connector-v2: Mainly occurs during E2E, because the E2E project depends on all Connectors We may plan to provide a separate test project for each Connector (or version) in the future","s":"03. E2E failure — Connector jar package conflict","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#03-e2e-failure--connector-jar-package-conflict","p":102244},{"i":102284,"t":"The FakeSource of the Connector-v2 version can only generate random data of a few fixed columns at present, and the community partners are optimizing it to make it better. https://github.com/apache/incubator-seatunnel/pull/2406 That said, we can temporarily solve this problem by simulating the data of the specified content through Transform sql:","s":"04. Insufficient E2E — Sink Logic Verification","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#04-insufficient-e2e--sink-logic-verification","p":102244},{"i":102286,"t":"The Assert Sink can configure column rules, but cannot do row-level value checking. For this problem, you can temporarily use other connector sinks with external storage for query verification data.","s":"05. Insufficient E2E — Source validation data","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#05-insufficient-e2e--source-validation-data","p":102244},{"i":102288,"t":"In many cases, when E2E starts, you might use Thread.sleep to wait for resource initialization. Here, sleep will cause fewer initialization failures but more time-wasting issues. In addition, due to the instability of resources, network, and other issues, you might be able to run it now but not later. To avoid this problem, Thread.sleep can be replaced with Awaitility.","s":"06. E2E stability improvement","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#06-e2e-stability-improvement","p":102244},{"i":102290,"t":"At present, I see that most people run E2E tests separately for both source and sink. If you want to speed up the PR process, it is recommended that you combine both the sink and source into one E2E testcase for verification, and run the testcase only once.","s":"07. A method to speed up E2E","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#07-a-method-to-speed-up-e2e","p":102244},{"i":102292,"t":"After completing the previous steps, please make sure you do some checks before submitting PR — including the following aspects: Complete recompile project: Codestyle validation, dependency validation The successful compilation before does not mean that it can be compiled successfully now Running E2E locally succeeds: Both Flink and Spark are verified Supplement or change the document and review it again before submitting: Review for places not covered by tests Places that hav been reviewed before and needs to be checked again Review for including all files, not just code The above operations and steps can greatly save CI resources, speed up PR Merged, and reduce the costs of community reviews.","s":"5. Checks Before Submitting a PR","u":"/blog/2022/09/20/A-tutorial-to-help-you develop-a-SeaTunnel-Connector-hand-by-hand-while-avoiding -pitfalls","h":"#5-checks-before-submitting-a-pr","p":102244},{"i":102294,"t":"This article provides a detailed walkthrough of how to achieve full data synchronization from MySQL to PostgreSQL using Apache SeaTunnel 2.3.9. We cover the complete end-to-end process — from environment setup to production validation. Let’s dive into the MySQL-to-PostgreSQL synchronization scenario. Version Requirements： MySQL： MySQL 8.3 PostgreSQL： PostgreSQL 13.2 Apache SeaTunnel： Apache-SeaTunnel-2.3.9","s":"Data Pipeline Tutorial：Synchronizing from MySQL to PostgreSQL Based on Apache SeaTunnel","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102297,"t":"Run the following SQL command to check the version： -- Check version information select version();","s":"Verify Version Information","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102299,"t":"-- View replication-related variables show variables where variable_name in ('log_bin', 'binlog_format', 'binlog_row_image', 'gtid_mode', 'enforce_gtid_consistency'); For MySQL CDC data synchronization, SeaTunnel needs to read the MySQLbinlogand act as a slave node in the MySQL cluster. Note： In MySQL 8.0+,binlogis enabled by default, but replication mode must be enabled manually. -- Enable master-slave replication (execute in sequence) -- SET GLOBAL gtid_mode=OFF; -- SET GLOBAL enforce_gtid_consistency=OFF; SET GLOBAL gtid_mode=OFF_PERMISSIVE; SET GLOBAL gtid_mode=ON_PERMISSIVE; SET GLOBAL enforce_gtid_consistency=ON; SET GLOBAL gtid_mode=ON;","s":"Enable Master-Slave Replication","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102301,"t":"A user must haveREPLICATION SLAVEandREPLICATION CLIENTprivileges： -- Grant privileges to the user CREATE USER 'test'@'%' IDENTIFIED BY 'password'; GRANT SELECT, RELOAD, SHOW DATABASES, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'test'; FLUSH PRIVILEGES;","s":"Grant Necessary User Permissions","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102304,"t":"By default, SeaTunnel logs output to a single file. For production, it’s preferable to have separate log files per job. Update the logging configuration inlog4j2.properties： ############################ log output to file ############################# # rootLogger.appenderRef.file.ref = fileAppender # Change log output to use independent log files for each job rootLogger.appenderRef.file.ref = routingAppender ############################ log output to file #############################","s":"Cluster Logging","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#cluster-logging","p":102293},{"i":102306,"t":"For production clusters, it is recommended to install SeaTunnel under the/optdirectory and point theSEATUNNEL_HOMEenvironment variable accordingly. If multiple versions exist, create a symbolic link to align with the server deployment directory： # Create a symlink ln -s /opt/apache-seatunnel-2.3.9 /opt/seatunnel # Set environment variable export SEATUNNEL_HOME=/opt/seatunnel","s":"Client Configuration","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#client-configuration","p":102293},{"i":102308,"t":"For Linux servers, add the following lines to/etc/profile.d/seatunnel.sh： echo 'export SEATUNNEL_HOME=/opt/seatunnel' >> /etc/profile.d/seatunnel.sh echo 'export PATH=$SEATUNNEL_HOME/bin：$PATH' >> /etc/profile.d/seatunnel.sh source /etc/profile.d/seatunnel.sh","s":"Environment Variables Configuration","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#environment-variables-configuration","p":102293},{"i":102310,"t":"Note： The configuration below does not cover all options but illustrates common production settings. env { job.mode = \"STREAMING\" job.name = \"DEMO\" parallelism = 3 checkpoint.interval = 30000 # 30 seconds checkpoint.timeout = 30000 # 30 seconds job.retry.times = 3 job.retry.interval.seconds = 3 # 3 seconds } The first step is setting up theenvmodule, which operates in a streaming mode. Therefore, it’s essential to specify the configuration mode asSTREAMING.","s":"Job Configuration","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102312,"t":"Configuring a task name is crucial for identifying and managing jobs in a production environment. Naming conventions based on database or table names can help with monitoring and administration.","s":"Task Naming and Management","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#task-naming-and-management","p":102293},{"i":102314,"t":"Here, we set the parallelism to3, but this value can be adjusted based on the cluster size and database performance.","s":"Parallelism Settings","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#parallelism-settings","p":102293},{"i":102316,"t":"Checkpoint Frequency： Set to 30 seconds. If higher precision is required, this can be reduced to 10 secondsor less. Checkpoint Timeout： If a checkpoint takes too long, the job is considered failed. Set to30 seconds. Automatic Retry： Configured to3 retries, with a retry interval of3 seconds(adjustable based on system requirements). source { MySQL-CDC { base-url = \"jdbc：mysql：//192.168.8.101：3306/test?serverTimezone=Asia/Shanghai\" username = \"test\" password = \"123456\" database-names = [\"test\"] # table-names = [\"test.test_001\",\"test.test_002\"] table-pattern = \"test\\\\.test_.*\" # The first dot is a literal character, requiring escaping; the second dot represents any single character. table-names-config = [ {\"table\"：\"test.test_002\",\"primaryKeys\"：[\"id\"]} ] startup.mode = \"initial\" # First sync all historical data, then incremental updates snapshot.split.size = \"8096\" snapshot.fetch.size = \"1024\" server-id = \"6500-8500\" connect.timeout.ms = 30000 connect.max-retries = 3 connection.pool.size = 20 exactly_once = false # In analytical scenarios, disabling exactly-once consistency allows some duplicates/losses for better performance. schema-changes.enabled = true # Enable schema evolution to avoid frequent modifications; supports add, rename, drop operations. } }","s":"Checkpoint Configuration","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#checkpoint-configuration","p":102293},{"i":102318,"t":"Time Zone Configuration： It’s recommended to specify the MySQL connection timezone to prevent discrepancies when extractingdatetimeortimestampdata. User Credentials： The username and password must have replication privileges , allowing access to the bin_log logs. The account should be able to query all tables under the designated databases.","s":"Key MySQL CDC Configurations","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102320,"t":"Typically, each database is assigned to a separate task. Here, we specify only thetestdatabase. Two methods can be used： Direct table name selection Regular expression-based table matching(recommended for large datasets or entire database synchronization). Important： When using regular expressions, both the database name and table name must be included. The.character, which separates them, must be escaped (\\\\.). For example, to match tables prefixed withtest_, we use： test\\\\.test_.* The first dot (.) represents a literal separator, requiring escaping (\\\\.). The second dot (.) representsany single characterin regex. Additionally, for tables without primary keys, logical primary keys can be specified manually to facilitate data synchronization.","s":"Database & Table Selection","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"#database--table-selection","p":102293},{"i":102322,"t":"The default startup mode isinitial, which means： Full historical data syncfirst Incremental updatesafterward Sharding & Fetching The default values for shard size and batch fetch size work well. If the server has higher performance, these values can be increased. Server ID MySQL requires unique server IDs for replication nodes. Apache SeaTunnel must masquerade as a MySQL replica. If not configured, a default value is used, but manual specification is recommended to avoid conflicts. The server ID range must be greater than the parallelism level, or errors may occur.","s":"Startup Mode","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102324,"t":"Connection Timeout： For large datasets, increase this value accordingly. Auto-Retry Interval： If handling a high volume of tables, consider extending retry intervals.","s":"Timeouts & Retries","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102326,"t":"For CDC-based data synchronization,exactly-once consistency is often not required in analytical scenarios. Disablingit can significantly boost performance. However, if strict consistency is required, it can be enabled at the cost of reduced performance.","s":"Exactly-Once Consistency","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102328,"t":"It’s highly recommended to enable schema evolution, which： Allows automatic table modifications (e.g., adding/removing fields) Reduces the need for manual job updates when the schema changes However,downstream tasks may fail if they rely on a field that was modified. Supported schema changes： ✔️ADD COLUMN ✔️DROP COLUMN ✔️RENAME COLUMN ✔️MODIFY COLUMN Note： Schema evolution does not supportCREATE TABLEorDROP TABLE.","s":"Schema Evolution","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102330,"t":"The sink configuration inserts data into PostgreSQL. sink { jdbc { url = \"jdbc：postgresql：//192.168.8.101：5432/test\" driver = \"org.postgresql.Driver\" user = \"postgres\" password = \"123456\" generate_sink_sql = true database = \"test\" table = \"${database_name}.${table_name}\" schema_save_mode = \"CREATE_SCHEMA_WHEN_NOT_EXIST\" data_save_mode = \"APPEND_DATA\" # enable_upsert = false } } Key Considerations： JDBC Connection： Specify PostgreSQL driver, user, and password. Auto SQL Generation： Enabling generate_sink_sql lets SeaTunnel automatically create tables and generate INSERT,DELETE, andUPDATEstatements. Schema Handling： PostgreSQL uses Database → Schema → Table, while MySQL has only Database → Table. Ensure the schema is correctly mapped to avoid data mismatches. User Permissions： The PostgreSQL user must have table creation permissions if using auto-schema generation. For more details, refer to the official documentation： 🔗SeaTunnel MySQL-CDC Connector Docs","s":"Configuring the Sink (PostgreSQL)","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102332,"t":"Apache SeaTunnel supports placeholders, which dynamically adjust table names based on the source data. For example： table = \"${database\\_name}.${table\\_name}\" Ensures each table syncs correctly without manual specification. Supports concatenation and dynamic formatting.","s":"Using Placeholders in Sink Configuration","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102334,"t":"Theschema_save_modeparameter plays a crucial role in database-wide synchronization. It simplifies the process by automatically creating tables in the target database, eliminating the need for manual table creation steps. Another key configuration isAPPEND_DATA, which is particularly useful when the target database already contains previously synchronized data. This setting prevents the accidental deletion of existing records , making it a safer choice for most scenarios. However, if your use case requires a different approach, you can modify this setting according to the official documentation guidelines.","s":"Schema Save Mode and Data Append Strategy","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102336,"t":"Another important parameter isenable_upsert. If you can guarantee that the source data contains no duplicate records , disabling upsert (enable_upsert = false) can significantly enhance synchronization performance . This is because, without upsert, the system does not need to check for existing records before inserting new ones. However, if there is a possibility of duplicate records in the source data, it is strongly recommended to keep Upsert enabled (enable_upsert = true). This ensures that records are inserted or updated based on their primary key** , preventing duplication issues. For detailed parameter explanations and further customization options , please refer to the official Apache SeaTunnel documentation.","s":"Enable Upsert for Performance Optimization","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102338,"t":"Once your configuration file is ready, submit the job using the SeaTunnel command-line tool： ./bin/start-seatunnel.sh --config /path/to/config.yaml --async Key Parameters： --config： Specifies the path to your configuration file. --async： Submits the job asynchronously, allowing the command line to exit while the job continues in the background. After submission, you can monitor the job via SeaTunnel’s cluster UI. In version 2.3.9, SeaTunnel provides a visual interface where you can view job logs, execution status, and data throughput details.","s":"Task Submission and Monitoring","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102340,"t":"For this demonstration, we created two tables (test_001andtest_002) and inserted sample data into MySQL. Using SeaTunnel's synchronization tasks, the data was successfully synchronized to PostgreSQL. The demonstration included insertions, deletions, updates, and even table schema modifications—all of which were reflected in real time on PostgreSQL. Key Points： Schema Synchronization： SeaTunnel supports automatic table schema synchronization. When the source MySQL table structure changes, the target PostgreSQL table automatically updates. Data Consistency： SeaTunnel ensures data consistency by accurately synchronizing all insert, delete, and update operations to the target database.","s":"Data Synchronization Demonstration","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102342,"t":"Apache SeaTunnel focuses on data integration and synchronization, addressing common challenges such as： Diverse Data Sources： Supporting hundreds of data sources, even as new ones emerge. Complex Sync Scenarios： Including full, incremental, CDC, real-time, and whole-database synchronizations. High Resource Demands： Traditional tools often require extensive computing or JDBC resources for real-time sync of many small tables. Monitoring and Quality： Sync processes can suffer from data loss or duplication, and effective monitoring is essential. Complex Technology Stacks： Multiple sync programs may be needed for different systems. Management Challenges： Offline and real-time sync are often developed and managed separately, increasing complexity.","s":"About SeaTunnel","u":"/blog/2025/03/19/Data_Pipeline_Tutorial_Synchronizing_from_MySQL_to_PostgreSQL_Based_on_Apache_SeaTunnel","h":"","p":102293},{"i":102346,"t":"Introduction​ With the development of big data technology, the demand for data integration and data stream processing is increasing. Apache SeaTunnel, as an open-source data integration framework, not only supports multiple data sources and targets but also provides flexible APIs to meet various complex business requirements. This article will deeply analyze the Apache SeaTunnel API to help developers better understand its usage scenarios and implementation methods.","s":"Apache SeaTunnel API Deep Dive","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"","p":102345},{"i":102348,"t":"With the development of big data technology, the demand for data integration and data stream processing is increasing. Apache SeaTunnel, as an open-source data integration framework, not only supports multiple data sources and targets but also provides flexible APIs to meet various complex business requirements. This article will deeply analyze the Apache SeaTunnel API to help developers better understand its usage scenarios and implementation methods.","s":"Introduction","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#introduction","p":102345},{"i":102350,"t":"From the official diagram, we can see that SeaTunnel defines the following types: Source API: Used to define data input sources. Transform API: Used to process and transform data. Sink API: Used to define data output targets. Three types of operators So I want to start by looking at Apache SeaTunnel's design philosophy from the interface definitions.","s":"Understanding SeaTunnel from Interface Definitions","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#understanding-seatunnel-from-interface-definitions","p":102345},{"i":102352,"t":"SeaTunnelSource is the interface definition for data reading. In this interface, it defines how to extract data from a data source. public interface SeaTunnelSource<T, SplitT extends SourceSplit, StateT extends Serializable> extends Serializable, PluginIdentifierInterface, SeaTunnelPluginLifeCycle, SeaTunnelJobAware { /** * Returns the type of current Source, whether it is [bounded batch data] or [unbounded stream data] */ Boundedness getBoundedness(); /** * This method will be deprecated in the future * Catalog will be used to represent data, allowing more metadata to describe the data */ default SeaTunnelDataType<T> getProducedType() { return (SeaTunnelDataType) getProducedCatalogTables().get(0).getSeaTunnelRowType(); } /** * SeaTunnel currently supports multi-table reading, so this returns a list structure * Each catalog contains metadata information about the table being read */ default List<CatalogTable> getProducedCatalogTables() { throw new UnsupportedOperationException( \"getProducedCatalogTables method has not been implemented.\"); } /** * Create Reader, which is the class that actually reads the data */ SourceReader<T, SplitT> createReader(SourceReader.Context readerContext) throws Exception; /** * These two methods are for creating/restoring SplitEnumerator */ SourceSplitEnumerator<SplitT, StateT> createEnumerator( SourceSplitEnumerator.Context<SplitT> enumeratorContext) throws Exception; SourceSplitEnumerator<SplitT, StateT> restoreEnumerator( SourceSplitEnumerator.Context<SplitT> enumeratorContext, StateT checkpointState) throws Exception; /** * These two methods are generally not modified, can be overridden if custom serialization is needed */ default Serializer<SplitT> getSplitSerializer() { return new DefaultSerializer<>(); } default Serializer<StateT> getEnumeratorStateSerializer() { return new DefaultSerializer<>(); } } From this interface, we can see two main methods: createReader createEnumerator The SourceSplitEnumerator created by the createEnumerator method is responsible for splitting the data extraction tasks. The SourceReader created by the createReader method will perform the actual task reading based on these split tasks. First, let's look at the code for SourceSplitEnumerator.","s":"SeaTunnelSource","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#seatunnelsource","p":102345},{"i":102354,"t":"public interface SourceSplitEnumerator<SplitT extends SourceSplit, StateT> extends AutoCloseable, CheckpointListener { void open(); void run() throws Exception; @Override void close() throws IOException; void addSplitsBack(List<SplitT> splits, int subtaskId); int currentUnassignedSplitSize(); void handleSplitRequest(int subtaskId); void registerReader(int subtaskId); StateT snapshotState(long checkpointId) throws Exception; default void handleSourceEvent(int subtaskId, SourceEvent sourceEvent) {} interface Context<SplitT extends SourceSplit> { int currentParallelism(); Set<Integer> registeredReaders(); void assignSplit(int subtaskId, List<SplitT> splits); default void assignSplit(int subtaskId, SplitT split) { assignSplit(subtaskId, Collections.singletonList(split)); } void signalNoMoreSplits(int subtask); void sendEventToSourceReader(int subtaskId, SourceEvent event); MetricsContext getMetricsContext(); EventListener getEventListener(); } } The SourceSplitEnumerator interface defines some methods and an inner class Context. First, let's look at its own methods. We can see there are 3 lifecycle-related methods: open(), run(), and close(). These methods require connectors to handle resource creation or cleanup based on their implementations. registerReader(int subtaskId) method: reader actively registers with the split enumerator handleSplitRequest(int subtaskId): reader actively requests from the split enumerator to get the extraction tasks it will execute. (However, looking at the code implementation, this approach is less common; most of the time, the enumerator actively pushes tasks to reader) addSplitsBack(List<SplitT> splits, int subtaskId): When a reader encounters an exception, its running tasks need to be reassigned. At this point, these tasks need to be added back to the queue for reassignment to other nodes for fault tolerance. In the Context interface definition, there are two key methods: assignSplit(int subtaskId, List<SplitT> splits): split enumerator actively pushes tasks to a specific reader signalNoMoreSplits(int subtaskId): split enumerator tells a specific reader that no more tasks will be assigned to it.","s":"SourceSplitEnumerator","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#sourcesplitenumerator","p":102345},{"i":102356,"t":"public interface SourceReader<T, SplitT extends SourceSplit> extends AutoCloseable, CheckpointListener { void open() throws Exception; @Override void close() throws IOException; void pollNext(Collector<T> output) throws Exception; List<SplitT> snapshotState(long checkpointId) throws Exception; void addSplits(List<SplitT> splits); void handleNoMoreSplits(); default void handleSourceEvent(SourceEvent sourceEvent) {} interface Context { int getIndexOfSubtask(); Boundedness getBoundedness(); void signalNoMoreElement(); void sendSplitRequest(); void sendSourceEventToEnumerator(SourceEvent sourceEvent); MetricsContext getMetricsContext(); EventListener getEventListener(); } } In the Reader interface definition, there is also an inner class Context. Let's look at some of the main methods: pollNext(Collector<T> output): The main method for data extraction. In this method, each connector will implement extracting data from its corresponding data source, converting it to SeaTunnel's internal data structure SeaTunnelRow, and then adding it to the Collector addSplits(List<SplitT> splits): reader's handling after receiving tasks assigned by the split enumerator snapshotState(long checkpointId): This method is called during checkpoint, requiring the reader to record some state for subsequent fault tolerance","s":"SourceReader","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#sourcereader","p":102345},{"i":102358,"t":"Here, a Split represents a task for splitting data source reads. It can be a partition of a Hive table, a partition of Kafka, or a split of a JDBC query statement. In short, the core idea is to split data reading into multiple independent reading tasks that can be assigned to different reader instances for execution, thereby speeding up queries. For example: In batch processing, data can be split into N parts, allowing data extraction to be executed in parallel to achieve speed improvement. For stream processing, there are two approaches. One is to split data into limited unbounded data streams (e.g., Kafka splits by partition, one partition per task, each task being an unbounded data stream). Another approach is to generate infinite bounded data streams (also using Kafka as an example, extracting part of the data from a partition each time, infinitely generating task definitions). As for how many Splits a data reading task will ultimately be divided into and how to implement the splitting, it's up to each connector's implementation. Each connector can decide based on the actual partitions being read or parameters. Next, let's look at the code related to Transform!","s":"Summary with a Diagram","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#summary-with-a-diagram","p":102345},{"i":102360,"t":"public interface SeaTunnelTransform<T> extends Serializable, PluginIdentifierInterface, SeaTunnelJobAware { default void open() {} default void setTypeInfo(SeaTunnelDataType<T> inputDataType) { throw new UnsupportedOperationException(\"setTypeInfo method is not supported\"); } // Get the data structure after Transform processing CatalogTable getProducedCatalogTable(); // From this, we can see that Transform in SeaTunnel only supports map operations // Operations like Join involving multiple data sources are not supported T map(T row); default void close() {} } In transform, there is one key method T map(T row), which performs map processing on an original data record to get a new data record. The structure of the new data is consistent with getProducedCatalogTable(). This code is based on version 2.3.6. The community is currently working on multi-table read/write functionality for Transform. Currently, only the map operator (one-to-one) is supported. I've seen the community also discussing whether to support the flatMap operator (one-to-many), which would allow data expansion operations during synchronization. Now let's look at the Sink code!","s":"SeaTunnelTransform","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#seatunneltransform","p":102345},{"i":102362,"t":"public interface SeaTunnelSink<IN, StateT, CommitInfoT, AggregatedCommitInfoT> extends Serializable, PluginIdentifierInterface, SeaTunnelPluginLifeCycle, SeaTunnelJobAware { @Deprecated // These two methods are also marked as deprecated, will use Catalog for representation in the future default void setTypeInfo(SeaTunnelRowType seaTunnelRowType) { throw new UnsupportedOperationException(\"setTypeInfo method is not supported\"); } @Deprecated default SeaTunnelDataType<IN> getConsumedType() { throw new UnsupportedOperationException(\"getConsumedType method is not supported\"); } /** * Create/restore SinkWriter, Writer is the class that actually performs data writing */ SinkWriter<IN, CommitInfoT, StateT> createWriter(SinkWriter.Context context) throws IOException; default SinkWriter<IN, CommitInfoT, StateT> restoreWriter( SinkWriter.Context context, List<StateT> states) throws IOException { return createWriter(context); } default Optional<Serializer<StateT>> getWriterStateSerializer() { return Optional.empty(); } default Optional<SinkCommitter<CommitInfoT>> createCommitter() throws IOException { return Optional.empty(); } default Optional<Serializer<CommitInfoT>> getCommitInfoSerializer() { return Optional.empty(); } default Optional<SinkAggregatedCommitter<CommitInfoT, AggregatedCommitInfoT> createAggregatedCommitter() throws IOException { return Optional.empty(); } default Optional<Serializer<AggregatedCommitInfoT>> getAggregatedCommitInfoSerializer() { return Optional.empty(); } } In Sink, there are several key methods: createWriter(SinkWriter.Context context): Create Writer instance. Similar to Source, the actual data writing is performed by the Writer. createCommitter(): Optional. When two-phase commit is needed, create a SinkCommitter to complete the two-phase commit. This approach is no longer recommended; it's recommended to use createAggregatedCommitter() for two-phase commit. createAggregatedCommitter(): Optional. Similar to SinkCommitter, used for two-phase commit during the commit phase. The difference is that SinkAggregatedCommitter runs as a single instance, not multiple instances, concentrating all commit tasks in one place for execution. So if a connector needs two-phase commit, it's recommended to use createAggregatedCommitter() to create it.","s":"SeaTunnelSink","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#seatunnelsink","p":102345},{"i":102364,"t":"public interface SinkWriter<T, CommitInfoT, StateT> { void write(T element) throws IOException; default void applySchemaChange(SchemaChangeEvent event) throws IOException {} Optional<CommitInfoT> prepareCommit() throws IOException; default List<StateT> snapshotState(long checkpointId) throws IOException { return Collections.emptyList(); } void abortPrepare(); void close() throws IOException; interface Context extends Serializable { int getIndexOfSubtask(); default int getNumberOfParallelSubtasks() { return 1; } MetricsContext getMetricsContext(); EventListener getEventListener(); } } We can see that the structure of SinkWriter is somewhat similar to SourceReader. Let's look at some key methods: write(T element): Implementation of writing to the target database when receiving upstream data; applySchemaChange(SchemaChangeEvent event): How downstream handles when upstream data table structure changes, such as adding/removing fields, modifying field names. But this depends on the specific implementation; prepareCommit(): When two-phase commit is needed, generate information for this commit, which will be handed over to SinkCommitter/SinkAggregatedCommitter for two-phase commit. This method is called during checkpoint, meaning information is committed to the target connector only during each checkpoint; snapshotState(): During checkpoint, store some state of the writer for subsequent fault tolerance;","s":"SinkWriter","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#sinkwriter","p":102345},{"i":102366,"t":"When data needs to be read from a data source, SourceSplitEnumerator first splits the tasks, then SourceReader executes the split data reading tasks. During reading, raw data needs to be converted to SeaTunnel's internal SeaTunnelRow, then passed downstream to Transform for data transformation. After transformation, it's handed to SinkWriter to write the SeaTunnelRow data to the corresponding connector. If two-phase commit is needed during writing, additional SinkCommitter related classes need to be implemented.","s":"Summary with a Diagram","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#summary-with-a-diagram-1","p":102345},{"i":102368,"t":"In the previous sections, we've described the functionality of source, transform, and sink. But how is data passed between source, transform, and sink? If a source has multiple downstream components, how are messages distributed to all downstream components? This is where the Collector interface comes in. From the data flow direction, only source to downstream or transform to downstream passes data, so we can see there are two Collector interface definitions, located in the source and transform packages respectively. First, let's look at the Collector definition in source: public interface Collector<T> { void collect(T record); default void markSchemaChangeBeforeCheckpoint() {} default void collect(SchemaChangeEvent event) {} default void markSchemaChangeAfterCheckpoint() {} Object getCheckpointLock(); default boolean isEmptyThisPollNext() { return false; } default void resetEmptyThisPollNext() {} } The Collector definition in transform is relatively simple: public interface Collector<T> { void collect(T record); void close(); } Collector decouples multiple operators. Each operator only needs to focus on how to process data, without worrying about who to send the result data to. Updating the previous diagram (adding another Transform to show the multi-downstream scenario):","s":"Collector","u":"/blog/2026/01/06/seatunnel-api-deep-dive","h":"#collector","p":102345},{"i":102370,"t":"Apache SeaTunnel Committer | Zongwen Li","s":"SeaTunnel engine, designed for tens-of-billions data integration","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"","p":102369},{"i":102372,"t":"Apache SeaTunnel is a very easy-to-use ultra-high-performance distributed data integration platform that supports real-time synchronization of massive data. Apache SeaTunnel will try its best to solve the problems that may be encountered in the process of mass data synchronization, such as data loss and duplication, task accumulation and delay, low throughput, etc.","s":"Introduction to Apache SeaTunnel","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#introduction-to-apache-seatunnel","p":102369},{"i":102374,"t":"SeaTunnel, formerly known as Waterdrop, was open-sourced on GitHub in 2017. In October 2021, the Waterdrop community joined the Apache incubator and changed its name to SeaTunnel.","s":"Milestones of SeaTunnel","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#milestones-of-seatunnel","p":102369},{"i":102376,"t":"When SeaTunnel entered the Apache incubator, the SeaTunnel community ushered in rapid growth. As of now, the SeaTunnel community has a total of 151 contributors, 4314 Stars, and 804 forks.","s":"SeaTunnel Growth","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#seatunnel-growth","p":102369},{"i":102378,"t":"There are many pain points faced by the existing computing engines in the field of data integration, and we will talk about this first. The pain points usually lie in three directions: The fault tolerance ability of the engine; Difficulty in configuration, operation, and maintenance of engine jobs; The resource usage of the engine.","s":"Pain points of Existing engines","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#pain-points-of-existing-engines","p":102369},{"i":102380,"t":"Global Failover For distributed streaming processing systems, high throughput and low latency are often the most important requirements. At the same time, fault tolerance is also very important in distributed systems. For scenarios that require high correctness, the implementation of exactly once is often very important. In a distributed streaming processing system, since the computing power, network, load, etc. of each node are different, the state of each node cannot be directly merged to obtain a true global state. To obtain consistent results, the distributed processing system needs to be resilient to node failure, that is, it can recover to consistent results when it fails. Although it is claimed in their official blog that Spark’s Structured Streaming uses the Chandy-Lamport algorithm for Failover processing, it does not disclose more details. Flink implemented Checkpoint as a fault-tolerant mechanism based on the above algorithm and published related papers: Lightweight Asynchronous Snapshots for Distributed Dataflows In the current industrial implementation, when a job fails, all nodes of the job DAG need to failover, and the whole process will last for a long time, which will cause a lot of upstream data to accumulate.","s":"fault tolerance","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#fault-tolerance","p":102369},{"i":102382,"t":"The previous problem will cause a long-time recovery, and the business service may accept a certain degree of data delay. In a worse case, a single sink node cannot be recovered for a long time, and the source data has a limited storage time, such as MySQL and Oracle log data, which will lead to data loss.","s":"Loss of Data","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#loss-of-data","p":102369},{"i":102384,"t":"Single table Configuration The previous examples are cases regarding a small number of tables, but in real business service development, we usually need to synchronize thousands of tables, which may be divided into databases and tables at the same time; The status quo is that we need to configure each table, a large number of table synchronization takes a lot of time for users, and it is prone to problems such as field mapping errors, which are difficult to maintain.","s":"Configuration is cumbersome","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#configuration-is-cumbersome","p":102369},{"i":102386,"t":"Besides, according to the research report of Fivetran, 60% of the company’s schema will change every month, and 30% will change every week. However, none of the existing engines supports Schema Evolution. After changing the Schema each time, the user needs to reconfigure the entire link, which makes the maintenance of the job very cumbersome.","s":"Not supporting Schema Evolution","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#not-supporting-schema-evolution","p":102369},{"i":102388,"t":"The database link takes up too much If our Source or Sink is of JDBC type, since the existing engine only supports one or more links per table, when there are many tables to be synchronized, more link resources will be occupied, which will bring a great burden to the database server.","s":"The high volume of resource usage","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#the-high-volume-of-resource-usage","p":102369},{"i":102390,"t":"In the existing engine, a buffer and other control operators are used to control the pressure, that is, the back pressure mechanism; since the back pressure is transmitted level by level, there will be pressure delay, and at the same time, the processing of data will not be smooth enough, increasing the GC time, fault-tolerant completion time, etc. Another case is that neither the source nor the sink has reached the maximum pressure, but the user still needs to control the synchronization rate to prevent too much impact on the source database or the target database, which cannot be controlled through the back pressure mechanism.","s":"Operator pressure is uncontrollable","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#operator-pressure-is-uncontrollable","p":102369},{"i":102392,"t":"To solve these severe issues faced by computing engines, we self-developed our engine expertise in big data integration. Firstly, let’s get through what goals this engine wants to achieve.","s":"Architecture goals of Apache SeaTunnel Engine","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#architecture-goals-of-apache-seatunnel-engine","p":102369},{"i":102394,"t":"In the data integration case, there is a possibility that a job can synchronize hundreds of sheets, and the failure of one node or one table will lead to the failure of all tables, which is too costly. We expect that unrelated Job Tasks will not affect each other during fault tolerance, so we call a vertex collection with upstream and downstream relationships a Pipeline, and a Job can consist of one or more pipelines.","s":"Pipeline Failover","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#pipeline-failover","p":102369},{"i":102396,"t":"Now if there is an exception in the pipeline, we still need to failover all the vertex in the pipeline; but can we restore only part of the vertex? For example, if the Source fails, the Sink does not need to restart. In the case of a single Source and multiple Sinks, if a single Sink fails, only the Sink and Source that failed will be restored; that is, only the node that failed and its upstream nodes will be restored. Obviously, the stateless vertex does not need to be restarted, and since SeaTunnel is a data integration framework, we do not have aggregation state vertexes such as Agg and Count, so we only need to consider Sink; Sink does not support idempotence & 2PC; no restart and restart will result in the same data duplication, which can only be solved by Sink without restarting; Sink supports idempotence, but does not support 2PC: because it is idempotent writing, it does not matter whether the source reads data inconsistently every time, and it does not need to be restarted; Sink supports 2PC: If the Source supports data consistency, if an abort is not executed, the processed old data will be automatically ignored through the channel data ID, and at the same time, it will face the problem that the transaction session time may time out; If the Source does not support data consistency, perform abort on the Sink to discard the last data, which has the same effect as restarting but does not require initialization operations such as re-establishing links; That is, the simplest implementation is to execute abort. We use the pipeline as the minimum granularity for fault-tolerant management, and use the Chandy-Lamport algorithm to realize fault-tolerant distributed jobs.","s":"Regional Failover","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#regional-failover","p":102369},{"i":102398,"t":"For sink failure, when data cannot be written, a possible solution is to work two jobs at the same time. One job reads the database logs using the CDC source connector and then writes the data to Kafka using the Kafka Sink connector. Another job reads data from Kafka using the Kafka source connector and writes data to the destination using the destination sink connector. This solution requires users to have a deep understanding of the underlying technology, and both tasks will increase the difficulty of operation and maintenance. Because every job needs JobMaster, it requires more resources. Ideally, the user only knows that they will be reading data from the source and writing data to the sink, and at the same time, during this process, the data can be cached in case the sink fails. The sync engine needs to automatically add caching operations to the execution plan and ensure that the source still works in the event of a sink failure. In this process, the engine needs to ensure that the data written to the cache and read from the cache are transactional, to ensure data consistency.","s":"Data Cache","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#data-cache","p":102369},{"i":102400,"t":"For a large number of table synchronization, we expect that a single Source can support reading multiple structural tables, and then use the side stream output to keep consistent with a single table stream. The advantage of this is that it can reduce the link occupation of the data source and improve the utilization rate of thread resources. At the same time, in SeaTunnel Engine, these multiple tables will be regarded as a pipeline, which will increase the granularity of fault tolerance; there are trade-offs, and the user can choose how many tables a pipeline can pass through.","s":"Sharding & Multi-table Sync","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#sharding--multi-table-sync","p":102369},{"i":102402,"t":"Schema Evolution is a feature that allows users to easily change the current schema of a table to accommodate changing data over time. Most commonly, it is used when performing an append or overwrite operation, to automatically adjust the schema to include one or more new columns. This feature is required for real-time data warehouse scenarios. Currently, the Flink and Spark engines do not support this feature. In SeaTunnel Engine, we will use the Chandy-Lamport algorithm to send DDL events, make them flow in the DAG graph and change the structure of each operator, and then synchronize them to the Sink.","s":"Schema Evolution","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#schema-evolution","p":102369},{"i":102404,"t":"The Multi-table feature can reduce the use of some Source and Sink link resources. At the same time, we have implemented Dynamic Thread Resource Sharing in SeaTunnel Engine, reducing the resource usage of the engine on the server.","s":"Shared Resource","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#shared-resource","p":102369},{"i":102406,"t":"As for the problems that cannot be solved by the back pressure mechanism, we will optimize the Buffer and Checkpoint mechanism: Firstly, We try to allow Buffer to control the amount of data in a period; Secondly, by the Checkpoint mechanism, the engine can lock the buffer after the Checkpoint reaches the maximum number of parallelism and executes an interval time, prohibiting the writing of Source data, achieving the result of taking the pressure proactively, avoiding issues like back pressure delay or failure to be delivered to Source. The above is the design goal of SeaTunnel Engine, hoping to help you better solve the problems that bother you in data integration. In the future, we will continue to optimize the experience of using SeaTunnel so that more people are willing to use it.","s":"Speed Control","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#speed-control","p":102369},{"i":102408,"t":"As an Apache incubator project, the Apache SeaTunnel community is developing rapidly. In the following community planning, we will focus on four directions: Support more data integration scenarios (Apache SeaTunnel Engine) It is used to solve the pain points that existing engines cannot solve, such as the synchronization of the entire database, the synchronization of table structure changes, and the large granularity of task failure; Guys who are interested in the engine can pay attention to this Umbrella: https://github.com/apache/incubator-seatunnel/issues/2272 Expand and improve Connector & Catalog ecology Support more Connector & Catalog, such as TiDB, Doris, Stripe, etc., and improve existing connectors, improve their usability and performance, etc.; Support CDC connector for real-time incremental synchronization scenarios. Guys who are interested in connectors can pay attention to this Umbrella: https://github.com/apache/incubator-seatunnel/issues/1946 Support for more versions of the engines Such as Spark 3.x, Flink 1.14.x, etc. Guys who are interested in supporting Spark 3.3 can pay attention to this PR: https://github.com/apache/incubator-seatunnel/pull/2574 Easier to use (Apache SeaTunnel Web) Provides a web interface to make operations more efficient in the form of DAG/SQL Simple and more intuitive display of Catalog, Connector, Job, etc.; Access to the scheduling platform to make task management easier Guys who are interested in Web can pay attention to our Web sub-project: https://github.com/apache/incubator-seatunnel-web","s":"The future of Apache SeaTunnel","u":"/blog/Apache SeaTunnel Committer | Zongwen Li","h":"#the-future-of-apache-seatunnel","p":102369},{"i":102410,"t":"Translator | Critina In the May joint Meetup between Apache SeaTunnel and Apache Inlong, Li Zongwen, a senior engineer at WhaleOps, shared his experiences about finding and refactoring of the the four major problems with Apache SeaTunnel (Incubating).i.e. the connectors of SeaTunnel have to be implemented many times,the inconsistent parameters, SeaTunnel is not supportive of multiple versions of the engine, and it’s difficult to upgrade the engine. In order to solve these problems, Li Zongwen aimed to decouple Apache SeaTunnel (Incubating) from thw computing engines, and re-factor the Source and Sink apis to improve the development experience. This speech mainly consists of five parts.The first part is about Apache SeaTunnel (Incubator) refactoring background and motivation. The second part introduces Apache SeaTunnel (Incubating) Target for refactoring.The third part discusses Apache SeaTunnel (Incubating) overall design for refactoring. The last two parts is about Apache SeaTunnel (Incubating) Source API design and Apache SeaTunnel (Incubating) Sink API design.","s":"How dose Apache SeaTunnel refactor the API to decouple with the computing engine","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"","p":102409},{"i":102412,"t":"Those of you who have used Apache SeaTunnel (Incubator) or developers should know that Apache SeaTunnel (Incubator) is now fully coupled with the engine, which is entirely based on Spark or Flink, and so are the configuration file parameters. From the perspective of contributors and users, we can find they face some problems. In the view of the contributors, repeated implementing connector is meaningless and it is unable for potential contributors to contribute to the community due to inconsistent engine versions. At present, many companies use Lambda architecture, Spark is used for offline operations and Flink is used for real-time operations. In the view of the users, it can be found that Spark may have the Connector of SeaTunnel, but Flink does not, and the parameters of the two engines for the Connector of the same storage engine are not unified, thus resulting a high cost of and deviating from its original intention of being easy to use. And some users question that Flink version 1.14 is not supported nowadays. While with the current SeaTunnel architecture, we must discard the previous version in order to support Flink version 1.14, which will bring great trouble for early version users. As a result, it was difficult for us to either upgrade engine or support more versions. In addition, Spark and Flink both adopt the Checkpoint fault-tolerant mechanism implemented by Chandy-Lamport algorithm and internally unify DataSet and DataStream. On this premise, we believe decoupling is feasible.","s":"01 Background and motivation for refactoring","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"#01-background-and-motivation-for-refactoring","p":102409},{"i":102414,"t":"Therefore, in order to solve the problems raised above, we set the following goals. Connector is only implemented once. To solve the problems that parameters are not unified and Connector is implemented for too many times, we hope to achieve a unified Source and Sink API; Multiple versions of Spark and Flink engines are supported. A translation layer above the Source and Sink API is added to support multiple versions of Spark and Flink engines. The logic for parallel shard of Source and the Sink submission should be clarified. We must provide a good API to support Connector development. The full database synchronization in real-time scenarios should be supported. This is a derivative requirement that many users have mentioned for CDC support. I once participated the Flink CDC community before and many users pointed out that in the CDC scenario, if you wanted to use the Flink CDC directly, each table would have a link and there would be thousands of links for thousands of tables when you need to synchronize the whole library, which was unacceptable for both the database and the DBA. To solve this problem, the simplest way was to introduce Canal、Debezium or other components, which were used to pull incremental data to Kafka or other MQ for intermediate storage, and then we could use Flink SQL for synchronization. This actually contradicted the original idea of the Flink CDC to reduce links. However, the Flink CDC aimed only a Connector and was unable to deal with the whole link, so the proposal was not seen in the SeaTunnel community. By the chance of the reconstruction, we submitted the proposal to the SeaTunnel community. Automatic discovery and storage of meta information are realized. The users should have awful experience due to the storage engines such as Kafka lacking of record of the data structure, when we need to read structured data, the user must define the topic of structured data types before read one topic at a time . We hope once the configuration is completed, there is no need to do any redundant work again. Some people may wonder why we don’t use Apache Beam directly. That is because Beam sources are divided into BOUNDED and UNBOUNDED sources, which means it needs to be implemented twice. Moreover, some features of Source and Sink are not supported, which will be mentioned later.","s":"02 Apache SeaTunnel (Incubating) decouples with computing engine","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"#02-apache-seatunnel-incubating-decouples-with-computing-engine","p":102409},{"i":102416,"t":"The Apache SeaTunnel(Incubating) API architecture is described in the picture above. The Source & Sink API is one of the core APIS of data integration, which defines the logic for parallel shard of Source and the commitment of Sink to realize the Connector. The Engine API includes the translation and the execution layers. The translation is used to translate Souce and Sink API of SeaTunnel into connectors that can be run inside the engine. The execution defines the execution logic of Source, Transform, Sink and other operations in the engine. The Table SPI is mainly used to expose the interface of Source and Sink in SPI mode, and to specify mandatory and optional parameters of Connector etc. The DataType includes SeaTunnel data structure used to isolate engines and declare Table schema. The Catalog is Used to obtain Table schemes and Options, etc. The Catalog Storage is used to store Table Schemes defined by unstructured engines such as Kafka. The execution flow we assumed nowadays can be see in the above picture. Obtain task parameters from configuration files or UI. Obtain the Table Schema, Option and other information by analyzing the parameters from Catalog. Pull up the Connector of SeaTunnel in SPI mode and inject Table information. Translate the Connector from SeaTunnel into the Connector within the engine. Execute the operation logic of the engine. The multi-table distribution in the picture only exists in the synchronization of the whole database of CDC, while other connectors are single tables and do not need the distribution logic. It can be seen that the hardest part of the plan is to translate Source and Sink into an internal Source and Sink in the engine. Many users today use Apache SeaTunnel (Incubating) not only as a data integration tool but also as a data storage tool, and use a lot of Spark and Flink SQLs. We want to preserve that SQL capability for users to upgrade seamlessly. According to our research, the feature above shows the ideal execution logic of Source and Sink. Since SeaTunnel is incubated as WaterDrop, the terms in the figure are tended towards Spark. Ideally, the Source and Sink coordinators can be run on the Driver, and the Source Reader and Sink Writer will run on the Worker. In terms of the Source Coordinator, we expect it to support several features. The first capability is that the slicing logic of data can be dynamically added to the Reader. The second is that the coordination of Reader can be supported. Source Reader is used to read data, and then send the data to the engine, and finally to the Source Writer for data writing. Meanwhile, Writer can support the two-phase transaction submission, and the coordinator of Sink supports the aggregation submission requirements of Connector such as Iceberg.","s":"03 Apache SeaTunnel(Incubating) overall design for refactoring","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"#03-apache-seatunnelincubating-overall-design-for-refactoring","p":102409},{"i":102418,"t":"After research, we found the following features that are required by Source. Unified offline and real-time API , which supports that source is implemented only once and supports both offline and real-time API; Supportive of parallel reading. For example that Kafka generates a reader for each partition and execute in parallel. Supporting dynamic slice-adding. For example, Kafka defines a regular topic, when a new topic needs to be added due to the volume of business, the Source API allows to dynamically add the slice to the job. Supporting the work of coordinating reader, which is currently only needed in the CDC Connector. CDC is currently supported by NetFilx’s DBlog parallel algorithms, which requires reader coordination between full synchronization and incremental synchronization. Supporting a single reader to process multiple tables, i.e. to allows the whole database synchronization in the real-time scenario as mentioned above. Based on the above requirements, we have created the basic API as shown in the figure above. And the code has been submitted to the API-Draft branch in the Apache SeaTunnel(Incubator) community. If you’re interested, you can view the code in detail.","s":"04 Source API","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"#04-source-api","p":102409},{"i":102420,"t":"Flink and Spark unify the API of DataSet and DataStream, and they can support the first two features. Then, for the remaining three features, how do we Support dynamic slice-adding？ Support the work of coordinating reader？ Support a single reader to process multiple tables？ Let's review the design with questions. We found that other connectors do not need coordinators, except for CDC. For those connectors that do not need coordinators, we have a Source that supports parallel execution and engine translation. As shown in the figure above, there is a slice enumerator on the left, which can list which slices the source needs and show what there are. After enumerating slices in real time, each slice would be distributed to SourceReader, the real data reading module. Boundedness marker is used to differentiate offline and real-time operations. Connector can mark whether there is a stop Offset in a slice. For example, Kafka can support real-time and offline operations. The degree of parallelism can be set for the ParallelSource in the engine to support parallel reading. As shown in the figure above, in a scenario where a coordinator is required, Event transmission is done between the Reader and Enumerator. Enumerator coordinates events by the Event sent by the Reader. The Coordinated Source needs to ensure single parallelism at the engine level to ensure data consistency. Of course, this does not make good use of the engine’s memory management mechanism, but trade-offs are necessary. For the last question, how can we support a single reader to process multiple tables? This involves the Table API layer. Once all the required tables have been read from the Catalog, some of the tables may belong to a single job and can be read by a link, and some may need to be separated, depending on how Source is implemented. Since this is a special requirement, we want to make it easier for the developers. In the Table API layer, we will provide a SupportMultipleTable interface to declare that Source supports multiple Table reads. The Source is implemented based on the corresponding deserializer of multiple tables. As for how to separate derived multi-table data, Flink will adopt Side Output mechanism, while Spark is going to use Filter or Partition mechanism.","s":"How to adapt to Spark and Flink engines","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"#how-to-adapt-to-spark-and-flink-engines","p":102409},{"i":102422,"t":"At present, there are not many features required by Sink, but three mojor requirements are considerable according to our research. The first is about idempotent writing, which requires no code and depends on whether the storage engine can support it. The second is about distributed transactions. The mainstream method is two-phase commitments, such as Kafka etc. The third is about the submission of aggregation. For Storage engines like Iceberg and Hoodie, we hope there is no issues triggered by small files, so we expect to aggregate these files into a single file and commit it as a whole. Based on these three requirements, we built three APIS: SinkWriter, SinkCommitter, and SinkAggregated Committer. SinkWriter plays a role of writing, which may or may not be idempotent. SinkCommitter supports for two-phase commitments. SinkAggregatedCommitter supports for aggregated commitments. Ideally, AggregatedCommitter runs in Driver in single or parallel, and Writer and Committer run in Worker with multiple parallels, with each parallel carrying its own pre-commit work and then send Aggregated messages to Aggregated committers. Current advanced versions of Spark and Flink all support AggregatedCommitter running on the Driver(Job Manager) and Writer and Committer running on the worker(Task Manager). However, for the lower versions of Flink, AggregatedCommitter cannot be supported to run in JM, so we are also carrying translation adaptation. Writer and Committer will act as pre-operators, packaged by Flink’s ProcessFunction, supports concurrent pre-delivery and write, and implement two-phase commitment based on Flink’s Checkpoint mechanism. This is also the current 2PC implementation of many of Flink connectors. The ProcessFunction can send messages about pre-commits to downstream Aggregated committers, which can be wrapped around operators such as SinkFunction or ProcessFunction. Of course, We need to ensure that only one single parallel will be started by the AggregatedCommitter in case of the broken of the logic of the aggregated commitment. Thank you for watching. If you’re interested in the specific implementations mentioned in my speech, you can refer to the Apache SeaTunnel (Incubating) community and check out the API-Draft branch code. Thank you again.","s":"5 Sink API","u":"/blog/How dose Apache SeaTunnel refactor the API to decouple with the computing engine","h":"#5-sink-api","p":102409},{"i":102424,"t":"ClickHouse is a distributed columnar DBMS for OLAP. Our department has stored all log data related to data analysis in ClickHouse, an excellent data warehouse, and the current daily data volume has reached 30 billion. In the previous article [How to quickly import data from HDFS into ClickHouse] (2021-12-30-hdfs-to-clickhouse.md), we mentioned the use of Seatunnel https://github.com/apache/incubator -seatunnel After a very simple operation on the data in HDFS, the data can be written to ClickHouse. The data in HDFS is generally unstructured data, so what should we do with the structured data stored in Hive?","s":"How to quickly import data from Hive into ClickHouse","u":"/blog/hive-to-clickhouse","h":"","p":102423},{"i":102426,"t":"Assuming that our data has been stored in Hive, we need to read the data in the Hive table and filter out the fields we care about, or convert the fields, and finally write the corresponding fields into the ClickHouse table.","s":"Hive to ClickHouse","u":"/blog/hive-to-clickhouse","h":"#hive-to-clickhouse","p":102423},{"i":102428,"t":"The structure of the data table we store in Hive is as follows, which stores common Nginx logs. CREATE TABLE `nginx_msg_detail`( `hostname` string, `domain` string, `remote_addr` string, `request_time` float, `datetime` string, `url` string, `status` int, `data_size` int, `referer` string, `cookie_info` string, `user_agent` string, `minute` string) PARTITIONED BY ( `date` string, `hour` string)","s":"Hive Schema","u":"/blog/hive-to-clickhouse","h":"#hive-schema","p":102423},{"i":102430,"t":"Our ClickHouse table creation statement is as follows, our table is partitioned by day CREATE TABLE cms.cms_msg ( date Date, datetime DateTime, url String, request_time Float32, status String, hostname String, domain String, remote_addr String, data_size Int32 ) ENGINE = MergeTree PARTITION BY date ORDER BY (date, hostname) SETTINGS index_granularity = 16384","s":"ClickHouse Schema","u":"/blog/hive-to-clickhouse","h":"#clickhouse-schema","p":102423},{"i":102432,"t":"Next, I will introduce to you how we write data from Hive to ClickHouse through Seatunnel.","s":"Seatunnel with ClickHouse","u":"/blog/hive-to-clickhouse","h":"#seatunnel-with-clickhouse","p":102423},{"i":102434,"t":"Seatunnel is a very easy-to-use, high-performance, real-time data processing product that can deal with massive data. It is built on Spark. Seatunnel has a very rich set of plug-ins that support reading data from Kafka, HDFS, and Kudu, performing various data processing, and writing the results to ClickHouse, Elasticsearch or Kafka. The environment preparation and installation steps of Seatunnel will not be repeated here. For specific installation steps, please refer to the previous article or visit Seatunnel Docs","s":"Seatunnel","u":"/blog/hive-to-clickhouse","h":"#seatunnel","p":102423},{"i":102436,"t":"We only need to write a configuration file of Seatunnel Pipeline to complete the data import. The configuration file includes four parts, namely Spark, Input, filter and Output. Spark​ This part is the related configuration of Spark, which mainly configures the resource size required for Spark execution. spark { // This configuration is required spark.sql.catalogImplementation = \"hive\" spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } Input​ This part defines the data source. The following is a configuration example of reading data in text format from a Hive file. input { hive { pre_sql = \"select * from access.nginx_msg_detail\" table_name = \"access_log\" } } See, a very simple configuration can read data from Hive. pre_sql is the SQL to read data from Hive, and table_name is the name of the table that will register the read data as a temporary table in Spark, which can be any field. It should be noted that it must be ensured that the metastore of hive is in the service state. When running in Cluster, Client, Local mode, the hive-site.xml file must be placed in the $HADOOP_CONF directory of the submit task node Filter​ In the Filter section, here we configure a series of transformations, and here we discard the unnecessary minute and hour fields. Of course, we can also not read these fields through pre_sql when reading Hive filter { remove { source_field = [\"minute\", \"hour\"] } } Output​ Finally, we write the processed structured data to ClickHouse output { clickhouse { host = \"your.clickhouse.host:8123\" database = \"seatunnel\" table = \"nginx_log\" fields = [\"date\", \"datetime\", \"hostname\", \"url\", \"http_code\", \"request_time\", \"data_size\", \"domain\"] username = \"username\" password = \"password\" } }","s":"Seatunnel Pipeline","u":"/blog/hive-to-clickhouse","h":"#seatunnel-pipeline","p":102423},{"i":102438,"t":"We combine the above four-part configuration into our configuration file config/batch.conf. vim config/batch.conf spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" // This configuration is required spark.sql.catalogImplementation = \"hive\" } input { hive { pre_sql = \"select * from access.nginx_msg_detail\" table_name = \"access_log\" } } filter { remove { source_field = [\"minute\", \"hour\"] } } output { clickhouse { host = \"your.clickhouse.host:8123\" database = \"seatunnel\" table = \"access_log\" fields = [\"date\", \"datetime\", \"hostname\", \"uri\", \"http_code\", \"request_time\", \"data_size\", \"domain\"] username = \"username\" password = \"password\" } } Execute the command, specify the configuration file, and run Seatunnel to write data to ClickHouse. Here we take the local mode as an example. ./bin/start-seatunnel.sh --config config/batch.conf -e client -m 'local[2]'","s":"Running Seatunnel","u":"/blog/hive-to-clickhouse","h":"#running-seatunnel","p":102423},{"i":102440,"t":"In this post, we covered how to import data from Hive into ClickHouse using Seatunnel. The data import can be completed quickly through only one configuration file without writing any code, which is very simple. If you want to know more functions and cases of Seatunnel combined with ClickHouse, Elasticsearch, Kafka, Hadoop, you can go directly to the official website https://seatunnel.apache.org/ -- Power by InterestingLab","s":"Conclusion","u":"/blog/hive-to-clickhouse","h":"#conclusion","p":102423},{"i":102442,"t":"ClickHouse is a distributed columnar DBMS for OLAP. Our department has now stored all log data related to data analysis in ClickHouse, an excellent data warehouse, and the current daily data volume has reached 30 billion. The experience of data processing and storage introduced earlier is based on real-time data streams. The data is stored in Kafka. We use Java or Golang to read, parse, and clean the data from Kafka and write it into ClickHouse, so that the data can be stored in ClickHouse. Quick access. However, in the usage scenarios of many students, the data is not real-time, and it may be necessary to import the data in HDFS or Hive into ClickHouse. Some students implement data import by writing Spark programs, so is there a simpler and more efficient way? At present, there is a tool Seatunnel in the open source community, the project address https://github.com/apache/incubator-seatunnel, can quickly Data in HDFS is imported into ClickHouse.","s":"How to quickly import data from HDFS into ClickHouse","u":"/blog/hdfs-to-clickhouse","h":"","p":102441},{"i":102444,"t":"Assuming that our logs are stored in HDFS, we need to parse the logs and filter out the fields we care about, and write the corresponding fields into the ClickHouse table.","s":"HDFS To ClickHouse","u":"/blog/hdfs-to-clickhouse","h":"#hdfs-to-clickhouse","p":102441},{"i":102446,"t":"The log format we store in HDFS is as follows, which is a very common Nginx log 10.41.1.28 github.com 114.250.140.241 0.001s \"127.0.0.1:80\" [26/Oct/2018:03:09:32 +0800] \"GET /Apache/Seatunnel HTTP/1.1\" 200 0 \"-\" - \"Dalvik/2.1.0 (Linux; U; Android 7.1.1; OPPO R11 Build/NMF26X)\" \"196\" \"-\" \"mainpage\" \"443\" \"-\" \"172.16.181.129\"","s":"Log Sample","u":"/blog/hdfs-to-clickhouse","h":"#log-sample","p":102441},{"i":102448,"t":"Our ClickHouse table creation statement is as follows, our table is partitioned by day CREATE TABLE cms.cms_msg ( date Date, datetime DateTime, url String, request_time Float32, status String, hostname String, domain String, remote_addr String, data_size Int32, pool String ) ENGINE = MergeTree PARTITION BY date ORDER BY date SETTINGS index_granularity = 16384","s":"ClickHouse Schema","u":"/blog/hdfs-to-clickhouse","h":"#clickhouse-schema","p":102441},{"i":102450,"t":"Next, I will introduce to you in detail how we can meet the above requirements through Seatunnel and write the data in HDFS into ClickHouse.","s":"Seatunnel with ClickHouse","u":"/blog/hdfs-to-clickhouse","h":"#seatunnel-with-clickhouse","p":102441},{"i":102452,"t":"Seatunnel is a very easy-to-use, high-performance, real-time data processing product that can deal with massive data. It is built on Spark. Seatunnel has a very rich set of plugins that support reading data from Kafka, HDFS, Kudu, performing various data processing, and writing the results to ClickHouse, Elasticsearch or Kafka.","s":"Seatunnel","u":"/blog/hdfs-to-clickhouse","h":"#seatunnel","p":102441},{"i":102454,"t":"First we need to install Seatunnel, the installation is very simple, no need to configure system environment variables Prepare the Spark environment Install Seatunnel Configure Seatunnel The following are simple steps, the specific installation can refer to Quick Start cd /usr/local wget https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz tar -xvf https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz wget https://github.com/InterestingLab/seatunnel/releases/download/v1.1.1/seatunnel-1.1.1.zip unzip seatunnel-1.1.1.zip cd seatunnel-1.1.1 vim config/seatunnel-env.sh # Specify the Spark installation path SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.2.0-bin-hadoop2.7}","s":"Prerequisites","u":"/blog/hdfs-to-clickhouse","h":"#prerequisites","p":102441},{"i":102456,"t":"We only need to write a configuration file of seatunnel Pipeline to complete the data import. The configuration file consists of four parts, Spark, Input, filter and Output. Spark​ This part is the related configuration of Spark, which mainly configures the size of the resources required for Spark to execute. spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } Input​ This part defines the data source. The following is a configuration example for reading data in text format from HDFS files. input { hdfs { path = \"hdfs://nomanode:8020/rowlog/accesslog\" table_name = \"access_log\" format = \"text\" } } Filter​ In the Filter section, here we configure a series of transformations, including regular parsing to split the log, time transformation to convert HTTPDATE to the date format supported by ClickHouse, type conversion to Number type fields, and field filtering through SQL, etc. filter { # Parse raw logs using regular expressions grok { source_field = \"raw_message\" pattern = '%{IP:ha_ip}\\\\s%{NOTSPACE:domain}\\\\s%{IP:remote_addr}\\\\s%{NUMBER:request_time}s\\\\s\\\"%{DATA:upstream_ip}\\\"\\\\s\\\\[%{HTTPDATE:timestamp}\\\\]\\\\s\\\"%{NOTSPACE:method}\\\\s%{DATA:url}\\\\s%{NOTSPACE:http_ver}\\\"\\\\s%{NUMBER:status}\\\\s%{NUMBER:body_bytes_send}\\\\s%{DATA:referer}\\\\s%{NOTSPACE:cookie_info}\\\\s\\\"%{DATA:user_agent}\\\"\\\\s%{DATA:uid}\\\\s%{DATA:session_id}\\\\s\\\"%{DATA:pool}\\\"\\\\s\\\"%{DATA:tag2}\\\"\\\\s%{DATA:tag3}\\\\s%{DATA:tag4}' } # Convert data in \"dd/MMM/yyyy:HH:mm:ss Z\" format to # Data in \"yyyy/MM/dd HH:mm:ss\" format date { source_field = \"timestamp\" target_field = \"datetime\" source_time_format = \"dd/MMM/yyyy:HH:mm:ss Z\" target_time_format = \"yyyy/MM/dd HH:mm:ss\" } # Use SQL to filter the fields of interest and process the fields # You can even filter out data you don't care about by filter conditions sql { table_name = \"access\" sql = \"select substring(date, 1, 10) as date, datetime, hostname, url, http_code, float(request_time), int(data_size), domain from access\" } } Output​ Finally, we write the processed structured data to ClickHouse output { clickhouse { host = \"your.clickhouse.host:8123\" database = \"seatunnel\" table = \"access_log\" fields = [\"date\", \"datetime\", \"hostname\", \"uri\", \"http_code\", \"request_time\", \"data_size\", \"domain\"] username = \"username\" password = \"password\" } }","s":"seatunnel Pipeline","u":"/blog/hdfs-to-clickhouse","h":"#seatunnel-pipeline","p":102441},{"i":102458,"t":"We combine the above four-part configuration into our configuration file config/batch.conf. vim config/batch.conf spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } input { hdfs { path = \"hdfs://nomanode:8020/rowlog/accesslog\" table_name = \"access_log\" format = \"text\" } } filter { # Parse raw logs using regular expressions grok { source_field = \"raw_message\" pattern = '%{IP:ha_ip}\\\\s%{NOTSPACE:domain}\\\\s%{IP:remote_addr}\\\\s%{NUMBER:request_time}s\\\\s\\\"%{DATA:upstream_ip}\\\"\\\\s\\\\[%{HTTPDATE:timestamp}\\\\]\\\\s\\\"%{NOTSPACE:method}\\\\s%{DATA:url}\\\\s%{NOTSPACE:http_ver}\\\"\\\\s%{NUMBER:status}\\\\s%{NUMBER:body_bytes_send}\\\\s%{DATA:referer}\\\\s%{NOTSPACE:cookie_info}\\\\s\\\"%{DATA:user_agent}\\\"\\\\s%{DATA:uid}\\\\s%{DATA:session_id}\\\\s\\\"%{DATA:pool}\\\"\\\\s\\\"%{DATA:tag2}\\\"\\\\s%{DATA:tag3}\\\\s%{DATA:tag4}' } # Convert data in \"dd/MMM/yyyy:HH:mm:ss Z\" format to # Data in \"yyyy/MM/dd HH:mm:ss\" format date { source_field = \"timestamp\" target_field = \"datetime\" source_time_format = \"dd/MMM/yyyy:HH:mm:ss Z\" target_time_format = \"yyyy/MM/dd HH:mm:ss\" } # Use SQL to filter the fields of interest and process the fields # You can even filter out data you don't care about by filter conditions sql { table_name = \"access\" sql = \"select substring(date, 1, 10) as date, datetime, hostname, url, http_code, float(request_time), int(data_size), domain from access\" } } output { clickhouse { host = \"your.clickhouse.host:8123\" database = \"seatunnel\" table = \"access_log\" fields = [\"date\", \"datetime\", \"hostname\", \"uri\", \"http_code\", \"request_time\", \"data_size\", \"domain\"] username = \"username\" password = \"password\" } } Execute the command, specify the configuration file, and run Seatunnel to write data to ClickHouse. Here we take the local mode as an example. ./bin/start-seatunnel.sh --config config/batch.conf -e client -m 'local[2]'","s":"Running seatunnel","u":"/blog/hdfs-to-clickhouse","h":"#running-seatunnel","p":102441},{"i":102460,"t":"In this post, we covered how to import Nginx log files from HDFS into ClickHouse using Seatunnel. Data can be imported quickly with only one configuration file without writing any code. In addition to supporting HDFS data sources, Seatunnel also supports real-time reading and processing of data from Kafka to ClickHouse. Our next article will describe how to quickly import data from Hive into ClickHouse. Of course, Seatunnel is not only a tool for ClickHouse data writing, but also plays a very important role in the writing of data sources such as Elasticsearch and Kafka. If you want to know more functions and cases of Seatunnel combined with ClickHouse, Elasticsearch and Kafka, you can go directly to the official website https://seatunnel.apache.org/ -- Power by InterestingLab","s":"Conclusion","u":"/blog/hdfs-to-clickhouse","h":"#conclusion","p":102441},{"i":102462,"t":"As we know, only manageable, callable, computable, and magnetizable data resources can be deposited as assets. The interconnection of information systems has created a huge demand for multi-source and multidimensional data integration, which imposes strict requirements on data processing and integration tools. In the era of intelligence, under the trends of “smart city”, “smart governance”, and “intelligent products”, enterprises are mostly faced with the challenge of how to achieve efficient data push, improve platform quality, and ensure data security. Only by choosing the right data integration tools and platforms can data play a key role. As a next-generation high-performance, distributed, and massive data integration framework, Apache SeaTunnel is committed to making data synchronization simpler and more efficient and accelerating the implementation of distributed data processing capabilities in the production environment. At the Apache SeaTunnel Meetup (April 16, 2022), the community will invite experienced Apache SeaTunnel users to share the best practices of the project in intelligent production environments. In addition, there will be contributors to analyze the source code of Apache SeaTunnel, guiding you to have a comprehensive and in-depth understanding of this powerful data integration tool. Whether you are a beginner who is interested in Apache SeaTunnel or users who encounter complex and difficult deployment problems in daily practice, you can come here to communicate with our instructors and get the answers you want.","s":"Innovation of Data Integration Technology in the Intelligent Era","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"","p":102461},{"i":102464,"t":"Apache SeaTunnel Meetup | April online live registration has been started, hurry up and register! Time: 2022–4–16 14:00–17:00 Format: live online Click the link to register (free): https://www.slidestalk.com/m/780 Join Slack: https://join.slack.com/t/apacheseatunnel/shared_invite/zt-10u1eujlc-g4E~ppbinD0oKpGeoo_dAw","s":"01 Sign up","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#01-sign-up","p":102461},{"i":102466,"t":"Detailed case study Feature Analysis Tips to avoid stepping into the pit from enterprises Open-source community growth strategy Face-to-face Q&A with industry technical experts Surprise gifts","s":"02 Highlights","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#02-highlights","p":102461},{"i":102468,"t":"On the day of the event, big data engineers from Kidswant and oppo will share the front-line practical experience, and senior engineers from WhaleOps will give a “hard-core” explanation of the important function updates of Apache SeaTunnel. Yuan Hongjun, Kidswant Big Data Expert, OLAP Platform Architect Years of experience in R&D and management of big data platforms, rich research experience in data assets, data linkage, data governance, OLAP, and other fields Time: 14:00–14:40 Topic: Application Practice of Apache SeaTunnel in Kidswant Speech outline: How to push data efficiently? How to improve the quality of the platform? How to ensure data security? What changes did Kidswant make to Apache SeaTunnel? Fan Jia, WhaleOps Senior Engineer Time: 14:40–15:20 Topic: Clickhouse Bulk Load Implementation Based on Apache SeaTunnel Speech outline: How to implement the bulk load data synchronization function of Clickhouse by extending the Connector of Apache SeaTunnel? Wang Zichao, Oppo Senior Backend Engineer Time: 15:50–16:30 Topic: The technological innovation of oppo intelligent recommendation sample center based on Apache SeaTunnel Speech outline: Introduce the evolution of oppo’s intelligent recommendation machine learning sample dealing process and the role of Apache SeaTunnel in it. In addition to the wonderful speeches, a number of lucky draw sessions were also set up on the meetup. Anyone participating in the lucky draw will have the opportunity to win exquisite customized gifts from Apache SeaTunnel, so stay tuned~","s":"03 Event Agenda","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#03-event-agenda","p":102461},{"i":102470,"t":"SeaTunnel (formerly Waterdrop) is an easy-to-use, ultra-high-performance distributed data integration platform that supports real-time synchronization of massive amounts of data and can synchronize hundreds of billions of data per day in a stable and efficient manner.","s":"About SeaTunnel","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#about-seatunnel","p":102461},{"i":102472,"t":"SeaTunnel does everything it can to solve the problems you may encounter in synchronizing massive amounts of data. Data loss and duplication Task buildup and latency Low throughput Long application-to-production cycle time Lack of application status monitoring","s":"Why do we need SeaTunnel?","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#why-do-we-need-seatunnel","p":102461},{"i":102474,"t":"Massive data synchronization Massive data integration ETL of large volumes of data Massive data aggregation Multi-source data processing","s":"SeaTunnel Usage Scenarios","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#seatunnel-usage-scenarios","p":102461},{"i":102476,"t":"Rich components High scalability Easy to use Mature and stable","s":"Features of SeaTunnel","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#features-of-seatunnel","p":102461},{"i":102478,"t":"Want to experience SeaTunnel quickly? SeaTunnel 2.1.0 takes 10 seconds to get you up and running. https://seatunnel.apache.org/docs/2.1.0/developement/setup","s":"How to get started with SeaTunnel quickly?","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#how-to-get-started-with-seatunnel-quickly","p":102461},{"i":102480,"t":"We invite all partners who are interested in making local open-source global to join the SeaTunnel contributors family and foster open-source together! Submit an issue: https://github.com/apache/incubator-seatunnel/issues Contribute code to: https://github.com/apache/incubator-seatunnel/pulls Subscribe to the community development mailing list : dev-subscribe@seatunnel.apache.org Development Mailing List : dev@seatunnel.apache.org Join Slack: https://the-asf.slack.com/archives/C053HND1D6X Follow Twitter: https://twitter.com/ASFSeaTunnel Come and join us!","s":"How can I contribute?","u":"/blog/Innovation of Data Integration Technology in the Intelligent Era","h":"#how-can-i-contribute","p":102461},{"i":102482,"t":"Bo Bi, data engineer at Mafengwo During the joint Apache SeaTunnel & IoTDB Meetup on October 15, Bo Bi, the data engineer at a leading Chinese travel-social e-commerce platform Mafengwo, introduced the basic principles of SeaTunnel and related enterprise practice thinking, the pain points and optimization thinking in typical scenarios of Mafengwo’s big data development and scheduling platform, and shared his experience of participating in community contributions. We hope to help you understand SeaTunnel and the paths and skills of community building at the same time.","s":"Mafengwo finally chose Apache SeaTunnel after analyzing these 9 points of how it works!","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"","p":102481},{"i":102484,"t":"SeaTunnel is a distributed, high-performance data integration platform for the synchronization and transformation of large volumes of data (offline and real-time) The diagram above shows the workflow of SeaTunnel, which in simple terms consists of 3 parts: input, transformation, and output; more complex data processing is just a combination of several actions. In a synchronization scenario, such as importing Kafka to Elasticsearch, Kafka is the Source of the process and Elasticsearch is the Sink of the process. If, during the import process, the field columns do not match the external data columns to be written and some column or type conversion is required, or if you need to join multiple data sources and then do some data widening, field expansion, etc., then you need to add some Transform in the process, corresponding to the middle part of the picture. This shows that the core of SeaTunnel is the Source, Transform and Sink process definitions. In Source we can define the data sources we need to read, in Sink, we can define the data pipeline and eventually write the external storage, and we can transform the data in between, either using SQL or custom functions.","s":"Introduction to the technical principle of SeaTunnel","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#introduction-to-the-technical-principle-of-seatunnel","p":102481},{"i":102486,"t":"For a mature component framework, there must be something unique about the design pattern of the API design implementation that makes the framework scalable. The SeaTunnel architecture consists of three main parts. 1、SeaTunnel Basic API. the implementation of the SeaTunnel base API. SeaTunnel’s plug-in system.","s":"SeaTunnel Connector API Version V1 Architecture Breakdown","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-connector-api-version-v1-architecture-breakdown","p":102481},{"i":102488,"t":"The above diagram shows the definition of the interface, the Plugin interface in SeaTunnel abstracts the various actions of data processing into a Plugin. The five parts of the diagram below, Basesource, Basetransfform, Basesink, Runtimeenv, and Execution, all inherit from the Plugin interface. As a process definition plug-in, Source is responsible for reading data, Transform is responsible for transforming, Sink is responsible for writing and Runtimeenv is setting the base environment variables. The overall SeaTunnel base API is shown below Execution, the data flow builder used to build the entire data flow based on the first three, is also part of the base API","s":"SeaTunnel Basic API","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-basic-api","p":102481},{"i":102490,"t":"Based on the previous basic APIs, SeaTunnel has been implemented in separate packages for different computing engines, currently the Spark API abstraction and the Flink API abstraction, which logically completes the process of building the data pipeline. Due to space constraints, we will focus on Spark batch processing. Based on the wrapped implementation of the previous base Api, the first is that Base spark source implements Base source, base Spark transform implements Base transform and Base Spark sink implements Base sink. The method definition uses Spark’s Dataset as the carrier of the data, and all data processing is based on the Dataset, including reading, processing and exporting. The SparkEnvironment, which internally encapsulates Spark’s Sparksession in an Env, makes it easy for individual plugins to use. The Spark batch process ends with SparkBatchExecution (the data stream builder), which is the core code snippet used to functionally build our data stream Pipeline, the most basic data stream on the left in the diagram below. The user-based definition of each process component is also the configuration of Source Sink, Transform. More complex data flow logic can be implemented, such as multi-source Join, multi-pipeline processing, etc., all of which can be built through Execution.","s":"SeaTunnel Base API Implementation","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-base-api-implementation","p":102481},{"i":102492,"t":"SeaTunnel’s API consists of three main parts. The first part is the SeaTunnel base API, which provides the basic abstract interfaces such as Source, Sink, Transform, and Plugin. The second part is based on a set of interfaces Transform, Sink, Source, Runtime, and Execution provided by the SeaTunnel base API, which is wrapped and implemented on the Flink and Spark engines respectively, i.e. Spark engine API layer abstraction and Flink engine API layer abstraction. Both Flink and Spark engines support stream and batch processing, so there are different ways to use streams/batches under the Flink API abstraction and Spark abstraction APIs, such as Flinkstream and Flinkbatch under the Flink abstraction API, and Sparkbatch and Sparkstreaming under the Spark abstraction API. The third part is the plug-in system, based on Spark abstraction and Flink API abstraction, SeaTunnel engine implements rich connectors and processing plug-ins, while developers can also be based on different engine API abstractions, and extensions to achieve their own Plugin. SeaTunnel Implementation Principle Currently, SeaTunnel offers a variety of ways to use Flink, Spark, and FlinkSQL. Due to space limitations, we will introduce the execution principles of the Spark method. First, the entry starts the command Start-seatunnel-spark.sh via the shell, which internally calls Sparkstarter’s Class, which parses the parameters passed by the shell script, and also parses the Config file to determine which Connectors are defined in the Config file, such as Fake, Console, etc. Then find the Connector path from the Connector plugin directory and stitch it into the Spark-submit launch command with — jar, so that the found Plugin jar package can be passed to the Spark cluster as a dependency. For Connector plugins, all Spark Connectors are packaged in the plugin directory of the distribution (this directory is managed centrally). After Spark-submit is executed, the task is submitted to the Spark cluster, and the Main class of the Spark job’s Driver builds the data flow Pipeline through the data flow builder Execution, combined with Souce, Sink, and Transform so that the whole chain is connected.","s":"SeaTunnel Connector V1 API Architecture Summary","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-connector-v1-api-architecture-summary","p":102481},{"i":102494,"t":"In the latest community release of SeaTunnel 2.2.0-beta, the refactoring of the Connectorapi, now known as the SeaTurnelV2 API, has been completed! Why do we need to reconfigure? As the Container is currently a strongly coupled engine, i.e. Flink and Spark API, if the Flink or Spark engine is upgraded, the Connector will also have to be adjusted, possibly with changes to parameters or interfaces. This can lead to multiple implementations for different engines and inconsistent parameters to develop a new Connector. Therefore, the community has designed and implemented the V2 version of the API based on these pain points.","s":"SeaTunnel Connector V2 API Architecture","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-connector-v2-api-architecture","p":102481},{"i":102496,"t":"SeaTunnel V2 API Architecture","s":"SeaTunnel V2 API Architecture","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-v2-api-architecture","p":102481},{"i":102498,"t":"·DataType: defines SeaTunnel’s data structure SeaTunnelRow, which is used to isolate the engine ·Catalog: used to obtain Table Scheme, Options, etc.. ·Catalog Storage: used to store user-defined Table Schemes etc. for unstructured engines such as Kafka. ·Table SPI: mainly used to expose the Source and Sink interfaces as an SPI","s":"1.Table API","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#1table-api","p":102481},{"i":102500,"t":"Define the Connector’s core programming interface for implementing the Connector","s":"2. Source & Sink API","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#2-source--sink-api","p":102481},{"i":102502,"t":"·Translation: The translation layer, which translates the Source and Sink APIs implemented by the Connector into a runnable API inside the engine. ·Execution: Execution logic, used to define the execution logic of Source, Transform, Sink and other operations within the engine. The Source & Sink API is the basis for the implementation of the connector and is very important for developers. The design of the v2 Source & Sink API is highlighted below","s":"3.Engine API","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#3engine-api","p":102481},{"i":102504,"t":"The current version of SeaTunnel’s API design draws on some of Flink’s design concepts, and the more core classes of the Source API are shown below. The core Source API interaction flow is shown above. In the case of concurrent reads, the enumerator SourceSplitEnumerator is required to split the task and send the SourceSplit down to the SourceReader, which receives the split and uses it to read the external data source. In order to support breakpoints and Eos semantics, it is necessary to preserve and restore the state, for example by preserving the current Reader’s Split consumption state and restoring it after a failure in each Reader through the Checkpoint state and Checkpoint mechanism, so that the data can be read from the place where it failed.","s":"SeaTunnel Connector V2 Source API","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-connector-v2-source-api","p":102481},{"i":102506,"t":"The overall Sink API interaction flow is shown in the diagram below. The SeaTunnel sink is currently designed to support distributed transactions, based on a two-stage transaction commit. First SinkWriter continuously writes data to an external data source, then when the engine does a checkpoint, it triggers a first-stage commit. SinkWriter needs to do a Prepare commit, which is the first stage of the commit. The engine will determine if all the Writer's first stage succeeds, and if they all succeed, the engine will combine the Subtask’s Commit info with the Commit method of the Committer to do the actual commit of the transaction and operate the database for the Commit, i.e. the second stage of the commit. This is the second stage of commit. For the Kafka sink connector implementation, the first stage is to do a pre-commit by calling KafkaProducerSender.prepareCommit(). The second commit is performed via Producer.commitTransaction();. flush(); flushes the data from the Broker’s system cache to disk. Finally, it is worth noting! Both SinkCommitter and SinkAggregatedCommitter can perform a second stage commit to replace the Committer in the diagram. The difference is that SinkCommitter can only do a partial commit of a single Subtask’s CommitInfo, which may be partially successful and partially unsuccessful, and cannot be handled globally. The difference is that the SinkCommitter can only do partial commits of a single Subtask’s CommitInfo, which may be partially successful and partially unsuccessful. SinkAggregatedCommitter is a single parallel, aggregating the CommitInfo of all Subtask, and can do the second stage commit as a whole, either all succeed or all fail, avoiding the problem of inconsistent status due to partial failure of the second stage. It is therefore recommended that the SinkAggregatedCommitter be used in preference.","s":"SeaTunnel Connector V2 Sink API","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-connector-v2-sink-api","p":102481},{"i":102508,"t":"We can look at the changes before and after the V1 V2 upgrade from a data processing perspective, which is more intuitive, Spark batch processing as an example: SeaTunnel V1: The entire data processing process is based on the Spark dataset API, and the Connector and the compute engine are strongly coupled. SeaTunnel V2: Thanks to the work of the engine translator, the Connector API, and the SeaTunnelRow, the data source of the SeaTunnel internal data structures accessed through the Connector, are translated by the translation layer into a runnable Spark API and spark dataset that is recognized inside the engine during data transformation. As data is written out, the Spark API and Spark dataset are translated through the translation layer into an executable connector API inside the SeaTunnel connector and a data source of internal SeaTunnel structures that can be used. Overall, the addition of a translation layer at the API and compute engine layers decouples the Connector API from the engine, and the Connector implementation no longer depends on the compute engine, making the extension and implementation more flexible. In terms of community planning, the V2 API will be the main focus of development, and more features will be supported in V2, while V1 will be stabilized and no longer maintained.","s":"Comparison of SeaTunnel V1 and V2 API processing flows","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#comparison-of-seatunnel-v1-and-v2-api-processing-flows","p":102481},{"i":102511,"t":"Hornet’s Nest Big Data Development Platform, which focuses on providing one-stop big data development and scheduling services, helps businesses solve complex problems such as data development management, task scheduling and task monitoring in offline scenarios. The offline development and scheduling platform plays the role of the top and the bottom. The top is to provide open interface API and UI to connect with various data application platforms and businesses, and the bottom is to drive various computations and storage, and then run in an orderly manner according to the task dependency and scheduling time.","s":"Practice and reflections on our off-line development scheduling platform","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#practice-and-reflections-on-our-off-line-development-scheduling-platform-1","p":102481},{"i":102513,"t":"Data development Task configuration, quality testing, release live ·Data synchronisation Data access, data processing, data distribution ·Scheduling capabilities Supports timed scheduling, triggered scheduling ·Operations and Maintenance Centre Job Diagnosis, Task O&M, Instance O&M ·Management Library table management, permission management, API management, script management In summary, the core capabilities of the offline development scheduling platform are openness, versatility, and one-stop shopping. Through standardized processes, the entire task development cycle is managed and a one-stop service experience is provided.","s":"Platform Capabilities","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#platform-capabilities","p":102481},{"i":102515,"t":"The Hornet’s Nest Big Data Development and Scheduling Platform consists of four main modules: the task component layer, the scheduling layer, the service layer, and the monitoring layer. The service layer is mainly responsible for job lifecycle management (e.g. job creation, testing, release, offline); Airflow dagphthon file building and generating, task bloodline dependency management, permission management, API (providing data readiness, querying of task execution status). The scheduling layer is based on Airflow and is responsible for the scheduling of all offline tasks. A task component layer that enables users to develop data through supported components that include tools such as SparkSQL/, HiveSQ, LMR), StarRocks import, etc., directly interfacing with underlying HDFS, MySQL, and other storage systems. The monitoring layer is responsible for all aspects of monitoring and alerting on scheduling resources, computing resources, task execution, etc.","s":"The architecture of the platform","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#the-architecture-of-the-platform","p":102481},{"i":102517,"t":"Challenges with open capabilities: Need to support multiple business scenarios and meet flexible data pipeline requirements (i.e. extend to support more task components such as hive2clickhourse, clickhourse2mysql, etc.) Extending task components based on Airflow: higher maintenance costs for extensions, need to reduce costs and increase efficiency (based on the limited provider's Airflow offers, less applicable in terms of usage requirements, Airflow is a Python technology stack, while our team is mainly based on the Java technology stack, so the technology stack difference brings higher iteration costs) Self-developed task components: the high cost of platform integration, long development cycle, high cost of the configuration of task components. (Research or implement task components by yourself, different ways of adapting the parameters of the components in the service layer, no uniform way of parameter configuration) We wanted to investigate a data integration tool that, firstly, supported a rich set of components, provided out-of-the-box capabilities, was easy to extend, and offered a uniform configuration of parameters and a uniform way of using them to facilitate platform integration and maintenance. Selection of data integration tools To address the pain points mentioned above, we actively explored solutions and conducted a selection analysis of several mainstream data integration products in the industry. As you can see from the comparison above, Datax and SeaTunnel both offer good scalability, and high stability, support rich connector plugins, provide scripted, uniformly configurable usage, and have active communities. However, Datax is limited by being distributed and is not well suited to massive data scenarios. In contrast, SeaTunnel offers the ability to provide distributed execution, distributed transactions, scalable levels of data handling, and the ability to provide a unified technical solution in data synchronization scenarios. In addition to the advantages and features described above and the applicable scenarios, more importantly, the current offline computing resources for big data are unified and managed by yarn, and for the subsequently extended tasks we also wish to execute on Yarn, we finally prefer SeaTunnel for our usage scenarios. Further performance testing of SeaTunnel and the development of an open data scheduling platform to integrate SeaTunnel may be carried out at a later stage, and its use will be rolled out gradually.","s":"Open Data Sync Capability Scenarios","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#open-data-sync-capability-scenarios","p":102481},{"i":102519,"t":"To briefly introduce the background, the Big Data platform has now completed the unification of the OLAP engine layer, using the StarRocks engine to replace the previous Kylin engine as the main query engine in OLAP scenarios. In the data processing process, after the data is modelled in the data warehouse, the upper model needs to be imported into the OLAP engine for query acceleration, so there are a lot of tasks to push data from Hive to StarRocks every day. task (based on a wrapper for the StarRocks Broker Load import method) to a StarRocks-based table. The current pain points are twofold. ·Long data synchronization links: Hive2StarRocks processing links, which require at least two tasks, are relatively redundant. ·Outbound efficiency: From the perspective of outbound efficiency, many Hive models themselves are processed by Spark SQL, and based on the processing the Spark Dataset in memory can be pushed directly to StarRocks without dropping the disk, improving the model’s regional time. StarRocks currently also supports Spark Load, based on the Spark bulk data import method, but our ETL is more complex, needs to support data conversion multi-table Join, data aggregation operations, etc., so temporarily can not meet. We know from the SeaTunnel community that there are plans to support the StarRocks Sink Connector, and we are working on that part as well, so we will continue to communicate with the community to build it together later.","s":"Outbound scenario: Hive data sync to StarRocks","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#outbound-scenario-hive-data-sync-to-starrocks","p":102481},{"i":102522,"t":"As mentioned earlier, the community has completed the refactoring of the V1 to V2 API and needs to implement more connector plug-ins based on the V2 version of the connector API, which I was lucky enough to contribute to. I am currently responsible for big data infrastructure work, which many mainstream big data components big data also use, so when the community proposed a connector issue, I was also very interested in it. As the platform is also investigating SeaTunnel, learning and being able to contribute pr to the community is a great way to learn about SeaTunnel. I remember at first I proposed a less difficult pr to implement the WeChat sink connector, but in the process of contributing I encountered many problems, bad coding style, code style did not take into account the rich output format supported by the extension, etc. Although the process was not so smooth, I was really excited and accomplished when the pr was merged. Although the process was not so smooth, it was very exciting and rewarding when the pr was merged. As I became more familiar with the process, I became much more efficient at submitting pr and was confident enough to attempt difficult issues.","s":"SeaTunnel Community Contribution","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#seatunnel-community-contribution","p":102481},{"i":102524,"t":"Good first issue Good first issue #3018 #2828 If you are a first-time community contributor, it is advisable to focus on the Good first issue first, as it is basically a relatively simple and newcomer-friendly issue. Through Good first issue, you can get familiar with the whole process of participating in the GitHub open source community contribution, for example, first fork the project, then submit the changes, and finally submit the pull request, waiting for the community to review, the community will target to you to put forward some suggestions for improvement, directly will leave a comment below, until when your pr is merged in, this will have completed a comp Subscribe to community mailings Once you’re familiar with the pr contribution process, you can subscribe to community emails to keep up to date with what’s happening in the community, such as what features are currently being worked on and what’s planned for future iterations. If you’re interested in a feature, you can contribute to it in your own situation! Familiarity with git use The main git commands used in development are git clone, git pull, git rebase and git merge. git rebase is recommended in the community development specification and does not generate additional commits compared to git merge. Familiarity with GitHub project collaboration process Open source projects are developed collaboratively by multiple people, and the collaboration method on GitHub is at its core outlined in fork For example, the apache st project, which is under the apache space, is first forked to our own space on GitHub Then modify the implementation, mention a pull request, and submit the pull request to be associated with the issue, in the commit, if we change a long time, in the upward commit, then the target branch has a lot of new commits exhausted this time we need to do a pull& merge or rebase. Source code compilation project It is important to be familiar with source compilation, as local source compilation can prove that the code added to a project can be compiled, and can be used as a preliminary check before committing to pr. Source compilation is generally slow and can be speeded up by using mvn -T for multi-threaded parallel compilation. Compilation checks Pre-compilation checks, including Licence header, Code checkstyle, and Document checkstyle, will be checked during Maven compilation, and if they fail, the CI will not be passed. So it is recommended to use some plug-in tools in the idea to improve the efficiency, such as Code checkstyle has a plug-in to automatically check the code specification, Licence header can add code templates in the idea, these have been shared by the community before how to do! Add full E2E Add full E2E testing and ensure that the E2E is passed before the Pull request. Finally, I hope more students will join the SeaTunnel community, where you can not only feel the open-source spirit and culture of Apache but also understand the management process of Apache projects and learn good code design ideas. We hope that by working together and growing together, we can build SeaTunnel into a top-notch data integration platform.","s":"How to get involved in community contributions quickly","u":"/blog/During the joint Apache SeaTunnel & IoTDB Meetup on October 15,","h":"#how-to-get-involved-in-community-contributions-quickly","p":102481},{"i":102526,"t":"Author | Fan Jia, Apache SeaTunnel(Incubating) Contributor Editor | Test Engineer Feng Xiulan For importing billions of batches of data, the traditional JDBC approach does not perform as well as it should in some massive data synchronization scenarios. To write data faster, Apache SeaTunnel (Incubating) has just released version 2.1.1 to provide support for ClickhouseFile-Connector to implement Bulk load data writing. Bulk load means synchronizing large amounts of data to the target DB. SeaTunnel currently supports data synchronization to ClickHouse. At the Apache SeaTunnel (Incubating) April Meetup, Apache SeaTunnel (Incubating) contributor Fan Jia shared the topic of \"ClickHouse bulk load implementation based on SeaTunnel\", explaining in detail the implementation principle and process of ClickHouseFile for efficient processing of large amounts of data. Thanks to the test engineer Feng Xiulan for the article arrangement! This presentation contains seven parts. State of ClickHouse Sink Scenarios that ClickHouse Sink isn't good at Introduction to the ClickHouseFile plugin ClickHouseFile core technologies Analysis of ClickHouseFile plugin implementation Comparison of plug-in capabilities Post-optimization directions Fan Jia, Apache SeaTunnel (Incubating) contributor, Senior Enginee of WhaleOps.","s":"How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"","p":102525},{"i":102528,"t":"At present, the process of synchronizing data from SeaTunnel to ClickHouse is as follows: as long as the data source is supported by SeaTunnel, the data can be extracted, converted (or not), and written directly to the ClickHouse sink connector, and then written to the ClickHouse server via JDBC. However, there are some problems with writing to the ClickHouse server via traditional JDBC. Firstly, the tool used now is the driver provided by ClickHouse and implemented via HTTP, however, HTTP is not very efficient to implement in certain scenarios. The second is the huge amount of data, if there is duplicate data or a large amount of data written at once, it needs to generate the corresponding insert statement and send it via HTTP to the ClickHouse server-side by the traditional method, where it is parsed and executed item by item or in batches, which does not allow data compression. Finally, there is the problem we often encounter, i.e. too much data may lead to an OOM on the SeaTunnel side or a server-side hang due to too much data being written to the server-side too often. So we thought, is there a faster way to send than HTTP? If data pre-processing or data compression could be done on the SeaTunnel side, then the network bandwidth pressure would be reduced and the transmission rate would be increased.","s":"01 Status of ClickHouse Sink","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#01-status-of-clickhouse-sink","p":102525},{"i":102530,"t":"If the HTTP transfer protocol is used, HTTP may not be able to handle it when the volume of data is too large and the batch is sending requests in micro-batches. Too many INSERT requests may put too much pressure on the server. The bandwidth can handle a large number of requests, but the server-side is not always able to carry them. The online server not only needs data inserts but more importantly, the query data can be used by other business teams. If the server cluster goes down due to too much-inserted data, it is more than worth the cost.","s":"02 Scenarios that ClickHouse Sink isn't good at","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#02-scenarios-that-clickhouse-sink-isnt-good-at","p":102525},{"i":102532,"t":"In response to these scenarios that ClickHouse is not good at, we wondered is there a way to do data compression right on the Spark side, without increasing the resource load on the Server when writing data, and with the ability to write large amounts of data quickly? So we developed the ClickHouseFile plugin to solve the problem. The key technology of the ClickHouseFile plugin is ClickHouse -local. ClickHouse-local mode allows users to perform fast processing of local files without having to deploy and configure a ClickHouse Server. C lickHouse-local uses the same core as ClickHouse Server, so it supports most features as well as the same format and table engine. These two features mean that users can work directly with local files without having to do the processing on the ClickHouse Server side. Because it is the same format, the data generated by the operations we perform on the remote or SeaTunnel side is seamlessly compatible with the server-side and can be written to using ClickHouse local. ClickHouse local is the core technology for the implementation of ClickHouseFile, which allows for implementing the ClickHouse file connector. ClickHouse local core is used in the following ways. First line: pass the data to the test_table table of the ClickHouse-local program via the Linux pipeline. Lines two to five: create a result_table for receiving data. The sixth line: pass data from test_table to the result_table. Line 7: Define the disk path for data processing. By calling the Clickhouse-local component, the Apache SeaTunnel (Incubating) is used to generate the data files and compress the data. By communicating with the Server, the generated data is sent directly to the different nodes of Clickhouse and the data files are then made available to the nodes for the query. Comparison of the original and current implementations. Originally, the data, including the insert statements was sent by Spark to the server, and the server did the SQL parsing, generated and compressed the table data files, generated the corresponding files, and created the corresponding indexes. If we use ClickHouse local technology, the data file generation, file compression and index creation are done by SeaTunnel, and the final output is a file or folder for the server-side, which is synchronized to the server and the server can queries the data without additional operations.","s":"03 ClickHouse File core technologies","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#03-clickhouse-file-core-technologies","p":102525},{"i":102534,"t":"The above process makes data synchronization more efficient, thanks to three optimizations we have made to it. Firstly, the data is transferred from the pipeline to the ClickHouseFile by the division, which imposes limitations in terms of length and memory. For this reason, we write the data received by the ClickHouse connector, i.e. the sink side, to a temporary file via MMAP technology, and then the ClickHouse local reads the data from the temporary file to generate our target local file, in order to achieve the effect of incremental data reading and solve the OM problem. Secondly, it supports sharding. If only one file or folder is generated in a cluster, the file is distributed to only one node, which will greatly reduce the performance of the query. Therefore, we carry out slicing support. Users can set the key for slicing in the configuration folder, and the algorithm will divide the data into multiple log files and write them to different cluster nodes, significantly improving the read performance. The third key optimization is file transfer. Currently, SeaTunnel supports two file transfer methods, one is SCP, which is characterized by security, versatility, and no additional configuration; the other is RSYNC, which is somewhat fast and efficient and supports breakpoint resume, but requires additional configuration, users can choose between the way suits their needs.","s":"04 Core technical points","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#04-core-technical-points","p":102525},{"i":102536,"t":"In summary, the general implementation process of ClickHouseFile is as follows. 1.caching data to the ClickHouse sink side. 2.calling ClickHouse-local to generate the file. 3.sending the data to the ClickHouse server. 4.Execution of the ATTACH command. With the above four steps, the generated data reaches a queryable state.","s":"05 Plugin implementation analysis","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#05-plugin-implementation-analysis","p":102525},{"i":102538,"t":"(a) In terms of data transfer, ClickHouseFile is more suitable for massive amounts of data, with the advantage that no additional configuration is required and it is highly versatile, while ClickHouseFile is more complex to configure and currently supports fewer engines. In terms of environmental complexity, ClickHouse is more suitable for complex environments and can be run directly without additional configuration. In terms of versatility, ClickHouse, due to being an officially supported JDBC diver by SeaTunnel, basically supports all engines for data writing, while ClickHouseFile supports relatively few engines. In terms of server pressure, ClickHouseFile's advantage shows when it comes to massive data transfers that don't put too much pressure on the server. However, the two are not in competition and the choice needs to be based on the usage scenario.","s":"06 Comparison of plug-in capabilities","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#06-comparison-of-plug-in-capabilities","p":102525},{"i":102540,"t":"Although SeaTunnel currently supports the ClickHouseFile plugin, there are still many defects that need to be optimized, mainly including Rsync support. Exactly-Once support. Zero Copy support for transferring data files. More Engine support. Anyone interested in the above issues is welcome to contribute to the follow-up plans, or tell me your ideas!","s":"07 Follow-up plans","u":"/blog/How to synchronize tens of billions of data based on SeaTunnel's ClickHouse","h":"#07-follow-up-plans","p":102525},{"i":102542,"t":"Amazon Aurora DSQL is a distributed SQL database launched by Amazon Web Services in December 2024. It is designed for building applications with infinite scalability, high availability, and no infrastructure management, featuring high availability, serverless architecture, strong compatibility, fault tolerance, and high security levels. Since Aurora DSQL's authentication mechanism is integrated with IAM, accessing the Aurora DSQL database requires generating a token via an IAM identity. This token is valid for only 15 minutes by default; therefore, some mainstream data synchronization tools currently do not support migrating data from other databases to Aurora DSQL. In view of this situation, the author developed a Sink Connector specifically for Aurora DSQL based on the data synchronization tool Apache SeaTunnel, to meet the demand for migrating data from other databases to Aurora DSQL.","s":"Share from AWS Architect - Migrating Data to Amazon Aurora DSQL Based on SeaTunnel","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"","p":102541},{"i":102544,"t":"SeaTunnel is a very easy-to-use, multi-modal, ultra-high-performance distributed data integration platform suited for data integration and data synchronization, mainly aiming to solve common problems in the field of data integration.","s":"Introduction to SeaTunnel","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#introduction-to-seatunnel","p":102541},{"i":102546,"t":"Rich and Extensible Connectors: Currently, SeaTunnel supports over 190 Connectors, and the number is increasing. Connectors for mainstream databases like MySQL, Oracle, SQLServer, and PostgreSQL are already supported. The plugin-based design allows users to easily develop their own Connectors and integrate them into the SeaTunnel project. Batch-Stream Integration: Connectors developed based on the SeaTunnel Connector API are perfectly compatible with scenarios such as offline synchronization, real-time synchronization, full synchronization, and incremental synchronization. They greatly reduce the difficulty of managing data integration tasks. Distributed Snapshot: Supports distributed snapshot algorithms to ensure data consistency. Multi-Engine Support: SeaTunnel uses the SeaTunnel engine (Zeta) for data synchronization by default. SeaTunnel also supports using Flink or Spark as the execution engine for Connectors to adapt to existing enterprise technical components. SeaTunnel supports multiple versions of Spark and Flink. JDBC Reuse & Multi-table Database Log Parsing: SeaTunnel supports multi-table or whole-database synchronization, solving the problem of excessive JDBC connections; it supports multi-table or whole-database log reading and parsing, solving the problem of repeated log reading and parsing in CDC multi-table synchronization scenarios. High Throughput, Low Latency: SeaTunnel supports parallel reading and writing, providing stable, reliable, high-throughput, and low-latency data synchronization capabilities. Comprehensive Real-time Monitoring: SeaTunnel supports detailed monitoring information for every step of the data synchronization process, allowing users to easily understand information such as the amount of data read and written, data size, and QPS of the synchronization task.","s":"SeaTunnel Features","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#seatunnel-features","p":102541},{"i":102548,"t":"Figure 1 SeaTunnel Workflow SeaTunnel's workflow is shown in the figure above. The user configures job information and selects the execution engine to submit the job. The Source Connector is responsible for reading source data in parallel and sending the data to the downstream Transform or directly to the Sink, and the Sink writes the data to the destination.","s":"SeaTunnel Workflow","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#seatunnel-workflow","p":102541},{"i":102550,"t":"git clone https://github.com/apache/seatunnel.git cd seatunnel sh ./mvnw clean install -DskipTests -Dskip.spotless=true cp seatunnel-dist/target/apache-seatunnel-${version}-bin.tar.gz /The-Path-You-Want-To-Copy cd /The-Path-You-Want-To-Copy tar -xzvf \"apache-seatunnel-${version}-bin.tar.gz\" After successfully building from source, all Connector plugins and some necessary dependencies (e.g., mysql driver) are included in the binary package. You can use the Connector plugins directly without installing them separately.","s":"Build SeaTunnel from Source","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#build-seatunnel-from-source","p":102541},{"i":102552,"t":"env { parallelism = 1 job.mode = \"STREAMING\" checkpoint.interval = 6000 checkpoint.timeout = 1200000 } source { MySQL-CDC { username = \"user name\" password = \"password\" table-names = [\"db.table1\"] url = \"jdbc:mysql://dbhost:3306/db?useSSL=false&allowPublicKeyRetrieval=true&serverTimezone=UTC&connectTimeout=120000&socketTimeout=120000&autoReconnect=true&failOverReadOnly=false&maxReconnects=10\" table-names-config = [ { table = \"db.table1\" primaryKeys = [\"id\"] } ] } } transform { } sink { Jdbc { url=\"jdbc:postgresql://<dsql_endpoint>:5432/postgres\" dialect=\"dsql\" driver = \"org.postgresql.Driver\" username = \"admin\" access_key_id = \"ACCESSKEYIDEXAMPLE\" secret_access_key = \"SECRETACCESSKEYEXAMPLE\" region = \"us-east-1\" database = \"postgres\" generate_sink_sql = true primary_keys = [\"id\"] max_retries=\"3\" batch_size =1000 } }","s":"Configuration Example for Synchronizing MySQL Data to Aurora DSQL using SeaTunnel","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#configuration-example-for-synchronizing-mysql-data-to-aurora-dsql-using-seatunnel","p":102541},{"i":102554,"t":"Save the above configuration as a mysql-to-dsql.conf file (please note that the values in the example need to be replaced with real parameters), store it in the config directory of apache-seatunnel-${version}, and execute the following command: cd \"apache-seatunnel-${version}\" ./bin/seatunnel.sh --config ./config/mysql-to-dsql.conf -m local Figure 2 Data Synchronization Log Information After the command is successfully executed, you can observe the task execution status through the newly generated logs. If an error occurs, you can also pinpoint it based on the exception information, such as database connection timeout or table not existing. Under normal circumstances, data will be successfully written to the target Aurora DSQL, as shown in the figure above.","s":"Run Data Synchronization Task","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#run-data-synchronization-task","p":102541},{"i":102556,"t":"Aurora DSQL is a highly secure, easily scalable, serverless infrastructure distributed database. Its authentication method combines with IAM identity, so currently, there is a lack of suitable tools to synchronize data to Aurora DSQL, especially in terms of real-time data synchronization. SeaTunnel is an excellent data integration and data synchronization tool. It currently supports data synchronization from a variety of data sources, and based on SeaTunnel, custom data synchronization requirements can be implemented very flexibly, such as full synchronization/incremental real-time synchronization. Based on this flexibility, the author developed a Sink Connector specifically for Aurora DSQL to meet the data synchronization needs for Aurora DSQL.","s":"Summary","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#summary","p":102541},{"i":102558,"t":"SeaTunnel Deployment: https://seatunnel.apache.org/docs/start-v2/locally/deployment Developing a new SeaTunnel Connector: https://github.com/apache/seatunnel/blob/dev/seatunnel-connectors-v2/README.md Generating authentication tokens in Aurora DSQL: https://docs.aws.amazon.com/aurora-dsql/latest/userguide/SECTION_authentication-token.html The aforementioned specific Amazon Web Services Generative AI-related services are currently available in Amazon Web Services overseas regions. Cloud services related to Amazon Web Services China Regions are operated by Sinnet and NWCD. Please refer to the official website of China Regions for specific information.","s":"References","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#references","p":102541},{"i":102560,"t":"Tan Zhiqiang, AWS Migration Solution Architect, is mainly responsible for enterprise customers' cloud migration or cross-cloud migration. He has more than ten years of experience in IT professional services and has served as a programmer, project manager, technical consultant, and solution architect.","s":"About the Author","u":"/blog/migrate-data-to-amazon-aurora-dsql","h":"#about-the-author","p":102541},{"i":102562,"t":"In large-scale data integration, the throughput bottleneck is often not the data pipeline itself, but the “metadata path”: loading connector JARs during startup, managing state and recovery during runtime, and fetching schemas/partitions from external systems (databases, Hive Metastore, etc.) while initializing jobs. Once job concurrency reaches thousands (or more), these seemingly small operations can easily turn into cluster-wide pressure. Apache SeaTunnel Engine (Zeta) caches high-frequency, reusable, and expensive metadata on the engine side, and combines it with distributed storage and lifecycle cleanup. This is a key reason why the engine can run massive numbers of sync jobs concurrently with better stability.","s":"Deep Dive into Apache SeaTunnel Metadata Cache: Running Tens of Thousands of Sync Jobs in Parallel","u":"/blog/seatunnel-metadata-cache","h":"","p":102561},{"i":102564,"t":"When you start a huge number of small jobs in parallel, the most common metadata bottlenecks usually come from three areas: Class loading and dependency isolation: creating a dedicated ClassLoader per job can repeatedly load the same connector dependencies and quickly raise JVM Metaspace pressure. State and recoverability: checkpoints, runtime state, and historical job information can become heavy in both memory and IO without tiered storage and automatic cleanup. External schema/catalog queries: repeated schema and partition lookups can overload databases or Hive Metastore and lead to instability. Below is a practical breakdown of SeaTunnel’s approach, together with configuration tips you can apply in production.","s":"Why metadata becomes the bottleneck","u":"/blog/seatunnel-metadata-cache","h":"#why-metadata-becomes-the-bottleneck","p":102561},{"i":102566,"t":"When many jobs reuse the same set of connectors, frequent creation/destruction of class loaders causes Metaspace churn and can even lead to metaspace-related OOMs. SeaTunnel Engine provides classloader-cache-mode to reuse class loaders across jobs and reduce repeated loads. Enable it in seatunnel.yaml (it is enabled by default; re-enable it if you previously turned it off): seatunnel: engine: classloader-cache-mode: true When it helps most: High job concurrency and frequent job starts, with a relatively small set of connector types. You observe consistent Metaspace growth or class-loading related memory alerts. Notes: If your cluster runs with a highly diverse set of connectors, caching increases the amount of resident metadata in Metaspace. Monitor your Metaspace trend and adjust accordingly.","s":"1) ClassLoader caching to reduce Metaspace pressure","u":"/blog/seatunnel-metadata-cache","h":"#1-classloader-caching-to-reduce-metaspace-pressure","p":102561},{"i":102568,"t":"SeaTunnel Engine’s fault tolerance is built on the Chandy–Lamport checkpoint idea. For both performance and reliability, it uses Hazelcast distributed data structures (such as IMap) for certain runtime information, and relies on external storage (shared/distributed storage) for durable recovery. In practice, you will usually care about three sets of settings:","s":"2) Distributed state and persistence for recoverability","u":"/blog/seatunnel-metadata-cache","h":"#2-distributed-state-and-persistence-for-recoverability","p":102561},{"i":102570,"t":"seatunnel: engine: checkpoint: interval: 300000 timeout: 10000 If your job config (env) specifies checkpoint.interval/checkpoint.timeout, the job config takes precedence.","s":"(1) Checkpoint parameters","u":"/blog/seatunnel-metadata-cache","h":"#1-checkpoint-parameters","p":102561},{"i":102572,"t":"For multi-node clusters, configure at least backup-count to reduce the risk of losing in-memory information when a node fails. If you want jobs to be automatically recoverable after a full cluster stop/restart, consider enabling external persistence for IMap as well. For details, see: /docs/seatunnel-engine/deployment /docs/seatunnel-engine/checkpoint-storage","s":"(2) IMap backup and persistence (recommended for production)","u":"/blog/seatunnel-metadata-cache","h":"#2-imap-backup-and-persistence-recommended-for-production","p":102561},{"i":102574,"t":"SeaTunnel stores completed job status, counters, and error logs in IMap. As the number of jobs grows, memory usage will grow too. Configure history-job-expire-minutes so expired job information is evicted automatically (default is 1440 minutes, i.e., 1 day). seatunnel: engine: history-job-expire-minutes: 1440","s":"(3) Automatic cleanup of historical job information","u":"/blog/seatunnel-metadata-cache","h":"#3-automatic-cleanup-of-historical-job-information","p":102561},{"i":102576,"t":"When many jobs start concurrently, schema/catalog requests (table schema, partitions, constraints, etc.) can turn into a “silent storm”. SeaTunnel applies caching and reuse patterns in connectors/catalogs to reduce repeated network round-trips and metadata parsing overhead. JDBC sources: startup typically fetches table schemas, types, and primary keys for validation and split planning. For large fan-out startups, avoid letting every job repeatedly fetch the same metadata (batch job starts or pre-warming can help). Hive sources: Hive Metastore is often a shared and sensitive service. Reusing catalog instances and already-loaded database/table/partition metadata helps reduce Metastore pressure, especially for highly partitioned tables.","s":"3) Catalog/schema metadata caching to reduce source-side pressure","u":"/blog/seatunnel-metadata-cache","h":"#3-catalogschema-metadata-caching-to-reduce-source-side-pressure","p":102561},{"i":102578,"t":"Flink is primarily designed for long-running streaming jobs and complex operator state; Spark is job/context oriented for batch processing. For the “tens of thousands of independent small jobs” goal, SeaTunnel Engine focuses on pushing reusable metadata down to the engine layer: minimizing repeated loads, minimizing repeated external queries, and managing the lifecycle of historical job metadata to keep the cluster stable under high concurrency.","s":"How this differs from Flink/Spark: optimized for “massive small jobs”","u":"/blog/seatunnel-metadata-cache","h":"#how-this-differs-from-flinkspark-optimized-for-massive-small-jobs","p":102561},{"i":102580,"t":"Enable reasonable backups: in production, set backup-count >= 1 and evaluate IMap persistence if you need automatic recovery after full restarts. Limit connector diversity: keeping connector combinations relatively stable improves the benefit of classloader-cache-mode. Monitor metadata-related signals: besides JVM metrics, watch checkpoint latency/failure rate, Hazelcast memory usage, IMap size and growth, and historical job accumulation. Set eviction policies: tune history-job-expire-minutes to balance observability and long-term memory safety.","s":"Production checklist","u":"/blog/seatunnel-metadata-cache","h":"#production-checklist","p":102561},{"i":102582,"t":"In the recently released SeaTunnel 2.3.0 official version, the community self-developed engine SeaTunnel Zeta which has been under preparation for more than a year——is officially released, and it will be used as the default engine of SeaTunnel in the future, providing users with high throughput, low latency, reliable consistent synchronization job operation guarantee. Why does SeaTunnel develop its synchronization engine? What is the positioning of the SeaTunnel Engine? How is it different from traditional computing engines? What is the design idea? What is unique about the architectural design? These questions will be answered in this article. Why develop our engine SeaTunnel Engine Positioning Design ideas Architecture design Unique advantages and features Current basic functions and features Future optimization plan","s":"In the recently released SeaTunnel 2.3.0 official version","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"","p":102581},{"i":102584,"t":"It was a year ago that the SeaTunnel community publicly stated for the first time that it would develop its engine. The reason why the team decided to develop a self-developed engine was that SeaTunnel's connector can run only on Flink or Spark, and Flink and Spark, as computing engines, have many unsolvable problems when integrating and synchronizing data. Refer to: Why do we self-develop the big data synchronization engine SeaTunnel Zeta? https://github.com/apache/incubator-seatunnel/issues/1954","s":"01 Why develop our engine","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#01-why-develop-our-engine","p":102581},{"i":102586,"t":"The general idea of engine design is as follows: Simple and easy to use, the new engine minimizes the dependence on third-party services, and can realize cluster management, snapshot storage, and cluster HA functions without relying on big data components such as Zookeeper and HDFS. This is very useful for users who do not have a big data platform or are unwilling to rely on a big data platform for data synchronization. More resource-saving, at the CPU level, Zeta Engine internally uses Dynamic Thread Sharing (dynamic thread sharing) technology. In the real-time synchronization scenario, if the number of tables is large but the amount of data in each table is small, Zeta Engine will Synchronous tasks run in shared threads, which can reduce unnecessary thread creation and save system resources. On the read and data write side, the Zeta Engine is designed to minimize the number of JDBC connections. In the CDC scenario, Zeta Engine will try to reuse log reading and parsing resources as much as possible. More stable. In this version, Zeta Engine uses Pipeline as the minimum granularity of Checkpoint and fault tolerance for data synchronization tasks. The failure of a task will only affect the tasks that have upstream and downstream relationships with it. Try to avoid task failures that cause the entire Job to fail. or rollback. At the same time, for scenarios where the source data has a storage time limit, Zeta Engine supports enabling data cache to automatically cache the data read from the source, and then the downstream tasks read the cached data and write it to the target. In this scenario, even if the target end fails and data cannot be written, it will not affect the normal reading of the source end, preventing the source end data from being deleted due to expiration. Faster, Zeta Engine’s execution plan optimizer will optimize the execution plan to reduce the possible network transmission of data, thereby reducing the loss of overall synchronization performance caused by data serialization and deserialization, and completing faster Data synchronization operations. Of course, it also supports speed limiting, so that sync jobs can be performed at a reasonable speed. Data synchronization support for all scenarios. SeaTunnel aims to support full synchronization and incremental synchronization under offline batch synchronization, and support real-time synchronization and CDC.","s":"02 Design ideas","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#02-design-ideas","p":102581},{"i":102588,"t":"SeaTunnel Engine is mainly composed of a set of APIs for data synchronization processing and a core computing engine. Here we mainly introduce the architecture design of the SeaTunnel Engine core engine. picture SeaTunnel Engine consists of three main services: CoordinatorService, TaskExecutionService, and SlotService.","s":"03 Architecture design","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#03-architecture-design","p":102581},{"i":102590,"t":"CoordinatorService is the Master service of the cluster, which provides the generation process of each job from LogicalDag to ExecutionDag, and then to PhysicalDag, and finally creates the JobMaster of the job for scheduling execution and status monitoring of the job. CoordinatorService is mainly composed of 4 large functional modules: JobMaster is responsible for the generation process from LogicalDag to ExecutionDag to PhysicalDag of a single job, and is scheduled to run by PipelineBaseScheduler. CheckpointCoordinator, responsible for the Checkpoint process control of the job. ResourceManager is responsible for the application and management of job resources. It currently supports Standalone mode and will support On Yarn and On K8s in the future. Metrics Service, responsible for the statistics and summary of job monitoring information.","s":"Coordinator Service","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#coordinator-service","p":102581},{"i":102592,"t":"TaskExecutionService is the Worker service of the cluster, which provides the real runtime environment of each Task in the job. TaskExecutionService uses Dynamic Thread Sharing technology to reduce CPU usage.","s":"TaskExecutionService","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#taskexecutionservice","p":102581},{"i":102594,"t":"SlotService runs on each node of the cluster and is mainly responsible for the division, application, and recycling of resources on the node.","s":"SlotService","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#slotservice","p":102581},{"i":102597,"t":"SeaTunnel Engine has realized autonomous clustering (no centralization). To achieve cluster autonomy and job fault tolerance without relying on third-party service components (such as Zookeeper), SeaTunnel Engine uses Hazelcast as the underlying dependency. Hazelcast provides a distributed memory network, allowing users to operate a distributed collection like a normal Java collection locally. SeaTunnel saves the status information of the job in the memory grid of Hazelcast. When the Master node switches, it can Job state recovery based on data in the Hazelcast in-memory grid. At the same time, we have also implemented the persistence of Hazelcast memory grid data, and persisted the job status information to the storage (database of JDBC protocol, HDFS, cloud storage) in the form of WAL. In this way, even if the entire cluster hangs and restarts, the runtime information of the job can be repaired.","s":"Autonomous cluster","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#autonomous-cluster","p":102581},{"i":102599,"t":"SeaTunnel Engine is different from the traditional Spark/Flink computing engine, it is an engine specially used for data synchronization. The SeaTunnel engine naturally supports data cache. When multiple synchronous jobs in the cluster share a data source, the SeaTunnel engine will automatically enable the data cache. The source of a job will read the data and write it into the cache, and all other jobs will no longer read data from the data source but are automatically optimized to read data from the Cache. The advantage of this is that it can reduce the reading pressure of the data source and reduce the impact of data synchronization on the data source.","s":"Data cache","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#data-cache","p":102581},{"i":102601,"t":"SeaTunnel Engine supports the speed limit during data synchronization, which is very useful when reading data sources with high concurrency. A reasonable speed limit can not only ensure that the data is synchronized on time, but also minimize the pressure on the data source.","s":"Speed control","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#speed-control","p":102581},{"i":102603,"t":"At present, the underlying operating tools and data synchronization tools provided by computing engines such as Spark/Flink cannot solve the problem that each table needs a JDBC connection when the entire database is synchronized. Database connections are resources for the database. Too many database connections will put great pressure on the database, resulting in a decrease in the stability of database read and write delays. This is a very serious accident for business databases. To solve this problem, SeaTunnel Engine uses a shared connection pool to ensure that multiple tables can share JDBC connections, thereby reducing the use of database connections.","s":"Shared connection pool to reduce database pressure","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#shared-connection-pool-to-reduce-database-pressure","p":102581},{"i":102605,"t":"SeaTunnel Engine supports resumed uploads under offline synchronization. When the amount of data is large, a data synchronization job often needs to run for tens of minutes or several hours. If the middle job hangs up and reruns, it means wasting time. SeaTunnel Engine will continue to save the state (checkpoint) during the offline synchronization process. When the job hangs up and reruns, it will continue to run from the last checkpoint, which effectively solves the data that may be caused by hardware problems such as node downtime. Delay.","s":"Breakpoint resume (incremental/full volume)","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#breakpoint-resume-incrementalfull-volume","p":102581},{"i":102607,"t":"Schema evolution is a feature that allows users to easily change a table's current schema to accommodate data that changes over time. Most commonly, it is used when performing an append or overwrite operation, to automatically adjust the schema to include one or more new columns. This capability is required in real-time data warehouse scenarios. Currently, the Flink and Spark engines do not support this feature.","s":"The Schema revolution route","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#the-schema-revolution-route","p":102581},{"i":102609,"t":"Flink's design is fault tolerance and rollback at the entire job level. If a task fails, the entire job will be rolled back and restarted. The design of SeaTunnel Engine takes into account that in the data synchronization scenario, in many q cases, the failure of a task should only need to focus on fault tolerance for tasks that have upstream and downstream relationships with it. Based on this design principle, SeaTunnel Engine will first generate a logical DAG according to the user-configured job configuration file, then optimize the logical DAG, and finally generate a pipeline (a connected subgraph in a job DAG) to call and execute jobs at the granularity. fault tolerance. A typical usage scenario is: Use the CDC connector to read data from MySQL's binlog and write it to another MySQL. If you use Flink or Spark engine, once the target MySQL cannot write, it will cause the task of CDC to read the binlog to be terminated. If MySQL is set If the expiration time of the log is set, the problem of the target MySQL is solved, but the log of the source MySQL is cleared, which leads to data loss and other problems. SeaTunnel Engine will automatically optimize this synchronization task, automatically add the source to the target Cache, and then further optimize this job into two Pipelines, pipeline#1 is responsible for reading data from the CDC and writing it to the SeaTunnel Cache, and pipeline#2 is responsible for reading data from the SeaTunnel Cache Cache reads data and writes to target MySQL. If there is a problem with the target MySQL and cannot be written, the pipeline#2 of this synchronization job will be terminated, and the pipeline#1 will still run normally. This design fundamentally solves the above problems and is more in line with the processing logic of the data synchronization engine.","s":"Fine-grained fault-tolerant design","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#fine-grained-fault-tolerant-design","p":102581},{"i":102611,"t":"SeaTunnel Engine's Task design uses shared thread technology. Different from Flink/Spark, SeaTunnel Engine does not simply allow a Task to occupy a thread, but through a dynamic perception method - Dynamic Thread Sharing (Dynamic Thread Sharing) To judge whether a Task should share a thread with other Tasks or should monopolize a thread. Compared with single-threaded serial computing, multi-threaded parallel computing has better performance advantages, but if each Task uses an independent thread to run, when there are many tables for data synchronization and the number of Tasks is large, it will be in the Worker node Start very many threads on it. When the number of CPU cores is fixed, the more threads, the better. When the number of threads is too large, the CPU needs to spend a lot of time on thread context switching, which will affect computing performance. Flink/Spark usually limits the maximum number of tasks running on each node. In this way, it can avoid starting too many threads. To run more tasks on one node, SeaTunnel Engine can share thread technology. Let those tasks with a small amount of data share threads, and tasks with a large amount of data exclusively use threads. This method makes it possible for SeaTunnel Engine to run hundreds or thousands of table synchronization tasks on one node, with less resource occupation. Complete the synchronization of more tables.","s":"Dynamically share threads to reduce resource usage","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#dynamically-share-threads-to-reduce-resource-usage","p":102581},{"i":102613,"t":"2.3.0 is the first official version of SeaTunnel Engine, which implements some basic functions. For the detailed design, please refer to: https://github.com/apache/incubator-seatunnel/issues/2272 [ Cluster Management ] Support stand-alone operation Support cluster operation Autonomous cluster (no centralization), no need to specify a Master node for the SeaTunnel Engine cluster, SeaTunnel Engine elects the Master node by itself during operation and automatically selects a new Master node after the Master node hangs up. Automatic discovery of cluster nodes, the nodes with the same cluster_name will automatically form a cluster. [ Core function ] Supports running jobs in Local mode. The cluster is automatically destroyed after the job runs. It supports running jobs in Cluster mode (single machine or cluster) and submitting jobs to the SeaTunnel Engine service through SeaTunnel Client. After the job is completed, the service continues to run and waits for the next job submission. Support offline batch synchronization. Support real-time synchronization. Batch and flow integration, all SeaTunnel V2 version connectors can run in SeaTunnel Engine. Supports distributed snapshot algorithm cooperates with SeaTunnel V2 connector to support two-phase commit, and ensures data exactly-once. Supports job invocation at the Pipeline level to ensure that it can be started even when resources are limited. Supports job fault tolerance at the Pipeline level. The failure of a Task only affects the Pipeline it is in, and only the Task under the Pipeline needs to be rolled back. Supports dynamic thread sharing to achieve real-time synchronization of a large number of small data sets.","s":"05 Basic functions and features","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#05-basic-functions-and-features","p":102581},{"i":102615,"t":"Support Cache mode, and first support Kafka as Cache Support JobHistory, support the persistence of JobHistory. Support indicator (Reader Rows, QPS, Reader Bytes) monitoring and indicator query Support dynamic modification of the execution plan. Support CDC. Support whole database synchronization Support multi-table synchronization Support for Schema Revolution","s":"06 Future optimization plan","u":"/blog/Reveal the core design of the SeaTunnel Zeta synchronization engine!","h":"#06-future-optimization-plan","p":102581},{"i":102617,"t":"At the Apache SeaTunnel (Incubating) Meetup in April, Yuan Hongjun, a big data expert and OLAP platform architect at Kidswant, shared a topic of SeaTunnel Application and Refactoring at Kidswant. The presentation contains five parts. Background of the introduction of Apache SeaTunnel (Incubating) by Kidswant A comparison of mainstream tools for big data processing The implementation of Apache SeaTunnel (Incubating) Common problems in Apache SeaTunnel (Incubating) refactoring Predictions on the future development of Kidswant Yuan Hongjun, Big data expert, OLAP platform architect of Kidswant. He has many years of experience in big data platform development and management, and has rich research experience in data assets, data lineage mapping, data governance, OLAP, and other fields.","s":"SeaTunnel Application and Refactoring at Kidswant","u":"/blog/SeaTunnel Application and Refactoring at Kidswant","h":"","p":102616},{"i":102619,"t":"At present, Kidswant’s OLAP platform consists of seven parts: metadata layer, task layer, storage layer, SQL layer, scheduling layer, service layer, and monitoring layer. This sharing focuses on offline tasks in the task layer. In fact, Kidswant had a complete internal collection and push system, but due to some historical legacy issues, the company’s existing platform could not quickly support the OLAP platform getting online, so at that time the company had to abandon its own platform and start developing a new system instead. There were three options in front of OLAP at the time. 1, Re-develop the collection and push system. 2、Self-R&D. 3, Participate in open source projects.","s":"01 Background","u":"/blog/SeaTunnel Application and Refactoring at Kidswant","h":"#01-background","p":102616},{"i":102621,"t":"These three options have their own pros and cons. Carrying re-research and development based on the collection and push system is convenient for us to take advantage of the experience of previous results and avoid repeatedly stepping into the pit. But the disadvantage is that it requires a large amount of code, time, a longer research period, and with less abstract code and lots of customized functions bound to the business, it’s difficult to do the re-development. If completely self-developed, though the development process is autonomous and controllable, some engines such as Spark can be done to fit our own architecture, while the disadvantage is that we may encounter some unknown problems. For the last choice, if we use open-source frameworks, the advantage is that there is more abstract code, and the framework can be guaranteed in terms of performance and stability after verification by other major companies. Therefore Kidswant mainly studied three open-source data synchronization tools, DATAX, Sqoop, and SeaTunnel in the early stages of OLAP data synchronization refactoring. From the diagram we can see that Sqoop’s main function is data synchronization for RDB, and its implementation is based on MAP/REDUCE. Sqoop has rich parameters and command lines to perform various operations. The advantage of Sqoop is that it fits Hadoop ecology, and already supports most of the conversion from RDB to HIVE arbitrary source, with a complete set of commands and APIs. The disadvantages are that Sqoop only supports RDB data synchronization and has some limitations on data files, and there is no concept of data cleansing yet. DataX mainly aims at synchronizing data from any source by configurable files + multi-threading, which runs three main processes: Reader, Framework, and Writer, where Framework mainly plays the role of communication and leaving empty space. The advantage of DataX is that it uses plug-in development, has its own flow control and data control, and is active in the community, with DataX’s official website offering data pushes from many different sources. The disadvantage of DataX, however, is that it is memory-based and there may be limitations on the amount of data available. Apache SeaTunnel (Incubating) also does data synchronization from any source and implements the process in three steps: source, transform and sink based on configuration files, Spark or Flink. The advantage is that the current 2.1.0 version has a very large number of plug-ins and source pushes, based on the idea of plug-ins also makes it very easy to extend and embrace Spark and Flink while with a distributed architecture. The only downside to Apache SeaTunnel (Incubating) is probably the lack of IP calls at the moment and the need to manage the UI interface by yourself. In summary, although Sqoop is distributed, it only supports data synchronization between RDB and HIVE, Hbase and has poor scalability, which is not convenient for re-development. DataX is scalable and stable overall, but because it is a standalone version, it cannot be deployed in a distributed cluster, and there is a strong dependency between data extraction capability and machine performance. SeaTunnel, on the other hand, is similar to DataX and makes up for the flaw of non-distributed DataX. It also supports real-time streaming, and the community is highly active as a new product. We chose SeaTunnel based on a number of factors such as whether it supported distributed or not, and whether it needed to be deployed on a separate machine.","s":"02 Big data processing mainstream tools comparison","u":"/blog/SeaTunnel Application and Refactoring at Kidswant","h":"#02-big-data-processing-mainstream-tools-comparison","p":102616},{"i":102623,"t":"On the Apache SeaTunnel (Incubating) website, we can see that the basic process of Apache SeaTunnel (Incubating) consists of three parts: source, transform and sink. According to the guidelines on the website, Apache SeaTunnel (Incubating) requires a configuration script to start, but after some research, we found that the final execution of Apache SeaTunnel (Incubating) is bansed on an application submitted by spark-submit that relies on the config file. This initialization, although simple, has the problem of having to rely on the config file, which is generated and then cleared after each run, and although it can be dynamically generated in the scheduling script, it raises two questions: 1) whether frequent disk operations make sense; and 2) whether there is a more efficient way to support Apache SeaTunnel (Incubating). With these considerations in mind, we added a Unified Configuration Template Platform module to the final design solution. Scheduling is done by initiating a commit command, and Apache SeaTunnel (Incubating) itself pulls the configuration information from the unified configuration template platform, then loads and initializes the parameters. The diagram above shows the business process for Kidswant’s OLAP, which is divided into three sections. The overall flow of data from Parquet, i.e. Hive, through the Parquet tables to KYLIN and CK source. This is the page where we construct the model, which is generated mainly through drag and drop, with some transactional operations between each table, and micro-processing for Apache SeaTunnel (Incubating) on the right. So we end up submitting the commands as above, where the first one marked in red is [-conf customconfig/jars], referring to the fact that the user can then unify the configuration template platform for processing, or specify it separately when modeling. The last one marked in red is [421 $start_time $end_time $taskType] Unicode, which is a unique encoding. Below, on the left, are the 38 commands submitted by our final dispatch script. Below, on the right, is a modification made for Apache SeaTunnel (Incubating), and you can see a more specific tool class called WaterdropContext. It can first determine if Unicode exists and then use Unicode_code to get the configuration information for the different templates, avoiding the need to manipulate the config file. In the end, the reportMeta is used to report some information after the task is completed, which is also done in Apache SeaTunnel (Incubating). In the finalized config file as above, it is worth noting that in terms of transforms, Kidswant has made some changes. The first is to do desensitization for mobile phones or ID numbers etc. If the user specifies a field, they do it by field, if not they will scan all fields and then desensitize and encrypt them according to pattern matching. Second, transform also supports custom processing, as mentioned above when talking about OLAP modeling. With the addition of HideStr, the first ten fields of a string of characters can be retained and all characters at the back encrypted, providing some security in the data. Then, on the sink side, we added pre_sql in order to support the idempotency of the task, which is mainly done for tasks such as data deletion, or partition deletion, as the task cannot be run only once during production, and this design needed to account for the data deviation and correctness once operations such as reruns or complement occur. On the right side of the diagram, on the Sink side of a Clickhouse, we have added an is_senseless_mode, which forms a read/write senseless mode, where the user does not perceive the whole area when querying and complementing but uses the CK partition conversion, i.e. the command called MOVE PARTITION TO TABLE to operate. A special note here is the Sink side of KYLIN. KYLIN is a very special source with its own set of data entry logic and its monitoring page, so the transformation we have done on KYLIN is simply a call to its API operation and a simple API call and constant polling of the state when using KYLIN, so the resources for KYLIN are limited in the Unified Template Configuration platform.","s":"03 Implementation","u":"/blog/SeaTunnel Application and Refactoring at Kidswant","h":"#03-implementation","p":102616},{"i":102625,"t":"01 OOM & Too Many Parts​ The problem usually arises during the Hive to Hive process, even if we go through automatic resource allocation, but there are cases where the data amount suddenly gets bigger, for example after holding several events. Such problems can only be avoided by manually and dynamically tuning the reference and adjusting the data synchronization batch time. In the future, we may try to control the data volume to achieve fine control. 02 Field and type inconsistency issues​ When the model runs, the user will make some changes to the upstream tables or fields that the task depends on, and these changes may lead to task failure if they are not perceived. The current solution is to rely on data lineage+ snapshots for advance awareness to avoid errors. 03 Custom data sources & custom separators​ If the finance department requires a customized separator or jar information, the user can now specify the loading of additional jar information as well as the separator information themselves in the unified configuration template platform. 04 Data skewing issues​ This may be due to users setting their parallelism but not being able to do so perfectly. We haven’t finished dealing with this issue yet, but we may add post-processing to the Source module to break up the data and complete the skew. 05 KYLIN global dictionary lock problem​ As the business grows, one cube will not be able to meet the needs of the users, so it will be necessary to create more than one cube. If the same fields are used between multiple cubes, the problem of KYLIN global dictionary lock will be encountered. The current solution is to separate the scheduling time between two or more tasks, or if this is not possible, we can make a distributed lock control, where the sink side of KYLIN has to get the lock to run.","s":"04 Common problems about the Apache SeaTunnel (Incubating) transformation","u":"/blog/SeaTunnel Application and Refactoring at Kidswant","h":"#04-common-problems-about-the-apache-seatunnel-incubating-transformation","p":102616},{"i":102627,"t":"Multi-source data synchronization, maybe processing for RDB sources Real-time Flink-based implementation Take over the existing collection and scheduling platform (mainly to solve the problem of splitting library and tables) Data quality verification, like some null values, the vacancy rate of the whole data, main time judgment, etc. This is all I have to share, I hope we can communicate more with the community in the future and make progress together, thanks!","s":"05 An outlook on the future of Kidswant","u":"/blog/SeaTunnel Application and Refactoring at Kidswant","h":"#05-an-outlook-on-the-future-of-kidswant","p":102616},{"i":102629,"t":"When it comes to writing data to Elasticsearch, the first thing that comes to mind must be Logstash. Logstash is accepted by the majority of users because of its simplicity, scalability, and scalability. However, the ruler is shorter and the inch is longer, and Logstash must have application scenarios that it cannot apply to, such as: Massive data ETL Massive data aggregation Multi-source data processing In order to meet these scenarios, many students will choose Spark, use Spark operators to process data, and finally write the processing results to Elasticsearch. Our department used Spark to analyze Nginx logs, counted our web service access, aggregated Nginx logs every minute and finally wrote the results to Elasticsearch, and then used Kibana to configure real-time monitoring of the Dashboard. Both Elasticsearch and Kibana are convenient and practical, but with more and more similar requirements, how to quickly write data to Elasticsearch through Spark has become a big problem for us. Today, I would like to recommend a black technology Seatunnel https://github.com/apache/incubator-seatunnel that can realize fast data writing. It is very easy to use , a high-performance, real-time data processing product that can deal with massive data. It is built on Spark and is easy to use, flexibly configured, and requires no development.","s":"How to quickly write data to Elasticsearch using Spark","u":"/blog/spark-execute-elasticsearch","h":"","p":102628},{"i":102631,"t":"Like Logstash, Seatunnel also supports multiple types of data input. Here we take the most common Kakfa as the input source as an example to explain how to use Seatunnel to quickly write data to Elasticsearch","s":"Kafka to Elasticsearch","u":"/blog/spark-execute-elasticsearch","h":"#kafka-to-elasticsearch","p":102628},{"i":102633,"t":"The original log format is as follows: 127.0.0.1 elasticsearch.cn 114.250.140.241 0.001s \"127.0.0.1:80\" [26/Oct/2018:21:54:32 +0800] \"GET /article HTTP/1.1\" 200 123 \"-\" - \"Dalvik/2.1.0 (Linux; U; Android 7.1.1; OPPO R11 Build/NMF26X)\"","s":"Log Sample","u":"/blog/spark-execute-elasticsearch","h":"#log-sample","p":102628},{"i":102635,"t":"We want to count the visits of each domain name in one minute. The aggregated data has the following fields: domain String hostname String status int datetime String count int","s":"Elasticsearch Document","u":"/blog/spark-execute-elasticsearch","h":"#elasticsearch-document","p":102628},{"i":102637,"t":"Next, I will introduce you in detail, how we read the data in Kafka through Seatunnel, parse and aggregate the data, and finally write the processing results into Elasticsearch.","s":"Seatunnel with Elasticsearch","u":"/blog/spark-execute-elasticsearch","h":"#seatunnel-with-elasticsearch","p":102628},{"i":102639,"t":"Seatunnel also has a very rich plug-in that supports reading data from Kafka, HDFS, Hive, performing various data processing, and converting the results Write to Elasticsearch, Kudu or Kafka.","s":"Seatunnel","u":"/blog/spark-execute-elasticsearch","h":"#seatunnel","p":102628},{"i":102641,"t":"First of all, we need to install seatunnel, the installation is very simple, no need to configure system environment variables Prepare the Spark environment Install Seatunnel Configure Seatunnel The following are simple steps, the specific installation can refer to Quick Start cd /usr/local wget https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz tar -xvf https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz wget https://github.com/InterestingLab/seatunnel/releases/download/v1.1.1/seatunnel-1.1.1.zip unzip seatunnel-1.1.1.zip cd seatunnel-1.1.1 vim config/seatunnel-env.sh # Specify the Spark installation path SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.2.0-bin-hadoop2.7}","s":"Prerequisites","u":"/blog/spark-execute-elasticsearch","h":"#prerequisites","p":102628},{"i":102643,"t":"Like Logstash, we only need to write a configuration file of Seatunnel Pipeline to complete the data import. I believe that friends who know Logstash can start Seatunnel configuration soon. The configuration file includes four parts, namely Spark, Input, filter and Output. Spark​ This part is the related configuration of Spark, which mainly configures the resource size required for Spark execution. spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" spark.streaming.batchDuration = 5 } Input​ This part defines the data source. The following is a configuration example of reading data from Kafka, kafkaStream { topics = \"seatunnel-es\" consumer.bootstrap.servers = \"localhost:9092\" consumer.group.id = \"seatunnel_es_group\" consumer.rebalance.max.retries = 100 } Filter​ In the Filter section, here we configure a series of conversions, including regular parsing to split logs, time conversion to convert HTTPDATE to a date format supported by Elasticsearch, type conversion for fields of type Number, and data aggregation through SQL filter { # Parse the original log using regex # The initial data is in the raw_message field grok { source_field = \"raw_message\" pattern = '%{NOTSPACE:hostname}\\\\s%{NOTSPACE:domain}\\\\s%{IP:remote_addr}\\\\s%{NUMBER:request_time}s\\\\s\\\"%{DATA:upstream_ip}\\\"\\\\s\\\\[%{HTTPDATE:timestamp}\\\\]\\\\s\\\"%{NOTSPACE:method}\\\\s%{DATA:url}\\\\s%{NOTSPACE:http_ver}\\\"\\\\s%{NUMBER:status}\\\\s%{NUMBER:body_bytes_send}\\\\s%{DATA:referer}\\\\s%{NOTSPACE:cookie_info}\\\\s\\\"%{DATA:user_agent}' } # Convert data in \"dd/MMM/yyyy:HH:mm:ss Z\" format to # format supported in Elasticsearch date { source_field = \"timestamp\" target_field = \"datetime\" source_time_format = \"dd/MMM/yyyy:HH:mm:ss Z\" target_time_format = \"yyyy-MM-dd'T'HH:mm:ss.SSS+08:00\" } ## Aggregate data with SQL sql { table_name = \"access_log\" sql = \"select domain, hostname, int(status), datetime, count(*) from access_log group by domain, hostname, status, datetime\" } } Output​ Finally, we write the processed structured data to Elasticsearch. output { elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-${now}\" es.batch.size.entries = 100000 index_time_format = \"yyyy.MM.dd\" } }","s":"Seatunnel Pipeline","u":"/blog/spark-execute-elasticsearch","h":"#seatunnel-pipeline","p":102628},{"i":102645,"t":"We combine the above four-part configuration into our configuration file config/batch.conf. vim config/batch.conf spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" spark.streaming.batchDuration = 5 } input { kafkaStream { topics = \"seatunnel-es\" consumer.bootstrap.servers = \"localhost:9092\" consumer.group.id = \"seatunnel_es_group\" consumer.rebalance.max.retries = 100 } } filter { # Parse the original log using regex # The initial data is in the raw_message field grok { source_field = \"raw_message\" pattern = '%{IP:hostname}\\\\s%{NOTSPACE:domain}\\\\s%{IP:remote_addr}\\\\s%{NUMBER:request_time}s\\\\s\\\"%{DATA:upstream_ip}\\\"\\\\s\\\\[%{HTTPDATE:timestamp}\\\\]\\\\s\\\"%{NOTSPACE:method}\\\\s%{DATA:url}\\\\s%{NOTSPACE:http_ver}\\\"\\\\s%{NUMBER:status}\\\\s%{NUMBER:body_bytes_send}\\\\s%{DATA:referer}\\\\s%{NOTSPACE:cookie_info}\\\\s\\\"%{DATA:user_agent}' } # Convert data in \"dd/MMM/yyyy:HH:mm:ss Z\" format to # format supported in Elasticsearch date { source_field = \"timestamp\" target_field = \"datetime\" source_time_format = \"dd/MMM/yyyy:HH:mm:ss Z\" target_time_format = \"yyyy-MM-dd'T'HH:mm:00.SSS+08:00\" } ## Aggregate data with SQL sql { table_name = \"access_log\" sql = \"select domain, hostname, status, datetime, count(*) from access_log group by domain, hostname, status, datetime\" } } output { elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-${now}\" es.batch.size.entries = 100000 index_time_format = \"yyyy.MM.dd\" } } Execute the command, specify the configuration file, and run Seatunnel to write data to Elasticsearch. Here we take the local mode as an example. ./bin/start-seatunnel.sh --config config/batch.conf -e client -m 'local[2]' Finally, the data written into Elasticsearch is as follows, and with Kibana, real-time monitoring of web services can be realized ^_^. \"_source\": { \"domain\": \"elasticsearch.cn\", \"hostname\": \"localhost\", \"status\": \"200\", \"datetime\": \"2018-11-26T21:54:00.000+08:00\", \"count\": 26 }","s":"Running Seatunnel","u":"/blog/spark-execute-elasticsearch","h":"#running-seatunnel","p":102628},{"i":102647,"t":"In this post, we introduced how to write data from Kafka to Elasticsearch via Seatunnel. You can quickly run a Spark Application with only one configuration file, complete data processing and writing, and do not need to write any code, which is very simple. When there are scenarios that Logstash cannot support or the performance of Logstah cannot meet expectations during data processing, you can try to use Seatunnel to solve the problem. If you want to know more functions and cases of using Seatunnel in combination with Elasticsearch, Kafka and Hadoop, you can go directly to the official website https://seatunnel.apache.org/ We will publish another article \"How to Use Spark and Elasticsearch for Interactive Data Analysis\" in the near future, so stay tuned.","s":"Conclusion","u":"/blog/spark-execute-elasticsearch","h":"#conclusion","p":102628},{"i":102649,"t":"Mailing list : dev@seatunnel.apache.org. Send anything to dev-subscribe@seatunnel.apache.org and subscribe to the mailing list according to the replies. Slack: Send a Request to join SeaTunnel slack email to the mailing list (dev@seatunnel.apache.org), and we will invite you to join (please make sure you are registered with Slack before doing so). bilibili B station video","s":"Contract us","u":"/blog/spark-execute-elasticsearch","h":"#contract-us","p":102628},{"i":102651,"t":"TiDB is a fusion database product targeting online transaction processing/online analytical processing. Distributed transactions, real-time OLAP and other important features. TiSpark is a product launched by PingCAP to solve the complex OLAP needs of users. It uses the Spark platform and integrates the advantages of TiKV distributed clusters. Completing OLAP operations with TiSpark directly requires knowledge of Spark and some development work. So, are there some out-of-the-box tools that can help us use TiSpark to complete OLAP analysis on TiDB more quickly? At present, there is a tool Seatunnel in the open source community, the project address https://github.com/apache/incubator-seatunnel, which can be based on Spark, Quickly implement TiDB data reading and OLAP analysis based on TiSpark.","s":"How to use Spark to do OLAP analysis on TiDB","u":"/blog/spark-execute-tidb","h":"","p":102650},{"i":102653,"t":"We have such a requirement online. Read the website access data of a certain day from TiDB, count the number of visits of each domain name and the status code returned by the service, and finally write the statistical results to another table in TiDB. Let's see how Seatunnel implements such a function.","s":"Operating TiDB with Seatunnel","u":"/blog/spark-execute-tidb","h":"#operating-tidb-with-seatunnel","p":102650},{"i":102655,"t":"Seatunnel is a very easy-to-use, high-performance, real-time data processing product that can deal with massive data. It is built on Spark. Seatunnel has a very rich set of plugins that support reading data from TiDB, Kafka, HDFS, Kudu, perform various data processing, and then write the results to TiDB, ClickHouse, Elasticsearch or Kafka. Ready to work​ 1. Introduction to TiDB table structure​ Input (table where access logs are stored) CREATE TABLE access_log ( domain VARCHAR(255), datetime VARCHAR(63), remote_addr VARCHAR(63), http_ver VARCHAR(15), body_bytes_send INT, status INT, request_time FLOAT, url TEXT ) +-----------------+--------------+------+------+---------+-------+ | Field | Type | Null | Key | Default | Extra | +-----------------+--------------+------+------+---------+-------+ | domain | varchar(255) | YES | | NULL | | | datetime | varchar(63) | YES | | NULL | | | remote_addr | varchar(63) | YES | | NULL | | | http_ver | varchar(15) | YES | | NULL | | | body_bytes_send | int(11) | YES | | NULL | | | status | int(11) | YES | | NULL | | | request_time | float | YES | | NULL | | | url | text | YES | | NULL | | +-----------------+--------------+------+------+---------+-------+ Output (table where result data is stored) CREATE TABLE access_collect ( date VARCHAR(23), domain VARCHAR(63), status INT, hit INT ) +--------+-------------+------+------+---------+-------+ | Field | Type | Null | Key | Default | Extra | +--------+-------------+------+------+---------+-------+ | date | varchar(23) | YES | | NULL | | | domain | varchar(63) | YES | | NULL | | | status | int(11) | YES | | NULL | | | hit | int(11) | YES | | NULL | | +--------+-------------+------+------+---------+-------+ 2. Install Seatunnel​ After we have the input and output tables of TiDB, we need to install Seatunnel. The installation is very simple, and there is no need to configure system environment variables Prepare the Spark environment Install Seatunnel Configure Seatunnel The following are simple steps, the specific installation can refer to Quick Start # Download and install Spark cd /usr/local wget https://archive.apache.org/dist/spark/spark-2.1.0/spark-2.1.0-bin-hadoop2.7.tgz tar -xvf https://archive.apache.org/dist/spark/spark-2.1.0/spark-2.1.0-bin-hadoop2.7.tgz wget # Download and install seatunnel https://github.com/InterestingLab/seatunnel/releases/download/v1.2.0/seatunnel-1.2.0.zip unzip seatunnel-1.2.0.zip cd seatunnel-1.2.0 vim config/seatunnel-env.sh # Specify the Spark installation path SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.1.0-bin-hadoop2.7}","s":"Seatunnel","u":"/blog/spark-execute-tidb","h":"#seatunnel","p":102650},{"i":102657,"t":"We only need to write a Seatunnel configuration file to read, process, and write data. The Seatunnel configuration file consists of four parts, Spark, Input, Filter and Output. The Input part is used to specify the input source of the data, the Filter part is used to define various data processing and aggregation, and the Output part is responsible for writing the processed data to the specified database or message queue. The whole processing flow is Input -> Filter -> Output, which constitutes the processing flow (Pipeline) of Seatunnel. The following is a specific configuration, which is derived from an online practical application, but simplified for demonstration. Input (TiDB)​ This part of the configuration defines the input source. The following is to read data from a table in TiDB. input { tidb { database = \"nginx\" pre_sql = \"select * from nginx.access_log\" table_name = \"spark_nginx_input\" } } Filter​ In the Filter section, here we configure a series of transformations, most of the data analysis requirements are completed in the Filter. Seatunnel provides a wealth of plug-ins enough to meet various data analysis needs. Here we complete the data aggregation operation through the SQL plugin. filter { sql { table_name = \"spark_nginx_log\" sql = \"select count(*) as hit, domain, status, substring(datetime, 1, 10) as date from spark_nginx_log where substring(datetime, 1, 10)='2019-01-20' group by domain, status, substring(datetime, 1, 10)\" } } Output (TiDB)​ Finally, we write the processed results to another table in TiDB. TiDB Output is implemented through JDBC output { tidb { url = \"jdbc:mysql://127.0.0.1:4000/nginx?useUnicode=true&characterEncoding=utf8\" table = \"access_collect\" user = \"username\" password = \"password\" save_mode = \"append\" } } Spark​ This part is related to Spark configuration. It mainly configures the resource size required for Spark execution and other Spark configurations. Our TiDB Input plugin is implemented based on TiSpark, which relies on TiKV cluster and Placement Driver (PD). So we need to specify PD node information and TiSpark related configuration spark.tispark.pd.addresses and spark.sql.extensions. spark { spark.app.name = \"seatunnel-tidb\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" # Set for TiSpark spark.tispark.pd.addresses = \"localhost:2379\" spark.sql.extensions = \"org.apache.spark.sql.TiExtensions\" } Run Seatunnel​ We combine the above four parts into our final configuration file conf/tidb.conf spark { spark.app.name = \"seatunnel-tidb\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" # Set for TiSpark spark.tispark.pd.addresses = \"localhost:2379\" spark.sql.extensions = \"org.apache.spark.sql.TiExtensions\" } input { tidb { database = \"nginx\" pre_sql = \"select * from nginx.access_log\" table_name = \"spark_table\" } } filter { sql { table_name = \"spark_nginx_log\" sql = \"select count(*) as hit, domain, status, substring(datetime, 1, 10) as date from spark_nginx_log where substring(datetime, 1, 10)='2019-01-20' group by domain, status, substring(datetime, 1, 10)\" } } output { tidb { url = \"jdbc:mysql://127.0.0.1:4000/nginx?useUnicode=true&characterEncoding=utf8\" table = \"access_collect\" user = \"username\" password = \"password\" save_mode = \"append\" } } Execute the command, specify the configuration file, and run Seatunnel to implement our data processing logic. Local ./bin/start-seatunnel.sh --config config/tidb.conf --deploy-mode client --master 'local[2]' yarn-client ./bin/start-seatunnel.sh --config config/tidb.conf --deploy-mode client --master yarn yarn-cluster ./bin/start-seatunnel.sh --config config/tidb.conf --deploy-mode cluster -master yarn If it is a local test and verification logic, you can use the local mode (Local). Generally, in the production environment, the yarn-client or yarn-cluster mode is used. test result​ mysql> select * from access_collect; +------------+--------+--------+------+ | date | domain | status | hit | +------------+--------+--------+------+ | 2019-01-20 | b.com | 200 | 63 | | 2019-01-20 | a.com | 200 | 85 | +------------+--------+--------+------+ 2 rows in set (0.21 sec)","s":"Implement the Seatunnel processing flow","u":"/blog/spark-execute-tidb","h":"#implement-the-seatunnel-processing-flow","p":102650},{"i":102659,"t":"In this article, we introduced how to use Seatunnel to read data from TiDB, do simple data processing and write it to another table in TiDB. Data can be imported quickly with only one configuration file without writing any code. In addition to supporting TiDB data sources, Seatunnel also supports Elasticsearch, Kafka, Kudu, ClickHouse and other data sources. At the same time, we are developing an important function, which is to use the transaction features of TiDB in Seatunnel to realize streaming data processing from Kafka to TiDB, and support Exactly-Once data from end (Kafka) to end (TiDB). consistency. If you want to know more functions and cases of Seatunnel combined with TiDB, ClickHouse, Elasticsearch and Kafka, you can go directly to the official website https://seatunnel.apache.org/","s":"Conclusion","u":"/blog/spark-execute-tidb","h":"#conclusion","p":102650},{"i":102661,"t":"Mailing list : dev@seatunnel.apache.org. Send anything to dev-subscribe@seatunnel.apache.org and subscribe to the mailing list according to the replies. Slack: Send a Request to join SeaTunnel slack email to the mailing list (dev@seatunnel.apache.org), and we will invite you to join (please make sure you are registered with Slack before doing so). bilibili B station video -- Power by InterestingLab","s":"Contract us","u":"/blog/spark-execute-tidb","h":"#contract-us","p":102650},{"i":102663,"t":"Foreword​ StructuredStreaming is a newly opened module after Spark 2.0. Compared with SparkStreaming, it has some prominent advantages: First, it can achieve lower latency; Second, real-time aggregation can be done, such as real-time calculation of the total sales of each commodity every day; Third, you can do the association between streams, for example, to calculate the click rate of an advertisement, you need to associate the exposure record of the advertisement with the click record. The above points may be cumbersome or difficult to implement if using SparkStreaming, but it will be easier to implement using StructuredStreaming.","s":"How to support Spark StructuredStreaming","u":"/blog/spark-structured-streaming","h":"","p":102662},{"i":102665,"t":"StructuredStreaming is a newly opened module after Spark 2.0. Compared with SparkStreaming, it has some prominent advantages: First, it can achieve lower latency; Second, real-time aggregation can be done, such as real-time calculation of the total sales of each commodity every day; Third, you can do the association between streams, for example, to calculate the click rate of an advertisement, you need to associate the exposure record of the advertisement with the click record. The above points may be cumbersome or difficult to implement if using SparkStreaming, but it will be easier to implement using StructuredStreaming.","s":"Foreword","u":"/blog/spark-structured-streaming","h":"#foreword","p":102662},{"i":102667,"t":"Maybe you have not studied StructuredStreaming in detail, but found that StructuredStreaming can solve your needs very well. How to quickly use StructuredStreaming to solve your needs? Currently there is a tool Seatunnel in the community, the project address: https://github.com/apache/incubator-seatunnel , It can help you use StructuredStreaming to complete your needs efficiently and at low cost.","s":"How to use StructuredStreaming","u":"/blog/spark-structured-streaming","h":"#how-to-use-structuredstreaming","p":102662},{"i":102669,"t":"Seatunnel is a very easy-to-use, high-performance, real-time data processing product that can deal with massive data. It is built on Spark. Seatunnel has a very rich set of plug-ins, supports reading data from Kafka, HDFS, Kudu, performs various data processing, and writes the results to ClickHouse, Elasticsearch or Kafka","s":"Seatunnel","u":"/blog/spark-structured-streaming","h":"#seatunnel","p":102662},{"i":102671,"t":"First we need to install Seatunnel, the installation is very simple, no need to configure system environment variables Prepare the Spark environment Install Seatunnel Configure Seatunnel The following are simple steps, the specific installation can refer to Quick Start cd /usr/local wget https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz tar -xvf https://archive.apache.org/dist/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz wget https://github.com/InterestingLab/seatunnel/releases/download/v1.3.0/seatunnel-1.3.0.zip unzip seatunnel-1.3.0.zip cd seatunnel-1.3.0 vim config/seatunnel-env.sh # Specify the Spark installation path SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.2.0-bin-hadoop2.7}","s":"Ready to work","u":"/blog/spark-structured-streaming","h":"#ready-to-work","p":102662},{"i":102673,"t":"We only need to write a configuration file of Seatunnel Pipeline to complete the data import. The configuration file includes four parts, namely Spark, Input, filter and Output. Spark​ This part is the related configuration of Spark, which mainly configures the resource size required for Spark execution. spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } Input​ Below is an example of reading data from kafka kafkaStream { topics = \"seatunnel\" consumer.bootstrap.servers = \"localhost:9092\" schema = \"{\\\"name\\\":\\\"string\\\",\\\"age\\\":\\\"integer\\\",\\\"addrs\\\":{\\\"country\\\":\\\"string\\\",\\\"city\\\":\\\"string\\\"}}\" } Through the above configuration, the data in kafka can be read. Topics is the topic of kafka to be subscribed to. Subscribing to multiple topics at the same time can be separated by commas. Consumer.bootstrap.servers is the list of Kafka servers, and schema is optional. Because the value read by StructuredStreaming from kafka (official fixed field value) is of binary type, see http://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html But if you are sure that the data in your kafka is a json string, you can specify the schema, and the input plugin will parse it according to the schema you specify Filter​ Here is a simple filter example filter{ sql{ table_name = \"student\" sql = \"select name,age from student\" } } table_name is the registered temporary table name for easy use in the following sql Output​ The processed data is output, assuming that our output is also kafka output{ kafka { topic = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" streaming_output_mode = \"update\" checkpointLocation = \"/your/path\" } } topic is the topic you want to output, producer.bootstrap.servers is a list of kafka clusters, streaming_output_mode is an output mode parameter of StructuredStreaming, there are three types of append|update|complete, for details, see the documentation http: //spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-modes checkpointLocation is the checkpoint path of StructuredStreaming. If configured, this directory will store the running information of the program. For example, if the program exits and restarts, it will continue to consume the last offset.","s":"Seatunnel Pipeline","u":"/blog/spark-structured-streaming","h":"#seatunnel-pipeline","p":102662},{"i":102675,"t":"The above is a simple example. Next, we will introduce a slightly more complex business scenario. Scenario 1: Real-time aggregation scenario​ Suppose there is now a mall with 10 kinds of products on it, and now it is necessary to find the daily sales of each product in real time, and even to find the number of buyers of each product (not very precise). The huge advantage of this is that massive data can be aggregated during real-time processing, and there is no need to write data into the data warehouse first, and then run offline scheduled tasks for aggregation. It is still very convenient to operate. The data of kafka is as follows {\"good_id\":\"abc\",\"price\":300,\"user_id\":123456,\"time\":1553216320} So how do we use Seatunnel to fulfill this requirement, of course, we only need to configure it. #The configuration in spark is configured according to business requirements spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } #configure input input { kafkaStream { topics = \"good_topic\" consumer.bootstrap.servers = \"localhost:9092\" schema = \"{\\\"good_id\\\":\\\"string\\\",\\\"price\\\":\\\"integer\\\",\\\"user_id\\\":\\\"Long\\\",\\\"time\\\":\\\"Long\\\"}\" } } #configure filter filter { #When the program is doing aggregation, it will internally store the aggregation state of the program since startup, which will lead to OOM over time. If the watermark is set, the program will automatically clean up the state other than the watermark. #Here means use the ts field to set the watermark, the limit is 1 day Watermark { time_field = \"time\" time_type = \"UNIX\" #UNIX represents a timestamp with a time field of 10, and other types can be found in the plugin documentation for details. time_pattern = \"yyyy-MM-dd\" #The reason why the ts is assigned to the day is because the daily sales are sought, if the hourly sales are sought, the hour can be assigned `yyyy-MM-dd HH` delay_threshold = \"1 day\" watermark_field = \"ts\" #After setting the watermark, a new field will be added, `ts` is the name of this field } #The reason for group by ts is to make the watermark take effect, approx_count_distinct is an estimate, not an exact count_distinct sql { table_name = \"good_table_2\" sql = \"select good_id,sum(price) total, approx_count_distinct(user_id) person from good_table_2 group by ts,good_id\" } } #Next we choose to output the results to Kafka in real time output{ kafka { topic = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" streaming_output_mode = \"update\" checkpointLocation = \"/your/path\" } } The above configuration is complete, start Seatunnel, and you can get the results you want. Scenario 2: Multiple stream association scenarios​ Suppose you have placed an advertisement on a certain platform, and now you need to calculate the CTR (click-through rate) of each advertisement in real time. The data comes from two topics, one is the advertisement exposure log, and the other is the advertisement click log. At this point, we need to associate the two stream data together for calculation, and Seatunnel also supports this function recently, let's take a look at how to do it: Click on topic data format {\"ad_id\":\"abc\",\"click_time\":1553216320,\"user_id\":12345} Exposure topic data format {\"ad_id\":\"abc\",\"show_time\":1553216220,\"user_id\":12345} #The configuration in spark is configured according to business requirements spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } #configure input input { kafkaStream { topics = \"click_topic\" consumer.bootstrap.servers = \"localhost:9092\" schema = \"{\\\"ad_id\\\":\\\"string\\\",\\\"user_id\\\":\\\"Long\\\",\\\"click_time\\\":\\\"Long\\\"}\" table_name = \"click_table\" } kafkaStream { topics = \"show_topic\" consumer.bootstrap.servers = \"localhost:9092\" schema = \"{\\\"ad_id\\\":\\\"string\\\",\\\"user_id\\\":\\\"Long\\\",\\\"show_time\\\":\\\"Long\\\"}\" table_name = \"show_table\" } } filter { #Left association right table must set watermark #Right off left and right tables must set watermark #http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#inner-joins-with-optional-watermarking Watermark { source_table_name = \"click_table\" #Here you can specify to add a watermark to a temporary table. If you don't specify it, it will be the first one in the input. time_field = \"time\" time_type = \"UNIX\" delay_threshold = \"3 hours\" watermark_field = \"ts\" result_table_name = \"click_table_watermark\" #After adding the watermark, it can be registered as a temporary table, which is convenient for subsequent use in sql } Watermark { source_table_name = \"show_table\" time_field = \"time\" time_type = \"UNIX\" delay_threshold = \"2 hours\" watermark_field = \"ts\" result_table_name = \"show_table_watermark\" } sql { table_name = \"show_table_watermark\" sql = \"select a.ad_id,count(b.user_id)/count(a.user_id) ctr from show_table_watermark as a left join click_table_watermark as b on a.ad_id = b.ad_id and a.user_id = b.user_id \" } } #Next we choose to output the results to Kafka in real time output { kafka { topic = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" streaming_output_mode = \"append\" #Stream association only supports append mode checkpointLocation = \"/your/path\" } } Through configuration, the case of stream association is also completed here.","s":"Scenario Analysis","u":"/blog/spark-structured-streaming","h":"#scenario-analysis","p":102662},{"i":102677,"t":"Through configuration, you can quickly use StructuredStreaming for real-time data processing, but you still need to understand some concepts of StructuredStreaming, such as the watermark mechanism, and the output mode of the program. Finally, Seatunnel also supports spark streaming and spark batching of course. If you are also interested in these two, you can read our previous article \"How to quickly import data from Hive into ClickHouse\", \"[Excellent data engineer, how to use Spark to do OLAP analysis on TiDB] (2021-12-30-spark-execute-tidb.md)\", \"[How to use Spark to quickly write data to Elasticsearch] (2021-12-30-spark-execute-elasticsearch.md)\" If you want to know more functions and cases of Seatunnel combined with HBase, ClickHouse, Elasticsearch, Kafka, MySQL and other data sources, you can go directly to the official website [https://seatunnel.apache.org/](https://seatunnel.apache. org/)","s":"Conclusion","u":"/blog/spark-structured-streaming","h":"#conclusion","p":102662},{"i":102679,"t":"Mailing list : dev@seatunnel.apache.org. Send anything to dev-subscribe@seatunnel.apache.org and subscribe to the mailing list according to the replies. Slack: Send a Request to join SeaTunnel slack email to the mailing list (dev@seatunnel.apache.org), and we will invite you to join (please make sure you are registered with Slack before doing so). bilibili B station video","s":"联系我们","u":"/blog/spark-structured-streaming","h":"#联系我们","p":102662},{"i":102681,"t":"After days of community development, the preliminary development of the new Connector API of SeaTunnel is completed. The next step is to adapt this new connector. In order to aid the developers to use this connector, this article provides guide to develop a new API.","s":"Upcoming API Connector Development Analysis","u":"/blog/Upcoming API Connector Development Analysis","h":"","p":102680},{"i":102683,"t":"Environment configuration: JDK8 and Scala2.11 are recommended. As before, we need to download the latest code locally through git and import it into the IDE, project address: https://github.com/apache/incubator-seatunnel . At the same time, switch the branch to api-draft, and currently use this branch to develop the new version of the API and the corresponding Connector. The project structure is as follows:","s":"Priliminary Setup","u":"/blog/Upcoming API Connector Development Analysis","h":"#priliminary-setup","p":102680},{"i":102685,"t":"At present, in order to distinguish different Connectors, we put the connectors that support Flink/Spark under the seatunnel-connectors/seatunnel-connectors-flink(spark) module. New version of the Connector is placed under the seatunnel-connectors/seatunnel-connectors-seatunnel module. As we can see from the above figure, we have implemented Fake, Console, Kafka Connector, and Clickhouse Connector is also being implemented. At present, the data type we support is SeaTunnelRow, so no matter the type of data generated by the Source or the type of data consumed by the Sink, it should be SeaTunnelRow.","s":"Prerequisites","u":"/blog/Upcoming API Connector Development Analysis","h":"#prerequisites","p":102680},{"i":102687,"t":"Taking Fake Connector as an example, let's introduce how to implement a new Connector: Create a corresponding module with a path under seatunnel-connectors-seatunnel, which is at the same level as other new connectors. Modify the seatunnel-connectors-seatunnel/pom.xml file, add a new module to modules, modify seatunnel-connectors-seatunnel/seatunnel-connector-seatunnel-fake/pom.xml, add seatunnel-api dependencies, and correct parent Quote. The resulting style is as follows: The next step is to create the corresponding package and related classes, create FakeSource, and need to inherit SeaTunnel Source. Note : The Source of SeaTunnel adopts the design of stream and batch integration. The Source of SeaTunnel determines whether current Source is a stream or batch through attribute getBoundedness. So you can specify a Source as a stream or batch by dynamic configuration (refer to the default method). The configuration defined by the user in the configuration file can be obtained through the prepare method to realize the customized configuration. Then create FakeSourceReader, FakeSource SplitEnumerator, and FakeSourceSplit to inherit the corresponding abstract classes (which can be found in the corresponding classes). As long as we implement the corresponding methods of these classes, then our SeaTunnel Source Connector is basically completed. Next, just follow the existing example to write the corresponding code. The most important one is the FakeSource Reader, which defines how we obtain data from the outside, which is the most critical part of the Source Connector. Every time a piece of data is generated, we need to place it in the collector as shown: After the code development is complete, we need to configure the configuration file plugin-mapping.properties located under seatunnel-connectors/modules. Adding a seatunnel .source.FakeSource = seatunnel-connector-fake means that SeaTunnel can find the jar package corresponding to the project by looking for a Source named FakeSource. This allows the Connector to be used in the normal configuration file. For a detailed description of writing Source and Sink and SeaTunnel API, please refer to the introduction at seatunnel-connectors/seatunnel-connectors-seatunnel/ README.zh.md.","s":"Development of Connector","u":"/blog/Upcoming API Connector Development Analysis","h":"","p":102680},{"i":102689,"t":"For testing, we can find the seatunnel-flink(spark)-new-connector-example module in seatunnel-examples, and test it against different engines to ensure that the performance of the Connector is as consistent as possible. If you find any discrepancies, you can mark them in the document, modify the configuration file under resource, add our Connector to the configuration, and introduce seatunnel-flink(spark)-new-connector-example/pom.xml dependency, you can execute SeaTunnelApiExample to test. The default is stream processing mode, and the execution mode is switched to batch mode by modifying job.mode=BATCH in the environment of the configuration file.","s":"Connector Testing","u":"/blog/Upcoming API Connector Development Analysis","h":"#connector-testing","p":102680},{"i":102691,"t":"When our Connector is ready, we can submit PR to github. After reviewing by other partners, our contributed Connector will become part of SeaTunnel!","s":"Submit PR","u":"/blog/Upcoming API Connector Development Analysis","h":"#submit-pr","p":102680},{"i":102696,"t":"The practice of SeaTunnel in Vip February 18, 2022 · 13 min read Guest speaker: Vip Senior Big Data Engineer Wang Yu Lecture preparation: Zhang Detong Introduction: Vip referenced SeaTunnel as early as version 1.0. We use SeaTunnel to perform some data interaction work between Hive and ClickHouse. Today's presentation will focus on the following points: Requirements and pain points of ClickHouse data import; Selection of ClickHouse warehousing and warehousing tools; Hive to ClickHouse; ClickHouse to Hive; Integration of SeaTunnel and Vipshop data platform; Future outlook; Requirements and pain points of ClickHouse data import 1. Vipshop Data OLAP Architecture​ The picture shows the OLAP architecture of Vipshop. The modules we are responsible for are the data service and the computing engine in the picture. The underlying data warehouses are divided into offline data warehouses, real-time data warehouses, and lake warehouses. For computing engines, we use Presto, Kylin and Clickhouse. Although Clickhouse is a storage-integrated OLAP database, we have included it in the computing engine part in order to take advantage of Clickhouse's excellent computing performance. Based on OLAP components, we provide SQL data services and non-SQL independent analysis of Vipshop to serve different intelligences. For example, non-SQL services are services that provide data analysis that is closer to the business for BI and commerce. Multiple data applications are abstracted on top of data services. 2. Requirements​ We connect the underlying Hive, Kudu, and Alluxio components through Presto Connector and Spark components. Big data components can import and export data to and from each other, and you can use appropriate components to analyze data according to the needs and scenarios of data analysis. But when we introduced Clickhouse, it was a data island, and it was difficult to import and export data. There is a lot of work between Hive and Clickhouse to implement import and export. Our first data import and export requirement is to improve the import and export efficiency and incorporate Clickhouse into the big data system. The second requirement is that Presto runs SQL relatively slowly. The figure shows an example of slow SQL. The SQL where condition in the figure sets the date, time range, and specific filter conditions. This kind of SQL usage runs slowly because Presto uses partition granularity to push down. Even after optimization by other methods such as Hive's bucket table and bucketing, the return time is a few seconds, which cannot meet business requirements. In this case, we need to use Clickhouse for offline OLAP computing acceleration. Our real-time data is written to Clickhouse through Kafka and Flink SQL. However, it is not enough to use real-time data for analysis. It is necessary to use the Hive dimension table and the T+1 real-time table with the ETL calculation number for accelerated transportation in Clickhouse. This requires importing Hive data into Clickhouse, which is our third requirement. 3. Pain points​ First, we introduce a data component to consider its performance. The granularity of the Hive table is five minutes. Is there a component that can support a short ETL process and import the ETL results into Clickhouse within five minutes? Second, we need to ensure the quality of the data, and the accuracy of the data needs to be guaranteed. The number of data entries in Hive and Clickhouse needs to be consistent. If there is a problem with the data quality, can the data be repaired by rerunning and other mechanisms? Third, are the data types that data import needs to support complete? The data types and some mechanisms between different databases are different. We have data types such as HiperLogLog and BitMap that are widely used in a certain storage engine. Whether they can be correctly transmitted and identified, and can be used well. Selection of ClickHouse and Hive warehousing and warehousing tools Based on the pain points in the data business, we have compared and selected data warehouse and warehouse tools. We mainly choose among open source tools, without considering commercial warehouse entry and exit tools, we mainly compare DataX, SeaTunnel, and write Spark programs and use jdbc to insert ClickHouse among the three options. SeaTunnel and Spark rely on Vipshop's own Yarn cluster, which can directly implement distributed reading and writing. DataX is non-distributed, and the startup process between Reader and Writer takes a long time, and the performance is ordinary. The performance of SeaTunnel and Spark for data processing can reach several times that of DataX. Data of more than one billion can run smoothly in SeaTunnel and Spark. DataX has great performance pressure after the amount of data is large, and it is difficult to process data of more than one billion. In terms of read and write plug-in scalability, SeaTunnel supports a variety of data sources and supports users to develop plug-ins. SeaTunnel supports data import into Redis. In terms of stability, since SeaTunnel and DataX are self-contained tools, the stability will be better. The stability aspect of Spark requires attention to code quality. The amount of data in our exposure table is in the billions of levels every day. We have the performance requirement to complete data processing within 5 minutes. We have the need to import and export data to Redis. We need import and export tools that can be connected to the data platform for task scheduling. . For the consideration of data volume, performance, scalability, and platform compatibility, we chose SeaTunnel as our data warehouse import and export tool. Import Hive data into ClickHouse The following will introduce our use of SeaTunnel. The picture is a Hive table, which is our three-level product dimension table, including category products, dimension categories, and user population information. The primary key of the table is a third-level category ct_third_id, and the following value is the bitmap of two uids, which is the bitmap type of the user id. We need to import this Hive table into Clickhouse. SeaTunnel is easy to install, and the official website documentation describes how to install it. The figure below shows the configuration of SeaTunnel. In the configuration, env, source and sink are essential. In the env part, the example in the figure is the Spark configuration. The configuration includes concurrency, etc. These parameters can be adjusted. The source part is the data source. The Hive data source is configured here, including a Hive Select statement. Spark runs the SQL in the source configuration to read the data. UDF is supported here for simple ETL; the sink part is configured with Clickhouse, and you can see output_type= rowbinary and rowbinary are the self-developed acceleration solutions of Vipshop; pre_sql and check_sql are self-developed functions for data verification, which will be described in detail later; clickhouse.socket_timeout and bulk_size can be adjusted according to the actual situation. Run SeaTunnel, execute the sh script file, configure the conf file address and yarn information, and then you can. Spark logs are generated during the running process, and both successful running and running errors can be viewed in the logs. In order to better fit the business, Vipshop will make some improvements to SeaTunnel. All our ETL tasks need to be rerun. We support pre_sql and check_sql to implement data rerun and logarithm. The main process is to execute pre_sql for preprocessing after the data is ready, delete the old partition data in Clickhouse, store it in a directory, and restore the partition and rename when it fails. check_sql will check, and the whole process will end after the check is passed; if the check fails, it will be rerun according to the configuration, and if the rerun fails, the corresponding person in charge will be alerted. Based on the 1.0 version of SeaTunnel, Vipshop has added RowBinary for acceleration, and it also makes it easier to import the binary files of HuperLogLog and BinaryBitmap from Hive to Clickhouse. We made changes in ClickHouse-jdbc, bulk_size, Hive-source. Use the extended api of CK-jdbc to write data to CK in rowbinary mode. Bulk_size introduces the control logic for writing to CK in rowbinary mode. Hive-source RDD is partitioned with HashPartitioner to break up data to prevent data from being skewed. We also let SeaTunnel support multiple types. In order to circle the crowd, corresponding methods need to be implemented in Clickhouse, Preso, and Spark. We added Callback, HttpEntity, and RowBinaryStream that support Batch feature to Clickhouse-jdbc, added bitmap type mapping to Clickhouse-jdbc and Clickhouse-sink code, and implemented UDF of Clickhouse's Hyperloglog and Bitmap functions in Presto and Spark. In the previous configuration, the clickhouse-sink part can specify the table name, and here is the difference between writing to the local table and the distributed table. The performance of writing to a distributed table is worse than that of writing to a local table, which will put more pressure on the Clickhouse cluster. However, in scenarios such as exposure meter, flow meter, and ABTest, two tables are required to join, and both tables are in the order of billions. . At this time, we hope that the Join key falls on the local machine, and the Join cost is smaller. When we built the table, we configured the murmurHash64 rule in the distributed table distribution rules of Clickhouse, and then directly configured the distributed table in the sink of Seatunnel, handed the writing rules to Clickhouse, and used the characteristics of the distributed table to write. Writing to the local table will put less pressure on Clickhouse, and the performance of writing will be better. In Seatunnel, we go to Clickhouse's System.cluster table to obtain the table distribution information and machine distribution host according to the sink's local table. Then write to these hosts according to the equalization rule. Put the distributed writing of data into Seatunnel. For the writing of local tables and distributed tables, our future transformation direction is to implement consistent hashing in Seatunnel, write directly according to certain rules, such as Clickhouse, without relying on Clickhouse itself for data distribution, and improve Clickhouse's high CPU load problem. ClickHouse data import into Hive We have the needs of people in the circle. Every day, Vipshop gathers 200,000 people in the supplier circle, such as people born in the 1980s, Gao Fushuai, and Bai Fumei. These Bitmap crowd information in Clickhouse needs to be exported to the Hive table, coordinated with other ETL tasks in Hive, and finally pushed to PIKA for use by external media. We made SeaTunnel back-push Clickhouse Bitmap crowd data to Hive. The figure shows the SeaTunnel configuration. We configure the source as Clickhouse, the sink as Hive, and the data verification is also configured in Hive. Since we access SeaTunnel earlier, we have processed some modules, including adding plugin-spark-sink-hive module, plugin-spark-source-ClickHouse module, and rewriting Spark Row related methods so that they can be packaged through The Clickhouse data mapped by Schem, reconstruct the StructField and generate the DataFrame that finally needs to land on Hive. The latest version has many source and sink components, which is more convenient to use in SeaTunnel. It is now also possible to integrate the Flink connector directly in SeaTunnel. Integration of SeaTunnel and Vipshop Data Platform Each company has its own scheduling system, such as Beluga, Zeus. The scheduling tool of Vipshop is Shufang, and the scheduling tool integrates the data transmission tool. The following is the architecture diagram of the scheduling system, which includes the entry and exit of various types of data. The SeaTunnel task type is integrated into the platform. The picture is a screenshot of the scheduled task of Shufang. You can see that the selected part is a configured SeaTunnel task. resource information. The following shows the historical running instance information. We integrated SeaTunnel into the scheduling system. The Shufang Scheduling Master will assign tasks to the corresponding Agents according to the task types, and assign them to the appropriate machines to run according to the Agent load. The controller pulls the task scheduling configuration and information in the foreground. After arriving, a SeaTunnel cluster is generated and executed in a virtual environment similar to k8s pod and cgroup isolation. The running result will be judged by the data quality monitoring of the scheduling platform whether the task is completed and whether the operation is successful, and if it fails, it will rerun and alarm. SeaTunnel itself is a tool-based component, which is used to manage and control data blood relationship, data quality, historical records, high-alert monitoring, and resource allocation. We integrate SeaTunnel into the platform, and we can take advantage of the platform to take advantage of SeaTunnel. SeaTunnel is used for processing in the deposit crowd. By managing data, we divide the circled people into different people according to their paths and usage conditions, or thousands of people and thousands of faces, tag users, and push a certain type of people circled to users, analysts and suppliers. The traffic enters Kafka, enters the warehouse through Flink, and then forms a user label table through ETL. After the user label table is generated, we use the BitMap method implemented by Presto to type the data into a wide table in Hive. Users create tasks by box-selecting entries on the crowd system page, submit to Tengqun, and generate SQL query Clickhouse BitMap. Clickhouse's BitMap query speed is very fast. Due to its inherent advantages, we need to import Hive's BitMap table into Clickhouse through SeaTunnel. After the crowd is circled, we need to land the table to form a partition or a record of Clickhouse, and then store the resulting BitMap table in Hive through SeaTunnel. Finally, the synchronization tool will synchronize Hive's BitMap crowd results to the external media repository Pika. Around 20w people are circled every day. During the whole process, SeaTunnel is responsible for exporting the data from Hive to Clickhouse. After the ETL process of Clickhouse is completed, SeaTunnel exports the data from Clickhouse to Hive. In order to fulfill this requirement, we implemented UDFs of ClickHouse's Hyperloglog and BitMap functions on Presto and Spark; we also developed the Seatunnel interface, so that the crowds circled by users using the Bitmap method in ClickHouse can be directly written to the Hive table through Seatunnel , without an intermediate landing step. Users can also call the SeaTunnel interface through spark to circle the crowd or reverse the crowd bitmap in Hive, so that the data can be directly transmitted to the ClickHouse result table without intermediate landing. Follow-up In the future, we will further improve the problem of high CPU load when Clickhouse writes data. In the next step, we will implement the CK-local mode of Clickhouse data source and read end in SeaTunnel, separate read and write, and reduce Clickhouse pressure. In the future we will also add more sink support, such as data push to Pika and corresponding data checking. Tags: Vip ClickHouse","s":"Requirements and pain points of ClickHouse data import","u":"/user_cases","h":"","p":102695},{"i":102698,"t":"Tags C ClickHouse1 V Vip1","s":"Tags","u":"/user_cases/tags","h":"","p":102697},{"i":102700,"t":"One post tagged with \"ClickHouse\" View All Tags The practice of SeaTunnel in Vip February 18, 2022 · 13 min read Guest speaker: Vip Senior Big Data Engineer Wang Yu Lecture preparation: Zhang Detong Introduction: Vip referenced SeaTunnel as early as version 1.0. We use SeaTunnel to perform some data interaction work between Hive and ClickHouse. Today's presentation will focus on the following points: Requirements and pain points of ClickHouse data import; Selection of ClickHouse warehousing and warehousing tools; Hive to ClickHouse; ClickHouse to Hive; Integration of SeaTunnel and Vipshop data platform; Future outlook; Requirements and pain points of ClickHouse data import 1. Vipshop Data OLAP Architecture​ The picture shows the OLAP architecture of Vipshop. The modules we are responsible for are the data service and the computing engine in the picture. The underlying data warehouses are divided into offline data warehouses, real-time data warehouses, and lake warehouses. For computing engines, we use Presto, Kylin and Clickhouse. Although Clickhouse is a storage-integrated OLAP database, we have included it in the computing engine part in order to take advantage of Clickhouse's excellent computing performance. Based on OLAP components, we provide SQL data services and non-SQL independent analysis of Vipshop to serve different intelligences. For example, non-SQL services are services that provide data analysis that is closer to the business for BI and commerce. Multiple data applications are abstracted on top of data services. 2. Requirements​ We connect the underlying Hive, Kudu, and Alluxio components through Presto Connector and Spark components. Big data components can import and export data to and from each other, and you can use appropriate components to analyze data according to the needs and scenarios of data analysis. But when we introduced Clickhouse, it was a data island, and it was difficult to import and export data. There is a lot of work between Hive and Clickhouse to implement import and export. Our first data import and export requirement is to improve the import and export efficiency and incorporate Clickhouse into the big data system. The second requirement is that Presto runs SQL relatively slowly. The figure shows an example of slow SQL. The SQL where condition in the figure sets the date, time range, and specific filter conditions. This kind of SQL usage runs slowly because Presto uses partition granularity to push down. Even after optimization by other methods such as Hive's bucket table and bucketing, the return time is a few seconds, which cannot meet business requirements. In this case, we need to use Clickhouse for offline OLAP computing acceleration. Our real-time data is written to Clickhouse through Kafka and Flink SQL. However, it is not enough to use real-time data for analysis. It is necessary to use the Hive dimension table and the T+1 real-time table with the ETL calculation number for accelerated transportation in Clickhouse. This requires importing Hive data into Clickhouse, which is our third requirement. 3. Pain points​ First, we introduce a data component to consider its performance. The granularity of the Hive table is five minutes. Is there a component that can support a short ETL process and import the ETL results into Clickhouse within five minutes? Second, we need to ensure the quality of the data, and the accuracy of the data needs to be guaranteed. The number of data entries in Hive and Clickhouse needs to be consistent. If there is a problem with the data quality, can the data be repaired by rerunning and other mechanisms? Third, are the data types that data import needs to support complete? The data types and some mechanisms between different databases are different. We have data types such as HiperLogLog and BitMap that are widely used in a certain storage engine. Whether they can be correctly transmitted and identified, and can be used well. Selection of ClickHouse and Hive warehousing and warehousing tools Based on the pain points in the data business, we have compared and selected data warehouse and warehouse tools. We mainly choose among open source tools, without considering commercial warehouse entry and exit tools, we mainly compare DataX, SeaTunnel, and write Spark programs and use jdbc to insert ClickHouse among the three options. SeaTunnel and Spark rely on Vipshop's own Yarn cluster, which can directly implement distributed reading and writing. DataX is non-distributed, and the startup process between Reader and Writer takes a long time, and the performance is ordinary. The performance of SeaTunnel and Spark for data processing can reach several times that of DataX. Data of more than one billion can run smoothly in SeaTunnel and Spark. DataX has great performance pressure after the amount of data is large, and it is difficult to process data of more than one billion. In terms of read and write plug-in scalability, SeaTunnel supports a variety of data sources and supports users to develop plug-ins. SeaTunnel supports data import into Redis. In terms of stability, since SeaTunnel and DataX are self-contained tools, the stability will be better. The stability aspect of Spark requires attention to code quality. The amount of data in our exposure table is in the billions of levels every day. We have the performance requirement to complete data processing within 5 minutes. We have the need to import and export data to Redis. We need import and export tools that can be connected to the data platform for task scheduling. . For the consideration of data volume, performance, scalability, and platform compatibility, we chose SeaTunnel as our data warehouse import and export tool. Import Hive data into ClickHouse The following will introduce our use of SeaTunnel. The picture is a Hive table, which is our three-level product dimension table, including category products, dimension categories, and user population information. The primary key of the table is a third-level category ct_third_id, and the following value is the bitmap of two uids, which is the bitmap type of the user id. We need to import this Hive table into Clickhouse. SeaTunnel is easy to install, and the official website documentation describes how to install it. The figure below shows the configuration of SeaTunnel. In the configuration, env, source and sink are essential. In the env part, the example in the figure is the Spark configuration. The configuration includes concurrency, etc. These parameters can be adjusted. The source part is the data source. The Hive data source is configured here, including a Hive Select statement. Spark runs the SQL in the source configuration to read the data. UDF is supported here for simple ETL; the sink part is configured with Clickhouse, and you can see output_type= rowbinary and rowbinary are the self-developed acceleration solutions of Vipshop; pre_sql and check_sql are self-developed functions for data verification, which will be described in detail later; clickhouse.socket_timeout and bulk_size can be adjusted according to the actual situation. Run SeaTunnel, execute the sh script file, configure the conf file address and yarn information, and then you can. Spark logs are generated during the running process, and both successful running and running errors can be viewed in the logs. In order to better fit the business, Vipshop will make some improvements to SeaTunnel. All our ETL tasks need to be rerun. We support pre_sql and check_sql to implement data rerun and logarithm. The main process is to execute pre_sql for preprocessing after the data is ready, delete the old partition data in Clickhouse, store it in a directory, and restore the partition and rename when it fails. check_sql will check, and the whole process will end after the check is passed; if the check fails, it will be rerun according to the configuration, and if the rerun fails, the corresponding person in charge will be alerted. Based on the 1.0 version of SeaTunnel, Vipshop has added RowBinary for acceleration, and it also makes it easier to import the binary files of HuperLogLog and BinaryBitmap from Hive to Clickhouse. We made changes in ClickHouse-jdbc, bulk_size, Hive-source. Use the extended api of CK-jdbc to write data to CK in rowbinary mode. Bulk_size introduces the control logic for writing to CK in rowbinary mode. Hive-source RDD is partitioned with HashPartitioner to break up data to prevent data from being skewed. We also let SeaTunnel support multiple types. In order to circle the crowd, corresponding methods need to be implemented in Clickhouse, Preso, and Spark. We added Callback, HttpEntity, and RowBinaryStream that support Batch feature to Clickhouse-jdbc, added bitmap type mapping to Clickhouse-jdbc and Clickhouse-sink code, and implemented UDF of Clickhouse's Hyperloglog and Bitmap functions in Presto and Spark. In the previous configuration, the clickhouse-sink part can specify the table name, and here is the difference between writing to the local table and the distributed table. The performance of writing to a distributed table is worse than that of writing to a local table, which will put more pressure on the Clickhouse cluster. However, in scenarios such as exposure meter, flow meter, and ABTest, two tables are required to join, and both tables are in the order of billions. . At this time, we hope that the Join key falls on the local machine, and the Join cost is smaller. When we built the table, we configured the murmurHash64 rule in the distributed table distribution rules of Clickhouse, and then directly configured the distributed table in the sink of Seatunnel, handed the writing rules to Clickhouse, and used the characteristics of the distributed table to write. Writing to the local table will put less pressure on Clickhouse, and the performance of writing will be better. In Seatunnel, we go to Clickhouse's System.cluster table to obtain the table distribution information and machine distribution host according to the sink's local table. Then write to these hosts according to the equalization rule. Put the distributed writing of data into Seatunnel. For the writing of local tables and distributed tables, our future transformation direction is to implement consistent hashing in Seatunnel, write directly according to certain rules, such as Clickhouse, without relying on Clickhouse itself for data distribution, and improve Clickhouse's high CPU load problem. ClickHouse data import into Hive We have the needs of people in the circle. Every day, Vipshop gathers 200,000 people in the supplier circle, such as people born in the 1980s, Gao Fushuai, and Bai Fumei. These Bitmap crowd information in Clickhouse needs to be exported to the Hive table, coordinated with other ETL tasks in Hive, and finally pushed to PIKA for use by external media. We made SeaTunnel back-push Clickhouse Bitmap crowd data to Hive. The figure shows the SeaTunnel configuration. We configure the source as Clickhouse, the sink as Hive, and the data verification is also configured in Hive. Since we access SeaTunnel earlier, we have processed some modules, including adding plugin-spark-sink-hive module, plugin-spark-source-ClickHouse module, and rewriting Spark Row related methods so that they can be packaged through The Clickhouse data mapped by Schem, reconstruct the StructField and generate the DataFrame that finally needs to land on Hive. The latest version has many source and sink components, which is more convenient to use in SeaTunnel. It is now also possible to integrate the Flink connector directly in SeaTunnel. Integration of SeaTunnel and Vipshop Data Platform Each company has its own scheduling system, such as Beluga, Zeus. The scheduling tool of Vipshop is Shufang, and the scheduling tool integrates the data transmission tool. The following is the architecture diagram of the scheduling system, which includes the entry and exit of various types of data. The SeaTunnel task type is integrated into the platform. The picture is a screenshot of the scheduled task of Shufang. You can see that the selected part is a configured SeaTunnel task. resource information. The following shows the historical running instance information. We integrated SeaTunnel into the scheduling system. The Shufang Scheduling Master will assign tasks to the corresponding Agents according to the task types, and assign them to the appropriate machines to run according to the Agent load. The controller pulls the task scheduling configuration and information in the foreground. After arriving, a SeaTunnel cluster is generated and executed in a virtual environment similar to k8s pod and cgroup isolation. The running result will be judged by the data quality monitoring of the scheduling platform whether the task is completed and whether the operation is successful, and if it fails, it will rerun and alarm. SeaTunnel itself is a tool-based component, which is used to manage and control data blood relationship, data quality, historical records, high-alert monitoring, and resource allocation. We integrate SeaTunnel into the platform, and we can take advantage of the platform to take advantage of SeaTunnel. SeaTunnel is used for processing in the deposit crowd. By managing data, we divide the circled people into different people according to their paths and usage conditions, or thousands of people and thousands of faces, tag users, and push a certain type of people circled to users, analysts and suppliers. The traffic enters Kafka, enters the warehouse through Flink, and then forms a user label table through ETL. After the user label table is generated, we use the BitMap method implemented by Presto to type the data into a wide table in Hive. Users create tasks by box-selecting entries on the crowd system page, submit to Tengqun, and generate SQL query Clickhouse BitMap. Clickhouse's BitMap query speed is very fast. Due to its inherent advantages, we need to import Hive's BitMap table into Clickhouse through SeaTunnel. After the crowd is circled, we need to land the table to form a partition or a record of Clickhouse, and then store the resulting BitMap table in Hive through SeaTunnel. Finally, the synchronization tool will synchronize Hive's BitMap crowd results to the external media repository Pika. Around 20w people are circled every day. During the whole process, SeaTunnel is responsible for exporting the data from Hive to Clickhouse. After the ETL process of Clickhouse is completed, SeaTunnel exports the data from Clickhouse to Hive. In order to fulfill this requirement, we implemented UDFs of ClickHouse's Hyperloglog and BitMap functions on Presto and Spark; we also developed the Seatunnel interface, so that the crowds circled by users using the Bitmap method in ClickHouse can be directly written to the Hive table through Seatunnel , without an intermediate landing step. Users can also call the SeaTunnel interface through spark to circle the crowd or reverse the crowd bitmap in Hive, so that the data can be directly transmitted to the ClickHouse result table without intermediate landing. Follow-up In the future, we will further improve the problem of high CPU load when Clickhouse writes data. In the next step, we will implement the CK-local mode of Clickhouse data source and read end in SeaTunnel, separate read and write, and reduce Clickhouse pressure. In the future we will also add more sink support, such as data push to Pika and corresponding data checking. Tags: Vip ClickHouse","s":"One post tagged with \"ClickHouse\"","u":"/user_cases/tags/click-house","h":"","p":102699},{"i":102702,"t":"One post tagged with \"Vip\" View All Tags The practice of SeaTunnel in Vip February 18, 2022 · 13 min read Guest speaker: Vip Senior Big Data Engineer Wang Yu Lecture preparation: Zhang Detong Introduction: Vip referenced SeaTunnel as early as version 1.0. We use SeaTunnel to perform some data interaction work between Hive and ClickHouse. Today's presentation will focus on the following points: Requirements and pain points of ClickHouse data import; Selection of ClickHouse warehousing and warehousing tools; Hive to ClickHouse; ClickHouse to Hive; Integration of SeaTunnel and Vipshop data platform; Future outlook; Requirements and pain points of ClickHouse data import 1. Vipshop Data OLAP Architecture​ The picture shows the OLAP architecture of Vipshop. The modules we are responsible for are the data service and the computing engine in the picture. The underlying data warehouses are divided into offline data warehouses, real-time data warehouses, and lake warehouses. For computing engines, we use Presto, Kylin and Clickhouse. Although Clickhouse is a storage-integrated OLAP database, we have included it in the computing engine part in order to take advantage of Clickhouse's excellent computing performance. Based on OLAP components, we provide SQL data services and non-SQL independent analysis of Vipshop to serve different intelligences. For example, non-SQL services are services that provide data analysis that is closer to the business for BI and commerce. Multiple data applications are abstracted on top of data services. 2. Requirements​ We connect the underlying Hive, Kudu, and Alluxio components through Presto Connector and Spark components. Big data components can import and export data to and from each other, and you can use appropriate components to analyze data according to the needs and scenarios of data analysis. But when we introduced Clickhouse, it was a data island, and it was difficult to import and export data. There is a lot of work between Hive and Clickhouse to implement import and export. Our first data import and export requirement is to improve the import and export efficiency and incorporate Clickhouse into the big data system. The second requirement is that Presto runs SQL relatively slowly. The figure shows an example of slow SQL. The SQL where condition in the figure sets the date, time range, and specific filter conditions. This kind of SQL usage runs slowly because Presto uses partition granularity to push down. Even after optimization by other methods such as Hive's bucket table and bucketing, the return time is a few seconds, which cannot meet business requirements. In this case, we need to use Clickhouse for offline OLAP computing acceleration. Our real-time data is written to Clickhouse through Kafka and Flink SQL. However, it is not enough to use real-time data for analysis. It is necessary to use the Hive dimension table and the T+1 real-time table with the ETL calculation number for accelerated transportation in Clickhouse. This requires importing Hive data into Clickhouse, which is our third requirement. 3. Pain points​ First, we introduce a data component to consider its performance. The granularity of the Hive table is five minutes. Is there a component that can support a short ETL process and import the ETL results into Clickhouse within five minutes? Second, we need to ensure the quality of the data, and the accuracy of the data needs to be guaranteed. The number of data entries in Hive and Clickhouse needs to be consistent. If there is a problem with the data quality, can the data be repaired by rerunning and other mechanisms? Third, are the data types that data import needs to support complete? The data types and some mechanisms between different databases are different. We have data types such as HiperLogLog and BitMap that are widely used in a certain storage engine. Whether they can be correctly transmitted and identified, and can be used well. Selection of ClickHouse and Hive warehousing and warehousing tools Based on the pain points in the data business, we have compared and selected data warehouse and warehouse tools. We mainly choose among open source tools, without considering commercial warehouse entry and exit tools, we mainly compare DataX, SeaTunnel, and write Spark programs and use jdbc to insert ClickHouse among the three options. SeaTunnel and Spark rely on Vipshop's own Yarn cluster, which can directly implement distributed reading and writing. DataX is non-distributed, and the startup process between Reader and Writer takes a long time, and the performance is ordinary. The performance of SeaTunnel and Spark for data processing can reach several times that of DataX. Data of more than one billion can run smoothly in SeaTunnel and Spark. DataX has great performance pressure after the amount of data is large, and it is difficult to process data of more than one billion. In terms of read and write plug-in scalability, SeaTunnel supports a variety of data sources and supports users to develop plug-ins. SeaTunnel supports data import into Redis. In terms of stability, since SeaTunnel and DataX are self-contained tools, the stability will be better. The stability aspect of Spark requires attention to code quality. The amount of data in our exposure table is in the billions of levels every day. We have the performance requirement to complete data processing within 5 minutes. We have the need to import and export data to Redis. We need import and export tools that can be connected to the data platform for task scheduling. . For the consideration of data volume, performance, scalability, and platform compatibility, we chose SeaTunnel as our data warehouse import and export tool. Import Hive data into ClickHouse The following will introduce our use of SeaTunnel. The picture is a Hive table, which is our three-level product dimension table, including category products, dimension categories, and user population information. The primary key of the table is a third-level category ct_third_id, and the following value is the bitmap of two uids, which is the bitmap type of the user id. We need to import this Hive table into Clickhouse. SeaTunnel is easy to install, and the official website documentation describes how to install it. The figure below shows the configuration of SeaTunnel. In the configuration, env, source and sink are essential. In the env part, the example in the figure is the Spark configuration. The configuration includes concurrency, etc. These parameters can be adjusted. The source part is the data source. The Hive data source is configured here, including a Hive Select statement. Spark runs the SQL in the source configuration to read the data. UDF is supported here for simple ETL; the sink part is configured with Clickhouse, and you can see output_type= rowbinary and rowbinary are the self-developed acceleration solutions of Vipshop; pre_sql and check_sql are self-developed functions for data verification, which will be described in detail later; clickhouse.socket_timeout and bulk_size can be adjusted according to the actual situation. Run SeaTunnel, execute the sh script file, configure the conf file address and yarn information, and then you can. Spark logs are generated during the running process, and both successful running and running errors can be viewed in the logs. In order to better fit the business, Vipshop will make some improvements to SeaTunnel. All our ETL tasks need to be rerun. We support pre_sql and check_sql to implement data rerun and logarithm. The main process is to execute pre_sql for preprocessing after the data is ready, delete the old partition data in Clickhouse, store it in a directory, and restore the partition and rename when it fails. check_sql will check, and the whole process will end after the check is passed; if the check fails, it will be rerun according to the configuration, and if the rerun fails, the corresponding person in charge will be alerted. Based on the 1.0 version of SeaTunnel, Vipshop has added RowBinary for acceleration, and it also makes it easier to import the binary files of HuperLogLog and BinaryBitmap from Hive to Clickhouse. We made changes in ClickHouse-jdbc, bulk_size, Hive-source. Use the extended api of CK-jdbc to write data to CK in rowbinary mode. Bulk_size introduces the control logic for writing to CK in rowbinary mode. Hive-source RDD is partitioned with HashPartitioner to break up data to prevent data from being skewed. We also let SeaTunnel support multiple types. In order to circle the crowd, corresponding methods need to be implemented in Clickhouse, Preso, and Spark. We added Callback, HttpEntity, and RowBinaryStream that support Batch feature to Clickhouse-jdbc, added bitmap type mapping to Clickhouse-jdbc and Clickhouse-sink code, and implemented UDF of Clickhouse's Hyperloglog and Bitmap functions in Presto and Spark. In the previous configuration, the clickhouse-sink part can specify the table name, and here is the difference between writing to the local table and the distributed table. The performance of writing to a distributed table is worse than that of writing to a local table, which will put more pressure on the Clickhouse cluster. However, in scenarios such as exposure meter, flow meter, and ABTest, two tables are required to join, and both tables are in the order of billions. . At this time, we hope that the Join key falls on the local machine, and the Join cost is smaller. When we built the table, we configured the murmurHash64 rule in the distributed table distribution rules of Clickhouse, and then directly configured the distributed table in the sink of Seatunnel, handed the writing rules to Clickhouse, and used the characteristics of the distributed table to write. Writing to the local table will put less pressure on Clickhouse, and the performance of writing will be better. In Seatunnel, we go to Clickhouse's System.cluster table to obtain the table distribution information and machine distribution host according to the sink's local table. Then write to these hosts according to the equalization rule. Put the distributed writing of data into Seatunnel. For the writing of local tables and distributed tables, our future transformation direction is to implement consistent hashing in Seatunnel, write directly according to certain rules, such as Clickhouse, without relying on Clickhouse itself for data distribution, and improve Clickhouse's high CPU load problem. ClickHouse data import into Hive We have the needs of people in the circle. Every day, Vipshop gathers 200,000 people in the supplier circle, such as people born in the 1980s, Gao Fushuai, and Bai Fumei. These Bitmap crowd information in Clickhouse needs to be exported to the Hive table, coordinated with other ETL tasks in Hive, and finally pushed to PIKA for use by external media. We made SeaTunnel back-push Clickhouse Bitmap crowd data to Hive. The figure shows the SeaTunnel configuration. We configure the source as Clickhouse, the sink as Hive, and the data verification is also configured in Hive. Since we access SeaTunnel earlier, we have processed some modules, including adding plugin-spark-sink-hive module, plugin-spark-source-ClickHouse module, and rewriting Spark Row related methods so that they can be packaged through The Clickhouse data mapped by Schem, reconstruct the StructField and generate the DataFrame that finally needs to land on Hive. The latest version has many source and sink components, which is more convenient to use in SeaTunnel. It is now also possible to integrate the Flink connector directly in SeaTunnel. Integration of SeaTunnel and Vipshop Data Platform Each company has its own scheduling system, such as Beluga, Zeus. The scheduling tool of Vipshop is Shufang, and the scheduling tool integrates the data transmission tool. The following is the architecture diagram of the scheduling system, which includes the entry and exit of various types of data. The SeaTunnel task type is integrated into the platform. The picture is a screenshot of the scheduled task of Shufang. You can see that the selected part is a configured SeaTunnel task. resource information. The following shows the historical running instance information. We integrated SeaTunnel into the scheduling system. The Shufang Scheduling Master will assign tasks to the corresponding Agents according to the task types, and assign them to the appropriate machines to run according to the Agent load. The controller pulls the task scheduling configuration and information in the foreground. After arriving, a SeaTunnel cluster is generated and executed in a virtual environment similar to k8s pod and cgroup isolation. The running result will be judged by the data quality monitoring of the scheduling platform whether the task is completed and whether the operation is successful, and if it fails, it will rerun and alarm. SeaTunnel itself is a tool-based component, which is used to manage and control data blood relationship, data quality, historical records, high-alert monitoring, and resource allocation. We integrate SeaTunnel into the platform, and we can take advantage of the platform to take advantage of SeaTunnel. SeaTunnel is used for processing in the deposit crowd. By managing data, we divide the circled people into different people according to their paths and usage conditions, or thousands of people and thousands of faces, tag users, and push a certain type of people circled to users, analysts and suppliers. The traffic enters Kafka, enters the warehouse through Flink, and then forms a user label table through ETL. After the user label table is generated, we use the BitMap method implemented by Presto to type the data into a wide table in Hive. Users create tasks by box-selecting entries on the crowd system page, submit to Tengqun, and generate SQL query Clickhouse BitMap. Clickhouse's BitMap query speed is very fast. Due to its inherent advantages, we need to import Hive's BitMap table into Clickhouse through SeaTunnel. After the crowd is circled, we need to land the table to form a partition or a record of Clickhouse, and then store the resulting BitMap table in Hive through SeaTunnel. Finally, the synchronization tool will synchronize Hive's BitMap crowd results to the external media repository Pika. Around 20w people are circled every day. During the whole process, SeaTunnel is responsible for exporting the data from Hive to Clickhouse. After the ETL process of Clickhouse is completed, SeaTunnel exports the data from Clickhouse to Hive. In order to fulfill this requirement, we implemented UDFs of ClickHouse's Hyperloglog and BitMap functions on Presto and Spark; we also developed the Seatunnel interface, so that the crowds circled by users using the Bitmap method in ClickHouse can be directly written to the Hive table through Seatunnel , without an intermediate landing step. Users can also call the SeaTunnel interface through spark to circle the crowd or reverse the crowd bitmap in Hive, so that the data can be directly transmitted to the ClickHouse result table without intermediate landing. Follow-up In the future, we will further improve the problem of high CPU load when Clickhouse writes data. In the next step, we will implement the CK-local mode of Clickhouse data source and read end in SeaTunnel, separate read and write, and reduce Clickhouse pressure. In the future we will also add more sink support, such as data push to Pika and corresponding data checking. Tags: Vip ClickHouse","s":"One post tagged with \"Vip\"","u":"/user_cases/tags/vip","h":"","p":102701},{"i":102704,"t":"2022 February 18, 2022 - The practice of SeaTunnel in Vip","s":"Archive","u":"/user_cases/archive","h":"","p":102703},{"i":102706,"t":"The practice of SeaTunnel in Vip February 18, 2022 · 13 min read Guest speaker: Vip Senior Big Data Engineer Wang Yu Lecture preparation: Zhang Detong Introduction: Vip referenced SeaTunnel as early as version 1.0. We use SeaTunnel to perform some data interaction work between Hive and ClickHouse. Today's presentation will focus on the following points: Requirements and pain points of ClickHouse data import; Selection of ClickHouse warehousing and warehousing tools; Hive to ClickHouse; ClickHouse to Hive; Integration of SeaTunnel and Vipshop data platform; Future outlook; Requirements and pain points of ClickHouse data import 1. Vipshop Data OLAP Architecture​ The picture shows the OLAP architecture of Vipshop. The modules we are responsible for are the data service and the computing engine in the picture. The underlying data warehouses are divided into offline data warehouses, real-time data warehouses, and lake warehouses. For computing engines, we use Presto, Kylin and Clickhouse. Although Clickhouse is a storage-integrated OLAP database, we have included it in the computing engine part in order to take advantage of Clickhouse's excellent computing performance. Based on OLAP components, we provide SQL data services and non-SQL independent analysis of Vipshop to serve different intelligences. For example, non-SQL services are services that provide data analysis that is closer to the business for BI and commerce. Multiple data applications are abstracted on top of data services. 2. Requirements​ We connect the underlying Hive, Kudu, and Alluxio components through Presto Connector and Spark components. Big data components can import and export data to and from each other, and you can use appropriate components to analyze data according to the needs and scenarios of data analysis. But when we introduced Clickhouse, it was a data island, and it was difficult to import and export data. There is a lot of work between Hive and Clickhouse to implement import and export. Our first data import and export requirement is to improve the import and export efficiency and incorporate Clickhouse into the big data system. The second requirement is that Presto runs SQL relatively slowly. The figure shows an example of slow SQL. The SQL where condition in the figure sets the date, time range, and specific filter conditions. This kind of SQL usage runs slowly because Presto uses partition granularity to push down. Even after optimization by other methods such as Hive's bucket table and bucketing, the return time is a few seconds, which cannot meet business requirements. In this case, we need to use Clickhouse for offline OLAP computing acceleration. Our real-time data is written to Clickhouse through Kafka and Flink SQL. However, it is not enough to use real-time data for analysis. It is necessary to use the Hive dimension table and the T+1 real-time table with the ETL calculation number for accelerated transportation in Clickhouse. This requires importing Hive data into Clickhouse, which is our third requirement. 3. Pain points​ First, we introduce a data component to consider its performance. The granularity of the Hive table is five minutes. Is there a component that can support a short ETL process and import the ETL results into Clickhouse within five minutes? Second, we need to ensure the quality of the data, and the accuracy of the data needs to be guaranteed. The number of data entries in Hive and Clickhouse needs to be consistent. If there is a problem with the data quality, can the data be repaired by rerunning and other mechanisms? Third, are the data types that data import needs to support complete? The data types and some mechanisms between different databases are different. We have data types such as HiperLogLog and BitMap that are widely used in a certain storage engine. Whether they can be correctly transmitted and identified, and can be used well. Selection of ClickHouse and Hive warehousing and warehousing tools Based on the pain points in the data business, we have compared and selected data warehouse and warehouse tools. We mainly choose among open source tools, without considering commercial warehouse entry and exit tools, we mainly compare DataX, SeaTunnel, and write Spark programs and use jdbc to insert ClickHouse among the three options. SeaTunnel and Spark rely on Vipshop's own Yarn cluster, which can directly implement distributed reading and writing. DataX is non-distributed, and the startup process between Reader and Writer takes a long time, and the performance is ordinary. The performance of SeaTunnel and Spark for data processing can reach several times that of DataX. Data of more than one billion can run smoothly in SeaTunnel and Spark. DataX has great performance pressure after the amount of data is large, and it is difficult to process data of more than one billion. In terms of read and write plug-in scalability, SeaTunnel supports a variety of data sources and supports users to develop plug-ins. SeaTunnel supports data import into Redis. In terms of stability, since SeaTunnel and DataX are self-contained tools, the stability will be better. The stability aspect of Spark requires attention to code quality. The amount of data in our exposure table is in the billions of levels every day. We have the performance requirement to complete data processing within 5 minutes. We have the need to import and export data to Redis. We need import and export tools that can be connected to the data platform for task scheduling. . For the consideration of data volume, performance, scalability, and platform compatibility, we chose SeaTunnel as our data warehouse import and export tool. Import Hive data into ClickHouse The following will introduce our use of SeaTunnel. The picture is a Hive table, which is our three-level product dimension table, including category products, dimension categories, and user population information. The primary key of the table is a third-level category ct_third_id, and the following value is the bitmap of two uids, which is the bitmap type of the user id. We need to import this Hive table into Clickhouse. SeaTunnel is easy to install, and the official website documentation describes how to install it. The figure below shows the configuration of SeaTunnel. In the configuration, env, source and sink are essential. In the env part, the example in the figure is the Spark configuration. The configuration includes concurrency, etc. These parameters can be adjusted. The source part is the data source. The Hive data source is configured here, including a Hive Select statement. Spark runs the SQL in the source configuration to read the data. UDF is supported here for simple ETL; the sink part is configured with Clickhouse, and you can see output_type= rowbinary and rowbinary are the self-developed acceleration solutions of Vipshop; pre_sql and check_sql are self-developed functions for data verification, which will be described in detail later; clickhouse.socket_timeout and bulk_size can be adjusted according to the actual situation. Run SeaTunnel, execute the sh script file, configure the conf file address and yarn information, and then you can. Spark logs are generated during the running process, and both successful running and running errors can be viewed in the logs. In order to better fit the business, Vipshop will make some improvements to SeaTunnel. All our ETL tasks need to be rerun. We support pre_sql and check_sql to implement data rerun and logarithm. The main process is to execute pre_sql for preprocessing after the data is ready, delete the old partition data in Clickhouse, store it in a directory, and restore the partition and rename when it fails. check_sql will check, and the whole process will end after the check is passed; if the check fails, it will be rerun according to the configuration, and if the rerun fails, the corresponding person in charge will be alerted. Based on the 1.0 version of SeaTunnel, Vipshop has added RowBinary for acceleration, and it also makes it easier to import the binary files of HuperLogLog and BinaryBitmap from Hive to Clickhouse. We made changes in ClickHouse-jdbc, bulk_size, Hive-source. Use the extended api of CK-jdbc to write data to CK in rowbinary mode. Bulk_size introduces the control logic for writing to CK in rowbinary mode. Hive-source RDD is partitioned with HashPartitioner to break up data to prevent data from being skewed. We also let SeaTunnel support multiple types. In order to circle the crowd, corresponding methods need to be implemented in Clickhouse, Preso, and Spark. We added Callback, HttpEntity, and RowBinaryStream that support Batch feature to Clickhouse-jdbc, added bitmap type mapping to Clickhouse-jdbc and Clickhouse-sink code, and implemented UDF of Clickhouse's Hyperloglog and Bitmap functions in Presto and Spark. In the previous configuration, the clickhouse-sink part can specify the table name, and here is the difference between writing to the local table and the distributed table. The performance of writing to a distributed table is worse than that of writing to a local table, which will put more pressure on the Clickhouse cluster. However, in scenarios such as exposure meter, flow meter, and ABTest, two tables are required to join, and both tables are in the order of billions. . At this time, we hope that the Join key falls on the local machine, and the Join cost is smaller. When we built the table, we configured the murmurHash64 rule in the distributed table distribution rules of Clickhouse, and then directly configured the distributed table in the sink of Seatunnel, handed the writing rules to Clickhouse, and used the characteristics of the distributed table to write. Writing to the local table will put less pressure on Clickhouse, and the performance of writing will be better. In Seatunnel, we go to Clickhouse's System.cluster table to obtain the table distribution information and machine distribution host according to the sink's local table. Then write to these hosts according to the equalization rule. Put the distributed writing of data into Seatunnel. For the writing of local tables and distributed tables, our future transformation direction is to implement consistent hashing in Seatunnel, write directly according to certain rules, such as Clickhouse, without relying on Clickhouse itself for data distribution, and improve Clickhouse's high CPU load problem. ClickHouse data import into Hive We have the needs of people in the circle. Every day, Vipshop gathers 200,000 people in the supplier circle, such as people born in the 1980s, Gao Fushuai, and Bai Fumei. These Bitmap crowd information in Clickhouse needs to be exported to the Hive table, coordinated with other ETL tasks in Hive, and finally pushed to PIKA for use by external media. We made SeaTunnel back-push Clickhouse Bitmap crowd data to Hive. The figure shows the SeaTunnel configuration. We configure the source as Clickhouse, the sink as Hive, and the data verification is also configured in Hive. Since we access SeaTunnel earlier, we have processed some modules, including adding plugin-spark-sink-hive module, plugin-spark-source-ClickHouse module, and rewriting Spark Row related methods so that they can be packaged through The Clickhouse data mapped by Schem, reconstruct the StructField and generate the DataFrame that finally needs to land on Hive. The latest version has many source and sink components, which is more convenient to use in SeaTunnel. It is now also possible to integrate the Flink connector directly in SeaTunnel. Integration of SeaTunnel and Vipshop Data Platform Each company has its own scheduling system, such as Beluga, Zeus. The scheduling tool of Vipshop is Shufang, and the scheduling tool integrates the data transmission tool. The following is the architecture diagram of the scheduling system, which includes the entry and exit of various types of data. The SeaTunnel task type is integrated into the platform. The picture is a screenshot of the scheduled task of Shufang. You can see that the selected part is a configured SeaTunnel task. resource information. The following shows the historical running instance information. We integrated SeaTunnel into the scheduling system. The Shufang Scheduling Master will assign tasks to the corresponding Agents according to the task types, and assign them to the appropriate machines to run according to the Agent load. The controller pulls the task scheduling configuration and information in the foreground. After arriving, a SeaTunnel cluster is generated and executed in a virtual environment similar to k8s pod and cgroup isolation. The running result will be judged by the data quality monitoring of the scheduling platform whether the task is completed and whether the operation is successful, and if it fails, it will rerun and alarm. SeaTunnel itself is a tool-based component, which is used to manage and control data blood relationship, data quality, historical records, high-alert monitoring, and resource allocation. We integrate SeaTunnel into the platform, and we can take advantage of the platform to take advantage of SeaTunnel. SeaTunnel is used for processing in the deposit crowd. By managing data, we divide the circled people into different people according to their paths and usage conditions, or thousands of people and thousands of faces, tag users, and push a certain type of people circled to users, analysts and suppliers. The traffic enters Kafka, enters the warehouse through Flink, and then forms a user label table through ETL. After the user label table is generated, we use the BitMap method implemented by Presto to type the data into a wide table in Hive. Users create tasks by box-selecting entries on the crowd system page, submit to Tengqun, and generate SQL query Clickhouse BitMap. Clickhouse's BitMap query speed is very fast. Due to its inherent advantages, we need to import Hive's BitMap table into Clickhouse through SeaTunnel. After the crowd is circled, we need to land the table to form a partition or a record of Clickhouse, and then store the resulting BitMap table in Hive through SeaTunnel. Finally, the synchronization tool will synchronize Hive's BitMap crowd results to the external media repository Pika. Around 20w people are circled every day. During the whole process, SeaTunnel is responsible for exporting the data from Hive to Clickhouse. After the ETL process of Clickhouse is completed, SeaTunnel exports the data from Clickhouse to Hive. In order to fulfill this requirement, we implemented UDFs of ClickHouse's Hyperloglog and BitMap functions on Presto and Spark; we also developed the Seatunnel interface, so that the crowds circled by users using the Bitmap method in ClickHouse can be directly written to the Hive table through Seatunnel , without an intermediate landing step. Users can also call the SeaTunnel interface through spark to circle the crowd or reverse the crowd bitmap in Hive, so that the data can be directly transmitted to the ClickHouse result table without intermediate landing. Follow-up In the future, we will further improve the problem of high CPU load when Clickhouse writes data. In the next step, we will implement the CK-local mode of Clickhouse data source and read end in SeaTunnel, separate read and write, and reduce Clickhouse pressure. In the future we will also add more sink support, such as data push to Pika and corresponding data checking. Tags: Vip ClickHouse","s":"The practice of SeaTunnel in Vip","u":"/user_cases/The-practice-of-SeaTunnel-in-Vip","h":"","p":102705},{"i":102711,"t":"Spark Flink bin/start-seatunnel-spark.sh bin/start-seatunnel-flink.sh","s":"Command Entrypoint","u":"/docs/2.2.0-beta/command/usage","h":"#command-entrypoint","p":102709},{"i":102713,"t":"Spark Flink bin/start-seatunnel-spark.sh \\ -c config-path \\ -m master \\ -e deploy-mode \\ -i city=beijing Use -m or --master to specify the cluster manager Use -e or --deploy-mode to specify the deployment mode bin/start-seatunnel-flink.sh \\ -c config-path \\ -i key=value \\ -r run-application \\ [other params] Use -r or --run-mode to specify the flink job run mode, you can use run-application or run (default value) Use -c or --config to specify the path of the configuration file Use -i or --variable to specify the variables in the configuration file, you can configure multiple","s":"Options","u":"/docs/2.2.0-beta/command/usage","h":"#options","p":102709},{"i":102715,"t":"Spark Flink # Yarn client mode ./bin/start-seatunnel-spark.sh \\ --master yarn \\ --deploy-mode client \\ --config ./config/application.conf # Yarn cluster mode ./bin/start-seatunnel-spark.sh \\ --master yarn \\ --deploy-mode cluster \\ --config ./config/application.conf env { execution.parallelism = 1 } source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { sql = \"select name,age from fake where name='\"${my_name}\"'\" } } sink { ConsoleSink {} } Run bin/start-seatunnel-flink.sh \\ -c config-path \\ -i my_name=kid-xiong This designation will replace \"${my_name}\" in the configuration file with kid-xiong All the configurations in the env section will be applied to Flink dynamic parameters with the format of -D, such as -Dexecution.parallelism=1 . For the rest of the parameters, refer to the original flink parameters. Check the flink parameter method: bin/flink run -h . The parameters can be added as needed. For example, -m yarn-cluster is specified as on yarn mode. bin/flink run -h For example: -p 2 specifies that the job parallelism is 2 bin/start-seatunnel-flink.sh \\ -p 2 \\ -c config-path Configurable parameters of flink yarn-cluster For example: -m yarn-cluster -ynm seatunnel specifies that the job is running on yarn, and the name of yarn WebUI is seatunnel bin/start-seatunnel-flink.sh \\ -m yarn-cluster \\ -ynm seatunnel \\ -c config-path","s":"Example","u":"/docs/2.2.0-beta/command/usage","h":"#example","p":102709},{"i":102717,"t":"In SeaTunnel, the most important thing is the Config file, through which users can customize their own data synchronization requirements to maximize the potential of SeaTunnel. So next, I will introduce you how to configure the Config file.","s":"Intro to config file","u":"/docs/2.2.0-beta/concept/config","h":"","p":102716},{"i":102719,"t":"Before you read on, you can find config file examples here and in distribute package's config directory.","s":"Example","u":"/docs/2.2.0-beta/concept/config","h":"#example","p":102716},{"i":102721,"t":"The Config file will be similar to the one below. env { execution.parallelism = 1 } source { FakeSource { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { sql = \"select name,age from fake\" } } sink { Clickhouse { host = \"clickhouse:8123\" database = \"default\" table = \"seatunnel_console\" fields = [\"name\"] username = \"default\" password = \"\" } } As you can see, the Config file contains several sections: env, source, transform, sink. Different modules have different functions. After you understand these modules, you will understand how SeaTunnel works.","s":"Config file structure","u":"/docs/2.2.0-beta/concept/config","h":"#config-file-structure","p":102716},{"i":102723,"t":"Used to add some engine optional parameters, no matter which engine (Spark or Flink), the corresponding optional parameters should be filled in here.","s":"env","u":"/docs/2.2.0-beta/concept/config","h":"#env","p":102716},{"i":102725,"t":"source is used to define where SeaTunnel needs to fetch data, and use the fetched data for the next step. Multiple sources can be defined at the same time. The supported source at now check Source of SeaTunnel. Each source has its own specific parameters to define how to fetch data, and SeaTunnel also extracts the parameters that each source will use, such as the result_table_name parameter, which is used to specify the name of the data generated by the current source, which is convenient for follow-up used by other modules.","s":"source","u":"/docs/2.2.0-beta/concept/config","h":"#source","p":102716},{"i":102727,"t":"When we have the data source, we may need to further process the data, so we have the transform module. Of course, this uses the word 'may', which means that we can also directly treat the transform as non-existent, directly from source to sink. Like below. transform { // no thing on here } Like source, transform has specific parameters that belong to each module. The supported source at now check. The supported transform at now check Transform of SeaTunnel","s":"transform","u":"/docs/2.2.0-beta/concept/config","h":"#transform","p":102716},{"i":102729,"t":"Our purpose with SeaTunnel is to synchronize data from one place to another, so it is critical to define how and where data is written. With the sink module provided by SeaTunnel, you can complete this operation quickly and efficiently. Sink and source are very similar, but the difference is reading and writing. So go check out our supported sinks.","s":"sink","u":"/docs/2.2.0-beta/concept/config","h":"#sink","p":102716},{"i":102731,"t":"You will find that when multiple sources and multiple sinks are defined, which data is read by each sink, and which is the data read by each transform? We use result_table_name and source_table_name two key configurations. Each source module will be configured with a result_table_name to indicate the name of the data source generated by the data source, and other transform and sink modules can use source_table_name to refer to the corresponding data source name, indicating that I want to read the data for processing. Then transform, as an intermediate processing module, can use both result_table_name and source_table_name configurations at the same time. But you will find that in the above example Config, not every module is configured with these two parameters, because in SeaTunnel, there is a default convention, if these two parameters are not configured, then the generated data from the last module of the previous node will be used. This is much more convenient when there is only one source.","s":"Other","u":"/docs/2.2.0-beta/concept/config","h":"#other","p":102716},{"i":102733,"t":"If you want to know the details of this format configuration, Please see HOCON.","s":"What's More","u":"/docs/2.2.0-beta/concept/config","h":"#whats-more","p":102716},{"i":102736,"t":"SeaTunnel uses a grading system for connectors to help you understand what to expect from a connector: Alpha Beta General Availability (GA) Expectations An alpha connector signifies a connector under development and helps SeaTunnel gather early feedback and issues reported by early adopters. We strongly discourage using alpha releases for production use cases A beta connector is considered stable and reliable with no backwards incompatible changes but has not been validated by a broader group of users. We expect to find and fix a few issues and bugs in the release before it’s ready for GA. A generally available connector has been deemed ready for use in a production environment and is officially supported by SeaTunnel. Its documentation is considered sufficient to support widespread adoption. Production Readiness No Yes Yes","s":"Connector Release Status","u":"/docs/2.2.0-beta/Connector-v2-release-state","h":"#connector-release-status","p":102734},{"i":102738,"t":"Connector Name Type Status Support Version Asset Sink Beta 2.2.0-beta ClickHouse Source Beta 2.2.0-beta ClickHouse Sink Beta 2.2.0-beta ClickHouseFile Sink Beta 2.2.0-beta Console Sink Beta 2.2.0-beta DataHub Sink Alpha 2.2.0-beta DingTalk Sink Alpha 2.2.0-beta Elasticsearch Sink Beta 2.2.0-beta Email Sink Alpha 2.2.0-beta Enterprise WeChat Sink Alpha 2.2.0-beta FeiShu Sink Alpha 2.2.0-beta Fake Source Alpha 2.2.0-beta FtpFile Sink Alpha 2.2.0-beta Greenplum Sink Alpha 2.2.0-beta Greenplum Source Alpha 2.2.0-beta HdfsFile Sink Beta 2.2.0-beta HdfsFile Source Beta 2.2.0-beta Hive Sink Alpha 2.2.0-beta Hive Source Beta 2.2.0-beta Http Sink Beta 2.2.0-beta Http Source Beta 2.2.0-beta Hudi Source Alpha 2.2.0-beta Iceberg Source Alpha 2.2.0-beta IoTDB Source Beta 2.2.0-beta IoTDB Sink Beta 2.2.0-beta Jdbc Source Beta 2.2.0-beta Jdbc Sink Beta 2.2.0-beta Kudu Source Alpha 2.2.0-beta Kudu Sink Alpha 2.2.0-beta LocalFile Sink Beta 2.2.0-beta LocalFile Source Beta 2.2.0-beta MongoDB Source Beta 2.2.0-beta MongoDB Sink Beta 2.2.0-beta Neo4j Sink Alpha 2.2.0-beta OssFile Sink Alpha 2.2.0-beta OssFile Source Beta 2.2.0-beta Phoenix Sink Alpha 2.2.0-beta Phoenix Source Alpha 2.2.0-beta Pulsar Source Beta 2.2.0-beta Redis Sink Beta 2.2.0-beta Redis Source Alpha 2.2.0-beta Sentry Sink Alpha 2.2.0-beta Socket Sink Alpha 2.2.0-beta Socket Source Alpha 2.2.0-beta","s":"Connector V2 Health","u":"/docs/2.2.0-beta/Connector-v2-release-state","h":"#connector-v2-health","p":102734},{"i":102740,"t":"Assert sink connector","s":"Assert","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"","p":102739},{"i":102742,"t":"A flink sink plugin which can assert illegal data by user defined rules","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#description","p":102739},{"i":102744,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#key-features","p":102739},{"i":102746,"t":"name type required default value rules ConfigList yes - rules.field_name string yes - rules.field_type string no - rules.field_value ConfigList no - rules.field_value.rule_type string no - rules.field_value.rule_value double no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#options","p":102739},{"i":102748,"t":"Rule definition of user's available data. Each rule represents one field validation.","s":"rules [ConfigList]","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#rules-configlist","p":102739},{"i":102750,"t":"field name（string）","s":"field_name [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#field_name-string","p":102739},{"i":102752,"t":"field type (string), e.g. string,boolean,byte,short,int,long,float,double,char,void,BigInteger,BigDecimal,Instant","s":"field_type [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#field_type-string","p":102739},{"i":102754,"t":"A list value rule define the data value validation","s":"field_value [ConfigList]","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#field_value-configlist","p":102739},{"i":102756,"t":"The following rules are supported for now NOT_NULL value can't be null MIN define the minimum value of data MAX define the maximum value of data MIN_LENGTH define the minimum string length of a string data MAX_LENGTH define the maximum string length of a string data","s":"rule_type [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#rule_type-string","p":102739},{"i":102758,"t":"the value related to rule type","s":"rule_value [double]","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#rule_value-double","p":102739},{"i":102760,"t":"the whole config obey with hocon style Assert { rules = [{ field_name = name field_type = string field_value = [ { rule_type = NOT_NULL }, { rule_type = MIN_LENGTH rule_value = 3 }, { rule_type = MAX_LENGTH rule_value = 5 } ] },{ field_name = age field_type = int field_value = [ { rule_type = NOT_NULL }, { rule_type = MIN rule_value = 10 }, { rule_type = MAX rule_value = 20 } ] } ] }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Assert","h":"#example","p":102739},{"i":102762,"t":"Clickhouse sink connector","s":"Clickhouse","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"","p":102761},{"i":102764,"t":"Used to write data to Clickhouse.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#description","p":102761},{"i":102766,"t":"exactly-once The Clickhouse sink plug-in can achieve accuracy once by implementing idempotent writing, and needs to cooperate with aggregatingmergetree and other engines that support deduplication. schema projection tip Write data to Clickhouse can also be done using JDBC","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#key-features","p":102761},{"i":102768,"t":"name type required default value host string yes - database string yes - table string yes - username string yes - password string yes - fields string yes - clickhouse.* string no bulk_size string no 20000 split_mode string no false sharding_key string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#options","p":102761},{"i":102770,"t":"ClickHouse cluster address, the format is host:port , allowing multiple hosts to be specified. Such as \"host1:8123,host2:8123\" .","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#host-string","p":102761},{"i":102772,"t":"The ClickHouse database","s":"database [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#database-string","p":102761},{"i":102774,"t":"The table name","s":"table [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#table-string","p":102761},{"i":102776,"t":"ClickHouse user username","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#username-string","p":102761},{"i":102778,"t":"ClickHouse user password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#password-string","p":102761},{"i":102780,"t":"The data field that needs to be output to ClickHouse , if not configured, it will be automatically adapted according to the sink table schema .","s":"fields [array]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#fields-array","p":102761},{"i":102782,"t":"In addition to the above mandatory parameters that must be specified by clickhouse-jdbc , users can also specify multiple optional parameters, which cover all the parameters provided by clickhouse-jdbc . The way to specify the parameter is to add the prefix clickhouse. to the original parameter name. For example, the way to specify socket_timeout is: clickhouse.socket_timeout = 50000 . If these non-essential parameters are not specified, they will use the default values given by clickhouse-jdbc.","s":"clickhouse [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#clickhouse-string","p":102761},{"i":102784,"t":"The number of rows written through Clickhouse-jdbc each time, the default is 20000 .","s":"bulk_size [number]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#bulk_size-number","p":102761},{"i":102786,"t":"This mode only support clickhouse table which engine is 'Distributed'.And internal_replication option should be true. They will split distributed table data in seatunnel and perform write directly on each shard. The shard weight define is clickhouse will be counted.","s":"split_mode [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#split_mode-boolean","p":102761},{"i":102788,"t":"When use split_mode, which node to send data to is a problem, the default is random selection, but the 'sharding_key' parameter can be used to specify the field for the sharding algorithm. This option only worked when 'split_mode' is true.","s":"sharding_key [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#sharding_key-string","p":102761},{"i":102790,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#common-options-string","p":102761},{"i":102792,"t":"sink { Clickhouse { host = \"localhost:8123\" database = \"default\" table = \"fake_all\" username = \"default\" password = \"\" split_mode = true sharding_key = \"age\" } } sink { Clickhouse { host = \"localhost:8123\" database = \"default\" table = \"fake_all\" username = \"default\" password = \"\" } }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/sink/Clickhouse","h":"#examples","p":102761},{"i":102794,"t":"Clickhouse file sink connector","s":"ClickhouseFile","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"","p":102793},{"i":102796,"t":"Generate the clickhouse data file with the clickhouse-local program, and then send it to the clickhouse server, also call bulk load. This connector only support clickhouse table which engine is 'Distributed'.And internal_replication option should be true. Supports Batch and Streaming mode.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#description","p":102793},{"i":102798,"t":"exactly-once schema projection tip Write data to Clickhouse can also be done using JDBC","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#key-features","p":102793},{"i":102800,"t":"name type required default value host string yes - database string yes - table string yes - username string yes - password string yes - clickhouse_local_path string yes - sharding_key string no - copy_method string no scp node_free_password boolean no false node_pass list no - node_pass.node_address string no - node_pass.username string no \"root\" node_pass.password string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#options","p":102793},{"i":102802,"t":"ClickHouse cluster address, the format is host:port , allowing multiple hosts to be specified. Such as \"host1:8123,host2:8123\" .","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#host-string","p":102793},{"i":102804,"t":"The ClickHouse database","s":"database [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#database-string","p":102793},{"i":102806,"t":"The table name","s":"table [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#table-string","p":102793},{"i":102808,"t":"ClickHouse user username","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#username-string","p":102793},{"i":102810,"t":"ClickHouse user password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#password-string","p":102793},{"i":102812,"t":"When ClickhouseFile split data, which node to send data to is a problem, the default is random selection, but the 'sharding_key' parameter can be used to specify the field for the sharding algorithm.","s":"sharding_key [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#sharding_key-string","p":102793},{"i":102814,"t":"The address of the clickhouse-local program on the spark node. Since each task needs to be called, clickhouse-local should be located in the same path of each spark node.","s":"clickhouse_local_path [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#clickhouse_local_path-string","p":102793},{"i":102816,"t":"Specifies the method used to transfer files, the default is scp, optional scp and rsync","s":"copy_method [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#copy_method-string","p":102793},{"i":102818,"t":"Because seatunnel need to use scp or rsync for file transfer, seatunnel need clickhouse server-side access. If each spark node and clickhouse server are configured with password-free login, you can configure this option to true, otherwise you need to configure the corresponding node password in the node_pass configuration","s":"node_free_password [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_free_password-boolean","p":102793},{"i":102820,"t":"Used to save the addresses and corresponding passwords of all clickhouse servers","s":"node_pass [list]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_pass-list","p":102793},{"i":102822,"t":"The address corresponding to the clickhouse server","s":"node_pass.node_address [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_passnode_address-string","p":102793},{"i":102824,"t":"The username corresponding to the clickhouse server, default root user.","s":"node_pass.username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_passusername-string","p":102793},{"i":102826,"t":"The password corresponding to the clickhouse server.","s":"node_pass.password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_passpassword-string","p":102793},{"i":102828,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#common-options-string","p":102793},{"i":102830,"t":"ClickhouseFile { host = \"192.168.0.1:8123\" database = \"default\" table = \"fake_all\" username = \"default\" password = \"\" clickhouse_local_path = \"/Users/seatunnel/Tool/clickhouse local\" sharding_key = \"age\" node_free_password = false node_pass = [{ node_address = \"192.168.0.1\" password = \"seatunnel\" }] }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/sink/ClickhouseFile","h":"#examples","p":102793},{"i":102832,"t":"Common parameters of sink connectors name type required default value source_table_name string no -","s":"Common Options","u":"/docs/2.2.0-beta/connector-v2/sink/common-options","h":"","p":102831},{"i":102834,"t":"When source_table_name is not specified, the current plug-in processes the data set dataset output by the previous plugin in the configuration file; When source_table_name is specified, the current plug-in is processing the data set corresponding to this parameter.","s":"source_table_name [string]","u":"/docs/2.2.0-beta/connector-v2/sink/common-options","h":"#source_table_name-string","p":102831},{"i":102836,"t":"source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { source_table_name = \"fake\" sql = \"select name from fake\" result_table_name = \"fake_name\" } sql { source_table_name = \"fake\" sql = \"select age from fake\" result_table_name = \"fake_age\" } } sink { console { source_table_name = \"fake_name\" } } If source_table_name is not specified, the console outputs the data of the last transform, and if it is set to fake_name , it will output the data of fake_name","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/sink/common-options","h":"#examples","p":102831},{"i":102838,"t":"Console sink connector","s":"Console","u":"/docs/2.2.0-beta/connector-v2/sink/Console","h":"","p":102837},{"i":102840,"t":"Used to send data to Console. Both support streaming and batch mode. For example, if the data from upstream is [age: 12, name: jared], the content send to console is the following: {\"name\":\"jared\",\"age\":17}","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Console","h":"#description","p":102837},{"i":102842,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Console","h":"#key-features","p":102837},{"i":102844,"t":"name type required default value","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Console","h":"#options","p":102837},{"i":102846,"t":"simple: Console { } test: Configuring the SeaTunnel config file env { execution.parallelism = 1 job.mode = \"STREAMING\" } source { FakeSource { result_table_name = \"fake\" schema = { fields { name = \"string\" age = \"int\" } } } } transform { sql { sql = \"select name, age from fake\" } } sink { Console { } } Start a SeaTunnel task Console print data row=1 : XTblOoJMBr, 1968671376 row=2 : NAoJoFrthI, 1603900622 row=3 : VHZBzqQAPr, 1713899051 row=4 : pfUYOOrPgA, 1412123956 row=5 : dCNFobURas, 202987936 row=6 : XGWVgFnfWA, 1879270917 row=7 : KIGOqnLhqe, 430165110 row=8 : goMdjHlRpX, 288221239 row=9 : VBtpiNGArV, 1906991577","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Console","h":"#example","p":102837},{"i":102848,"t":"Datahub sink connector","s":"Datahub","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"","p":102847},{"i":102850,"t":"A sink plugin which use send message to datahub","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#description","p":102847},{"i":102852,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#key-features","p":102847},{"i":102854,"t":"name type required default value endpoint string yes - accessId string yes - accessKey string yes - project string yes - topic string yes - timeout int yes - retryTimes int yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#options","p":102847},{"i":102856,"t":"your datahub endpoint start with http （string）","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#url-string","p":102847},{"i":102858,"t":"your datahub accessId which cloud be access from Alibaba Cloud (string)","s":"accessId [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#accessid-string","p":102847},{"i":102860,"t":"your datahub accessKey which cloud be access from Alibaba Cloud (string)","s":"accessKey[string]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#accesskeystring","p":102847},{"i":102862,"t":"your datahub project which is created in Alibaba Cloud (string)","s":"project [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#project-string","p":102847},{"i":102864,"t":"your datahub topic (string)","s":"topic [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#topic-string","p":102847},{"i":102866,"t":"the max connection timeout (int)","s":"timeout [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#timeout-int","p":102847},{"i":102868,"t":"the max retry times when your client put record failed (int)","s":"retryTimes [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#retrytimes-int","p":102847},{"i":102870,"t":"sink { DataHub { endpoint=\"yourendpoint\" accessId=\"xxx\" accessKey=\"xxx\" project=\"projectname\" topic=\"topicname\" timeout=3000 retryTimes=3 } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Datahub","h":"#example","p":102847},{"i":102872,"t":"DinkTalk sink connector","s":"DingTalk","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"","p":102871},{"i":102874,"t":"A sink plugin which use DingTalk robot send message","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"#description","p":102871},{"i":102876,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"#key-features","p":102871},{"i":102878,"t":"name type required default value url string yes - secret string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"#options","p":102871},{"i":102880,"t":"DingTalk robot address format is https://oapi.dingtalk.com/robot/send?access_token=XXXXXX（string）","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"#url-string","p":102871},{"i":102882,"t":"DingTalk robot secret (string)","s":"secret [string]","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"#secret-string","p":102871},{"i":102884,"t":"sink { DingTalk { url=\"https://oapi.dingtalk.com/robot/send?access_token=ec646cccd028d978a7156ceeac5b625ebd94f586ea0743fa501c100007890\" secret=\"SEC093249eef7aa57d4388aa635f678930c63db3d28b2829d5b2903fc1e5c10000\" } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/dingtalk","h":"#example","p":102871},{"i":102887,"t":"Output data to Elasticsearch.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#description","p":102885},{"i":102889,"t":"exactly-once schema projection tip Engine Supported supported ElasticSearch version is >= 2.x and < 8.x","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#key-features","p":102885},{"i":102891,"t":"name type required default value hosts array yes - index string yes - index_type string no username string no password string no max_retry_size int no 3 max_batch_size int no 10","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#options","p":102885},{"i":102893,"t":"Elasticsearch cluster http address, the format is host:port , allowing multiple hosts to be specified. Such as [\"host1:9200\", \"host2:9200\"].","s":"hosts [array]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#hosts-array","p":102885},{"i":102895,"t":"Elasticsearch index name.Index support contains variables of field name,such as seatunnel_${age},and the field must appear at seatunnel row. If not, we will treat it as a normal index.","s":"index [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#index-string","p":102885},{"i":102897,"t":"Elasticsearch index type, it is recommended not to specify in elasticsearch 6 and above","s":"index_type [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#index_type-string","p":102885},{"i":102899,"t":"x-pack username","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#username-string","p":102885},{"i":102901,"t":"x-pack password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#password-string","p":102885},{"i":102903,"t":"one bulk request max try size","s":"max_retry_size [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#max_retry_size-int","p":102885},{"i":102905,"t":"batch bulk doc max size","s":"max_batch_size [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#max_batch_size-int","p":102885},{"i":102907,"t":"Elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-${age}\" }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/sink/Elasticsearch","h":"#examples","p":102885},{"i":102909,"t":"Email source connector","s":"Email","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"","p":102908},{"i":102911,"t":"Send the data as a file to email. The tested email version is 1.5.6.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#description","p":102908},{"i":102913,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#key-features","p":102908},{"i":102915,"t":"name type required default value email_from_address string yes - email_to_address string yes - email_host string yes - email_transport_protocol string yes - email_smtp_auth string yes - email_authorization_code string yes - email_message_headline string yes - email_message_content string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#options","p":102908},{"i":102917,"t":"Sender Email Address .","s":"email_from_address [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_from_address-string","p":102908},{"i":102919,"t":"Address to receive mail.","s":"email_to_address [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_to_address-string","p":102908},{"i":102921,"t":"SMTP server to connect to.","s":"email_host [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_host-string","p":102908},{"i":102923,"t":"The protocol to load the session .","s":"email_transport_protocol [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_transport_protocol-string","p":102908},{"i":102925,"t":"Whether to authenticate the customer.","s":"email_smtp_auth [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_smtp_auth-string","p":102908},{"i":102927,"t":"authorization code,You can obtain the authorization code from the mailbox Settings.","s":"email_authorization_code [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_authorization_code-string","p":102908},{"i":102929,"t":"The subject line of the entire message.","s":"email_message_headline [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_message_headline-string","p":102908},{"i":102931,"t":"The body of the entire message.","s":"email_message_content [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#email_message_content-string","p":102908},{"i":102933,"t":"EmailSink { email_from_address = \"xxxxxx@qq.com\" email_to_address = \"xxxxxx@163.com\" email_host=\"smtp.qq.com\" email_transport_protocol=\"smtp\" email_smtp_auth=\"true\" email_authorization_code=\"\" email_message_headline=\"\" email_message_content=\"\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Email","h":"#example","p":102908},{"i":102935,"t":"Enterprise WeChat sink connector","s":"Enterprise WeChat","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"","p":102934},{"i":102937,"t":"A sink plugin which use Enterprise WeChat robot send message For example, if the data from upstream is [\"alarmStatus\": \"firing\", \"alarmTime\": \"2022-08-03 01:38:49\"，\"alarmContent\": \"The disk usage exceeds the threshold\"], the output content to WeChat Robot is the following: alarmStatus: firing alarmTime: 2022-08-03 01:38:49 alarmContent: The disk usage exceeds the threshold Tips: WeChat sink only support string webhook and the data from source will be treated as body content in web hook.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#description","p":102934},{"i":102939,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#key-features","p":102934},{"i":102941,"t":"name type required default value url String Yes - mentioned_list array No - mentioned_mobile_list array No -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#options","p":102934},{"i":102943,"t":"Enterprise WeChat webhook url format is https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=XXXXXX（string）","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#url-string","p":102934},{"i":102945,"t":"A list of userids to remind the specified members in the group (@ a member), @ all means to remind everyone. If the developer can't get the userid, he can use called mobile list","s":"mentioned_list [array]","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#mentioned_list-array","p":102934},{"i":102947,"t":"Mobile phone number list, remind the group member corresponding to the mobile phone number (@ a member), @ all means remind everyone","s":"mentioned_mobile_list [array]","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#mentioned_mobile_list-array","p":102934},{"i":102949,"t":"simple: WeChat { url = \"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=693axxx6-7aoc-4bc4-97a0-0ec2sifa5aaa\" } WeChat { url = \"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=693axxx6-7aoc-4bc4-97a0-0ec2sifa5aaa\" mentioned_list=[\"wangqing\",\"@all\"] mentioned_mobile_list=[\"13800001111\",\"@all\"] }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Enterprise-WeChat","h":"#example","p":102934},{"i":102951,"t":"Ftp file sink connector","s":"FtpFile","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"","p":102950},{"i":102953,"t":"Output data to Ftp .","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#description","p":102950},{"i":102955,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#key-features","p":102950},{"i":102957,"t":"name type required default value host string yes - port int yes - username string yes - password string yes - path string yes - file_name_expression string no \"${transactionId}\" file_format string no \"text\" filename_time_format string no \"yyyy.MM.dd\" field_delimiter string no '\\001' row_delimiter string no \"\\n\" partition_by array no - partition_dir_expression string no \"\\${k0}=\\${v0}\\/\\${k1}=\\${v1}\\/...\\/\\${kn}=\\${vn}\\/\" is_partition_field_write_in_file boolean no false sink_columns array no When this parameter is empty, all fields are sink columns is_enable_transaction boolean no true save_mode string no \"error\"","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#options","p":102950},{"i":102959,"t":"The target ftp host is required","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#host-string","p":102950},{"i":102961,"t":"The target ftp port is required","s":"port [int]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#port-int","p":102950},{"i":102963,"t":"The target ftp username is required","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#username-string","p":102950},{"i":102965,"t":"The target ftp password is required","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#password-string","p":102950},{"i":102967,"t":"The target dir path is required.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#path-string","p":102950},{"i":102969,"t":"file_name_expression describes the file expression which will be created into the path. We can add the variable ${now} or ${uuid} in the file_name_expression, like test_${uuid}_${now}, ${now} represents the current time, and its format can be defined by specifying the option filename_time_format. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#file_name_expression-string","p":102950},{"i":102971,"t":"We supported as the following file types: text json csv orc parquet Please note that, The final file name will end with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#file_format-string","p":102950},{"i":102973,"t":"When the format in the file_name_expression parameter is xxxx-${now} , filename_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute","s":"filename_time_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#filename_time_format-string","p":102950},{"i":102975,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#field_delimiter-string","p":102950},{"i":102977,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#row_delimiter-string","p":102950},{"i":102979,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#partition_by-array","p":102950},{"i":102981,"t":"If the partition_by is specified, we will generate the corresponding partition directory based on the partition information, and the final file will be placed in the partition directory. Default partition_dir_expression is ${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/. k0 is the first partition field and v0 is the value of the first partition field.","s":"partition_dir_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#partition_dir_expression-string","p":102950},{"i":102983,"t":"If is_partition_field_write_in_file is true, the partition field and the value of it will be write into data file. For example, if you want to write a Hive Data File, Its value should be false.","s":"is_partition_field_write_in_file [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#is_partition_field_write_in_file-boolean","p":102950},{"i":102985,"t":"Which columns need be wrote to file, default value is all the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.","s":"sink_columns [array]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#sink_columns-array","p":102950},{"i":102987,"t":"If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#is_enable_transaction-boolean","p":102950},{"i":102989,"t":"Storage mode, currently supports overwrite. This means we will delete the old file when a new file have a same name with it. If is_enable_transaction is true, Basically, we won't encounter the same file name. Because we will add the transaction id to file name. For the specific meaning of each mode, see save-modes","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#save_mode-string","p":102950},{"i":102991,"t":"For text file format FtpFile { host=\"xxx.xxx.xxx.xxx\" port=21 username=\"username\" password=\"password\" path=\"/data/ftp\" field_delimiter=\"\\t\" row_delimiter=\"\\n\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"text\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/FtpFile","h":"#example","p":102950},{"i":102993,"t":"Feishu sink connector","s":"Feishu","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"","p":102992},{"i":102995,"t":"Used to launch feishu web hooks using data. For example, if the data from upstream is [age: 12, name: tyrantlucifer], the body content is the following: {\"age\": 12, \"name\": \"tyrantlucifer\"} Tips: Feishu sink only support post json webhook and the data from source will be treated as body content in web hook.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"#description","p":102992},{"i":102997,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"#key-features","p":102992},{"i":102999,"t":"name type required default value url String Yes - headers Map No -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"#options","p":102992},{"i":103001,"t":"Feishu webhook url","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"#url-string","p":102992},{"i":103003,"t":"Http request headers","s":"headers [Map]","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"#headers-map","p":102992},{"i":103005,"t":"simple: Feishu { url = \"https://www.feishu.cn/flow/api/trigger-webhook/108bb8f208d9b2378c8c7aedad715c19\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Feishu","h":"#example","p":102992},{"i":103007,"t":"Greenplum sink connector","s":"Greenplum","u":"/docs/2.2.0-beta/connector-v2/sink/Greenplum","h":"","p":103006},{"i":103009,"t":"Write data to Greenplum using Jdbc connector.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Greenplum","h":"#description","p":103006},{"i":103011,"t":"exactly-once schema projection tip Not support exactly-once semantics (XA transaction is not yet supported in Greenplum database).","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Greenplum","h":"#key-features","p":103006},{"i":103014,"t":"Optional jdbc drivers: org.postgresql.Driver com.pivotal.jdbc.GreenplumDriver Warn: for license compliance, if you use GreenplumDriver the have to provide Greenplum JDBC driver yourself, e.g. copy greenplum-xxx.jar to $SEATNUNNEL_HOME/lib for Standalone.","s":"driver [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Greenplum","h":"#driver-string","p":103006},{"i":103016,"t":"The URL of the JDBC connection. if you use postgresql driver the value is jdbc:postgresql://${yous_host}:${yous_port}/${yous_database}, or you use greenplum driver the value is jdbc:pivotal:greenplum://${yous_host}:${yous_port};DatabaseName=${yous_database}","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Greenplum","h":"#url-string","p":103006},{"i":103018,"t":"HDFS file sink connector","s":"HdfsFile","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"","p":103017},{"i":103020,"t":"Output data to hdfs file","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#description","p":103017},{"i":103022,"t":"exactly-once By default, we use 2PC commit to ensure exactly-once schema projection file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#key-features","p":103017},{"i":103024,"t":"In order to use this connector, You must ensure your spark/flink cluster already integrated hadoop. The tested hadoop version is 2.x. name type required default value fs.defaultFS string yes - path string yes - file_name_expression string no \"${transactionId}\" file_format string no \"text\" filename_time_format string no \"yyyy.MM.dd\" field_delimiter string no '\\001' row_delimiter string no \"\\n\" partition_by array no - partition_dir_expression string no \"${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/\" is_partition_field_write_in_file boolean no false sink_columns array no When this parameter is empty, all fields are sink columns is_enable_transaction boolean no true save_mode string no \"error\"","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#options","p":103017},{"i":103026,"t":"The hadoop cluster address that start with hdfs://, for example: hdfs://hadoopcluster","s":"fs.defaultFS [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#fsdefaultfs-string","p":103017},{"i":103028,"t":"The target dir path is required.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#path-string","p":103017},{"i":103030,"t":"file_name_expression describes the file expression which will be created into the path. We can add the variable ${now} or ${uuid} in the file_name_expression, like test_${uuid}_${now}, ${now} represents the current time, and its format can be defined by specifying the option filename_time_format. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#file_name_expression-string","p":103017},{"i":103032,"t":"We supported as the following file types: text csv parquet orc json Please note that, The final file name will ends with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#file_format-string","p":103017},{"i":103034,"t":"When the format in the file_name_expression parameter is xxxx-${now} , filename_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"filename_time_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#filename_time_format-string","p":103017},{"i":103036,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#field_delimiter-string","p":103017},{"i":103038,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#row_delimiter-string","p":103017},{"i":103040,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#partition_by-array","p":103017},{"i":103042,"t":"If the partition_by is specified, we will generate the corresponding partition directory based on the partition information, and the final file will be placed in the partition directory. Default partition_dir_expression is ${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/. k0 is the first partition field and v0 is the value of the first partition field.","s":"partition_dir_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#partition_dir_expression-string","p":103017},{"i":103044,"t":"If is_partition_field_write_in_file is true, the partition field and the value of it will be write into data file. For example, if you want to write a Hive Data File, Its value should be false.","s":"is_partition_field_write_in_file [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#is_partition_field_write_in_file-boolean","p":103017},{"i":103046,"t":"Which columns need be write to file, default value is all of the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.","s":"sink_columns [array]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#sink_columns-array","p":103017},{"i":103048,"t":"If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#is_enable_transaction-boolean","p":103017},{"i":103050,"t":"Storage mode, currently supports overwrite. This means we will delete the old file when a new file have a same name with it. If is_enable_transaction is true, Basically, we won't encounter the same file name. Because we will add the transaction id to file name. For the specific meaning of each mode, see save-modes","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#save_mode-string","p":103017},{"i":103052,"t":"For text file format HdfsFile { fs.defaultFS=\"hdfs://hadoopcluster\" path=\"/tmp/hive/warehouse/test2\" field_delimiter=\"\\t\" row_delimiter=\"\\n\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"text\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true } For parquet file format HdfsFile { fs.defaultFS=\"hdfs://hadoopcluster\" path=\"/tmp/hive/warehouse/test2\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"parquet\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true } For orc file format HdfsFile { fs.defaultFS=\"hdfs://hadoopcluster\" path=\"/tmp/hive/warehouse/test2\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"orc\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/HdfsFile","h":"#example","p":103017},{"i":103054,"t":"Hive sink connector","s":"Hive","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"","p":103053},{"i":103056,"t":"Write data to Hive. In order to use this connector, You must ensure your spark/flink cluster already integrated hive. The tested hive version is 2.3.9. Tips: Hive Sink Connector not support array, map and struct datatype now","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#description","p":103053},{"i":103058,"t":"exactly-once By default, we use 2PC commit to ensure exactly-once schema projection file format text parquet orc","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#key-features","p":103053},{"i":103060,"t":"name type required default value table_name string yes - metastore_uri string yes - partition_by array required if hive sink table have partitions - sink_columns array no When this parameter is empty, all fields are sink columns is_enable_transaction boolean no true save_mode string no \"append\"","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#options","p":103053},{"i":103062,"t":"Target Hive table name eg: db1.table1","s":"table_name [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#table_name-string","p":103053},{"i":103064,"t":"Hive metastore uri","s":"metastore_uri [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#metastore_uri-string","p":103053},{"i":103066,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#partition_by-array","p":103053},{"i":103068,"t":"Which columns need be write to hive, default value is all of the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.","s":"sink_columns [array]","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#sink_columns-array","p":103053},{"i":103070,"t":"If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory. Only support true now.","s":"is_enable_transaction [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#is_enable_transaction-boolean","p":103053},{"i":103072,"t":"Storage mode, we need support overwrite and append. append is now supported. Streaming Job not support overwrite.","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#save_mode-string","p":103053},{"i":103074,"t":"Hive { table_name = \"default.seatunnel_orc\" metastore_uri = \"thrift://namenode001:9083\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#example","p":103053},{"i":103076,"t":"We have a source table like this: create table test_hive_source( test_tinyint TINYINT, test_smallint SMALLINT, test_int INT, test_bigint BIGINT, test_boolean BOOLEAN, test_float FLOAT, test_double DOUBLE, test_string STRING, test_binary BINARY, test_timestamp TIMESTAMP, test_decimal DECIMAL(8,2), test_char CHAR(64), test_varchar VARCHAR(64), test_date DATE, test_array ARRAY<INT>, test_map MAP<STRING, FLOAT>, test_struct STRUCT<street:STRING, city:STRING, state:STRING, zip:INT> ) PARTITIONED BY (test_par1 STRING, test_par2 STRING); We need read data from the source table and write to another table: create table test_hive_sink_text_simple( test_tinyint TINYINT, test_smallint SMALLINT, test_int INT, test_bigint BIGINT, test_boolean BOOLEAN, test_float FLOAT, test_double DOUBLE, test_string STRING, test_binary BINARY, test_timestamp TIMESTAMP, test_decimal DECIMAL(8,2), test_char CHAR(64), test_varchar VARCHAR(64), test_date DATE ) PARTITIONED BY (test_par1 STRING, test_par2 STRING); The job config file can like this: env { # You can set flink configuration here execution.parallelism = 3 job.name=\"test_hive_source_to_hive\" } source { Hive { table_name = \"test_hive.test_hive_source\" metastore_uri = \"thrift://ctyun7:9083\" } } transform { } sink { # choose stdout output plugin to output data to console Hive { table_name = \"test_hive.test_hive_sink_text_simple\" metastore_uri = \"thrift://ctyun7:9083\" partition_by = [\"test_par1\", \"test_par2\"] sink_columns = [\"test_tinyint\", \"test_smallint\", \"test_int\", \"test_bigint\", \"test_boolean\", \"test_float\", \"test_double\", \"test_string\", \"test_binary\", \"test_timestamp\", \"test_decimal\", \"test_char\", \"test_varchar\", \"test_date\", \"test_par1\", \"test_par2\"] } }","s":"example 1","u":"/docs/2.2.0-beta/connector-v2/sink/Hive","h":"#example-1","p":103053},{"i":103078,"t":"Http sink connector","s":"Http","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"","p":103077},{"i":103080,"t":"Used to launch web hooks using data. For example, if the data from upstream is [age: 12, name: tyrantlucifer], the body content is the following: {\"age\": 12, \"name\": \"tyrantlucifer\"} Tips: Http sink only support post json webhook and the data from source will be treated as body content in web hook.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#description","p":103077},{"i":103082,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#key-features","p":103077},{"i":103084,"t":"name type required default value url String Yes - headers Map No - params Map No - retry int No - retry_backoff_multiplier_ms int No 100 retry_backoff_max_ms int No 10000","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#options","p":103077},{"i":103086,"t":"http request url","s":"url [String]","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#url-string","p":103077},{"i":103088,"t":"http headers","s":"headers [Map]","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#headers-map","p":103077},{"i":103090,"t":"http params","s":"params [Map]","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#params-map","p":103077},{"i":103092,"t":"The max retry times if request http return to IOException","s":"retry [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#retry-int","p":103077},{"i":103094,"t":"The retry-backoff times(millis) multiplier if request http failed","s":"retry_backoff_multiplier_ms [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#retry_backoff_multiplier_ms-int","p":103077},{"i":103096,"t":"The maximum retry-backoff times(millis) if request http failed","s":"retry_backoff_max_ms [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#retry_backoff_max_ms-int","p":103077},{"i":103098,"t":"simple: Http { url = \"http://localhost/test/webhook\" headers { token = \"9e32e859ef044462a257e1fc76730066\" } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Http","h":"#example","p":103077},{"i":103100,"t":"IoTDB sink connector","s":"IoTDB","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"","p":103099},{"i":103102,"t":"Used to write data to IoTDB.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#description","p":103099},{"i":103104,"t":"exactly-once IoTDB supports the exactly-once feature through idempotent writing. If two pieces of data have the same key and timestamp, the new data will overwrite the old one. schema projection tip There is a conflict of thrift version between IoTDB and Spark.Therefore, you need to execute rm -f $SPARK_HOME/jars/libthrift* and cp $IOTDB_HOME/lib/libthrift* $SPARK_HOME/jars/ to resolve it.","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#key-features","p":103099},{"i":103106,"t":"name type required default value node_urls list yes - username string yes - password string yes - batch_size int no 1024 batch_interval_ms int no - max_retries int no - retry_backoff_multiplier_ms int no - max_retry_backoff_ms int no - default_thrift_buffer_size int no - max_thrift_frame_size int no - zone_id string no - enable_rpc_compression boolean no - connection_timeout_in_ms int no - timeseries_options list no - timeseries_options.path string no - timeseries_options.data_type string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#options","p":103099},{"i":103108,"t":"IoTDB cluster address, the format is [\"host:port\", ...]","s":"node_urls [list]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#node_urls-list","p":103099},{"i":103110,"t":"IoTDB user username","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#username-string","p":103099},{"i":103112,"t":"IoTDB user password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#password-string","p":103099},{"i":103114,"t":"For batch writing, when the number of buffers reaches the number of batch_size or the time reaches batch_interval_ms, the data will be flushed into the IoTDB","s":"batch_size [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#batch_size-int","p":103099},{"i":103116,"t":"For batch writing, when the number of buffers reaches the number of batch_size or the time reaches batch_interval_ms, the data will be flushed into the IoTDB","s":"batch_interval_ms [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#batch_interval_ms-int","p":103099},{"i":103118,"t":"The number of retries to flush failed","s":"max_retries [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#max_retries-int","p":103099},{"i":103120,"t":"Using as a multiplier for generating the next delay for backoff","s":"retry_backoff_multiplier_ms [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#retry_backoff_multiplier_ms-int","p":103099},{"i":103122,"t":"The amount of time to wait before attempting to retry a request to IoTDB","s":"max_retry_backoff_ms [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#max_retry_backoff_ms-int","p":103099},{"i":103124,"t":"Thrift init buffer size in IoTDB client","s":"default_thrift_buffer_size [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#default_thrift_buffer_size-int","p":103099},{"i":103126,"t":"Thrift max frame size in IoTDB client","s":"max_thrift_frame_size [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#max_thrift_frame_size-int","p":103099},{"i":103128,"t":"java.time.ZoneId in IoTDB client","s":"zone_id [string]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#zone_id-string","p":103099},{"i":103130,"t":"Enable rpc compression in IoTDB client","s":"enable_rpc_compression [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#enable_rpc_compression-boolean","p":103099},{"i":103132,"t":"The maximum time (in ms) to wait when connect IoTDB","s":"connection_timeout_in_ms [int]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#connection_timeout_in_ms-int","p":103099},{"i":103134,"t":"Timeseries options","s":"timeseries_options [list]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#timeseries_options-list","p":103099},{"i":103136,"t":"Timeseries path","s":"timeseries_options.path [string]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#timeseries_optionspath-string","p":103099},{"i":103138,"t":"Timeseries data type","s":"timeseries_options.data_type [string]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#timeseries_optionsdata_type-string","p":103099},{"i":103140,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#common-options-string","p":103099},{"i":103142,"t":"sink { IoTDB { node_urls = [\"localhost:6667\"] username = \"root\" password = \"root\" batch_size = 1024 batch_interval_ms = 1000 } }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/sink/IoTDB","h":"#examples","p":103099},{"i":103144,"t":"Kudu sink connector","s":"Kudu","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"","p":103143},{"i":103146,"t":"Write data to Kudu. The tested kudu version is 1.11.1.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#description","p":103143},{"i":103148,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#key-features","p":103143},{"i":103150,"t":"name type required default value kudu_master string yes - kudu_table string yes - save_mode string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#options","p":103143},{"i":103152,"t":"kudu_master The address of kudu master,such as '192.168.88.110:7051'.","s":"kudu_master [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#kudu_master-string","p":103143},{"i":103154,"t":"kudu_table The name of kudu table..","s":"kudu_table [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#kudu_table-string","p":103143},{"i":103156,"t":"Storage mode, we need support overwrite and append. append is now supported.","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#save_mode-string","p":103143},{"i":103158,"t":"kuduSink { kudu_master = \"192.168.88.110:7051\" kudu_table = \"studentlyhresultflink\" save_mode=\"append\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Kudu","h":"#example","p":103143},{"i":103160,"t":"Local file sink connector","s":"LocalFile","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"","p":103159},{"i":103162,"t":"Output data to local file.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#description","p":103159},{"i":103164,"t":"exactly-once By default, we use 2PC commit to ensure exactly-once schema projection file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#key-features","p":103159},{"i":103166,"t":"name type required default value path string yes - file_name_expression string no \"${transactionId}\" file_format string no \"text\" filename_time_format string no \"yyyy.MM.dd\" field_delimiter string no '\\001' row_delimiter string no \"\\n\" partition_by array no - partition_dir_expression string no \"${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/\" is_partition_field_write_in_file boolean no false sink_columns array no When this parameter is empty, all fields are sink columns is_enable_transaction boolean no true save_mode string no \"error\"","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#options","p":103159},{"i":103168,"t":"The target dir path is required.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#path-string","p":103159},{"i":103170,"t":"file_name_expression describes the file expression which will be created into the path. We can add the variable ${now} or ${uuid} in the file_name_expression, like test_${uuid}_${now}, ${now} represents the current time, and its format can be defined by specifying the option filename_time_format. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#file_name_expression-string","p":103159},{"i":103172,"t":"We supported as the following file types: text csv parquet orc json Please note that, The final file name will ends with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#file_format-string","p":103159},{"i":103174,"t":"When the format in the file_name_expression parameter is xxxx-${now} , filename_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"filename_time_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#filename_time_format-string","p":103159},{"i":103176,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#field_delimiter-string","p":103159},{"i":103178,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#row_delimiter-string","p":103159},{"i":103180,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#partition_by-array","p":103159},{"i":103182,"t":"If the partition_by is specified, we will generate the corresponding partition directory based on the partition information, and the final file will be placed in the partition directory. Default partition_dir_expression is ${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/. k0 is the first partition field and v0 is the value of the first partition field.","s":"partition_dir_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#partition_dir_expression-string","p":103159},{"i":103184,"t":"If is_partition_field_write_in_file is true, the partition field and the value of it will be write into data file. For example, if you want to write a Hive Data File, Its value should be false.","s":"is_partition_field_write_in_file [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#is_partition_field_write_in_file-boolean","p":103159},{"i":103186,"t":"Which columns need be write to file, default value is all of the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.","s":"sink_columns [array]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#sink_columns-array","p":103159},{"i":103188,"t":"If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#is_enable_transaction-boolean","p":103159},{"i":103190,"t":"Storage mode, currently supports overwrite. This means we will delete the old file when a new file have a same name with it. If is_enable_transaction is true, Basically, we won't encounter the same file name. Because we will add the transaction id to file name. For the specific meaning of each mode, see save-modes","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#save_mode-string","p":103159},{"i":103192,"t":"For text file format LocalFile { path=\"/tmp/hive/warehouse/test2\" field_delimiter=\"\\t\" row_delimiter=\"\\n\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"text\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true } For parquet file format LocalFile { path=\"/tmp/hive/warehouse/test2\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"parquet\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true } For orc file format LocalFile { path=\"/tmp/hive/warehouse/test2\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"orc\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/LocalFile","h":"#example","p":103159},{"i":103194,"t":"MongoDB sink connector","s":"MongoDb","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"","p":103193},{"i":103196,"t":"Write data to MongoDB","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#description","p":103193},{"i":103198,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#key-features","p":103193},{"i":103200,"t":"name type required default value uri string yes - database string yes - collection string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#options","p":103193},{"i":103202,"t":"uri to write to mongoDB","s":"uri [string]","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#uri-string","p":103193},{"i":103204,"t":"database to write to mongoDB","s":"database [string]","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#database-string","p":103193},{"i":103206,"t":"collection to write to mongoDB","s":"collection [string]","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#collection-string","p":103193},{"i":103208,"t":"mongodb { uri = \"mongodb://username:password@127.0.0.1:27017/mypost?retryWrites=true&writeConcern=majority\" database = \"mydatabase\" collection = \"mycollection\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/MongoDB","h":"#example","p":103193},{"i":103210,"t":"Neo4j sink connector","s":"Neo4j","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"","p":103209},{"i":103212,"t":"Write data to Neo4j. neo4j-java-driver version 4.4.9","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#description","p":103209},{"i":103214,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#key-features","p":103209},{"i":103216,"t":"name type required default value uri String Yes - username String No - password String No - bearer_token String No - kerberos_ticket String No - database String Yes - query String Yes - queryParamPosition Object Yes - max_transaction_retry_time Long No 30 max_connection_timeout Long No 30","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#options","p":103209},{"i":103218,"t":"The URI of the Neo4j database. Refer to a case: neo4j://localhost:7687","s":"uri [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#uri-string","p":103209},{"i":103220,"t":"username of the Neo4j","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#username-string","p":103209},{"i":103222,"t":"password of the Neo4j. required if username is provided","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#password-string","p":103209},{"i":103224,"t":"base64 encoded bearer token of the Neo4j. for Auth.","s":"bearer_token [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#bearer_token-string","p":103209},{"i":103226,"t":"base64 encoded kerberos ticket of the Neo4j. for Auth.","s":"kerberos_ticket [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#kerberos_ticket-string","p":103209},{"i":103228,"t":"database name.","s":"database [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#database-string","p":103209},{"i":103230,"t":"Query statement. contain parameter placeholders that are substituted with the corresponding values at runtime","s":"query [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#query-string","p":103209},{"i":103232,"t":"position mapping information for query parameters. key name is parameter placeholder name. associated value is position of field in input data row.","s":"queryParamPosition [object]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#queryparamposition-object","p":103209},{"i":103234,"t":"maximum transaction retry time(seconds). transaction fail if exceeded","s":"max_transaction_retry_time [long]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#max_transaction_retry_time-long","p":103209},{"i":103236,"t":"The maximum amount of time to wait for a TCP connection to be established (seconds)","s":"max_connection_timeout [long]","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#max_connection_timeout-long","p":103209},{"i":103238,"t":"sink { Neo4j { uri = \"neo4j://localhost:7687\" username = \"neo4j\" password = \"1234\" database = \"neo4j\" max_transaction_retry_time = 10 max_connection_timeout = 10 query = \"CREATE (a:Person {name: $name, age: $age})\" queryParamPosition = { name = 0 age = 1 } } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Neo4j","h":"#example","p":103209},{"i":103240,"t":"Oss file sink connector","s":"OssFile","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"","p":103239},{"i":103242,"t":"Output data to oss file system. Tips: We made some trade-offs in order to support more file types, so we used the HDFS protocol for internal access to OSS and this connector need some hadoop dependencies. It's only support hadoop version 2.9.X+.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#description","p":103239},{"i":103244,"t":"exactly-once By default, we use 2PC commit to ensure exactly-once schema projection file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#key-features","p":103239},{"i":103246,"t":"name type required default value path string yes - bucket string yes - access_key string yes - access_secret string yes - endpoint string yes - file_name_expression string no \"${transactionId}\" file_format string no \"text\" filename_time_format string no \"yyyy.MM.dd\" field_delimiter string no '\\001' row_delimiter string no \"\\n\" partition_by array no - partition_dir_expression string no \"${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/\" is_partition_field_write_in_file boolean no false sink_columns array no When this parameter is empty, all fields are sink columns is_enable_transaction boolean no true save_mode string no \"error\"","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#options","p":103239},{"i":103248,"t":"The target dir path is required.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#path-string","p":103239},{"i":103250,"t":"The bucket address of oss file system, for example: oss://tyrantlucifer-image-bed","s":"bucket [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#bucket-string","p":103239},{"i":103252,"t":"The access key of oss file system.","s":"access_key [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#access_key-string","p":103239},{"i":103254,"t":"The access secret of oss file system.","s":"access_secret [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#access_secret-string","p":103239},{"i":103256,"t":"The endpoint of oss file system.","s":"endpoint [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#endpoint-string","p":103239},{"i":103258,"t":"file_name_expression describes the file expression which will be created into the path. We can add the variable ${now} or ${uuid} in the file_name_expression, like test_${uuid}_${now}, ${now} represents the current time, and its format can be defined by specifying the option filename_time_format. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#file_name_expression-string","p":103239},{"i":103260,"t":"We supported as the following file types: text csv parquet orc json Please note that, The final file name will end with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#file_format-string","p":103239},{"i":103262,"t":"When the format in the file_name_expression parameter is xxxx-${now} , filename_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"filename_time_format [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#filename_time_format-string","p":103239},{"i":103264,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#field_delimiter-string","p":103239},{"i":103266,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#row_delimiter-string","p":103239},{"i":103268,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#partition_by-array","p":103239},{"i":103270,"t":"If the partition_by is specified, we will generate the corresponding partition directory based on the partition information, and the final file will be placed in the partition directory. Default partition_dir_expression is ${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/. k0 is the first partition field and v0 is the value of the first partition field.","s":"partition_dir_expression [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#partition_dir_expression-string","p":103239},{"i":103272,"t":"If is_partition_field_write_in_file is true, the partition field and the value of it will be written into data file. For example, if you want to write a Hive Data File, Its value should be false.","s":"is_partition_field_write_in_file [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#is_partition_field_write_in_file-boolean","p":103239},{"i":103274,"t":"Which columns need be written to file, default value is all the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.","s":"sink_columns [array]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#sink_columns-array","p":103239},{"i":103276,"t":"If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory. Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#is_enable_transaction-boolean","p":103239},{"i":103278,"t":"Storage mode, currently supports overwrite. This means we will delete the old file when a new file have a same name with it. If is_enable_transaction is true, Basically, we won't encounter the same file name. Because we will add the transaction id to file name. For the specific meaning of each mode, see save-modes","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#save_mode-string","p":103239},{"i":103280,"t":"For text file format OssFile { path=\"/seatunnel/sink\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxx\" access_secret = \"xxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" field_delimiter=\"\\t\" row_delimiter=\"\\n\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"text\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true save_mode=\"error\" } For parquet file format OssFile { path=\"/seatunnel/sink\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxx\" access_secret = \"xxxxxxxxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" field_delimiter=\"\\t\" row_delimiter=\"\\n\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"parquet\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true save_mode=\"error\" } For orc file format OssFile { path=\"/seatunnel/sink\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxx\" access_secret = \"xxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" field_delimiter=\"\\t\" row_delimiter=\"\\n\" partition_by=[\"age\"] partition_dir_expression=\"${k0}=${v0}\" is_partition_field_write_in_file=true file_name_expression=\"${transactionId}_${now}\" file_format=\"orc\" sink_columns=[\"name\",\"age\"] filename_time_format=\"yyyy.MM.dd\" is_enable_transaction=true save_mode=\"error\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/OssFile","h":"#example","p":103239},{"i":103282,"t":"JDBC sink connector","s":"JDBC","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"","p":103281},{"i":103284,"t":"Write data through jdbc. Support Batch mode and Streaming mode, support concurrent writing, support exactly-once semantics (using XA transaction guarantee).","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#description","p":103281},{"i":103286,"t":"exactly-once Use Xa transactions to ensure exactly-once. So only support exactly-once for the database which is support Xa transactions. You can set is_exactly_once=true to enable it. schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#key-features","p":103281},{"i":103288,"t":"name type required default value url String Yes - driver String Yes - user String No - password String No - query String Yes - connection_check_timeout_sec Int No 30 max_retries Int No 3 batch_size Int No 300 batch_interval_ms Int No 1000 is_exactly_once Boolean No false xa_data_source_class_name String No - max_commit_attempts Int No 3 transaction_timeout_sec Int No -1","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#options","p":103281},{"i":103290,"t":"The jdbc class name used to connect to the remote data source, if you use MySQL the value is com.mysql.cj.jdbc.Driver. Warn: for license compliance, you have to provide MySQL JDBC driver yourself, e.g. copy mysql-connector-java-xxx.jar to $SEATNUNNEL_HOME/lib for Standalone.","s":"driver [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#driver-string","p":103281},{"i":103292,"t":"userName","s":"user [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#user-string","p":103281},{"i":103294,"t":"password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#password-string","p":103281},{"i":103296,"t":"The URL of the JDBC connection. Refer to a case: jdbc:postgresql://localhost/test","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#url-string","p":103281},{"i":103298,"t":"Query statement","s":"query [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#query-string","p":103281},{"i":103300,"t":"The time in seconds to wait for the database operation used to validate the connection to complete.","s":"connection_check_timeout_sec [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#connection_check_timeout_sec-int","p":103281},{"i":103302,"t":"The number of retries to submit failed (executeBatch)","s":"max_retries[int]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#max_retriesint","p":103281},{"i":103304,"t":"For batch writing, when the number of buffers reaches the number of batch_size or the time reaches batch_interval_ms, the data will be flushed into the database","s":"batch_size[int]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#batch_sizeint","p":103281},{"i":103306,"t":"For batch writing, when the number of buffers reaches the number of batch_size or the time reaches batch_interval_ms, the data will be flushed into the database","s":"batch_interval_ms[int]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#batch_interval_msint","p":103281},{"i":103308,"t":"Whether to enable exactly-once semantics, which will use Xa transactions. If on, you need to set xa_data_source_class_name.","s":"is_exactly_once[boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#is_exactly_onceboolean","p":103281},{"i":103310,"t":"The xa data source class name of the database Driver, for example, mysql is com.mysql.cj.jdbc.MysqlXADataSource, and please refer to appendix for other data sources","s":"xa_data_source_class_name[string]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#xa_data_source_class_namestring","p":103281},{"i":103312,"t":"The number of retries for transaction commit failures","s":"max_commit_attempts[int]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#max_commit_attemptsint","p":103281},{"i":103314,"t":"The timeout after the transaction is opened, the default is -1 (never timeout). Note that setting the timeout may affect exactly-once semantics","s":"transaction_timeout_sec[int]","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#transaction_timeout_secint","p":103281},{"i":103316,"t":"In the case of is_exactly_once = \"true\", Xa transactions are used. This requires database support, and some databases require some setup : 1 postgres needs to set max_prepared_transactions > 1 such as ALTER SYSTEM set max_prepared_transactions to 10. 2 mysql version need >= 8.0.29 and Non-root users need to grant XA_RECOVER_ADMIN permissions. such as grant XA_RECOVER_ADMIN on test_db.* to 'user1'@'%'.","s":"tips","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#tips","p":103281},{"i":103318,"t":"there are some reference value for params above. datasource driver url xa_data_source_class_name maven mysql com.mysql.cj.jdbc.Driver jdbc:mysql://localhost:3306/test com.mysql.cj.jdbc.MysqlXADataSource https://mvnrepository.com/artifact/mysql/mysql-connector-java postgresql org.postgresql.Driver jdbc:postgresql://localhost:5432/postgres org.postgresql.xa.PGXADataSource https://mvnrepository.com/artifact/org.postgresql/postgresql dm dm.jdbc.driver.DmDriver jdbc:dm://localhost:5236 dm.jdbc.driver.DmdbXADataSource https://mvnrepository.com/artifact/com.dameng/DmJdbcDriver18","s":"appendix","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#appendix","p":103281},{"i":103320,"t":"Simple jdbc { url = \"jdbc:mysql://localhost/test\" driver = \"com.mysql.cj.jdbc.Driver\" user = \"root\" password = \"123456\" query = \"insert into test_table(name,age) values(?,?)\" } Exactly-once jdbc { url = \"jdbc:mysql://localhost/test\" driver = \"com.mysql.cj.jdbc.Driver\" max_retries = 0 user = \"root\" password = \"123456\" query = \"insert into test_table(name,age) values(?,?)\" is_exactly_once = \"true\" xa_data_source_class_name = \"com.mysql.cj.jdbc.MysqlXADataSource\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Jdbc","h":"#example","p":103281},{"i":103322,"t":"Phoenix sink connector","s":"Phoenix","u":"/docs/2.2.0-beta/connector-v2/sink/Phoenix","h":"","p":103321},{"i":103324,"t":"Write Phoenix data through Jdbc connector. Support Batch mode and Streaming mode. The tested Phoenix version is 4.xx and 5.xx On the underlying implementation, through the jdbc driver of Phoenix, execute the upsert statement to write data to HBase. Two ways of connecting Phoenix with Java JDBC. One is to connect to zookeeper through JDBC, and the other is to connect to queryserver through JDBC thin client. Tips: By default, the (thin) driver jar is used. If you want to use the (thick) driver or other versions of Phoenix (thin) driver, you need to recompile the jdbc connector module Tips: Not support exactly-once semantics (XA transaction is not yet supported in Phoenix).","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Phoenix","h":"#description","p":103321},{"i":103326,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Phoenix","h":"#key-features","p":103321},{"i":103329,"t":"if you use phoenix (thick) driver the value is org.apache.phoenix.jdbc.PhoenixDriver or you use (thin) driver the value is org.apache.phoenix.queryserver.client.Driver","s":"driver [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Phoenix","h":"#driver-string","p":103321},{"i":103331,"t":"if you use phoenix (thick) driver the value is jdbc:phoenix:localhost:2182/hbase or you use (thin) driver the value is jdbc:phoenix:thin:url=http://localhost:8765;serialization=PROTOBUF","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Phoenix","h":"#url-string","p":103321},{"i":103333,"t":"use thick client drive Jdbc { driver = org.apache.phoenix.jdbc.PhoenixDriver url = \"jdbc:phoenix:localhost:2182/hbase\" query = \"upsert into test.sink(age, name) values(?, ?)\" } use thin client drive Jdbc { driver = org.apache.phoenix.queryserver.client.Driver url = \"jdbc:phoenix:thin:url=http://spark_e2e_phoenix_sink:8765;serialization=PROTOBUF\" query = \"upsert into test.sink(age, name) values(?, ?)\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Phoenix","h":"#example","p":103321},{"i":103335,"t":"Redis sink connector","s":"Redis","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"","p":103334},{"i":103337,"t":"Used to write data to Redis.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#description","p":103334},{"i":103339,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#key-features","p":103334},{"i":103341,"t":"name type required default value host string yes - port int yes - key string yes - data_type string yes - auth string No - format string No json","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#options","p":103334},{"i":103343,"t":"Redis host","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#host-string","p":103334},{"i":103345,"t":"Redis port","s":"port [int]","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#port-int","p":103334},{"i":103347,"t":"The value of key you want to write to redis. For example, if you want to use value of a field from upstream data as key, you can assign it to the field name. Upstream data is the following: code data success 200 get success true 500 internal error false If you assign field name to code and data_type to key, two data will be written to redis: 200 -> {code: 200, message: true, data: get success} 500 -> {code: 500, message: false, data: internal error} If you assign field name to value and data_type to key, only one data will be written to redis because value is not existed in upstream data's fields: value -> {code: 500, message: false, data: internal error} Please see the data_type section for specific writing rules. Of course, the format of the data written here I just take json as an example, the specific or user-configured format prevails.","s":"key [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#key-string","p":103334},{"i":103349,"t":"Redis data types, support key hash list set zset key Each data from upstream will be updated to the configured key, which means the later data will overwrite the earlier data, and only the last data will be stored in the key. hash Each data from upstream will be split according to the field and written to the hash key, also the data after will overwrite the data before. list Each data from upstream will be added to the configured list key. set Each data from upstream will be added to the configured set key. zset Each data from upstream will be added to the configured zset key with a weight of 1. So the order of data in zset is based on the order of data consumption.","s":"data_type [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#data_type-string","p":103334},{"i":103351,"t":"Redis authentication password, you need it when you connect to an encrypted cluster","s":"auth [String]","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#auth-string","p":103334},{"i":103353,"t":"The format of upstream data, now only support json, text will be supported later, default json. When you assign format is json, for example: Upstream data is the following: code data success 200 get success true Connector will generate data as the following and write it to redis: {\"code\": 200, \"data\": \"get success\", \"success\": \"true\"}","s":"format [String]","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#format-string","p":103334},{"i":103355,"t":"simple: Redis { host = localhost port = 6379 key = age data_type = list }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Redis","h":"#example","p":103334},{"i":103358,"t":"Write message to Sentry.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#description","p":103356},{"i":103360,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#key-features","p":103356},{"i":103362,"t":"name type required default value dsn string yes - env string no - release string no - cacheDirPath string no - enableExternalConfiguration boolean no - maxCacheItems number no - flushTimeoutMills number no - maxQueueSize number no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#options","p":103356},{"i":103364,"t":"The DSN tells the SDK where to send the events to.","s":"dsn [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#dsn-string","p":103356},{"i":103366,"t":"specify the environment","s":"env [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#env-string","p":103356},{"i":103368,"t":"specify the release","s":"release [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#release-string","p":103356},{"i":103370,"t":"the cache dir path for caching offline events","s":"cacheDirPath [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#cachedirpath-string","p":103356},{"i":103372,"t":"if loading properties from external sources is enabled.","s":"enableExternalConfiguration [boolean]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#enableexternalconfiguration-boolean","p":103356},{"i":103374,"t":"The max cache items for capping the number of events Default is 30","s":"maxCacheItems [number]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#maxcacheitems-number","p":103356},{"i":103376,"t":"Controls how many seconds to wait before flushing down. Sentry SDKs cache events from a background queue and this queue is given a certain amount to drain pending events Default is 15000 = 15s","s":"flushTimeoutMillis [number]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#flushtimeoutmillis-number","p":103356},{"i":103378,"t":"Max queue size before flushing events/envelopes to the disk","s":"maxQueueSize [number]","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#maxqueuesize-number","p":103356},{"i":103380,"t":"Sentry { dsn = \"https://xxx@sentry.xxx.com:9999/6\" enableExternalConfiguration = true maxCacheItems = 1000 env = prod }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Sentry","h":"#example","p":103356},{"i":103382,"t":"Socket sink connector","s":"Socket","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"","p":103381},{"i":103384,"t":"Used to send data to Socket Server. Both support streaming and batch mode. For example, if the data from upstream is [age: 12, name: jared], the content send to socket server is the following: {\"name\":\"jared\",\"age\":17}","s":"Description","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#description","p":103381},{"i":103386,"t":"exactly-once schema projection","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#key-features","p":103381},{"i":103388,"t":"name type required default value host String Yes - port Integer yes - max_retries Integer No 3","s":"Options","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#options","p":103381},{"i":103390,"t":"socket server host","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#host-string","p":103381},{"i":103392,"t":"socket server port","s":"port [integer]","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#port-integer","p":103381},{"i":103394,"t":"The number of retries to send record failed","s":"max_retries [integer]","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#max_retries-integer","p":103381},{"i":103396,"t":"simple: Socket { host = \"localhost\" port = 9999 } test: Configuring the SeaTunnel config file env { execution.parallelism = 1 job.mode = \"STREAMING\" } source { FakeSource { result_table_name = \"fake\" schema = { fields { name = \"string\" age = \"int\" } } } } transform { sql = \"select name, age from fake\" } sink { Socket { host = \"localhost\" port = 9999 } } Start a port listening nc -l -v 9999 Start a SeaTunnel task Socket Server Console print data {\"name\":\"jared\",\"age\":17}","s":"Example","u":"/docs/2.2.0-beta/connector-v2/sink/Socket","h":"#example","p":103381},{"i":103398,"t":"Clickhouse source connector","s":"Clickhouse","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"","p":103397},{"i":103400,"t":"Used to read data from Clickhouse.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#description","p":103397},{"i":103402,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split tip Reading data from Clickhouse can also be done using JDBC","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#key-features","p":103397},{"i":103404,"t":"name type required default value host string yes - database string yes - sql string yes - username string yes - password string yes - common-options string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#options","p":103397},{"i":103406,"t":"ClickHouse cluster address, the format is host:port , allowing multiple hosts to be specified. Such as \"host1:8123,host2:8123\" .","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#host-string","p":103397},{"i":103408,"t":"The ClickHouse database","s":"database [string]","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#database-string","p":103397},{"i":103410,"t":"The query sql used to search data though Clickhouse server","s":"sql [string]","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#sql-string","p":103397},{"i":103412,"t":"ClickHouse user username","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#username-string","p":103397},{"i":103414,"t":"ClickHouse user password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#password-string","p":103397},{"i":103416,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#common-options-string","p":103397},{"i":103418,"t":"source { Clickhouse { host = \"localhost:8123\" database = \"default\" sql = \"select * from test where age = 20 limit 100\" username = \"default\" password = \"\" result_table_name = \"test\" } }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/source/Clickhouse","h":"#examples","p":103397},{"i":103420,"t":"Common parameters of source connectors name type required default value result_table_name string no - field_name string no -","s":"Common Options","u":"/docs/2.2.0-beta/connector-v2/source/common-options","h":"","p":103419},{"i":103422,"t":"When result_table_name is not specified, the data processed by this plugin will not be registered as a data set (dataStream/dataset) that can be directly accessed by other plugins, or called a temporary table (table) ; When result_table_name is specified, the data processed by this plugin will be registered as a data set (dataStream/dataset) that can be directly accessed by other plugins, or called a temporary table (table) . The data set (dataStream/dataset) registered here can be directly accessed by other plugins by specifying source_table_name .","s":"result_table_name [string]","u":"/docs/2.2.0-beta/connector-v2/source/common-options","h":"#result_table_name-string","p":103419},{"i":103424,"t":"When the data is obtained from the upper-level plug-in, you can specify the name of the obtained field, which is convenient for use in subsequent sql plugins.","s":"field_name [string]","u":"/docs/2.2.0-beta/connector-v2/source/common-options","h":"#field_name-string","p":103419},{"i":103426,"t":"source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } The result of the data source FakeSourceStream will be registered as a temporary table named fake . This temporary table can be used by any Transform or Sink plugin by specifying source_table_name . field_name names the two columns of the temporary table name and age respectively.","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/common-options","h":"#example","p":103419},{"i":103428,"t":"FakeSource connector","s":"FakeSource","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"","p":103427},{"i":103430,"t":"The FakeSource is a virtual data source, which randomly generates the number of rows according to the data structure of the user-defined schema, just for testing, such as type conversion and feature testing","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"#description","p":103427},{"i":103432,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"#key-features","p":103427},{"i":103434,"t":"name type required default value result_table_name string yes - schema config yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"#options","p":103427},{"i":103436,"t":"The table name.","s":"result_table_name [string]","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"#result_table_name-string","p":103427},{"i":103438,"t":"Table structure description ,you should assign schema option to tell connector how to parse data to the row you want. Tips: Most of Unstructured-Datasource contain this param, such as LocalFile,HdfsFile. Example: schema = { fields { c_map = \"map<string, string>\" c_array = \"array<tinyint>\" c_string = string c_boolean = boolean c_tinyint = tinyint c_smallint = smallint c_int = int c_bigint = bigint c_float = float c_double = double c_decimal = \"decimal(30, 8)\" c_null = \"null\" c_bytes = bytes c_date = date c_time = time c_timestamp = timestamp } }","s":"type [string]","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"#type-string","p":103427},{"i":103440,"t":"Simple source for FakeSource which contains enough datatype source { FakeSource { schema = { fields { c_map = \"map<string, string>\" c_array = \"array<tinyint>\" c_string = string c_boolean = boolean c_tinyint = tinyint c_smallint = smallint c_int = int c_bigint = bigint c_float = float c_double = double c_decimal = \"decimal(30, 8)\" c_null = \"null\" c_bytes = bytes c_date = date c_time = time c_timestamp = timestamp } } result_table_name = \"fake\" } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/FakeSource","h":"#example","p":103427},{"i":103442,"t":"Ftp file source connector","s":"FtpFile","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"","p":103441},{"i":103444,"t":"Read data from ftp file server.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#description","p":103441},{"i":103446,"t":"batch stream exactly-once schema projection parallelism support user-defined split file format text csv json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#key-features","p":103441},{"i":103448,"t":"name type required default value host string yes - port int yes - user string yes - password string yes - path string yes - type string yes - schema config no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#options","p":103441},{"i":103450,"t":"The target ftp host is required","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#host-string","p":103441},{"i":103452,"t":"The target ftp port is required","s":"port [int]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#port-int","p":103441},{"i":103454,"t":"The target ftp username is required","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#username-string","p":103441},{"i":103456,"t":"The target ftp password is required","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#password-string","p":103441},{"i":103458,"t":"The source file path.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#path-string","p":103441},{"i":103460,"t":"File type, supported as the following file types: text csv json If you assign file type to json, you should also assign schema option to tell connector how to parse data to the row you want. For example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} you should assign schema as the following: schema { fields { code = int data = string success = boolean } } connector will generate data as the following: code data success 200 get success true If you assign file type to text csv, schema option not supported temporarily, but the subsequent features will support. Now connector will treat the upstream data as the following: lines The content of every line in file","s":"type [string]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#type-string","p":103441},{"i":103462,"t":"The schema information of upstream data.","s":"schema [config]","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#schema-config","p":103441},{"i":103464,"t":"FtpFile { path = \"/tmp/seatunnel/sink/parquet\" host = \"192.168.31.48\" port = 21 user = tyrantlucifer password = tianchao type = \"text\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/FtpFile","h":"#example","p":103441},{"i":103466,"t":"Greenplum source connector","s":"Greenplum","u":"/docs/2.2.0-beta/connector-v2/source/Greenplum","h":"","p":103465},{"i":103468,"t":"Read Greenplum data through Jdbc connector.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Greenplum","h":"#description","p":103465},{"i":103470,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split tip Optional jdbc drivers: org.postgresql.Driver com.pivotal.jdbc.GreenplumDriver Warn: for license compliance, if you use GreenplumDriver the have to provide Greenplum JDBC driver yourself, e.g. copy greenplum-xxx.jar to $SEATNUNNEL_HOME/lib for Standalone.","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Greenplum","h":"#key-features","p":103465},{"i":103472,"t":"Hdfs file source connector","s":"HdfsFile","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"","p":103471},{"i":103474,"t":"Read data from hdfs file system.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#description","p":103471},{"i":103476,"t":"batch stream exactly-once Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot. schema projection parallelism support user-defined split file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#key-features","p":103471},{"i":103478,"t":"name type required default value path string yes - type string yes - fs.defaultFS string yes - schema config no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#options","p":103471},{"i":103480,"t":"The source file path.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#path-string","p":103471},{"i":103482,"t":"File type, supported as the following file types: text csv parquet orc json If you assign file type to json, you should also assign schema option to tell connector how to parse data to the row you want. For example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} You can also save multiple pieces of data in one file and split them by newline: {\"code\": 200, \"data\": \"get success\", \"success\": true} {\"code\": 300, \"data\": \"get failed\", \"success\": false} you should assign schema as the following: schema { fields { code = int data = string success = boolean } } connector will generate data as the following: code data success 200 get success true If you assign file type to parquet orc, schema option not required, connector can find the schema of upstream data automatically. If you assign file type to text csv, schema option not supported temporarily, but the subsequent features will support. Now connector will treat the upstream data as the following: lines The content of every line in file","s":"type [string]","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#type-string","p":103471},{"i":103484,"t":"Hdfs cluster address.","s":"fs.defaultFS [string]","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#fsdefaultfs-string","p":103471},{"i":103486,"t":"HdfsFile { path = \"/apps/hive/demo/student\" type = \"parquet\" fs.defaultFS = \"hdfs://namenode001\" } HdfsFile { schema { fields { name = string age = int } } path = \"/apps/hive/demo/student\" type = \"json\" fs.defaultFS = \"hdfs://namenode001\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/HdfsFile","h":"#example","p":103471},{"i":103488,"t":"Hive source connector","s":"Hive","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"","p":103487},{"i":103490,"t":"Read data from Hive. In order to use this connector, You must ensure your spark/flink cluster already integrated hive. The tested hive version is 2.3.9. Tips: Hive Sink Connector can not add partition field to the output data now","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"#description","p":103487},{"i":103492,"t":"batch stream exactly-once Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot. schema projection parallelism support user-defined split file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"#key-features","p":103487},{"i":103494,"t":"name type required default value table_name string yes - metastore_uri string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"#options","p":103487},{"i":103496,"t":"Target Hive table name eg: db1.table1","s":"table_name [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"#table_name-string","p":103487},{"i":103498,"t":"Hive metastore uri","s":"metastore_uri [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"#metastore_uri-string","p":103487},{"i":103500,"t":"Hive { table_name = \"default.seatunnel_orc\" metastore_uri = \"thrift://namenode001:9083\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Hive","h":"#example","p":103487},{"i":103502,"t":"Http source connector","s":"Http","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"","p":103501},{"i":103504,"t":"Used to read data from Http.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#description","p":103501},{"i":103506,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#key-features","p":103501},{"i":103508,"t":"name type required default value url String Yes - schema Config No - schema.fields Config No - format String No json method String No get headers Map No - params Map No - body String No - poll_interval_ms int No - retry int No - retry_backoff_multiplier_ms int No 100 retry_backoff_max_ms int No 10000","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#options","p":103501},{"i":103510,"t":"http request url","s":"url [String]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#url-string","p":103501},{"i":103512,"t":"http request method, only supports GET, POST method.","s":"method [String]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#method-string","p":103501},{"i":103514,"t":"http headers","s":"headers [Map]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#headers-map","p":103501},{"i":103516,"t":"http params","s":"params [Map]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#params-map","p":103501},{"i":103518,"t":"http body","s":"body [String]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#body-string","p":103501},{"i":103520,"t":"request http api interval(millis) in stream mode","s":"poll_interval_ms [int]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#poll_interval_ms-int","p":103501},{"i":103522,"t":"The max retry times if request http return to IOException","s":"retry [int]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#retry-int","p":103501},{"i":103524,"t":"The retry-backoff times(millis) multiplier if request http failed","s":"retry_backoff_multiplier_ms [int]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#retry_backoff_multiplier_ms-int","p":103501},{"i":103526,"t":"The maximum retry-backoff times(millis) if request http failed","s":"retry_backoff_max_ms [int]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#retry_backoff_max_ms-int","p":103501},{"i":103528,"t":"the format of upstream data, now only support json text, default json. when you assign format is json, you should also assign schema option, for example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} you should assign schema as the following: schema { fields { code = int data = string success = boolean } } connector will generate data as the following: code data success 200 get success true when you assign format is text, connector will do nothing for upstream data, for example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} connector will generate data as the following: content {\"code\": 200, \"data\": \"get success\", \"success\": true}","s":"format [String]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#format-string","p":103501},{"i":103530,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#schema-config","p":103501},{"i":103532,"t":"simple: Http { url = \"https://tyrantlucifer.com/api/getDemoData\" schema { fields { code = int message = string data = string ok = boolean } } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Http","h":"#example","p":103501},{"i":103534,"t":"Apache Iceberg source connector","s":"Apache Iceberg","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"","p":103533},{"i":103536,"t":"Source connector for Apache Iceberg. It can support batch and stream mode.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#description","p":103533},{"i":103538,"t":"batch stream exactly-once schema projection parallelism support user-defined split data format parquet orc avro iceberg catalog hadoop(2.7.5) hive(2.3.9)","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#key-features","p":103533},{"i":103540,"t":"name type required default value catalog_name string yes - catalog_type string yes - uri string false - warehouse string yes - namespace string yes - table string yes - case_sensitive boolean false false start_snapshot_timestamp long false - start_snapshot_id long false - end_snapshot_id long false - use_snapshot_id long false - use_snapshot_timestamp long false - stream_scan_strategy enum false FROM_LATEST_SNAPSHOT","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#options","p":103533},{"i":103542,"t":"User-specified catalog name.","s":"catalog_name [string]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#catalog_name-string","p":103533},{"i":103544,"t":"The optional values are: hive: The hive metastore catalog. hadoop: The hadoop catalog.","s":"catalog_type [string]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#catalog_type-string","p":103533},{"i":103546,"t":"The Hive metastore’s thrift URI.","s":"uri [string]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#uri-string","p":103533},{"i":103548,"t":"The location to store metadata files and data files.","s":"warehouse [string]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#warehouse-string","p":103533},{"i":103550,"t":"The iceberg database name in the backend catalog.","s":"namespace [string]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#namespace-string","p":103533},{"i":103552,"t":"The iceberg table name in the backend catalog.","s":"table [string]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#table-string","p":103533},{"i":103554,"t":"If data columns where selected via fields(Collection), controls whether the match to the schema will be done with case sensitivity.","s":"case_sensitive [boolean]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#case_sensitive-boolean","p":103533},{"i":103556,"t":"Use projection to select data columns and columns order.","s":"fields [array]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#fields-array","p":103533},{"i":103558,"t":"Instructs this scan to look for changes starting from a particular snapshot (exclusive).","s":"start_snapshot_id [long]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#start_snapshot_id-long","p":103533},{"i":103560,"t":"Instructs this scan to look for changes starting from the most recent snapshot for the table as of the timestamp. timestamp – the timestamp in millis since the Unix epoch","s":"start_snapshot_timestamp [long]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#start_snapshot_timestamp-long","p":103533},{"i":103562,"t":"Instructs this scan to look for changes up to a particular snapshot (inclusive).","s":"end_snapshot_id [long]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#end_snapshot_id-long","p":103533},{"i":103564,"t":"Instructs this scan to look for use the given snapshot ID.","s":"use_snapshot_id [long]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#use_snapshot_id-long","p":103533},{"i":103566,"t":"Instructs this scan to look for use the most recent snapshot as of the given time in milliseconds. timestamp – the timestamp in millis since the Unix epoch","s":"use_snapshot_timestamp [long]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#use_snapshot_timestamp-long","p":103533},{"i":103568,"t":"Starting strategy for stream mode execution, Default to use FROM_LATEST_SNAPSHOT if don’t specify any value. The optional values are: TABLE_SCAN_THEN_INCREMENTAL: Do a regular table scan then switch to the incremental mode. FROM_LATEST_SNAPSHOT: Start incremental mode from the latest snapshot inclusive. FROM_EARLIEST_SNAPSHOT: Start incremental mode from the earliest snapshot inclusive. FROM_SNAPSHOT_ID: Start incremental mode from a snapshot with a specific id inclusive. FROM_SNAPSHOT_TIMESTAMP: Start incremental mode from a snapshot with a specific timestamp inclusive.","s":"stream_scan_strategy [enum]","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#stream_scan_strategy-enum","p":103533},{"i":103570,"t":"simple source { Iceberg { catalog_name = \"seatunnel\" catalog_type = \"hadoop\" warehouse = \"hdfs://your_cluster//tmp/seatunnel/iceberg/\" namespace = \"your_iceberg_database\" table = \"your_iceberg_table\" } } Or source { Iceberg { catalog_name = \"seatunnel\" catalog_type = \"hive\" uri = \"thrift://localhost:9083\" warehouse = \"hdfs://your_cluster//tmp/seatunnel/iceberg/\" namespace = \"your_iceberg_database\" table = \"your_iceberg_table\" } } schema projection source { Iceberg { catalog_name = \"seatunnel\" catalog_type = \"hadoop\" warehouse = \"hdfs://your_cluster/tmp/seatunnel/iceberg/\" namespace = \"your_iceberg_database\" table = \"your_iceberg_table\" fields { f2 = \"boolean\" f1 = \"bigint\" f3 = \"int\" f4 = \"bigint\" } } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Iceberg","h":"#example","p":103533},{"i":103572,"t":"Hudi source connector","s":"Hudi","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"","p":103571},{"i":103574,"t":"Used to read data from Hudi. Currently, only supports hudi cow table and Snapshot Query with Batch Mode. In order to use this connector, You must ensure your spark/flink cluster already integrated hive. The tested hive version is 2.3.9.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#description","p":103571},{"i":103576,"t":"batch Currently, only supports hudi cow table and Snapshot Query with Batch Mode stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#key-features","p":103571},{"i":103578,"t":"name type required default value table.path string yes - table.type string yes - conf.files string yes - use.kerberos boolean no false kerberos.principal string no - kerberos.principal.file string no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#options","p":103571},{"i":103580,"t":"table.path The hdfs root path of hudi table,such as 'hdfs://nameserivce/data/hudi/hudi_table/'.","s":"table.path [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#tablepath-string","p":103571},{"i":103582,"t":"table.type The type of hudi table. Now we only support 'cow', 'mor' is not support yet.","s":"table.type [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#tabletype-string","p":103571},{"i":103584,"t":"conf.files The environment conf file path list(local path), which used to init hdfs client to read hudi table file. The example is '/home/test/hdfs-site.xml;/home/test/core-site.xml;/home/test/yarn-site.xml'.","s":"conf.files [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#conffiles-string","p":103571},{"i":103586,"t":"use.kerberos Whether to enable Kerberos, default is false.","s":"use.kerberos [boolean]","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#usekerberos-boolean","p":103571},{"i":103588,"t":"kerberos.principal When use kerberos, we should set kerberos princal such as 'test_user@xxx'.","s":"kerberos.principal [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#kerberosprincipal-string","p":103571},{"i":103590,"t":"kerberos.principal.file When use kerberos, we should set kerberos princal file such as '/home/test/test_user.keytab'.","s":"kerberos.principal.file [string]","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#kerberosprincipalfile-string","p":103571},{"i":103592,"t":"source { Hudi { table.path = \"hdfs://nameserivce/data/hudi/hudi_table/\" table.type = \"cow\" conf.files = \"/home/test/hdfs-site.xml;/home/test/core-site.xml;/home/test/yarn-site.xml\" use.kerberos = true kerberos.principal = \"test_user@xxx\" kerberos.principal.file = \"/home/test/test_user.keytab\" } }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/source/Hudi","h":"#examples","p":103571},{"i":103594,"t":"IoTDB source connector","s":"IoTDB","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"","p":103593},{"i":103596,"t":"Read external data source data through IoTDB.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#description","p":103593},{"i":103598,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#key-features","p":103593},{"i":103600,"t":"name type required default value host string yes - port Int yes - node_urls string yes - sql string yes fields config yes - fetch_size int no - username string no - password string no - lower_bound long no - upper_bound long no - num_partitions int no - thrift_default_buffer_size int no - enable_cache_leader boolean no - version string no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#options","p":103593},{"i":103602,"t":"host [string] the host of the IoTDB when you select host of the IoTDB port [int] the port of the IoTDB when you select","s":"single node, you need to set host and port to connect to the remote data source.","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#single-node-you-need-to-set-host-and-port-to-connect-to-the-remote-data-source","p":103593},{"i":103604,"t":"node_urls [string] the node_urls of the IoTDB when you select e.g. 127.0.0.1:8080,127.0.0.2:8080","s":"multi node, you need to set node_urls to connect to the remote data source.","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#multi-node-you-need-to-set-node_urls-to-connect-to-the-remote-data-source","p":103593},{"i":103606,"t":"sql [string] execute sql statement e.g. select name,age from test","s":"other parameters","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#other-parameters","p":103593},{"i":103608,"t":"the fields of the IoTDB when you select the field type is SeaTunnel field type org.apache.seatunnel.api.table.type.SqlType e.g. fields{ name=STRING age=INT }","s":"fields [string]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#fields-string","p":103593},{"i":103611,"t":"the fetch_size of the IoTDB when you select","s":"fetch_size [int]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#fetch_size-int","p":103593},{"i":103613,"t":"the username of the IoTDB when you select","s":"username [string]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#username-string","p":103593},{"i":103615,"t":"the password of the IoTDB when you select","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#password-string","p":103593},{"i":103617,"t":"the lower_bound of the IoTDB when you select","s":"lower_bound [long]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#lower_bound-long","p":103593},{"i":103619,"t":"the upper_bound of the IoTDB when you select","s":"upper_bound [long]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#upper_bound-long","p":103593},{"i":103621,"t":"the num_partitions of the IoTDB when you select","s":"num_partitions [int]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#num_partitions-int","p":103593},{"i":103623,"t":"the thrift_default_buffer_size of the IoTDB when you select","s":"thrift_default_buffer_size [int]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#thrift_default_buffer_size-int","p":103593},{"i":103625,"t":"enable_cache_leader of the IoTDB when you select","s":"enable_cache_leader [boolean]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#enable_cache_leader-boolean","p":103593},{"i":103627,"t":"Version represents the SQL semantic version used by the client, which is used to be compatible with the SQL semantics of 0.12 when upgrading 0.13. The possible values are: V_0_12, V_0_13.","s":"version [string]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#version-string","p":103593},{"i":103629,"t":"we can split the partitions of the IoTDB and we used time column split num_partitions [int]​ split num","s":"split partitions","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#split-partitions","p":103593},{"i":103631,"t":"upper bound of the time column","s":"upper_bound [long]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#upper_bound-long-1","p":103593},{"i":103633,"t":"lower bound of the time column split the time range into numPartitions parts if numPartitions is 1, use the whole time range if numPartitions < (upper_bound - lower_bound), use (upper_bound - lower_bound) partitions eg: lower_bound = 1, upper_bound = 10, numPartitions = 2 sql = \"select * from test where age > 0 and age < 10\" split result split 1: select * from test where (time >= 1 and time < 6) and ( age > 0 and age < 10 ) split 2: select * from test where (time >= 6 and time < 11) and ( age > 0 and age < 10 )","s":"lower_bound [long]","u":"/docs/2.2.0-beta/connector-v2/source/IoTDB","h":"#lower_bound-long-1","p":103593},{"i":103635,"t":"Kudu source connector","s":"Kudu","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"","p":103634},{"i":103637,"t":"Used to read data from Kudu. The tested kudu version is 1.11.1.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#description","p":103634},{"i":103639,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#key-features","p":103634},{"i":103641,"t":"name type required default value kudu_master string yes - kudu_table string yes - columnsList string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#options","p":103634},{"i":103643,"t":"kudu_master The address of kudu master,such as '192.168.88.110:7051'.","s":"kudu_master [string]","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#kudu_master-string","p":103634},{"i":103645,"t":"kudu_table The name of kudu table..","s":"kudu_table [string]","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#kudu_table-string","p":103634},{"i":103647,"t":"columnsList Specifies the column names of the table.","s":"columnsList [string]","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#columnslist-string","p":103634},{"i":103649,"t":"source { KuduSource { result_table_name = \"studentlyh2\" kudu_master = \"192.168.88.110:7051\" kudu_table = \"studentlyh2\" columnsList = \"id,name,age,sex\" } }","s":"Examples","u":"/docs/2.2.0-beta/connector-v2/source/Kudu","h":"#examples","p":103634},{"i":103651,"t":"Local file source connector","s":"LocalFile","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"","p":103650},{"i":103653,"t":"Read data from local file system.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#description","p":103650},{"i":103655,"t":"batch stream exactly-once Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot. schema projection parallelism support user-defined split file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#key-features","p":103650},{"i":103657,"t":"name type required default value path string yes - type string yes - schema config no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#options","p":103650},{"i":103659,"t":"The source file path.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#path-string","p":103650},{"i":103661,"t":"File type, supported as the following file types: text csv parquet orc json If you assign file type to json, you should also assign schema option to tell connector how to parse data to the row you want. For example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} You can also save multiple pieces of data in one file and split them by newline: {\"code\": 200, \"data\": \"get success\", \"success\": true} {\"code\": 300, \"data\": \"get failed\", \"success\": false} you should assign schema as the following: schema { fields { code = int data = string success = boolean } } connector will generate data as the following: code data success 200 get success true If you assign file type to parquet orc, schema option not required, connector can find the schema of upstream data automatically. If you assign file type to text csv, schema option not supported temporarily, but the subsequent features will support. Now connector will treat the upstream data as the following: lines The content of every line in file","s":"type [string]","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#type-string","p":103650},{"i":103663,"t":"The schema information of upstream data.","s":"schema [config]","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#schema-config","p":103650},{"i":103665,"t":"LocalFile { path = \"/apps/hive/demo/student\" type = \"parquet\" } LocalFile { schema { fields { name = string age = int } } path = \"/apps/hive/demo/student\" type = \"json\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/LocalFile","h":"#example","p":103650},{"i":103667,"t":"JDBC source connector","s":"JDBC","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"","p":103666},{"i":103669,"t":"Read external data source data through JDBC.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#description","p":103666},{"i":103671,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#key-features","p":103666},{"i":103673,"t":"name type required default value url String Yes - driver String Yes - user String No - password String No - query String Yes - connection_check_timeout_sec Int No 30 partition_column String No - partition_upper_bound Long No - partition_lower_bound Long No -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#options","p":103666},{"i":103675,"t":"The jdbc class name used to connect to the remote data source, if you use MySQL the value is com.mysql.cj.jdbc.Driver. Warn: for license compliance, you have to provide MySQL JDBC driver yourself, e.g. copy mysql-connector-java-xxx.jar to $SEATNUNNEL_HOME/lib for Standalone.","s":"driver [string]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#driver-string","p":103666},{"i":103677,"t":"userName","s":"user [string]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#user-string","p":103666},{"i":103679,"t":"password","s":"password [string]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#password-string","p":103666},{"i":103681,"t":"The URL of the JDBC connection. Refer to a case: jdbc:postgresql://localhost/test","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#url-string","p":103666},{"i":103683,"t":"Query statement","s":"query [string]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#query-string","p":103666},{"i":103685,"t":"The time in seconds to wait for the database operation used to validate the connection to complete.","s":"connection_check_timeout_sec [int]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#connection_check_timeout_sec-int","p":103666},{"i":103687,"t":"The column name for parallelism's partition, only support numeric type.","s":"partition_column [string]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#partition_column-string","p":103666},{"i":103689,"t":"The partition_column max value for scan, if not set SeaTunnel will query database get max value.","s":"partition_upper_bound [long]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#partition_upper_bound-long","p":103666},{"i":103691,"t":"The partition_column min value for scan, if not set SeaTunnel will query database get min value.","s":"partition_lower_bound [long]","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#partition_lower_bound-long","p":103666},{"i":103693,"t":"If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks.","s":"tips","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#tips","p":103666},{"i":103695,"t":"there are some reference value for params above. datasource driver url maven mysql com.mysql.cj.jdbc.Driver jdbc:mysql://localhost:3306/test https://mvnrepository.com/artifact/mysql/mysql-connector-java postgresql org.postgresql.Driver jdbc:postgresql://localhost:5432/postgres https://mvnrepository.com/artifact/org.postgresql/postgresql dm dm.jdbc.driver.DmDriver jdbc:dm://localhost:5236 https://mvnrepository.com/artifact/com.dameng/DmJdbcDriver18","s":"appendix","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#appendix","p":103666},{"i":103697,"t":"simple: Jdbc { url = \"jdbc:mysql://localhost/test?serverTimezone=GMT%2b8\" driver = \"com.mysql.cj.jdbc.Driver\" connection_check_timeout_sec = 100 user = \"root\" password = \"123456\" query = \"select * from type_bin\" } parallel: Jdbc { url = \"jdbc:mysql://localhost/test?serverTimezone=GMT%2b8\" driver = \"com.mysql.cj.jdbc.Driver\" connection_check_timeout_sec = 100 user = \"root\" password = \"123456\" query = \"select * from type_bin\" partition_column= \"id\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Jdbc","h":"#example","p":103666},{"i":103699,"t":"Oss file source connector","s":"OssFile","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"","p":103698},{"i":103701,"t":"Read data from aliyun oss file system. Tips: We made some trade-offs in order to support more file types, so we used the HDFS protocol for internal access to OSS and this connector need some hadoop dependencies. It's only support hadoop version 2.9.X+.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#description","p":103698},{"i":103703,"t":"batch stream exactly-once Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot. schema projection parallelism support user-defined split file format text csv parquet orc json","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#key-features","p":103698},{"i":103705,"t":"name type required default value path string yes - type string yes - bucket string yes - access_key string yes - access_secret string yes - endpoint string yes - schema config no -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#options","p":103698},{"i":103707,"t":"The source file path.","s":"path [string]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#path-string","p":103698},{"i":103709,"t":"File type, supported as the following file types: text csv parquet orc json If you assign file type to json, you should also assign schema option to tell connector how to parse data to the row you want. For example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} You can also save multiple pieces of data in one file and split them by newline: {\"code\": 200, \"data\": \"get success\", \"success\": true} {\"code\": 300, \"data\": \"get failed\", \"success\": false} you should assign schema as the following: schema { fields { code = int data = string success = boolean } } connector will generate data as the following: code data success 200 get success true If you assign file type to parquet orc, schema option not required, connector can find the schema of upstream data automatically. If you assign file type to text csv, schema option not supported temporarily, but the subsequent features will support. Now connector will treat the upstream data as the following: lines The content of every line in file","s":"type [string]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#type-string","p":103698},{"i":103711,"t":"The bucket address of oss file system, for example: oss://tyrantlucifer-image-bed","s":"bucket [string]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#bucket-string","p":103698},{"i":103713,"t":"The access key of oss file system.","s":"access_key [string]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#access_key-string","p":103698},{"i":103715,"t":"The access secret of oss file system.","s":"access_secret [string]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#access_secret-string","p":103698},{"i":103717,"t":"The endpoint of oss file system.","s":"endpoint [string]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#endpoint-string","p":103698},{"i":103719,"t":"The schema of upstream data.","s":"schema [config]","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#schema-config","p":103698},{"i":103721,"t":"OssFile { path = \"/seatunnel/orc\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxxxxxxxx\" access_secret = \"xxxxxxxxxxxxxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" type = \"orc\" } OssFile { path = \"/seatunnel/json\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxxxxxxxx\" access_secret = \"xxxxxxxxxxxxxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" type = \"json\" schema { fields { id = int name = string } } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/OssFile","h":"#example","p":103698},{"i":103723,"t":"MongoDb source connector","s":"MongoDb","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"","p":103722},{"i":103725,"t":"Read data from MongoDB.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#description","p":103722},{"i":103727,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#key-features","p":103722},{"i":103729,"t":"name type required default value uri string yes - database string yes - collection string yes - schema object yes - common-options string yes -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#options","p":103722},{"i":103731,"t":"MongoDB uri","s":"uri [string]","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#uri-string","p":103722},{"i":103733,"t":"MongoDB database","s":"database [string]","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#database-string","p":103722},{"i":103735,"t":"MongoDB collection","s":"collection [string]","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#collection-string","p":103722},{"i":103737,"t":"Because MongoDB does not have the concept of schema, when engine reads MongoDB , it will sample MongoDB data and infer the schema . In fact, this process will be slow and may be inaccurate. This parameter can be manually specified. Avoid these problems. such as: schema { fields { id = int key_aa = string key_bb = string } }","s":"schema [object]","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#schema-object","p":103722},{"i":103739,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#common-options-string","p":103722},{"i":103741,"t":"mongodb { uri = \"mongodb://username:password@127.0.0.1:27017/mypost?retryWrites=true&writeConcern=majority\" database = \"mydatabase\" collection = \"mycollection\" schema { fields { id = int key_aa = string key_bb = string } } result_table_name = \"mongodb_result_table\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/MongoDB","h":"#example","p":103722},{"i":103743,"t":"Phoenix source connector","s":"Phoenix","u":"/docs/2.2.0-beta/connector-v2/source/Phoenix","h":"","p":103742},{"i":103745,"t":"Read Phoenix data through Jdbc connector. Support Batch mode and Streaming mode. The tested Phoenix version is 4.xx and 5.xx On the underlying implementation, through the jdbc driver of Phoenix, execute the upsert statement to write data to HBase. Two ways of connecting Phoenix with Java JDBC. One is to connect to zookeeper through JDBC, and the other is to connect to queryserver through JDBC thin client. Tips: By default, the (thin) driver jar is used. If you want to use the (thick) driver or other versions of Phoenix (thin) driver, you need to recompile the jdbc connector module","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Phoenix","h":"#description","p":103742},{"i":103747,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Phoenix","h":"#key-features","p":103742},{"i":103750,"t":"if you use phoenix (thick) driver the value is org.apache.phoenix.jdbc.PhoenixDriver or you use (thin) driver the value is org.apache.phoenix.queryserver.client.Driver","s":"driver [string]","u":"/docs/2.2.0-beta/connector-v2/source/Phoenix","h":"#driver-string","p":103742},{"i":103752,"t":"if you use phoenix (thick) driver the value is jdbc:phoenix:localhost:2182/hbase or you use (thin) driver the value is jdbc:phoenix:thin:url=http://localhost:8765;serialization=PROTOBUF","s":"url [string]","u":"/docs/2.2.0-beta/connector-v2/source/Phoenix","h":"#url-string","p":103742},{"i":103754,"t":"use thick client drive Jdbc { driver = org.apache.phoenix.jdbc.PhoenixDriver url = \"jdbc:phoenix:localhost:2182/hbase\" query = \"select age, name from test.source\" } use thin client drive Jdbc { driver = org.apache.phoenix.queryserver.client.Driver url = \"jdbc:phoenix:thin:url=http://spark_e2e_phoenix_sink:8765;serialization=PROTOBUF\" query = \"select age, name from test.source\" }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Phoenix","h":"#example","p":103742},{"i":103756,"t":"Apache Pulsar source connector","s":"Apache Pulsar","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"","p":103755},{"i":103758,"t":"Source connector for Apache Pulsar.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#description","p":103755},{"i":103760,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#key-features","p":103755},{"i":103762,"t":"name type required default value topic String No - topic-pattern String No - topic-discovery.interval Long No -1 subscription.name String Yes - client.service-url String Yes - admin.service-url String Yes - auth.plugin-class String No - auth.params String No - poll.timeout Integer No 100 poll.interval Long No 50 poll.batch.size Integer No 500 cursor.startup.mode Enum No LATEST cursor.startup.timestamp Long No - cursor.reset.mode Enum No LATEST cursor.stop.mode Enum No NEVER cursor.stop.timestamp Long No -","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#options","p":103755},{"i":103764,"t":"Topic name(s) to read data from when the table is used as source. It also supports topic list for source by separating topic by semicolon like 'topic-1;topic-2'. Note, only one of \"topic-pattern\" and \"topic\" can be specified for sources.","s":"topic [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#topic-string","p":103755},{"i":103766,"t":"The regular expression for a pattern of topic names to read from. All topics with names that match the specified regular expression will be subscribed by the consumer when the job starts running. Note, only one of \"topic-pattern\" and \"topic\" can be specified for sources.","s":"topic-pattern [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#topic-pattern-string","p":103755},{"i":103768,"t":"The interval (in ms) for the Pulsar source to discover the new topic partitions. A non-positive value disables the topic partition discovery. Note, This option only works if the 'topic-pattern' option is used.","s":"topic-discovery.interval [Long]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#topic-discoveryinterval-long","p":103755},{"i":103770,"t":"Specify the subscription name for this consumer. This argument is required when constructing the consumer.","s":"subscription.name [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#subscriptionname-string","p":103755},{"i":103772,"t":"Service URL provider for Pulsar service. To connect to Pulsar using client libraries, you need to specify a Pulsar protocol URL. You can assign Pulsar protocol URLs to specific clusters and use the Pulsar scheme. For example, localhost: pulsar://localhost:6650,localhost:6651.","s":"client.service-url [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#clientservice-url-string","p":103755},{"i":103774,"t":"The Pulsar service HTTP URL for the admin endpoint. For example, http://my-broker.example.com:8080, or https://my-broker.example.com:8443 for TLS.","s":"admin.service-url [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#adminservice-url-string","p":103755},{"i":103776,"t":"Name of the authentication plugin.","s":"auth.plugin-class [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#authplugin-class-string","p":103755},{"i":103778,"t":"Parameters for the authentication plugin. For example, key1:val1,key2:val2","s":"auth.params [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#authparams-string","p":103755},{"i":103780,"t":"The maximum time (in ms) to wait when fetching records. A longer time increases throughput but also latency.","s":"poll.timeout [Integer]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#polltimeout-integer","p":103755},{"i":103782,"t":"The interval time(in ms) when fetcing records. A shorter time increases throughput, but also increases CPU load.","s":"poll.interval [Long]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#pollinterval-long","p":103755},{"i":103784,"t":"The maximum number of records to fetch to wait when polling. A longer time increases throughput but also latency.","s":"poll.batch.size [Integer]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#pollbatchsize-integer","p":103755},{"i":103786,"t":"Startup mode for Pulsar consumer, valid values are 'EARLIEST', 'LATEST', 'SUBSCRIPTION', 'TIMESTAMP'.","s":"cursor.startup.mode [Enum]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#cursorstartupmode-enum","p":103755},{"i":103788,"t":"Start from the specified epoch timestamp (in milliseconds). Note, This option is required when the \"cursor.startup.mode\" option used 'TIMESTAMP'.","s":"cursor.startup.timestamp [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#cursorstartuptimestamp-string","p":103755},{"i":103790,"t":"Cursor reset strategy for Pulsar consumer valid values are 'EARLIEST', 'LATEST'. Note, This option only works if the \"cursor.startup.mode\" option used 'SUBSCRIPTION'.","s":"cursor.reset.mode [Enum]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#cursorresetmode-enum","p":103755},{"i":103792,"t":"Stop mode for Pulsar consumer, valid values are 'NEVER', 'LATEST'and 'TIMESTAMP'. Note, When 'NEVER' is specified, it is a real-time job, and other mode are off-line jobs.","s":"cursor.stop.mode [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#cursorstopmode-string","p":103755},{"i":103794,"t":"Stop from the specified epoch timestamp (in milliseconds). Note, This option is required when the \"cursor.stop.mode\" option used 'TIMESTAMP'.","s":"cursor.startup.timestamp [String]","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#cursorstartuptimestamp-string-1","p":103755},{"i":103796,"t":"source { Pulsar { topic = \"example\" subscription.name = \"seatunnel\" client.service-url = \"localhost:pulsar://localhost:6650\" admin.service-url = \"http://my-broker.example.com:8080\" result_table_name = \"test\" } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/pulsar","h":"#example","p":103755},{"i":103798,"t":"Redis source connector","s":"Redis","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"","p":103797},{"i":103800,"t":"Used to read data from Redis.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#description","p":103797},{"i":103802,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#key-features","p":103797},{"i":103804,"t":"name type required default value host string yes - port int yes - keys string yes - data_type string yes - auth string No - schema config No - format string No json","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#options","p":103797},{"i":103806,"t":"redis host","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#host-string","p":103797},{"i":103808,"t":"redis port","s":"port [int]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#port-int","p":103797},{"i":103810,"t":"keys pattern Tips:Redis source connector support fuzzy key matching, user needs to ensure that the matched keys are the same type","s":"keys [string]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#keys-string","p":103797},{"i":103812,"t":"redis data types, support key hash list set zset key The value of each key will be sent downstream as a single row of data. For example, the value of key is SeaTunnel test message, the data received downstream is SeaTunnel test message and only one message will be received. hash The hash key-value pairs will be formatted as json to be sent downstream as a single row of data. For example, the value of hash is name:tyrantlucifer age:26, the data received downstream is {\"name\":\"tyrantlucifer\", \"age\":\"26\"} and only one message will be received. list Each element in the list will be sent downstream as a single row of data. For example, the value of list is [tyrantlucier, CalvinKirs], the data received downstream are tyrantlucifer and CalvinKirs and only two message will be received. set Each element in the set will be sent downstream as a single row of data For example, the value of set is [tyrantlucier, CalvinKirs], the data received downstream are tyrantlucifer and CalvinKirs and only two message will be received. zset Each element in the sorted set will be sent downstream as a single row of data For example, the value of sorted set is [tyrantlucier, CalvinKirs], the data received downstream are tyrantlucifer and CalvinKirs and only two message will be received.","s":"data_type [string]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#data_type-string","p":103797},{"i":103814,"t":"redis authentication password, you need it when you connect to an encrypted cluster","s":"auth [String]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#auth-string","p":103797},{"i":103816,"t":"the format of upstream data, now only support json text, default json. when you assign format is json, you should also assign schema option, for example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} you should assign schema as the following: schema { fields { code = int data = string success = boolean } } connector will generate data as the following: code data success 200 get success true when you assign format is text, connector will do nothing for upstream data, for example: upstream data is the following: {\"code\": 200, \"data\": \"get success\", \"success\": true} connector will generate data as the following: content {\"code\": 200, \"data\": \"get success\", \"success\": true}","s":"format [String]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#format-string","p":103797},{"i":103818,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#schema-config","p":103797},{"i":103820,"t":"simple: Redis { host = localhost port = 6379 keys = \"key_test*\" data_type = key format = text } Redis { host = localhost port = 6379 keys = \"key_test*\" data_type = key format = json schema { fields { name = string age = int } } }","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Redis","h":"#example","p":103797},{"i":103822,"t":"Socket source connector","s":"Socket","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"","p":103821},{"i":103824,"t":"Used to read data from Socket.","s":"Description","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"#description","p":103821},{"i":103826,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"#key-features","p":103821},{"i":103828,"t":"name type required default value host String No localhost port Integer No 9999","s":"Options","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"#options","p":103821},{"i":103830,"t":"socket server host","s":"host [string]","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"#host-string","p":103821},{"i":103832,"t":"socket server port","s":"port [integer]","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"#port-integer","p":103821},{"i":103834,"t":"simple: Socket { host = \"localhost\" port = 9999 } test: Configuring the SeaTunnel config file env { execution.parallelism = 1 job.mode = \"STREAMING\" } source { Socket { host = \"localhost\" port = 9999 } } transform { } sink { Console {} } Start a port listening nc -l 9999 Start a SeaTunnel task Socket Source send test data ~ nc -l 9999 test hello flink spark Console Sink print data [test] [hello] [flink] [spark]","s":"Example","u":"/docs/2.2.0-beta/connector-v2/source/Socket","h":"#example","p":103821},{"i":103836,"t":"ElasticSearch connector based flink sql","s":"Flink SQL ElasticSearch Connector","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"","p":103835},{"i":103838,"t":"With elasticsearch connector, you can use the Flink SQL to write data into ElasticSearch.","s":"Description","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"#description","p":103835},{"i":103840,"t":"Let us have a brief example to show how to use the connector.","s":"Usage","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"#usage","p":103835},{"i":103842,"t":"Please refer to the Elastic Doc to prepare elastic environment.","s":"1. Elastic prepare","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"#1-elastic-prepare","p":103835},{"i":103844,"t":"ElasticSearch provide different connectors for different version: version 6.x: flink-sql-connector-elasticsearch6 version 7.x: flink-sql-connector-elasticsearch7 Here is a simple example of seatunnel configuration. SET table.dml-sync = true; CREATE TABLE events ( id INT, name STRING ) WITH ( 'connector' = 'datagen' ); CREATE TABLE es_sink ( id INT, name STRING ) WITH ( 'connector' = 'elasticsearch-7', -- or 'elasticsearch-6' 'hosts' = 'http://localhost:9200', 'index' = 'users' ); INSERT INTO es_sink SELECT * FROM events;","s":"2. prepare seatunnel configuration","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"#2-prepare-seatunnel-configuration","p":103835},{"i":103846,"t":"Execute the following command in seatunnel home path to start the Flink SQL job. $ bin/start-seatunnel-sql.sh -c config/elasticsearch.sql.conf","s":"3. start Flink SQL job","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"#3-start-flink-sql-job","p":103835},{"i":103848,"t":"Verify result from elasticsearch.","s":"4. verify result","u":"/docs/2.2.0-beta/connector/flink-sql/ElasticSearch","h":"#4-verify-result","p":103835},{"i":103850,"t":"JDBC connector based flink sql","s":"Flink SQL JDBC Connector","u":"/docs/2.2.0-beta/connector/flink-sql/Jdbc","h":"","p":103849},{"i":103852,"t":"We can use the Flink SQL JDBC Connector to connect to a JDBC database. Refer to the Flink SQL JDBC Connector for more information.","s":"Description","u":"/docs/2.2.0-beta/connector/flink-sql/Jdbc","h":"#description","p":103849},{"i":103855,"t":"A driver dependency is also required to connect to a specified database. Here are drivers currently supported: Driver Group Id Artifact Id JAR MySQL mysql mysql-connector-java Download PostgreSQL org.postgresql postgresql Download Derby org.apache.derby derby Download After downloading the driver jars, you need to place the jars into $FLINK_HOME/lib/.","s":"1. download driver","u":"/docs/2.2.0-beta/connector/flink-sql/Jdbc","h":"#1-download-driver","p":103849},{"i":103857,"t":"Start mysql server locally, and create a database named \"test\" and a table named \"test_table\" in the database. The table \"test_table\" could be created by the following SQL: CREATE TABLE IF NOT EXISTS `test_table`( `id` INT UNSIGNED AUTO_INCREMENT, `name` VARCHAR(100) NOT NULL, PRIMARY KEY ( `id` ) )ENGINE=InnoDB DEFAULT CHARSET=utf8; Insert some data into the table \"test_table\".","s":"2. prepare data","u":"/docs/2.2.0-beta/connector/flink-sql/Jdbc","h":"#2-prepare-data","p":103849},{"i":103859,"t":"Prepare a seatunnel config file with the following content: SET table.dml-sync = true; CREATE TABLE test ( id BIGINT, name STRING ) WITH ( 'connector'='jdbc', 'url' = 'jdbc:mysql://localhost:3306/test', 'table-name' = 'test_table', 'username' = '<replace with your username>', 'password' = '<replace with your password>' ); CREATE TABLE print_table ( id BIGINT, name STRING ) WITH ( 'connector' = 'print', 'sink.parallelism' = '1' ); INSERT INTO print_table SELECT * FROM test;","s":"3. seatunnel config","u":"/docs/2.2.0-beta/connector/flink-sql/Jdbc","h":"#3-seatunnel-config","p":103849},{"i":103861,"t":"./bin/start-seatunnel-sql.sh --config <path/to/your/config>","s":"4. run job","u":"/docs/2.2.0-beta/connector/flink-sql/Jdbc","h":"#4-run-job","p":103849},{"i":103863,"t":"Kafka connector based by flink sql","s":"Flink SQL Kafka Connector","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"","p":103862},{"i":103865,"t":"With kafka connector, we can read data from kafka and write data to kafka using Flink SQL. Refer to the Kafka connector for more details.","s":"Description","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#description","p":103862},{"i":103867,"t":"Let us have a brief example to show how to use the connector from end to end.","s":"Usage","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#usage","p":103862},{"i":103869,"t":"Please refer to the Kafka QuickStart to prepare kafka environment and produce data like following: $ bin/kafka-console-producer.sh --topic <topic-name> --bootstrap-server localhost:9092 After executing the command, we will come to the interactive mode. Print the following message to send data to kafka. {\"id\":1,\"name\":\"abc\"} >{\"id\":2,\"name\":\"def\"} >{\"id\":3,\"name\":\"dfs\"} >{\"id\":4,\"name\":\"eret\"} >{\"id\":5,\"name\":\"yui\"}","s":"1. kafka prepare","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#1-kafka-prepare","p":103862},{"i":103871,"t":"Here is a simple example of seatunnel configuration. SET table.dml-sync = true; CREATE TABLE events ( id INT, name STRING ) WITH ( 'connector' = 'kafka', 'topic'='<topic-name>', 'properties.bootstrap.servers' = 'localhost:9092', 'properties.group.id' = 'testGroup', 'scan.startup.mode' = 'earliest-offset', 'format' = 'json' ); CREATE TABLE print_table ( id INT, name STRING ) WITH ( 'connector' = 'print', 'sink.parallelism' = '1' ); INSERT INTO print_table SELECT * FROM events;","s":"2. prepare seatunnel configuration","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#2-prepare-seatunnel-configuration","p":103862},{"i":103873,"t":"$ ${FLINK_HOME}/bin/start-cluster.sh","s":"3. start flink local cluster","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#3-start-flink-local-cluster","p":103862},{"i":103875,"t":"Execute the following command in seatunnel home path to start the Flink SQL job. $ bin/start-seatunnel-sql.sh -c config/kafka.sql.conf","s":"4. start Flink SQL job","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#4-start-flink-sql-job","p":103862},{"i":103877,"t":"After the job submitted, we can see the data printing by connector 'print' in taskmanager's log . +I[1, abc] +I[2, def] +I[3, dfs] +I[4, eret] +I[5, yui]","s":"5. verify result","u":"/docs/2.2.0-beta/connector/flink-sql/Kafka","h":"#5-verify-result","p":103862},{"i":103879,"t":"Tutorial of flink sql module","s":"How to use flink sql module","u":"/docs/2.2.0-beta/connector/flink-sql/usage","h":"","p":103878},{"i":103882,"t":"bin/start-seatunnel-sql.sh","s":"1. Command Entrypoint","u":"/docs/2.2.0-beta/connector/flink-sql/usage","h":"#1-command-entrypoint","p":103878},{"i":103884,"t":"Change the file flink.sql.conf.template in the config/ directory to flink.sql.conf mv flink.sql.conf.template flink.sql.conf Prepare a seatunnel config file with the following content: SET table.dml-sync = true; CREATE TABLE events ( f_type INT, f_uid INT, ts AS localtimestamp, WATERMARK FOR ts AS ts ) WITH ( 'connector' = 'datagen', 'rows-per-second'='5', 'fields.f_type.min'='1', 'fields.f_type.max'='5', 'fields.f_uid.min'='1', 'fields.f_uid.max'='1000' ); CREATE TABLE print_table ( type INT, uid INT, lstmt TIMESTAMP ) WITH ( 'connector' = 'print', 'sink.parallelism' = '1' ); INSERT INTO print_table SELECT * FROM events where f_type = 1;","s":"2. seatunnel config","u":"/docs/2.2.0-beta/connector/flink-sql/usage","h":"#2-seatunnel-config","p":103878},{"i":103886,"t":"Standalone Cluster​ bin/start-seatunnel-sql.sh --config config/flink.sql.conf # -p 2 specifies that the parallelism of flink job is 2. You can also specify more parameters, use flink run -h to view bin/start-seatunnel-flink.sh \\ -p 2 \\ --config config/flink.sql.conf Yarn Cluster​ bin/start-seatunnel-sql.sh -m yarn-cluster --config config/flink.sql.conf bin/start-seatunnel-sql.sh -t yarn-per-job --config config/flink.sql.conf # -p 2 specifies that the parallelism of flink job is 2. You can also specify more parameters, use flink run -h to view bin/start-seatunnel-flink.sh \\ -p 2 \\ -m yarn-cluster \\ --config config/flink.sql.conf Other Options​ -p 2 specifies that the job parallelism is 2 bin/start-seatunnel-sql.sh -p 2 --config config/flink.sql.conf","s":"3. run job","u":"/docs/2.2.0-beta/connector/flink-sql/usage","h":"#3-run-job","p":103878},{"i":103888,"t":"How to implement flink sql interval join with seatunnel flink-sql module intervaljoin.sql.conf CREATE TABLE basic ( `id` BIGINT, `name` STRING, `ts` STRING ) WITH ( 'connector' = 'kafka', 'topic' = 'basic', 'properties.bootstrap.servers' = 'XX.XX.XX.XX:9092', 'properties.group.id' = 'testGroup', 'scan.startup.mode' = 'latest-offset', 'format' = 'json' ); CREATE TABLE infos ( `id` BIGINT, `age` BIGINT, `ts` STRING ) WITH ( 'connector' = 'kafka', 'topic' = 'info', 'properties.bootstrap.servers' = 'XX.XX.XX.XX:9092', 'properties.group.id' = 'testGroup', 'scan.startup.mode' = 'latest-offset', 'format' = 'json' ); CREATE TABLE stream2_join_result ( id BIGINT , name STRING, age BIGINT, ts1 STRING , ts2 STRING, PRIMARY KEY(id) NOT ENFORCED ) WITH ( 'connector' = 'jdbc', 'url' = 'jdbc:mysql://XX.XX.XX.XX:3306/testDB', 'username' = 'root', 'password' = 'taia@2021', 'table-name' = 'stream2_join_result' ); insert into stream2_join_result select basic.id, basic.name, infos.age,basic.ts,infos.ts from basic join infos on (basic.id = infos.id) where TO_TIMESTAMP(basic.ts,'yyyy-MM-dd HH:mm:ss') BETWEEN TO_TIMESTAMP(infos.ts,'yyyy-MM-dd HH:mm:ss') - INTERVAL '10' SECOND AND TO_TIMESTAMP(infos.ts,'yyyy-MM-dd HH:mm:ss') + INTERVAL '10' SECOND; bin/start-seatunnel-sql.sh -m yarn-cluster --config config/intervaljoin.sql.conf How to implement flink sql dim join (using mysql) with seatunnel flink-sql module dimjoin.sql.conf CREATE TABLE code_set_street ( area_code STRING, area_name STRING, town_code STRING , town_name STRING , PRIMARY KEY(town_code) NOT ENFORCED ) WITH ( 'connector' = 'jdbc', 'url' = 'jdbc:mysql://XX.XX.XX.XX:3306/testDB', 'username' = 'root', 'password' = '2021', 'table-name' = 'code_set_street', 'lookup.cache.max-rows' = '5000' , 'lookup.cache.ttl' = '5min' ); CREATE TABLE people ( `id` STRING, `name` STRING, `ts` TimeStamp(3) , proctime AS PROCTIME() ) WITH ( 'connector' = 'kafka', 'topic' = 'people', 'properties.bootstrap.servers' = 'XX.XX.XX.XX:9092', 'properties.group.id' = 'testGroup', 'scan.startup.mode' = 'latest-offset', 'format' = 'json' ); CREATE TABLE mysql_dim_join_result ( id STRING , name STRING, area_name STRING, town_code STRING , town_name STRING, ts TimeStamp , PRIMARY KEY(id,town_code) NOT ENFORCED ) WITH ( 'connector' = 'jdbc', 'url' = 'jdbc:mysql://XX.XX.XX.XX:3306/testDB', 'username' = 'root', 'password' = '2021', 'table-name' = 'mysql_dim_join_result' ); insert into mysql_dim_join_result select people.id , people.name ,code_set_street.area_name ,code_set_street.town_code, code_set_street.town_name , people.ts from people inner join code_set_street FOR SYSTEM_TIME AS OF people.proctime on (people.id = code_set_street.town_code); bin/start-seatunnel-sql.sh -m yarn-cluster --config config/dimjoin.sql.conf How to implement flink SQL cdc dim join (using mysql-cdc) with seatunnel flink-sql module First , Need create mysql table in mysql database​ CREATE TABLE `dim_cdc_join_result` ( `id` varchar(255) NOT NULL, `name` varchar(255) DEFAULT NULL, `area_name` varchar(255) NOT NULL, `town_code` varchar(255) NOT NULL, `town_name` varchar(255) DEFAULT NULL, `ts` varchar(255) DEFAULT NULL, PRIMARY KEY (`id`,`town_code`,`ts`) USING BTREE ) ENGINE=InnoDB DEFAULT CHARSET=utf8 ROW_FORMAT=COMPACT; cdcjoin.sql.conf CREATE TABLE code_set_street_cdc ( area_code STRING, area_name STRING, town_code STRING , town_name STRING , PRIMARY KEY(town_code) NOT ENFORCED ) WITH ( 'connector' = 'mysql-cdc', 'hostname' = 'XX.XX.XX.XX', 'port' = '3306', 'username' = 'root', 'password' = '2021', 'database-name' = 'flink', 'table-name' = 'code_set_street' ); CREATE TABLE people ( `id` STRING, `name` STRING, `ts` STRING ) WITH ( 'connector' = 'kafka', 'topic' = 'people', 'properties.bootstrap.servers' = 'XX.XX.XX.XX:9092', 'properties.group.id' = 'testGroup', 'scan.startup.mode' = 'latest-offset', 'format' = 'json' ); # create mysql sink table in flink CREATE TABLE dim_cdc_join_result ( id STRING , name STRING, area_name STRING, town_code STRING , town_name STRING, ts STRING , PRIMARY KEY(id,town_code) NOT ENFORCED ) WITH ( 'connector' = 'jdbc', 'url' = 'jdbc:mysql://XX.XX.XX.XX:3306/flink', 'username' = 'root', 'password' = '2021', 'table-name' = 'dim_cdc_join_result' ); insert into dim_cdc_join_result select a.id , a.name ,b.area_name ,b.town_code, b.town_name , a.ts from people a inner join code_set_street_cdc b on (a.id = b.town_code); bin/start-seatunnel-sql.sh -m yarn-cluster --config config/cdcjoin.sql.conf","s":"Example","u":"/docs/2.2.0-beta/connector/flink-sql/usage","h":"#example","p":103878},{"i":103890,"t":"Assert sink connector","s":"Assert","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"","p":103889},{"i":103892,"t":"A flink sink plugin which can assert illegal data by user defined rules tip Engine Supported and plugin name Spark：Assert Flink: Assert","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#description","p":103889},{"i":103894,"t":"name type required default value rules ConfigList yes - rules.field_name string yes - rules.field_type string no - rules.field_value ConfigList no - rules.field_value.rule_type string no - rules.field_value.rule_value double no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#options","p":103889},{"i":103896,"t":"Rule definition of user's available data. Each rule represents one field validation.","s":"rules [ConfigList]","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#rules-configlist","p":103889},{"i":103898,"t":"field name（string）","s":"field_name [string]","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#field_name-string","p":103889},{"i":103900,"t":"field type (string), e.g. string,boolean,byte,short,int,long,float,double,char,void,BigInteger,BigDecimal,Instant","s":"field_type [string]","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#field_type-string","p":103889},{"i":103902,"t":"A list value rule define the data value validation","s":"field_value [ConfigList]","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#field_value-configlist","p":103889},{"i":103904,"t":"The following rules are supported for now NOT_NULL, // value can't be null MIN, // define the minimum value of data MAX, // define the maximum value of data MIN_LENGTH, // define the minimum string length of a string data MAX_LENGTH // define the maximum string length of a string data","s":"rule_type [string]","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#rule_type-string","p":103889},{"i":103906,"t":"the value related to rule type","s":"rule_value [double]","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#rule_value-double","p":103889},{"i":103908,"t":"the whole config obey with hocon style Assert { rules = [{ field_name = name field_type = string field_value = [ { rule_type = NOT_NULL }, { rule_type = MIN_LENGTH rule_value = 3 }, { rule_type = MAX_LENGTH rule_value = 5 } ] },{ field_name = age field_type = int field_value = [ { rule_type = NOT_NULL }, { rule_type = MIN rule_value = 10 }, { rule_type = MAX rule_value = 20 } ] } ] }","s":"Example","u":"/docs/2.2.0-beta/connector/sink/Assert","h":"#example","p":103889},{"i":103910,"t":"Clickhouse sink connector","s":"Clickhouse","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"","p":103909},{"i":103912,"t":"Use Clickhouse-jdbc to correspond the data source according to the field name and write it into ClickHouse. The corresponding data table needs to be created in advance before use tip Engine Supported and plugin name Spark: Clickhouse Flink: Clickhouse","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#description","p":103909},{"i":103914,"t":"name type required default value bulk_size number no 20000 clickhouse.* string no database string yes - fields array no - host string yes - password string no - retry number no 1 retry_codes array no [ ] table string yes - username string no - split_mode boolean no false sharding_key string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#options","p":103909},{"i":103916,"t":"The number of rows written through Clickhouse-jdbc each time, the default is 20000 .","s":"bulk_size [number]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#bulk_size-number","p":103909},{"i":103918,"t":"database name","s":"database [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#database-string","p":103909},{"i":103920,"t":"The data field that needs to be output to ClickHouse , if not configured, it will be automatically adapted according to the data schema .","s":"fields [array]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#fields-array","p":103909},{"i":103922,"t":"ClickHouse cluster address, the format is host:port , allowing multiple hosts to be specified. Such as \"host1:8123,host2:8123\" .","s":"host [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#host-string","p":103909},{"i":103924,"t":"ClickHouse user password . This field is only required when the permission is enabled in ClickHouse .","s":"password [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#password-string","p":103909},{"i":103926,"t":"The number of retries, the default is 1","s":"retry [number]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#retry-number","p":103909},{"i":103928,"t":"When an exception occurs, the ClickHouse exception error code of the operation will be retried. For a detailed list of error codes, please refer to ClickHouseErrorCode If multiple retries fail, this batch of data will be discarded, use with caution! !","s":"retry_codes [array]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#retry_codes-array","p":103909},{"i":103930,"t":"table name","s":"table [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#table-string","p":103909},{"i":103932,"t":"ClickHouse user username, this field is only required when permission is enabled in ClickHouse","s":"username [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#username-string","p":103909},{"i":103934,"t":"In addition to the above mandatory parameters that must be specified by clickhouse-jdbc , users can also specify multiple optional parameters, which cover all the parameters provided by clickhouse-jdbc . The way to specify the parameter is to add the prefix clickhouse. to the original parameter name. For example, the way to specify socket_timeout is: clickhouse.socket_timeout = 50000 . If these non-essential parameters are not specified, they will use the default values given by clickhouse-jdbc.","s":"clickhouse [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#clickhouse-string","p":103909},{"i":103936,"t":"This mode only support clickhouse table which engine is 'Distributed'.And internal_replication option should be true. They will split distributed table data in seatunnel and perform write directly on each shard. The shard weight define is clickhouse will be counted.","s":"split_mode [boolean]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#split_mode-boolean","p":103909},{"i":103938,"t":"When use split_mode, which node to send data to is a problem, the default is random selection, but the 'sharding_key' parameter can be used to specify the field for the sharding algorithm. This option only worked when 'split_mode' is true.","s":"sharding_key [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#sharding_key-string","p":103909},{"i":103940,"t":"Sink plugin common parameters, please refer to common options for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#common-options-string","p":103909},{"i":103942,"t":"ClickHouse field type Convert plugin conversion goal type SQL conversion expression Description Date string string() yyyy-MM-dd Format string DateTime string string() yyyy-MM-dd HH:mm:ss Format string String string string() Int8 integer int() Uint8 integer int() Int16 integer int() Uint16 integer int() Int32 integer int() Uint32 long bigint() Int64 long bigint() Uint64 long bigint() Float32 float float() Float64 double double() Decimal(P, S) - CAST(source AS DECIMAL(P, S)) Decimal32(S), Decimal64(S), Decimal128(S) Can be used Array(T) - - Nullable(T) Depends on T Depends on T LowCardinality(T) Depends on T Depends on T","s":"ClickHouse type comparison table","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#clickhouse-type-comparison-table","p":103909},{"i":103944,"t":"clickhouse { host = \"localhost:8123\" clickhouse.socket_timeout = 50000 database = \"nginx\" table = \"access_msg\" fields = [\"date\", \"datetime\", \"hostname\", \"http_code\", \"data_size\", \"ua\", \"request_time\"] username = \"username\" password = \"password\" bulk_size = 20000 } ClickHouse { host = \"localhost:8123\" database = \"nginx\" table = \"access_msg\" fields = [\"date\", \"datetime\", \"hostname\", \"http_code\", \"data_size\", \"ua\", \"request_time\"] username = \"username\" password = \"password\" bulk_size = 20000 retry_codes = [209, 210] retry = 3 } In case of network timeout or network abnormality, retry writing 3 times","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Clickhouse","h":"#examples","p":103909},{"i":103946,"t":"Clickhouse file sink connector","s":"ClickhouseFile","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"","p":103945},{"i":103948,"t":"Generate the clickhouse data file with the clickhouse-local program, and then send it to the clickhouse server, also call bulk load. tip Engine Supported and plugin name Spark: ClickhouseFile Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#description","p":103945},{"i":103950,"t":"name type required default value database string yes - fields array no - host string yes - password string no - table string yes - username string no - sharding_key string no - clickhouse_local_path string yes - tmp_batch_cache_line int no 100000 copy_method string no scp node_free_password boolean no false node_pass list no - node_pass.node_address string no - node_pass.password string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#options","p":103945},{"i":103952,"t":"database name","s":"database [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#database-string","p":103945},{"i":103954,"t":"The data field that needs to be output to ClickHouse , if not configured, it will be automatically adapted according to the data schema .","s":"fields [array]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#fields-array","p":103945},{"i":103956,"t":"ClickHouse cluster address, the format is host:port , allowing multiple hosts to be specified. Such as \"host1:8123,host2:8123\" .","s":"host [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#host-string","p":103945},{"i":103958,"t":"ClickHouse user password . This field is only required when the permission is enabled in ClickHouse .","s":"password [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#password-string","p":103945},{"i":103960,"t":"table name","s":"table [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#table-string","p":103945},{"i":103962,"t":"ClickHouse user username, this field is only required when permission is enabled in ClickHouse","s":"username [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#username-string","p":103945},{"i":103964,"t":"When use split_mode, which node to send data to is a problem, the default is random selection, but the 'sharding_key' parameter can be used to specify the field for the sharding algorithm. This option only worked when 'split_mode' is true.","s":"sharding_key [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#sharding_key-string","p":103945},{"i":103966,"t":"The address of the clickhouse-local program on the spark node. Since each task needs to be called, clickhouse-local should be located in the same path of each spark node.","s":"clickhouse_local_path [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#clickhouse_local_path-string","p":103945},{"i":103968,"t":"SeaTunnel will use memory map technology to write temporary data to the file to cache the data that the user needs to write to clickhouse. This parameter is used to configure the number of data pieces written to the file each time. Most of the time you don't need to modify it.","s":"tmp_batch_cache_line [int]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#tmp_batch_cache_line-int","p":103945},{"i":103970,"t":"Specifies the method used to transfer files, the default is scp, optional scp and rsync","s":"copy_method [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#copy_method-string","p":103945},{"i":103972,"t":"Because seatunnel need to use scp or rsync for file transfer, seatunnel need clickhouse server-side access. If each spark node and clickhouse server are configured with password-free login, you can configure this option to true, otherwise you need to configure the corresponding node password in the node_pass configuration","s":"node_free_password [boolean]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#node_free_password-boolean","p":103945},{"i":103974,"t":"Used to save the addresses and corresponding passwords of all clickhouse servers","s":"node_pass [list]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#node_pass-list","p":103945},{"i":103976,"t":"The address corresponding to the clickhouse server","s":"node_pass.node_address [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#node_passnode_address-string","p":103945},{"i":103978,"t":"The password corresponding to the clickhouse server, only support root user yet.","s":"node_pass.node_password [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#node_passnode_password-string","p":103945},{"i":103980,"t":"Sink plugin common parameters, please refer to common options for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#common-options-string","p":103945},{"i":103982,"t":"ClickHouse field type Convert plugin conversion goal type SQL conversion expression Description Date string string() yyyy-MM-dd Format string DateTime string string() yyyy-MM-dd HH:mm:ss Format string String string string() Int8 integer int() Uint8 integer int() Int16 integer int() Uint16 integer int() Int32 integer int() Uint32 long bigint() Int64 long bigint() Uint64 long bigint() Float32 float float() Float64 double double() Decimal(P, S) - CAST(source AS DECIMAL(P, S)) Decimal32(S), Decimal64(S), Decimal128(S) Can be used Array(T) - - Nullable(T) Depends on T Depends on T LowCardinality(T) Depends on T Depends on T","s":"ClickHouse type comparison table","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#clickhouse-type-comparison-table","p":103945},{"i":103984,"t":"ClickhouseFile { host = \"localhost:8123\" database = \"nginx\" table = \"access_msg\" fields = [\"date\", \"datetime\", \"hostname\", \"http_code\", \"data_size\", \"ua\", \"request_time\"] username = \"username\" password = \"password\" clickhouse_local_path = \"/usr/bin/clickhouse-local\" node_free_password = true } ClickhouseFile { host = \"localhost:8123\" database = \"nginx\" table = \"access_msg\" fields = [\"date\", \"datetime\", \"hostname\", \"http_code\", \"data_size\", \"ua\", \"request_time\"] username = \"username\" password = \"password\" sharding_key = \"age\" clickhouse_local_path = \"/usr/bin/Clickhouse local\" node_pass = [ { node_address = \"localhost1\" password = \"password\" } { node_address = \"localhost2\" password = \"password\" } ] }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/ClickhouseFile","h":"#examples","p":103945},{"i":103986,"t":"Doris sink connector","s":"Doris","u":"/docs/2.2.0-beta/connector/sink/Doris","h":"","p":103985},{"i":103988,"t":"Write Data to a Doris Table. tip Engine Supported and plugin name Spark: Doris Flink: DorisSink","s":"Description:","u":"/docs/2.2.0-beta/connector/sink/Doris","h":"#description","p":103985},{"i":103990,"t":"Spark Flink name type required default value fenodes string yes - database string yes - table string yes - user string yes - password string yes - batch_size int yes 100 doris.* string no - fenodes [string]​ Doris FE address:8030 database [string]​ Doris target database name table [string]​ Doris target table name user [string]​ Doris user name password [string]​ Doris user's password batch_size [string]​ Doris number of submissions per batch Default value：5000 doris. [string]​ Doris stream_load properties,you can use 'doris.' prefix + stream_load properties More Doris stream_load Configurations name type required default value fenodes string yes - database string yes - table string yes - user string yes - password string yes - batch_size int no 100 interval int no 1000 max_retries int no 1 doris.* - no - parallelism int no - fenodes [string]​ Doris FE http address database [string]​ Doris database name table [string]​ Doris table name user [string]​ Doris username password [string]​ Doris password batch_size [int]​ Maximum number of lines in a single write Doris,default value is 5000. interval [int]​ The flush interval millisecond, after which the asynchronous thread will write the data in the cache to Doris.Set to 0 to turn off periodic writing. Default value ：5000 max_retries [int]​ Number of retries after writing Doris failed doris.* [string]​ The doris stream load parameters.you can use 'doris.' prefix + stream_load properties. eg:doris.column_separator' = ',' More Doris stream_load Configurations parallelism [Int]​ The parallelism of an individual operator, for DorisSink","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Doris","h":"#options","p":103985},{"i":103992,"t":"The parallelism of an individual operator, for DorisSink","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/sink/Doris","h":"#parallelism-int","p":103985},{"i":103994,"t":"Spark Flink Doris { fenodes=\"0.0.0.0:8030\" database=\"test\" table=\"user\" user=\"doris\" password=\"doris\" batch_size=10000 doris.column_separator=\"\\t\" doris.columns=\"id,user_name,user_name_cn,create_time,last_login_time\" } DorisSink { fenodes = \"127.0.0.1:8030\" database = database table = table user = root password = password batch_size = 1 doris.column_separator=\"\\t\" doris.columns=\"id,user_name,user_name_cn,create_time,last_login_time\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Doris","h":"#examples","p":103985},{"i":103996,"t":"Common parameters of sink connectors name type required default value source_table_name string no -","s":"Common Options","u":"/docs/2.2.0-beta/connector/sink/common-options","h":"","p":103995},{"i":103998,"t":"When source_table_name is not specified, the current plug-in processes the data set dataset output by the previous plugin in the configuration file; When source_table_name is specified, the current plug-in is processing the data set corresponding to this parameter.","s":"source_table_name [string]","u":"/docs/2.2.0-beta/connector/sink/common-options","h":"#source_table_name-string","p":103995},{"i":104000,"t":"source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { source_table_name = \"fake\" sql = \"select name from fake\" result_table_name = \"fake_name\" } sql { source_table_name = \"fake\" sql = \"select age from fake\" result_table_name = \"fake_age\" } } sink { console { source_table_name = \"fake_name\" } } If source_table_name is not specified, the console outputs the data of the last transform, and if it is set to fake_name , it will output the data of fake_name","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/common-options","h":"#examples","p":103995},{"i":104002,"t":"Console sink connector","s":"Console","u":"/docs/2.2.0-beta/connector/sink/Console","h":"","p":104001},{"i":104004,"t":"Output data to standard terminal or Flink taskManager, which is often used for debugging and easy to observe the data. tip Engine Supported and plugin name Spark: Console Flink: Console","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#description","p":104001},{"i":104006,"t":"Spark Flink name type required default value limit number no 100 serializer string no plain common-options string no - limit [number]​ Limit the number of rows to be output, the legal range is [-1, 2147483647] , -1 means that the output is up to 2147483647 rows serializer [string]​ The format of serialization when outputting. Available serializers include: json , plain common options [string]​ Sink plugin common parameters, please refer to Sink Plugin for details Options​ name type required default value limit int no INT_MAX common-options string no - limit [int]​ limit console result lines common options [string]​ Sink plugin common parameters, please refer to Sink Plugin for details","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#options","p":104001},{"i":104008,"t":"Limit the number of rows to be output, the legal range is [-1, 2147483647] , -1 means that the output is up to 2147483647 rows","s":"limit [number]","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#limit-number","p":104001},{"i":104010,"t":"The format of serialization when outputting. Available serializers include: json , plain","s":"serializer [string]","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#serializer-string","p":104001},{"i":104012,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#common-options-string","p":104001},{"i":104014,"t":"name type required default value limit int no INT_MAX common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#options-1","p":104001},{"i":104016,"t":"limit console result lines","s":"limit [int]","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#limit-int","p":104001},{"i":104018,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#common-options-string-1","p":104001},{"i":104020,"t":"Spark Flink console { limit = 10, serializer = \"json\" } Output 10 rows of data in Json format ConsoleSink{} Note​ Flink's console output is in flink's WebUI","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#examples","p":104001},{"i":104022,"t":"Flink's console output is in flink's WebUI","s":"Note","u":"/docs/2.2.0-beta/connector/sink/Console","h":"#note","p":104001},{"i":104024,"t":"Druid sink connector","s":"Druid","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"","p":104023},{"i":104026,"t":"Write data to Apache Druid. tip Engine Supported and plugin name Spark Flink: Druid","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#description","p":104023},{"i":104028,"t":"name type required default value coordinator_url String yes - datasource String yes - timestamp_column String no timestamp timestamp_format String no auto timestamp_missing_value String no - parallelism Int no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#options","p":104023},{"i":104030,"t":"The URL of Coordinator service in Apache Druid.","s":"coordinator_url [String]","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#coordinator_url-string","p":104023},{"i":104032,"t":"The DataSource name in Apache Druid.","s":"datasource [String]","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#datasource-string","p":104023},{"i":104034,"t":"The timestamp column name in Apache Druid, the default value is timestamp.","s":"timestamp_column [String]","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#timestamp_column-string","p":104023},{"i":104036,"t":"The timestamp format in Apache Druid, the default value is auto, it could be: iso ISO8601 with 'T' separator, like \"2000-01-01T01:02:03.456\" posix seconds since epoch millis milliseconds since epoch micro microseconds since epoch nano nanoseconds since epoch auto automatically detects ISO (either 'T' or space separator) or millis format any Joda DateTimeFormat string","s":"timestamp_format [String]","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#timestamp_format-string","p":104023},{"i":104038,"t":"The timestamp missing value in Apache Druid, which is used for input records that have a null or missing timestamp. The value of timestamp_missing_value should be in ISO 8601 format, for example \"2022-02-02T02:02:02.222\".","s":"timestamp_missing_value [String]","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#timestamp_missing_value-string","p":104023},{"i":104040,"t":"The parallelism of an individual operator, for DruidSink","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#parallelism-int","p":104023},{"i":104043,"t":"DruidSink { coordinator_url = \"http://localhost:8081/\" datasource = \"wikipedia\" }","s":"Simple","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#simple","p":104023},{"i":104045,"t":"DruidSink { coordinator_url = \"http://localhost:8081/\" datasource = \"wikipedia\" timestamp_column = \"timestamp\" timestamp_format = \"auto\" }","s":"Specified timestamp column and format","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#specified-timestamp-column-and-format","p":104023},{"i":104047,"t":"DruidSink { coordinator_url = \"http://localhost:8081/\" datasource = \"wikipedia\" timestamp_column = \"timestamp\" timestamp_format = \"auto\" timestamp_missing_value = \"2022-02-02T02:02:02.222\" }","s":"Specified timestamp column, format and missing value","u":"/docs/2.2.0-beta/connector/sink/Druid","h":"#specified-timestamp-column-format-and-missing-value","p":104023},{"i":104049,"t":"Elasticsearch sink connector","s":"Elasticsearch","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"","p":104048},{"i":104051,"t":"Output data to Elasticsearch. tip Engine Supported and plugin name Spark: Elasticsearch(supported ElasticSearch version is >= 2.x and <7.0.0) Flink: Elasticsearch(supported ElasticSearch version = 7.x, if you want use Elasticsearch version is 6.x, please use the source code to repackage by execute mvn clean package -Delasticsearch=6)","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#description","p":104048},{"i":104053,"t":"Spark Flink name type required default value hosts array yes - index_type string no - index_time_format string no yyyy.MM.dd index string no seatunnel es.* string no common-options string no - name type required default value hosts array yes - index_type string no log index_time_format string no yyyy.MM.dd index string no seatunnel common-options string no - parallelism int no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#options","p":104048},{"i":104055,"t":"Elasticsearch cluster address, the format is host:port , allowing multiple hosts to be specified. Such as [\"host1:9200\", \"host2:9200\"] .","s":"hosts [array]","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#hosts-array","p":104048},{"i":104057,"t":"Elasticsearch index type, it is recommended not to specify in elasticsearch 7 and above index_time_format [string]​ When the format in the index parameter is xxxx-${now} , index_time_format can specify the time format of the index name, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"index_type [string]","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#index_type-string","p":104048},{"i":104059,"t":"Elasticsearch index name. If you need to generate an index based on time, you can specify a time variable, such as seatunnel-${now} . now represents the current data processing time. Spark Flink es.* [string]​ Users can also specify multiple optional parameters. For a detailed list of parameters, see Parameters Supported by Elasticsearch. For example, the way to specify es.batch.size.entries is: es.batch.size.entries = 100000 . If these non-essential parameters are not specified, they will use the default values given in the official documentation. parallelism [Int]​ The parallelism of an individual operator, data source, or data sink","s":"index [string]","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#index-string","p":104048},{"i":104061,"t":"Users can also specify multiple optional parameters. For a detailed list of parameters, see Parameters Supported by Elasticsearch. For example, the way to specify es.batch.size.entries is: es.batch.size.entries = 100000 . If these non-essential parameters are not specified, they will use the default values given in the official documentation.","s":"es.* [string]","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#es-string","p":104048},{"i":104063,"t":"The parallelism of an individual operator, data source, or data sink","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#parallelism-int","p":104048},{"i":104065,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#common-options-string","p":104048},{"i":104067,"t":"elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Elasticsearch","h":"#examples","p":104048},{"i":104069,"t":"Email sink connector","s":"Email","u":"/docs/2.2.0-beta/connector/sink/Email","h":"","p":104068},{"i":104071,"t":"Supports data output through email attachments. The attachments are in the xlsx format that supports excel to open, which can be used to notify the task statistics results through email attachments. tip Engine Supported and plugin name Spark: Email Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#description","p":104068},{"i":104073,"t":"name type required default value subject string yes - from string yes - to string yes - bodyText string no - bodyHtml string no - cc string no - bcc string no - host string yes - port string yes - password string yes - limit string no 100000 use_ssl boolean no false use_tls boolean no false","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#options","p":104068},{"i":104075,"t":"Email Subject","s":"subject [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#subject-string","p":104068},{"i":104077,"t":"Email sender","s":"from [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#from-string","p":104068},{"i":104079,"t":"Email recipients, multiple recipients separated by ,","s":"to [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#to-string","p":104068},{"i":104081,"t":"Email content, text format","s":"bodyText [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#bodytext-string","p":104068},{"i":104083,"t":"Email content, hypertext content","s":"bodyHtml [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#bodyhtml-string","p":104068},{"i":104085,"t":"Email CC, multiple CCs separated by ,","s":"cc [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#cc-string","p":104068},{"i":104087,"t":"Email Bcc, multiple Bccs separated by ,","s":"bcc [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#bcc-string","p":104068},{"i":104089,"t":"Email server address, for example: stmp.exmail.qq.com","s":"host [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#host-string","p":104068},{"i":104091,"t":"Email server port For example: 25","s":"port [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#port-string","p":104068},{"i":104093,"t":"The password of the email sender, the user name is the sender specified by from","s":"password [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#password-string","p":104068},{"i":104095,"t":"The number of rows to include, the default is 100000","s":"limit [string]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#limit-string","p":104068},{"i":104097,"t":"The security properties for encrypted link to smtp server, the default is false","s":"use_ssl [boolean]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#use_ssl-boolean","p":104068},{"i":104099,"t":"The security properties for encrypted link to smtp server, the default is false","s":"use_tls [boolean]","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#use_tls-boolean","p":104068},{"i":104101,"t":"Email { subject = \"Report statistics\", from = \"xxxx@qq.com\", to = \"xxxxx1@qq.com,xxxxx2@qq.com\", cc = \"xxxxx3@qq.com,xxxxx4@qq.com\", bcc = \"xxxxx5@qq.com,xxxxx6@qq.com\", host= \"stmp.exmail.qq.com\", port= \"25\", password = \"***********\", limit = \"1000\", use_ssl = true }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Email","h":"#examples","p":104068},{"i":104103,"t":"File sink connector","s":"File","u":"/docs/2.2.0-beta/connector/sink/File","h":"","p":104102},{"i":104105,"t":"Output data to local or hdfs file. tip Engine Supported and plugin name Spark: File Flink: File","s":"Description","u":"/docs/2.2.0-beta/connector/sink/File","h":"#description","p":104102},{"i":104107,"t":"Spark Flink name type required default value options object no - partition_by array no - path string yes - path_time_format string no yyyyMMddHHmmss save_mode string no error serializer string no json common-options string no - options [object]​ Custom parameters partition_by [array]​ Partition data based on selected fields path [string]​ The file path is required. The hdfs file starts with hdfs:// , and the local file starts with file://, we can add the variable ${now} or ${uuid} in the path, like hdfs:///test_${uuid}_${now}.txt, ${now} represents the current time, and its format can be defined by specifying the option path_time_format path_time_format [string]​ When the format in the path parameter is xxxx-${now} , path_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax. save_mode [string]​ Storage mode, currently supports overwrite , append , ignore and error . For the specific meaning of each mode, see save-modes serializer [string]​ Serialization method, currently supports csv , json , parquet , orc and text common options [string]​ Sink plugin common parameters, please refer to Sink Plugin for details name type required default value format string yes - path string yes - path_time_format string no yyyyMMddHHmmss write_mode string no - common-options string no - parallelism int no - rollover_interval long no 1 max_part_size long no 1024 prefix string no seatunnel suffix string no .ext format [string]​ Currently, csv , json , and text are supported. The streaming mode currently only supports text path [string]​ The file path is required. The hdfs file starts with hdfs:// , and the local file starts with file://, we can add the variable ${now} or ${uuid} in the path, like hdfs:///test_${uuid}_${now}.txt, ${now} represents the current time, and its format can be defined by specifying the option path_time_format path_time_format [string]​ When the format in the path parameter is xxxx-${now} , path_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax. write_mode [string]​ NO_OVERWRITE No overwrite, there is an error in the path OVERWRITE Overwrite, delete and then write if the path exists common options [string]​ Sink plugin common parameters, please refer to Sink Plugin for details parallelism [Int]​ The parallelism of an individual operator, for FileSink rollover_interval [long]​ The new file part rollover interval, unit min. max_part_size [long]​ The max size of each file part, unit MB. prefix [string]​ The prefix of each file part. suffix [string]​ The suffix of each file part.","s":"Options","u":"/docs/2.2.0-beta/connector/sink/File","h":"#options","p":104102},{"i":104109,"t":"Custom parameters","s":"options [object]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#options-object","p":104102},{"i":104111,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#partition_by-array","p":104102},{"i":104113,"t":"The file path is required. The hdfs file starts with hdfs:// , and the local file starts with file://, we can add the variable ${now} or ${uuid} in the path, like hdfs:///test_${uuid}_${now}.txt, ${now} represents the current time, and its format can be defined by specifying the option path_time_format","s":"path [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#path-string","p":104102},{"i":104115,"t":"When the format in the path parameter is xxxx-${now} , path_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"path_time_format [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#path_time_format-string","p":104102},{"i":104117,"t":"Storage mode, currently supports overwrite , append , ignore and error . For the specific meaning of each mode, see save-modes","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#save_mode-string","p":104102},{"i":104119,"t":"Serialization method, currently supports csv , json , parquet , orc and text","s":"serializer [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#serializer-string","p":104102},{"i":104121,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#common-options-string","p":104102},{"i":104123,"t":"Currently, csv , json , and text are supported. The streaming mode currently only supports text","s":"format [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#format-string","p":104102},{"i":104125,"t":"The file path is required. The hdfs file starts with hdfs:// , and the local file starts with file://, we can add the variable ${now} or ${uuid} in the path, like hdfs:///test_${uuid}_${now}.txt, ${now} represents the current time, and its format can be defined by specifying the option path_time_format","s":"path [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#path-string-1","p":104102},{"i":104127,"t":"When the format in the path parameter is xxxx-${now} , path_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"path_time_format [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#path_time_format-string-1","p":104102},{"i":104129,"t":"NO_OVERWRITE No overwrite, there is an error in the path OVERWRITE Overwrite, delete and then write if the path exists","s":"write_mode [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#write_mode-string","p":104102},{"i":104131,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#common-options-string-1","p":104102},{"i":104133,"t":"The parallelism of an individual operator, for FileSink","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#parallelism-int","p":104102},{"i":104135,"t":"The new file part rollover interval, unit min.","s":"rollover_interval [long]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#rollover_interval-long","p":104102},{"i":104137,"t":"The max size of each file part, unit MB.","s":"max_part_size [long]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#max_part_size-long","p":104102},{"i":104139,"t":"The prefix of each file part.","s":"prefix [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#prefix-string","p":104102},{"i":104141,"t":"The suffix of each file part.","s":"suffix [string]","u":"/docs/2.2.0-beta/connector/sink/File","h":"#suffix-string","p":104102},{"i":104143,"t":"Spark Flink file { path = \"file:///var/logs\" serializer = \"text\" } FileSink { format = \"json\" path = \"hdfs://localhost:9000/flink/output/\" write_mode = \"OVERWRITE\" }","s":"Example","u":"/docs/2.2.0-beta/connector/sink/File","h":"#example","p":104102},{"i":104145,"t":"Hbase sink connector","s":"Hbase","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"","p":104144},{"i":104147,"t":"Use hbase-connectors to output data to Hbase , Hbase (>=2.1.0) and Spark (>=2.0.0) version compatibility depends on hbase-connectors . The hbase-connectors in the official Apache Hbase documentation is also one of the Apache Hbase Repos. tip Engine Supported and plugin name Spark: Hbase Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#description","p":104144},{"i":104149,"t":"name type required default value hbase.zookeeper.quorum string yes catalog string yes staging_dir string yes save_mode string no append hbase.* string no","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#options","p":104144},{"i":104151,"t":"The address of the zookeeper cluster, the format is: host01:2181,host02:2181,host03:2181","s":"hbase.zookeeper.quorum [string]","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#hbasezookeeperquorum-string","p":104144},{"i":104153,"t":"The structure of the hbase table is defined by catalog , the name of the hbase table and its namespace , which columns are used as rowkey, and the correspondence between column family and columns can be defined by catalog hbase table catalog","s":"catalog [string]","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#catalog-string","p":104144},{"i":104155,"t":"A path on HDFS that will generate data that needs to be loaded into hbase . After the data is loaded, the data file will be deleted and the directory is still there.","s":"staging_dir [string]","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#staging_dir-string","p":104144},{"i":104157,"t":"Two write modes are supported, overwrite and append . overwrite means that if there is data in the hbase table , truncate will be performed and then the data will be loaded. append means that the original data of the hbase table will not be cleared, and the load operation will be performed directly.","s":"save_mode [string]","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#save_mode-string","p":104144},{"i":104159,"t":"Users can also specify multiple optional parameters. For a detailed list of parameters, see Hbase Supported Parameters. If these non-essential parameters are not specified, they will use the default values given in the official documentation.","s":"hbase.* [string]","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#hbase-string","p":104144},{"i":104161,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#common-options-string","p":104144},{"i":104163,"t":"hbase { source_table_name = \"hive_dataset\" hbase.zookeeper.quorum = \"host01:2181,host02:2181,host03:2181\" catalog = \"{\\\"table\\\":{\\\"namespace\\\":\\\"default\\\", \\\"name\\\":\\\"customer\\\"},\\\"rowkey\\\":\\\"c_custkey\\\",\\\"columns\\\":{\\\"c_custkey\\\":{\\\"cf\\\":\\\"rowkey\\\", \\\"col\\\":\\\"c_custkey\\\", \\\"type\\\":\\\"bigint\\\"},\\\"c_name\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_name\\\", \\\"type\\\":\\\"string\\\"},\\\"c_address\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_address\\\", \\\"type\\\":\\\"string\\\"},\\\"c_city\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_city\\\", \\\"type\\\":\\\"string\\\"},\\\"c_nation\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_nation\\\", \\\"type\\\":\\\"string\\\"},\\\"c_region\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_region\\\", \\\"type\\\":\\\"string\\\"},\\\"c_phone\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_phone\\\", \\\"type\\\":\\\"string\\\"},\\\"c_mktsegment\\\":{\\\"cf\\\":\\\"info\\\", \\\"col\\\":\\\"c_mktsegment\\\", \\\"type\\\":\\\"string\\\"}}}\" staging_dir = \"/tmp/hbase-staging/\" save_mode = \"overwrite\" } This plugin of Hbase does not provide users with the function of creating tables, because the pre-partitioning method of the hbase table will be related to business logic, so when running the plugin, the user needs to create the hbase table and its pre-partition in advance; for rowkey Design, catalog itself supports multi-column combined rowkey=\"col1:col2:col3\" , but if there are other design requirements for rowkey , such as add salt , etc., it can be completely decoupled based on the transform plugin pair rowkey is modified.","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Hbase","h":"#examples","p":104144},{"i":104165,"t":"Hive sink connector","s":"Hive","u":"/docs/2.2.0-beta/connector/sink/Hive","h":"","p":104164},{"i":104167,"t":"Write Rows to Apache Hive. tip Engine Supported and plugin name Spark: Hive Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Hive","h":"#description","p":104164},{"i":104169,"t":"name type required default value sql string no - source_table_name string no - result_table_name string no - sink_columns string no - save_mode string no - partition_by Array[string] no - sql [string]​ Hive sql：the whole insert data sql, such as insert into/overwrite $table select * from xxx_table , If this option exists, other options will be ignored. Source_table_name [string]​ Datasource of this plugin. result_table_name [string]​ The output hive table name if the sql option doesn't specified. save_mode [string]​ Same with option spark.mode in Spark, combined with result_table_name if the sql option doesn't specified. sink_columns [string]​ Specify the selected fields which write to result_table_name, separated by commas, combined with result_table_name if the sql option doesn't specified. partition_by [Array[string]]​ Hive partition fields, combined with result_table_name if the sql option doesn't specified.","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Hive","h":"#options","p":104164},{"i":104171,"t":"sink { Hive { sql = \"insert overwrite table seatunnel.test1 partition(province) select name,age,province from myTable2\" } } sink { Hive { source_table_name = \"myTable2\" result_table_name = \"seatunnel.test1\" save_mode = \"overwrite\" sink_columns = \"name,age,province\" partition_by = [\"province\"] } }","s":"Example","u":"/docs/2.2.0-beta/connector/sink/Hive","h":"#example","p":104164},{"i":104173,"t":"Hudi sink connector","s":"Hudi","u":"/docs/2.2.0-beta/connector/sink/Hudi","h":"","p":104172},{"i":104175,"t":"Write Rows to a Hudi. tip Engine Supported and plugin name Spark: Hudi Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Hudi","h":"#description","p":104172},{"i":104177,"t":"name type required default value engine hoodie.base.path string yes - Spark hoodie.table.name string yes - Spark save_mode string no append Spark More hudi Configurations","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Hudi","h":"#options","p":104172},{"i":104179,"t":"Base path on lake storage, under which all the table data is stored. Always prefix it explicitly with the storage scheme (e.g hdfs://, s3:// etc). Hudi stores all the main meta-data about commits, savepoints, cleaning audit logs etc in .hoodie directory under this base path directory.","s":"hoodie.base.path [string]","u":"/docs/2.2.0-beta/connector/sink/Hudi","h":"#hoodiebasepath-string","p":104172},{"i":104181,"t":"Table name that will be used for registering with Hive. Needs to be same across runs.","s":"hoodie.table.name [string]","u":"/docs/2.2.0-beta/connector/sink/Hudi","h":"#hoodietablename-string","p":104172},{"i":104183,"t":"hudi { hoodie.base.path = \"hdfs://\" hoodie.table.name = \"seatunnel_hudi\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Hudi","h":"#examples","p":104172},{"i":104185,"t":"InfluxDB sink connector","s":"InfluxDB","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"","p":104184},{"i":104187,"t":"Write data to InfluxDB. tip Engine Supported and plugin name Spark Flink: InfluxDB","s":"Description","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#description","p":104184},{"i":104189,"t":"name type required default value server_url String yes - username String no - password String no - database String yes - measurement String yes - tags List<String> yes - fields List<String> yes - parallelism Int no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#options","p":104184},{"i":104191,"t":"The URL of InfluxDB Server.","s":"server_url [String]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#server_url-string","p":104184},{"i":104193,"t":"The username of InfluxDB Server.","s":"username [String]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#username-string","p":104184},{"i":104195,"t":"The password of InfluxDB Server.","s":"password [String]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#password-string","p":104184},{"i":104197,"t":"The database name in InfluxDB.","s":"database [String]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#database-string","p":104184},{"i":104199,"t":"The Measurement name in InfluxDB.","s":"measurement [String]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#measurement-string","p":104184},{"i":104201,"t":"The list of Tag in InfluxDB.","s":"tags [List<String>]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#tags-liststring","p":104184},{"i":104203,"t":"The list of Field in InfluxDB.","s":"fields [List<String>]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#fields-liststring","p":104184},{"i":104205,"t":"The parallelism of an individual operator, for InfluxDbSink","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#parallelism-int","p":104184},{"i":104208,"t":"InfluxDbSink { server_url = \"http://127.0.0.1:8086/\" database = \"influxdb\" measurement = \"m\" tags = [\"country\", \"city\"] fields = [\"count\"] }","s":"Simple","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#simple","p":104184},{"i":104210,"t":"InfluxDbSink { server_url = \"http://127.0.0.1:8086/\" username = \"admin\" password = \"password\" database = \"influxdb\" measurement = \"m\" tags = [\"country\", \"city\"] fields = [\"count\"] }","s":"Auth","u":"/docs/2.2.0-beta/connector/sink/InfluxDb","h":"#auth","p":104184},{"i":104212,"t":"JDBC sink connector","s":"Jdbc","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"","p":104211},{"i":104214,"t":"Write data through jdbc tip Engine Supported and plugin name Spark: Jdbc Flink: Jdbc","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#description","p":104211},{"i":104216,"t":"Spark Flink name type required default value driver string yes - url string yes - user string yes - password string yes - dbTable string yes - saveMode string no update useSsl string no false customUpdateStmt string no - duplicateIncs string no - showSql string no true url [string]​ The URL of the JDBC connection. Refer to a case: jdbc:mysql://localhost/dbName user [string]​ username password [string]​ user password dbTable [string]​ Sink table name, if the table does not exist, it will be created. saveMode [string]​ Storage mode, add mode update , perform data overwrite in a specified way when inserting data key conflicts Basic mode, currently supports overwrite , append , ignore and error . For the specific meaning of each mode, see save-modes useSsl [string]​ Configure when saveMode is specified as update , whether to enable ssl, the default value is false isolationLevel [string]​ The transaction isolation level, which applies to current connection. The default value is READ_UNCOMMITTED customUpdateStmt [string]​ Configure when saveMode is specified as update , which is used to specify the update statement template for key conflicts. If customUpdateStmt is empty, the sql will auto-generate for all columns, else use the sql which refer to the usage of INSERT INTO table (...) values (...) ON DUPLICATE KEY UPDATE... of mysql , use placeholders or fixed values in values tips: the tableName of sql should be consistent with the dbTable. duplicateIncs [string]​ Configure when saveMode is specified as update , and when the specified key conflicts, the value is updated to the existing value plus the original value showSql​ Configure when saveMode is specified as update , whether to show sql name type required default value driver string yes - url string yes - username string yes - password string no - query string yes - batch_size int no - source_table_name string yes - common-options string no - parallelism int no - pre_sql string no - post_sql string no - ignore_post_sql_exceptions boolean no - driver [string]​ Driver name, such as com.mysql.cj.jdbc.Driver for MySQL. Warn: for license compliance, you have to provide MySQL JDBC driver yourself, e.g. copy mysql-connector-java-xxx.jar to $FLINK_HOME/lib for Standalone. url [string]​ The URL of the JDBC connection. Such as: jdbc:mysql://localhost:3306/test username [string]​ username password [string]​ password query [string]​ Insert statement batch_size [int]​ Number of writes per batch parallelism [int]​ The parallelism of an individual operator, for JdbcSink. pre_sql [string]​ This sql can be executed before output. post_sql [string]​ This sql can be executed after output, and just supports for batch job. ignore_post_sql_exceptions [boolean]​ Whether to ignore post_sql exceptions. common options [string]​ Sink plugin common parameters, please refer to Sink Plugin for details","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#options","p":104211},{"i":104218,"t":"The URL of the JDBC connection. Refer to a case: jdbc:mysql://localhost/dbName","s":"url [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#url-string","p":104211},{"i":104220,"t":"username password [string]​ user password","s":"user [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#user-string","p":104211},{"i":104222,"t":"Sink table name, if the table does not exist, it will be created.","s":"dbTable [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#dbtable-string","p":104211},{"i":104224,"t":"Storage mode, add mode update , perform data overwrite in a specified way when inserting data key conflicts Basic mode, currently supports overwrite , append , ignore and error . For the specific meaning of each mode, see save-modes","s":"saveMode [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#savemode-string","p":104211},{"i":104226,"t":"Configure when saveMode is specified as update , whether to enable ssl, the default value is false","s":"useSsl [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#usessl-string","p":104211},{"i":104228,"t":"The transaction isolation level, which applies to current connection. The default value is READ_UNCOMMITTED","s":"isolationLevel [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#isolationlevel-string","p":104211},{"i":104230,"t":"Configure when saveMode is specified as update , which is used to specify the update statement template for key conflicts. If customUpdateStmt is empty, the sql will auto-generate for all columns, else use the sql which refer to the usage of INSERT INTO table (...) values (...) ON DUPLICATE KEY UPDATE... of mysql , use placeholders or fixed values in values tips: the tableName of sql should be consistent with the dbTable.","s":"customUpdateStmt [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#customupdatestmt-string","p":104211},{"i":104232,"t":"Configure when saveMode is specified as update , and when the specified key conflicts, the value is updated to the existing value plus the original value","s":"duplicateIncs [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#duplicateincs-string","p":104211},{"i":104234,"t":"Configure when saveMode is specified as update , whether to show sql","s":"showSql","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#showsql","p":104211},{"i":104236,"t":"Driver name, such as com.mysql.cj.jdbc.Driver for MySQL. Warn: for license compliance, you have to provide MySQL JDBC driver yourself, e.g. copy mysql-connector-java-xxx.jar to $FLINK_HOME/lib for Standalone.","s":"driver [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#driver-string","p":104211},{"i":104238,"t":"The URL of the JDBC connection. Such as: jdbc:mysql://localhost:3306/test","s":"url [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#url-string-1","p":104211},{"i":104240,"t":"username","s":"username [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#username-string","p":104211},{"i":104242,"t":"password","s":"password [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#password-string-1","p":104211},{"i":104244,"t":"Insert statement","s":"query [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#query-string","p":104211},{"i":104246,"t":"Number of writes per batch","s":"batch_size [int]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#batch_size-int","p":104211},{"i":104248,"t":"The parallelism of an individual operator, for JdbcSink.","s":"parallelism [int]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#parallelism-int","p":104211},{"i":104250,"t":"This sql can be executed before output.","s":"pre_sql [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#pre_sql-string","p":104211},{"i":104252,"t":"This sql can be executed after output, and just supports for batch job.","s":"post_sql [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#post_sql-string","p":104211},{"i":104254,"t":"Whether to ignore post_sql exceptions.","s":"ignore_post_sql_exceptions [boolean]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#ignore_post_sql_exceptions-boolean","p":104211},{"i":104256,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#common-options-string","p":104211},{"i":104258,"t":"Spark Flink jdbc { driver = \"com.mysql.cj.jdbc.Driver\", saveMode = \"update\", url = \"jdbc:mysql://ip:3306/database\", user = \"userName\", password = \"***********\", dbTable = \"tableName\", customUpdateStmt = \"INSERT INTO table (column1, column2, created, modified, yn) values(?, ?, now(), now(), 1) ON DUPLICATE KEY UPDATE column1 = IFNULL(VALUES (column1), column1), column2 = IFNULL(VALUES (column2), column2)\" } Insert data through JDBC jdbc { driver = \"com.mysql.cj.jdbc.Driver\", saveMode = \"update\", truncate = \"true\", url = \"jdbc:mysql://ip:3306/database\", user = \"userName\", password = \"***********\", dbTable = \"tableName\", customUpdateStmt = \"INSERT INTO tableName (column1, column2, created, modified, yn) values(?, ?, now(), now(), 1) ON DUPLICATE KEY UPDATE column1 = IFNULL(VALUES (column1), column1), column2 = IFNULL(VALUES (column2), column2)\" jdbc.connect_timeout = 10000 jdbc.socket_timeout = 10000 } Timeout config JdbcSink { source_table_name = fake driver = com.mysql.cj.jdbc.Driver url = \"jdbc:mysql://localhost/test\" username = root query = \"insert into test(name,age) values(?,?)\" batch_size = 2 }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Jdbc","h":"#examples","p":104211},{"i":104260,"t":"Iceberg sink connector","s":"Iceberg","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"","p":104259},{"i":104262,"t":"Write data to Iceberg. tip Engine Supported and plugin name Spark: Iceberg Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#description","p":104259},{"i":104264,"t":"name type required default value path string yes - saveMode string no append target-file-size-bytes long no - check-nullability bool no - snapshot-property.custom-key string no - fanout-enabled bool no - check-ordering bool no - Refer to iceberg write options for more configurations.","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#options","p":104259},{"i":104266,"t":"Iceberg table location.","s":"path","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#path","p":104259},{"i":104268,"t":"append or overwrite. Only these two modes are supported by iceberg. The default value is append.","s":"saveMode","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#savemode","p":104259},{"i":104270,"t":"Overrides this table’s write.target-file-size-bytes","s":"target-file-size-bytes","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#target-file-size-bytes","p":104259},{"i":104272,"t":"Sets the nullable check on fields","s":"check-nullability","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#check-nullability","p":104259},{"i":104274,"t":"Adds an entry with custom-key and corresponding value in the snapshot summary eg: snapshot-property.aaaa=\"bbbb\"","s":"snapshot-property.custom-key","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#snapshot-propertycustom-key","p":104259},{"i":104276,"t":"Overrides this table’s write.spark.fanout.enabled","s":"fanout-enabled","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#fanout-enabled","p":104259},{"i":104278,"t":"Checks if input schema and table schema are same","s":"check-ordering","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#check-ordering","p":104259},{"i":104280,"t":"iceberg { path = \"hdfs://localhost:9000/iceberg/warehouse/db/table\" }","s":"Example","u":"/docs/2.2.0-beta/connector/sink/Iceberg","h":"#example","p":104259},{"i":104282,"t":"Kafka sink connector","s":"Kafka","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"","p":104281},{"i":104284,"t":"Write Rows to a Kafka topic. tip Engine Supported and plugin name Spark: Kafka Flink: Kafka","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#description","p":104281},{"i":104286,"t":"name type required default value producer.bootstrap.servers string yes - topic string yes - producer.* string no - semantic string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#options","p":104281},{"i":104288,"t":"Kafka Brokers List","s":"producer.bootstrap.servers [string]","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#producerbootstrapservers-string","p":104281},{"i":104290,"t":"Kafka Topic","s":"topic [string]","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#topic-string","p":104281},{"i":104292,"t":"In addition to the above parameters that must be specified by the Kafka producer client, the user can also specify multiple non-mandatory parameters for the producer client, covering all the producer parameters specified in the official Kafka document. The way to specify the parameter is to add the prefix producer. to the original parameter name. For example, the way to specify request.timeout.ms is: producer.request.timeout.ms = 60000 . If these non-essential parameters are not specified, they will use the default values given in the official Kafka documentation.","s":"producer [string]","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#producer-string","p":104281},{"i":104294,"t":"Semantics that can be chosen. exactly_once/at_least_once/none, default is at_least_once In exactly_once, flink producer will write all messages in a Kafka transaction that will be committed to Kafka on a checkpoint. In at_least_once, flink producer will wait for all outstanding messages in the Kafka buffers to be acknowledged by the Kafka producer on a checkpoint. NONE does not provide any guarantees: messages may be lost in case of issues on the Kafka broker and messages may be duplicated in case of a Flink failure. please refer to Flink Kafka Fault Tolerance","s":"semantic [string]","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#semantic-string","p":104281},{"i":104296,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#common-options-string","p":104281},{"i":104298,"t":"kafka { topics = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Kafka","h":"#examples","p":104281},{"i":104300,"t":"Kudu sink connector","s":"Kudu","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"","p":104299},{"i":104302,"t":"Write data to Kudu. tip Engine Supported and plugin name Spark: Kudu Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"#description","p":104299},{"i":104304,"t":"name type required default value kudu_master string yes - kudu_table string yes - mode string no insert","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"#options","p":104299},{"i":104306,"t":"Kudu master, multiple masters are separated by commas","s":"kudu_master [string]","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"#kudu_master-string","p":104299},{"i":104308,"t":"The name of the table to be written in kudu, the table must already exist","s":"kudu_table [string]","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"#kudu_table-string","p":104299},{"i":104310,"t":"Write the mode adopted in kudu, support insert|update|upsert|insertIgnore, the default is insert.","s":"mode [string]","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"#mode-string","p":104299},{"i":104312,"t":"kudu { kudu_master=\"hadoop01:7051,hadoop02:7051,hadoop03:7051\" kudu_table=\"my_kudu_table\" mode=\"upsert\" }","s":"Example","u":"/docs/2.2.0-beta/connector/sink/Kudu","h":"#example","p":104299},{"i":104314,"t":"MongoDB sink connector","s":"MongoDB","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"","p":104313},{"i":104316,"t":"Write data to MongoDB tip Engine Supported and plugin name Spark: MongoDB Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#description","p":104313},{"i":104318,"t":"name type required default value writeconfig.uri string yes - writeconfig.database string yes - writeconfig.collection string yes - writeconfig.* string no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#options","p":104313},{"i":104320,"t":"uri to write to mongoDB","s":"writeconfig.uri [string]","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#writeconfiguri-string","p":104313},{"i":104322,"t":"database to write to mongoDB","s":"writeconfig.database [string]","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#writeconfigdatabase-string","p":104313},{"i":104324,"t":"collection to write to mongoDB","s":"writeconfig.collection [string]","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#writeconfigcollection-string","p":104313},{"i":104326,"t":"More other parameters can be configured here, see MongoDB Configuration for details, see the Output Configuration section. The way to specify parameters is to add a prefix to the original parameter name writeconfig. For example, the way to set localThreshold is writeconfig.localThreshold=20 . If you do not specify these optional parameters, the default values of the official MongoDB documentation will be used.","s":"writeconfig.* [string]","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#writeconfig-string","p":104313},{"i":104328,"t":"mongodb { writeconfig.uri = \"mongodb://username:password@127.0.0.1:27017/test_db\" writeconfig.database = \"test_db\" writeconfig.collection = \"test_collection\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/MongoDB","h":"#examples","p":104313},{"i":104330,"t":"Phoenix sink connector","s":"Phoenix","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"","p":104329},{"i":104332,"t":"Export data to Phoenix , compatible with Kerberos authentication tip Engine Supported and plugin name Spark: Phoenix Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#description","p":104329},{"i":104334,"t":"name type required default value zk-connect array yes - table string yes - tenantId string no - skipNormalizingIdentifier boolean no false common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#options","p":104329},{"i":104336,"t":"Connection string, configuration example: host1:2181,host2:2181,host3:2181 [/znode]","s":"zk-connect [string]","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#zk-connect-string","p":104329},{"i":104338,"t":"Target table name tenantId [string]​ Tenant ID, optional configuration item","s":"table [string]","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#table-string","p":104329},{"i":104340,"t":"Whether to skip the normalized identifier, if the column name is surrounded by double quotes, it is used as is, otherwise the name is uppercase. Optional configuration items, the default is false","s":"skipNormalizingIdentifier [boolean]","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#skipnormalizingidentifier-boolean","p":104329},{"i":104342,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#common-options-string","p":104329},{"i":104344,"t":"Phoenix { zk-connect = \"host1:2181,host2:2181,host3:2181\" table = \"tableName\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Phoenix","h":"#examples","p":104329},{"i":104346,"t":"Redis sink connector","s":"Redis","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"","p":104345},{"i":104348,"t":"Write Rows to a Redis. tip Engine Supported and plugin name Spark: Redis Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#description","p":104345},{"i":104350,"t":"name type required default value host string no \"localhost\" port int no 6379 auth string no db_num int no 0 data_type string no \"KV\" hash_name string no list_name string no set_name string no zset_name string no timeout int no 2000 ttl int no 0 is_self_achieved boolean no false","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#options","p":104345},{"i":104352,"t":"Redis server address, default \"localhost\"","s":"host [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#host-string","p":104345},{"i":104354,"t":"Redis service port, default 6379","s":"port [int]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#port-int","p":104345},{"i":104356,"t":"Redis authentication password","s":"auth [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#auth-string","p":104345},{"i":104358,"t":"Redis database index ID. It is connected to db 0 by default","s":"db_num [int]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#db_num-int","p":104345},{"i":104360,"t":"Redis timeout","s":"redis_timeout [int]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#redis_timeout-int","p":104345},{"i":104362,"t":"Redis data type eg: KV HASH LIST SET ZSET","s":"data_type [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#data_type-string","p":104345},{"i":104364,"t":"if redis data type is HASH must config hash name","s":"hash_name [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#hash_name-string","p":104345},{"i":104366,"t":"if redis data type is LIST must config list name","s":"list_name [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#list_name-string","p":104345},{"i":104368,"t":"if redis data type is ZSET must config zset name","s":"zset_name [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#zset_name-string","p":104345},{"i":104370,"t":"if redis data type is SET must config set name","s":"set_name [string]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#set_name-string","p":104345},{"i":104372,"t":"redis data expiration ttl, 0 means no expiration.","s":"ttl [int]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#ttl-int","p":104345},{"i":104374,"t":"If a redis access by a self-achieved redis proxy, which is not support redis function of \"info Replication\"","s":"is_self_achieved [boolean]","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#is_self_achieved-boolean","p":104345},{"i":104376,"t":"redis { host = \"localhost\" port = 6379 auth = \"myPassword\" db_num = 1 data_type = \"HASH\" hash_name = \"test\" is_self_achieved = false }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Redis","h":"#examples","p":104345},{"i":104378,"t":"TiDB sink connector","s":"TiDb","u":"/docs/2.2.0-beta/connector/sink/Tidb","h":"","p":104377},{"i":104380,"t":"Write data to TiDB. tip Engine Supported and plugin name Spark: TiDb Flink","s":"Description","u":"/docs/2.2.0-beta/connector/sink/Tidb","h":"#description","p":104377},{"i":104382,"t":"name type required default value spark.tispark.pd.addresses string yes - spark.sql.extensions string yes org.apache.spark.sql.TiExtensions spark.tispark.pd.addresses [string]​ TiDB Pd Address spark.sql.extensions [string]​ Spark Sql Extensions","s":"Env Options","u":"/docs/2.2.0-beta/connector/sink/Tidb","h":"#env-options","p":104377},{"i":104384,"t":"name type required default value addr string yes - port string yes - user string yes - password string yes - table string yes - database string yes addr [string]​ TiDB address, which currently only supports one instance port [string]​ TiDB port user [string]​ TiDB user password [string]​ TiDB password table [string]​ TiDB table name database [string]​ TiDB database name options​ Refer to TiSpark Configurations","s":"Options","u":"/docs/2.2.0-beta/connector/sink/Tidb","h":"#options","p":104377},{"i":104386,"t":"env { spark.tispark.pd.addresses = \"127.0.0.1:2379\" spark.sql.extensions = \"org.apache.spark.sql.TiExtensions\" } tidb { addr = \"127.0.0.1\", port = \"4000\" database = \"database\", table = \"tableName\", user = \"userName\", password = \"***********\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/sink/Tidb","h":"#examples","p":104377},{"i":104388,"t":"Common parameters of source connectors Spark Flink name type required default value result_table_name string yes - table_name string no - result_table_name [string]​ When result_table_name is not specified, the data processed by this plug-in will not be registered as a data set that can be directly accessed by other plugins, or called a temporary table (table); When result_table_name is specified, the data processed by this plug-in will be registered as a data set (dataset) that can be directly accessed by other plug-ins, or called a temporary table (table). The dataset registered here can be directly accessed by other plugins by specifying source_table_name. table_name [string]​ [Deprecated since v1.4] The function is the same as result_table_name , this parameter will be deleted in subsequent Release versions, and result_table_name parameter is recommended. name type required default value result_table_name string no - field_name string no - result_table_name [string]​ When result_table_name is not specified, the data processed by this plugin will not be registered as a data set (dataStream/dataset) that can be directly accessed by other plugins, or called a temporary table (table) ; When result_table_name is specified, the data processed by this plugin will be registered as a data set (dataStream/dataset) that can be directly accessed by other plugins, or called a temporary table (table) . The data set (dataStream/dataset) registered here can be directly accessed by other plugins by specifying source_table_name . field_name [string]​ When the data is obtained from the upper-level plug-in, you can specify the name of the obtained field, which is convenient for use in subsequent sql plugins.","s":"Common Options","u":"/docs/2.2.0-beta/connector/source/common-options","h":"","p":104387},{"i":104390,"t":"When result_table_name is not specified, the data processed by this plug-in will not be registered as a data set that can be directly accessed by other plugins, or called a temporary table (table); When result_table_name is specified, the data processed by this plug-in will be registered as a data set (dataset) that can be directly accessed by other plug-ins, or called a temporary table (table). The dataset registered here can be directly accessed by other plugins by specifying source_table_name.","s":"result_table_name [string]","u":"/docs/2.2.0-beta/connector/source/common-options","h":"#result_table_name-string","p":104387},{"i":104392,"t":"[Deprecated since v1.4] The function is the same as result_table_name , this parameter will be deleted in subsequent Release versions, and result_table_name parameter is recommended.","s":"table_name [string]","u":"/docs/2.2.0-beta/connector/source/common-options","h":"#table_name-string","p":104387},{"i":104394,"t":"When result_table_name is not specified, the data processed by this plugin will not be registered as a data set (dataStream/dataset) that can be directly accessed by other plugins, or called a temporary table (table) ; When result_table_name is specified, the data processed by this plugin will be registered as a data set (dataStream/dataset) that can be directly accessed by other plugins, or called a temporary table (table) . The data set (dataStream/dataset) registered here can be directly accessed by other plugins by specifying source_table_name .","s":"result_table_name [string]","u":"/docs/2.2.0-beta/connector/source/common-options","h":"#result_table_name-string-1","p":104387},{"i":104396,"t":"When the data is obtained from the upper-level plug-in, you can specify the name of the obtained field, which is convenient for use in subsequent sql plugins.","s":"field_name [string]","u":"/docs/2.2.0-beta/connector/source/common-options","h":"#field_name-string","p":104387},{"i":104398,"t":"Spark Flink fake { result_table_name = \"view_table\" } The result of the data source fake will be registered as a temporary table named view_table . This temporary table can be used by any Filter or Output plugin by specifying source_table_name . source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } The result of the data source FakeSourceStream will be registered as a temporary table named fake . This temporary table can be used by any Transform or Sink plugin by specifying source_table_name . field_name names the two columns of the temporary table name and age respectively.","s":"Example","u":"/docs/2.2.0-beta/connector/source/common-options","h":"#example","p":104387},{"i":104400,"t":"Druid source connector","s":"Druid","u":"/docs/2.2.0-beta/connector/source/Druid","h":"","p":104399},{"i":104402,"t":"Read data from Apache Druid. tip Engine Supported and plugin name Spark Flink: Druid","s":"Description","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#description","p":104399},{"i":104404,"t":"name type required default value jdbc_url String yes - datasource String yes - start_date String no - end_date String no - columns List<String> no * parallelism Int no -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#options","p":104399},{"i":104406,"t":"The URL of JDBC of Apache Druid.","s":"jdbc_url [String]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#jdbc_url-string","p":104399},{"i":104408,"t":"The DataSource name in Apache Druid.","s":"datasource [String]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#datasource-string","p":104399},{"i":104410,"t":"The start date of DataSource, for example, '2016-06-27', '2016-06-27 00:00:00', etc.","s":"start_date [String]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#start_date-string","p":104399},{"i":104412,"t":"The end date of DataSource, for example, '2016-06-28', '2016-06-28 00:00:00', etc.","s":"end_date [String]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#end_date-string","p":104399},{"i":104414,"t":"These columns that you want to query of DataSource.","s":"columns [List<String>]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#columns-liststring","p":104399},{"i":104416,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#common-options-string","p":104399},{"i":104418,"t":"The parallelism of an individual operator, for DruidSource","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#parallelism-int","p":104399},{"i":104420,"t":"DruidSource { jdbc_url = \"jdbc:avatica:remote:url=http://localhost:8082/druid/v2/sql/avatica/\" datasource = \"wikipedia\" start_date = \"2016-06-27 00:00:00\" end_date = \"2016-06-28 00:00:00\" columns = [\"flags\",\"page\"] }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Druid","h":"#example","p":104399},{"i":104422,"t":"Fake source connector","s":"Fake","u":"/docs/2.2.0-beta/connector/source/Fake","h":"","p":104421},{"i":104424,"t":"Fake is mainly used to conveniently generate user-specified data, which is used as input for functional verification, testing, and performance testing of seatunnel. note Engine Supported and plugin name Spark: Fake, FakeStream Flink: FakeSource, FakeSourceStream Flink Fake Source is mainly used to automatically generate data. The data has only two columns. The first column is of String type and the content is a random one from [\"Gary\", \"Ricky Huo\", \"Kid Xiong\"] . The second column is of Int type , which is the current 13-digit timestamp is used as input for functional verification and testing of seatunnel .","s":"Description","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#description","p":104421},{"i":104426,"t":"Spark Flink note These options is for Spark:FakeStream, and Spark:Fake do not have any options name type required default value content array no - rate number yes - common-options string yes - content [array]​ List of test data strings rate [number]​ Number of test cases generated per second name type required default value parallelism Int no - common-options string no - mock_data_schema list [column_config] no see details. mock_data_size int no 300 mock_data_interval int (second) no 1 parallelism [Int]​ The parallelism of an individual operator, for Fake Source Stream","s":"Options","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#options","p":104421},{"i":104428,"t":"List of test data strings","s":"content [array]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#content-array","p":104421},{"i":104430,"t":"Number of test cases generated per second","s":"rate [number]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#rate-number","p":104421},{"i":104432,"t":"The parallelism of an individual operator, for Fake Source Stream","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#parallelism-int","p":104421},{"i":104434,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#common-options-string","p":104421},{"i":104436,"t":"Config mock data's schema. Each is column_config option. When mock_data_schema is not defined. Data will generate with schema like this: mock_data_schema = [ { name = \"name\", type = \"string\", mock_config = { string_seed = [\"Gary\", \"Ricky Huo\", \"Kid Xiong\"] size_range = [1,1] } } { name = \"age\", type = \"int\", mock_config = { int_range = [1, 100] } } ] column_config option type. name type required default value support values name string yes string - type string yes string int,integer,byte,boolean,char, character,short,long,float,double, date,timestamp,decimal,bigdecimal, bigint,int[],byte[], boolean[],char[],character[],short[], long[],float[],double[],string[], binary,varchar mock_config mock_config no - - mock_config Option name type required default value sample byte_range list[byte][size=2] no - [0,127] boolean_seed list[boolean] no - [true, true, false] char_seed list[char][size=2] no - ['a','b','c'] date_range list[string][size=2] no - [\"1970-01-01\", \"2100-12-31\"] decimal_scale int no - 2 double_range list[double][size=2] no - [0.0, 10000.0] float_range list[flout][size=2] no - [0.0, 10000.0] int_range list[int][size=2] no - [0, 100] long_range list[long][size=2] no - [0, 100000] number_regex string no - \"[1-9]{1}\\d?\" time_range list[int][size=6] no - [0,24,0,60,0,60] size_range list[int][size=2] no - [6,10] string_regex string no - \"[a-z0-9]{5}\\@\\w{3}\\.[a-z]{3}\" string_seed list[string] no - [\"Gary\", \"Ricky Huo\", \"Kid Xiong\"]","s":"mock_data_schema Option  [list[column_config]]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#mock_data_schema-option--listcolumn_config","p":104421},{"i":104438,"t":"Config mock data size.","s":"mock_data_size Option [int]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#mock_data_size-option-int","p":104421},{"i":104440,"t":"Config the data can mock with interval, The unit is SECOND.","s":"mock_data_interval Option [int]","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#mock_data_interval-option-int","p":104421},{"i":104442,"t":"Spark Flink Fake​ Fake { result_table_name = \"my_dataset\" } FakeStream​ fakeStream { content = ['name=ricky&age=23', 'name=gary&age=28'] rate = 5 } The generated data is as follows, randomly extract the string from the content list +-----------------+ |raw_message | +-----------------+ |name=gary&age=28 | |name=ricky&age=23| +-----------------+ FakeSourceStream​ source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } FakeSource​ source { FakeSource { result_table_name = \"fake\" field_name = \"name,age\" mock_data_size = 100 // will generate 100 rows mock data. } }","s":"Examples","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#examples","p":104421},{"i":104443,"t":"Fake { result_table_name = \"my_dataset\" }","s":"Fake","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#fake-1","p":104421},{"i":104445,"t":"fakeStream { content = ['name=ricky&age=23', 'name=gary&age=28'] rate = 5 } The generated data is as follows, randomly extract the string from the content list +-----------------+ |raw_message | +-----------------+ |name=gary&age=28 | |name=ricky&age=23| +-----------------+","s":"FakeStream","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#fakestream","p":104421},{"i":104447,"t":"source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } }","s":"FakeSourceStream","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#fakesourcestream","p":104421},{"i":104449,"t":"source { FakeSource { result_table_name = \"fake\" field_name = \"name,age\" mock_data_size = 100 // will generate 100 rows mock data. } }","s":"FakeSource","u":"/docs/2.2.0-beta/connector/source/Fake","h":"#fakesource","p":104421},{"i":104451,"t":"Feishu sheet source connector","s":"Feishu Sheet","u":"/docs/2.2.0-beta/connector/source/FeishuSheet","h":"","p":104450},{"i":104453,"t":"Get data from Feishu sheet tip Engine Supported and plugin name Spark: FeishuSheet Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/FeishuSheet","h":"#description","p":104450},{"i":104455,"t":"name type required default value appId string yes - appSecret string yes - sheetToken string yes - range string no all values in sheet sheetNum int no 1 titleLineNum int no 1 ignoreTitleLine bool no true appId and appSecret These two parameters need to get from Feishu open platform. And open the sheet permission in permission management tab. sheetToken If you Feishu sheet link is https://xxx.feishu.cn/sheets/shtcnGxninxxxxxxx and the \"shtcnGxninxxxxxxx\" is sheetToken. range The format is A1:D5 or A2:C4 and so on. sheetNum If you want import first sheet you can input 1 and the default value is 1. If you want import second one you should input 2. titleLineNum The default title line the first line. If you title line is not first, you can change number for it. Like 2, 3 or 5. ignoreTitleLine The title line it not save to data, if you want to save title line to data, you can set value as false.","s":"Options","u":"/docs/2.2.0-beta/connector/source/FeishuSheet","h":"#options","p":104450},{"i":104457,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/FeishuSheet","h":"#common-options-string","p":104450},{"i":104459,"t":"FeishuSheet { result_table_name = \"my_dataset\" appId = \"cli_a2cbxxxxxx\" appSecret = \"IvhtW7xxxxxxxxxxxxxxx\" sheetToken = \"shtcn6K3DIixxxxxxxxxxxx\" # range = \"A1:D4\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/FeishuSheet","h":"#example","p":104450},{"i":104461,"t":"Elasticsearch source connector","s":"Elasticsearch","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"","p":104460},{"i":104463,"t":"Read data from Elasticsearch tip Engine Supported and plugin name Spark: Elasticsearch Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#description","p":104460},{"i":104465,"t":"name type required default value hosts array yes - index string yes es.* string no common-options string yes -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#options","p":104460},{"i":104467,"t":"ElasticSearch cluster address, the format is host:port, allowing multiple hosts to be specified. Such as [\"host1:9200\", \"host2:9200\"] .","s":"hosts [array]","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#hosts-array","p":104460},{"i":104469,"t":"ElasticSearch index name, support * fuzzy matching","s":"index [string]","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#index-string","p":104460},{"i":104471,"t":"Users can also specify multiple optional parameters. For a detailed list of parameters, see Parameters Supported by Elasticsearch. For example, the way to specify es.read.metadata is: es.read.metadata = true . If these non-essential parameters are not specified, they will use the default values given in the official documentation.","s":"es.* [string]","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#es-string","p":104460},{"i":104473,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#common-options-string","p":104460},{"i":104475,"t":"elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-20190424\" result_table_name = \"my_dataset\" } elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-*\" es.read.field.include = \"name, age\" resulttable_name = \"my_dataset\" } Matches all indexes starting with seatunnel- , and only reads the two fields name and age .","s":"Examples","u":"/docs/2.2.0-beta/connector/source/Elasticsearch","h":"#examples","p":104460},{"i":104477,"t":"File source connector","s":"File","u":"/docs/2.2.0-beta/connector/source/File","h":"","p":104476},{"i":104479,"t":"Read data from local or hdfs file. tip Engine Supported and plugin name Spark: File Flink: File","s":"Description","u":"/docs/2.2.0-beta/connector/source/File","h":"#description","p":104476},{"i":104481,"t":"Spark Flink name type required default value format string no json path string yes - common-options string yes - format [string]​ Format for reading files, currently supports text, parquet, json, orc, csv. name type required default value format.type string yes - path string yes - schema string yes - common-options string no - parallelism int no - format.type [string]​ The format for reading files from the file system, currently supports csv , json , parquet , orc and text . schema [string]​ csv The schema of csv is a string of jsonArray , such as \"[{\\\"type\\\":\\\"long\\\"},{\\\"type\\\":\\\"string\\\"}]\" , this can only specify the type of the field , The field name cannot be specified, and the common configuration parameter field_name is generally required. json The schema parameter of json is to provide a json string of the original data, and the schema can be automatically generated, but the original data with the most complete content needs to be provided, otherwise the fields will be lost. parquet The schema of parquet is an Avro schema string , such as {\\\"type\\\":\\\"record\\\",\\\"name\\\":\\\"test\\\",\\\"fields\\\":[{\\\"name\\\" :\\\"a\\\",\\\"type\\\":\\\"int\\\"},{\\\"name\\\":\\\"b\\\",\\\"type\\\":\\\"string\\\"}]} . orc The schema of orc is the string of orc schema , such as \"struct<name:string,addresses:array<struct<street:string,zip:smallint>>>\" . text The schema of text can be filled with string . parallelism [Int]​ The parallelism of an individual operator, for FileSource path [string]​ If read data from hdfs , the file path should start with hdfs:// If read data from local , the file path should start with file://","s":"Options","u":"/docs/2.2.0-beta/connector/source/File","h":"#options","p":104476},{"i":104483,"t":"The format for reading files from the file system, currently supports csv , json , parquet , orc and text .","s":"format.type [string]","u":"/docs/2.2.0-beta/connector/source/File","h":"#formattype-string","p":104476},{"i":104485,"t":"csv The schema of csv is a string of jsonArray , such as \"[{\\\"type\\\":\\\"long\\\"},{\\\"type\\\":\\\"string\\\"}]\" , this can only specify the type of the field , The field name cannot be specified, and the common configuration parameter field_name is generally required. json The schema parameter of json is to provide a json string of the original data, and the schema can be automatically generated, but the original data with the most complete content needs to be provided, otherwise the fields will be lost. parquet The schema of parquet is an Avro schema string , such as {\\\"type\\\":\\\"record\\\",\\\"name\\\":\\\"test\\\",\\\"fields\\\":[{\\\"name\\\" :\\\"a\\\",\\\"type\\\":\\\"int\\\"},{\\\"name\\\":\\\"b\\\",\\\"type\\\":\\\"string\\\"}]} . orc The schema of orc is the string of orc schema , such as \"struct<name:string,addresses:array<struct<street:string,zip:smallint>>>\" . text The schema of text can be filled with string .","s":"schema [string]","u":"/docs/2.2.0-beta/connector/source/File","h":"#schema-string","p":104476},{"i":104487,"t":"The parallelism of an individual operator, for FileSource","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/source/File","h":"#parallelism-int","p":104476},{"i":104489,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/File","h":"#common-options-string","p":104476},{"i":104491,"t":"Spark Flink file { path = \"hdfs:///var/logs\" result_table_name = \"access_log\" } file { path = \"file:///var/logs\" result_table_name = \"access_log\" } FileSource{ path = \"hdfs://localhost:9000/input/\" format.type = \"json\" schema = \"{\\\"data\\\":[{\\\"a\\\":1,\\\"b\\\":2},{\\\"a\\\":3,\\\"b\\\":4}],\\\"db\\\":\\\"string\\\",\\\"q\\\":{\\\"s\\\":\\\"string\\\"}}\" result_table_name = \"test\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/source/File","h":"#examples","p":104476},{"i":104493,"t":"Hbase source connector","s":"HBase","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"","p":104492},{"i":104495,"t":"Get data from HBase tip Engine Supported and plugin name Spark: HBase Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"#description","p":104492},{"i":104497,"t":"name type required default value hbase.zookeeper.quorum string yes catalog string yes common-options string yes -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"#options","p":104492},{"i":104499,"t":"The address of the zookeeper cluster, the format is: host01:2181,host02:2181,host03:2181","s":"hbase.zookeeper.quorum [string]","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"#hbasezookeeperquorum-string","p":104492},{"i":104501,"t":"The structure of the hbase table is defined by catalog , the name of the hbase table and its namespace , which columns are used as rowkey, and the correspondence between column family and columns can be defined by catalog hbase table catalog","s":"catalog [string]","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"#catalog-string","p":104492},{"i":104503,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"#common-options-string","p":104492},{"i":104505,"t":"Hbase { hbase.zookeeper.quorum = \"localhost:2181\" catalog = \"{\\\"table\\\":{\\\"namespace\\\":\\\"default\\\", \\\"name\\\":\\\"test\\\"},\\\"rowkey\\\":\\\"id\\\",\\\"columns\\\":{\\\"id\\\":{\\\"cf\\\":\\\"rowkey\\\", \\\"col\\\":\\\"id\\\", \\\"type\\\":\\\"string\\\"},\\\"a\\\":{\\\"cf\\\":\\\"f1\\\", \\\"col\\\":\\\"a\\\", \\\"type\\\":\\\"string\\\"},\\\"b\\\":{\\\"cf\\\":\\\"f1\\\", \\\"col\\\":\\\"b\\\", \\\"type\\\":\\\"string\\\"},\\\"c\\\":{\\\"cf\\\":\\\"f1\\\", \\\"col\\\":\\\"c\\\", \\\"type\\\":\\\"string\\\"}}}\" result_table_name = \"my_dataset\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Hbase","h":"#example","p":104492},{"i":104507,"t":"Hive source connector","s":"Hive","u":"/docs/2.2.0-beta/connector/source/Hive","h":"","p":104506},{"i":104509,"t":"Get data from hive tip Engine Supported and plugin name Spark: Hive Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Hive","h":"#description","p":104506},{"i":104511,"t":"name type required default value pre_sql string yes - common-options string yes -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Hive","h":"#options","p":104506},{"i":104513,"t":"For preprocessed sql , if preprocessing is not required, you can use select * from hive_db.hive_table .","s":"pre_sql [string]","u":"/docs/2.2.0-beta/connector/source/Hive","h":"#pre_sql-string","p":104506},{"i":104515,"t":"Source plugin common parameters, please refer to Source Plugin for details Note: The following configuration must be done to use hive source： # In the spark section in the seatunnel configuration file： env { ... spark.sql.catalogImplementation = \"hive\" ... }","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Hive","h":"#common-options-string","p":104506},{"i":104517,"t":"env { ... spark.sql.catalogImplementation = \"hive\" ... } source { hive { pre_sql = \"select * from mydb.mytb\" result_table_name = \"myTable\" } } ...","s":"Example","u":"/docs/2.2.0-beta/connector/source/Hive","h":"#example","p":104506},{"i":104519,"t":"It must be ensured that the metastore of hive is in service. Start the command hive --service metastore service default port 9083 cluster , client , local mode, hive-site.xml must be placed in the $HADOOP_CONF directory of the task submission node (or placed under $SPARK_HOME/conf ), IDE local Debug put it in the resources directory","s":"Notes","u":"/docs/2.2.0-beta/connector/source/Hive","h":"#notes","p":104506},{"i":104521,"t":"Http source connector","s":"Http","u":"/docs/2.2.0-beta/connector/source/Http","h":"","p":104520},{"i":104523,"t":"Get data from http or https interface tip Engine Supported and plugin name Spark: Http Flink: Http","s":"Description","u":"/docs/2.2.0-beta/connector/source/Http","h":"#description","p":104520},{"i":104525,"t":"name type required default vale url string yes - method string no GET header string no request_params string no sync_path string no","s":"Options","u":"/docs/2.2.0-beta/connector/source/Http","h":"#options","p":104520},{"i":104527,"t":"HTTP request path, starting with http:// or https://.","s":"url [string]","u":"/docs/2.2.0-beta/connector/source/Http","h":"#url-string","p":104520},{"i":104529,"t":"HTTP request method, GET or POST, default GET.","s":"method[string]","u":"/docs/2.2.0-beta/connector/source/Http","h":"#methodstring","p":104520},{"i":104531,"t":"HTTP request header, json format.","s":"header[string]","u":"/docs/2.2.0-beta/connector/source/Http","h":"#headerstring","p":104520},{"i":104533,"t":"HTTP request parameters, json format. Use string with escapes to save json","s":"request_params[string]","u":"/docs/2.2.0-beta/connector/source/Http","h":"#request_paramsstring","p":104520},{"i":104535,"t":"HTTP multiple requests, the storage path of parameters used for synchronization (hdfs).","s":"sync_path[string]","u":"/docs/2.2.0-beta/connector/source/Http","h":"#sync_pathstring","p":104520},{"i":104537,"t":"Source plugin common parameters, please refer to Source Plugin for details.","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Http","h":"#common-options-string","p":104520},{"i":104539,"t":"Http { url = \"http://date.jsontest.com/\" result_table_name= \"response_body\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Http","h":"#example","p":104520},{"i":104541,"t":"According to the processing result of the http call, to determine whether the synchronization parameters need to be updated, it needs to be written to hdfs through the hdfs sink plugin after the judgment is made outside the http source plugin.","s":"Notes","u":"/docs/2.2.0-beta/connector/source/Http","h":"#notes","p":104520},{"i":104543,"t":"Hudi source connector","s":"Hudi","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"","p":104542},{"i":104545,"t":"Read data from Hudi. tip Engine Supported and plugin name Spark: Hudi Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#description","p":104542},{"i":104547,"t":"name type required default value hoodie.datasource.read.paths string yes - hoodie.file.index.enable boolean no - hoodie.datasource.read.end.instanttime string no - hoodie.datasource.write.precombine.field string no - hoodie.datasource.read.incr.filters string no - hoodie.datasource.merge.type string no - hoodie.datasource.read.begin.instanttime string no - hoodie.enable.data.skipping string no - as.of.instant string no - hoodie.datasource.query.type string no - hoodie.datasource.read.schema.use.end.instanttime string no - Refer to hudi read options for configurations.","s":"Options","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#options","p":104542},{"i":104549,"t":"Comma separated list of file paths to read within a Hudi table.","s":"hoodie.datasource.read.paths","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadpaths","p":104542},{"i":104551,"t":"Enables use of the spark file index implementation for Hudi, that speeds up listing of large tables.","s":"hoodie.file.index.enable","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiefileindexenable","p":104542},{"i":104553,"t":"Instant time to limit incrementally fetched data to. New data written with an instant_time <= END_INSTANTTIME are fetched out.","s":"hoodie.datasource.read.end.instanttime","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadendinstanttime","p":104542},{"i":104555,"t":"Field used in preCombining before actual write. When two records have the same key value, we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..)","s":"hoodie.datasource.write.precombine.field","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcewriteprecombinefield","p":104542},{"i":104557,"t":"For use-cases like DeltaStreamer which reads from Hoodie Incremental table and applies opaque map functions, filters appearing late in the sequence of transformations cannot be automatically pushed down. This option allows setting filters directly on Hoodie Source.","s":"hoodie.datasource.read.incr.filters","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadincrfilters","p":104542},{"i":104559,"t":"For Snapshot query on merge on read table, control whether we invoke the record payload implementation to merge (payload_combine) or skip merging altogetherskip_merge","s":"hoodie.datasource.merge.type","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcemergetype","p":104542},{"i":104561,"t":"Instant time to start incrementally pulling data from. The instanttime here need not necessarily correspond to an instant on the timeline. New data written with an instant_time > BEGIN_INSTANTTIME are fetched out. For e.g: ‘20170901080000’ will get all new data written after Sep 1, 2017 08:00AM.","s":"hoodie.datasource.read.begin.instanttime","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadbegininstanttime","p":104542},{"i":104563,"t":"enable data skipping to boost query after doing z-order optimize for current table","s":"hoodie.enable.data.skipping","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodieenabledataskipping","p":104542},{"i":104565,"t":"The query instant for time travel. Without specified this option, we query the latest snapshot.","s":"as.of.instant","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#asofinstant","p":104542},{"i":104567,"t":"Whether data needs to be read, in incremental mode (new data since an instantTime) (or) Read Optimized mode (obtain latest view, based on base files) (or) Snapshot mode (obtain latest view, by merging base and (if any) log files)","s":"hoodie.datasource.query.type","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcequerytype","p":104542},{"i":104569,"t":"Uses end instant schema when incrementally fetched data to. Default: users latest instant schema.","s":"hoodie.datasource.read.schema.use.end.instanttime","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadschemauseendinstanttime","p":104542},{"i":104571,"t":"hudi { hoodie.datasource.read.paths = \"hdfs://\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Hudi","h":"#example","p":104542},{"i":104573,"t":"Iceberg source connector","s":"Iceberg","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"","p":104572},{"i":104575,"t":"Read data from Iceberg. tip Engine Supported and plugin name Spark: Iceberg Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#description","p":104572},{"i":104577,"t":"name type required default value common-options yes - path string yes - pre_sql string yes - snapshot-id long no - as-of-timestamp long no - Refer to iceberg read options for more configurations.","s":"Options","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#options","p":104572},{"i":104579,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common-options","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#common-options","p":104572},{"i":104581,"t":"Iceberg table location.","s":"path","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#path","p":104572},{"i":104583,"t":"SQL statements queried from iceberg table. Note that the table name is result_table_name configuration","s":"pre_sql","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#pre_sql","p":104572},{"i":104585,"t":"Snapshot ID of the table snapshot to read","s":"snapshot-id","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#snapshot-id","p":104572},{"i":104587,"t":"A timestamp in milliseconds; the snapshot used will be the snapshot current at this time.","s":"as-of-timestamp","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#as-of-timestamp","p":104572},{"i":104589,"t":"iceberg { path = \"hdfs://localhost:9000/iceberg/warehouse/db/table\" result_table_name = \"my_source\" pre_sql=\"select * from my_source where dt = '2019-01-01'\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Iceberg","h":"#example","p":104572},{"i":104591,"t":"InfluxDb source connector","s":"InfluxDb","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"","p":104590},{"i":104593,"t":"Read data from InfluxDB. tip Engine Supported and plugin name Spark Flink: InfluxDb","s":"Description","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#description","p":104590},{"i":104595,"t":"name type required default value server_url String yes - username String no - password String no - database String yes - measurement String yes - fields List<String> yes - field_types List<String> yes - parallelism Int no -","s":"Options","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#options","p":104590},{"i":104597,"t":"The URL of InfluxDB Server.","s":"server_url [String]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#server_url-string","p":104590},{"i":104599,"t":"The username of InfluxDB Server.","s":"username [String]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#username-string","p":104590},{"i":104601,"t":"The password of InfluxDB Server.","s":"password [String]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#password-string","p":104590},{"i":104603,"t":"The database name in InfluxDB.","s":"database [String]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#database-string","p":104590},{"i":104605,"t":"The Measurement name in InfluxDB.","s":"measurement [String]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#measurement-string","p":104590},{"i":104607,"t":"The list of Field in InfluxDB.","s":"fields [List<String>]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#fields-liststring","p":104590},{"i":104609,"t":"The list of Field Types in InfluxDB.","s":"field_types [List<String>]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#field_types-liststring","p":104590},{"i":104611,"t":"The parallelism of an individual operator, for InfluxDbSource.","s":"parallelism [Int]","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#parallelism-int","p":104590},{"i":104614,"t":"InfluxDbSource { server_url = \"http://127.0.0.1:8086/\" database = \"influxdb\" measurement = \"m\" fields = [\"time\", \"temperature\"] field_types = [\"STRING\", \"DOUBLE\"] }","s":"Simple","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#simple","p":104590},{"i":104616,"t":"InfluxDbSource { server_url = \"http://127.0.0.1:8086/\" username = \"admin\" password = \"password\" database = \"influxdb\" measurement = \"m\" fields = [\"time\", \"temperature\"] field_types = [\"STRING\", \"DOUBLE\"] }","s":"Auth","u":"/docs/2.2.0-beta/connector/source/InfluxDb","h":"#auth","p":104590},{"i":104618,"t":"JDBC source connector","s":"Jdbc","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"","p":104617},{"i":104620,"t":"Read external data source data through JDBC tip Engine Supported and plugin name Spark: Jdbc Flink: Jdbc","s":"Description","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#description","p":104617},{"i":104622,"t":"Spark Flink name type required default value driver string yes - jdbc.* string no password string yes - table string yes - url string yes - user string yes - common-options string yes - name type required default value driver string yes - url string yes - username string yes - password string no - query string yes - fetch_size int no - partition_column string no - partition_upper_bound long no - partition_lower_bound long no - common-options string no - parallelism int no -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#options","p":104617},{"i":104624,"t":"The jdbc class name used to connect to the remote data source, if you use MySQL the value is com.mysql.cj.jdbc.Driver. Warn: for license compliance, you have to provide MySQL JDBC driver yourself, e.g. copy mysql-connector-java-xxx.jar to $FLINK_HOME/lib for Standalone.","s":"driver [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#driver-string","p":104617},{"i":104626,"t":"password​","s":"password [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#password-string","p":104617},{"i":104628,"t":"The URL of the JDBC connection. Refer to a case: jdbc:postgresql://localhost/test Spark Flink jdbc [string]​ In addition to the parameters that must be specified above, users can also specify multiple optional parameters, which cover all the parameters provided by Spark JDBC. The way to specify parameters is to add the prefix jdbc. to the original parameter name. For example, the way to specify fetchsize is: jdbc.fetchsize = 50000 . If these non-essential parameters are not specified, they will use the default values given by Spark JDBC. user [string]​ username table [string]​ table name username [string]​ username query [string]​ Query statement fetch_size [int]​ fetch size parallelism [int]​ The parallelism of an individual operator, for JdbcSource. partition_column [string]​ The column name for parallelism's partition, only support numeric type. partition_upper_bound [long]​ The partition_column max value for scan, if not set SeaTunnel will query database get max value. partition_lower_bound [long]​ The partition_column min value for scan, if not set SeaTunnel will query database get min value.","s":"url [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#url-string","p":104617},{"i":104630,"t":"In addition to the parameters that must be specified above, users can also specify multiple optional parameters, which cover all the parameters provided by Spark JDBC. The way to specify parameters is to add the prefix jdbc. to the original parameter name. For example, the way to specify fetchsize is: jdbc.fetchsize = 50000 . If these non-essential parameters are not specified, they will use the default values given by Spark JDBC.","s":"jdbc [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#jdbc-string","p":104617},{"i":104632,"t":"username","s":"user [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#user-string","p":104617},{"i":104634,"t":"table name","s":"table [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#table-string","p":104617},{"i":104636,"t":"username","s":"username [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#username-string","p":104617},{"i":104638,"t":"Query statement","s":"query [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#query-string","p":104617},{"i":104640,"t":"fetch size","s":"fetch_size [int]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#fetch_size-int","p":104617},{"i":104642,"t":"The parallelism of an individual operator, for JdbcSource.","s":"parallelism [int]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#parallelism-int","p":104617},{"i":104644,"t":"The column name for parallelism's partition, only support numeric type.","s":"partition_column [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#partition_column-string","p":104617},{"i":104646,"t":"The partition_column max value for scan, if not set SeaTunnel will query database get max value.","s":"partition_upper_bound [long]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#partition_upper_bound-long","p":104617},{"i":104648,"t":"The partition_column min value for scan, if not set SeaTunnel will query database get min value.","s":"partition_lower_bound [long]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#partition_lower_bound-long","p":104617},{"i":104650,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#common-options-string","p":104617},{"i":104652,"t":"Spark Flink jdbc { driver = \"com.mysql.cj.jdbc.Driver\" url = \"jdbc:mysql://localhost:3306/info\" table = \"access\" result_table_name = \"access_log\" user = \"username\" password = \"password\" } Read MySQL data through JDBC jdbc { driver = \"com.mysql.cj.jdbc.Driver\" url = \"jdbc:mysql://localhost:3306/info\" table = \"access\" result_table_name = \"access_log\" user = \"username\" password = \"password\" jdbc.partitionColumn = \"item_id\" jdbc.numPartitions = \"10\" jdbc.lowerBound = 0 jdbc.upperBound = 100 } Divide partitions based on specified fields jdbc { driver = \"com.mysql.cj.jdbc.Driver\" url = \"jdbc:mysql://localhost:3306/info\" table = \"access\" result_table_name = \"access_log\" user = \"username\" password = \"password\" jdbc.connect_timeout = 10000 jdbc.socket_timeout = 10000 } Timeout config JdbcSource { driver = com.mysql.cj.jdbc.Driver url = \"jdbc:mysql://localhost/test\" username = root query = \"select * from test\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Jdbc","h":"#example","p":104617},{"i":104654,"t":"Kafka source connector","s":"Kafka","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"","p":104653},{"i":104656,"t":"To consume data from Kafka , supported Kafka version >= 0.10.0 . tip Engine Supported and plugin name Spark: KafkaStream Flink: Kafka","s":"Description","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#description","p":104653},{"i":104658,"t":"Spark Flink name type required default value topics string yes - consumer.group.id string yes - consumer.bootstrap.servers string yes - consumer.* string no - common-options string yes - name type required default value topics string yes - consumer.group.id string yes - consumer.bootstrap.servers string yes - schema string yes - format.type string yes - format.* string no - consumer.* string no - rowtime.field string no - watermark long no - offset.reset string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#options","p":104653},{"i":104660,"t":"Kafka topic name. If there are multiple topics, use , to split, for example: \"tpc1,tpc2\"","s":"topics [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#topics-string","p":104653},{"i":104662,"t":"Kafka consumer group id, used to distinguish different consumer groups","s":"consumer.group.id [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#consumergroupid-string","p":104653},{"i":104664,"t":"Kafka cluster address, separated by , Spark Flink format.type [string]​ Currently supports three formats json csv avro format.* [string]​ The csv format uses this parameter to set the separator and so on. For example, set the column delimiter to \\t , format.field-delimiter=\\\\t schema [string]​ csv The schema of csv is a string of jsonArray , such as \"[{\\\"field\\\":\\\"name\\\",\\\"type\\\":\\\"string\\\"},{\\\"field\\\":\\\"age\\ \",\\\"type\\\":\\\"int\\\"}]\" . json The schema parameter of json is to provide a json string of the original data, and the schema can be automatically generated, but the original data with the most complete content needs to be provided, otherwise the fields will be lost. avro The schema parameter of avro is to provide a standard avro schema JSON string , such as {\\\"name\\\":\\\"test\\\",\\\"type\\\":\\\"record\\\",\\\"fields\\\":[{ \\\"name\\\":\\\"name\\\",\\\"type\\\":\\\"string\\\"},{\\\"name\\\":\\\"age\\\",\\\"type\\\":\\\"long\\\"} ,{\\\"name\\\":\\\"addrs\\\",\\\"type\\\":{\\\"name\\\":\\\"addrs\\\",\\\"type\\\":\\\"record\\\",\\\"fields\\\" :[{\\\"name\\\":\\\"province\\\",\\\"type\\\":\\\"string\\\"},{\\\"name\\\":\\\"city\\\",\\\"type\\\":\\\"string \\\"}]}}]} To learn more about how the Avro Schema JSON string should be defined, please refer to: https://avro.apache.org/docs/current/spec.html rowtime.field [string]​ Extract timestamp using current configuration field for flink event time watermark watermark [long]​ Sets a built-in watermark strategy for rowtime.field attributes which are out-of-order by a bounded time interval. Emits watermarks which are the maximum observed timestamp minus the specified delay. offset.reset [string]​ The consumer's initial offset is only valid for new consumers. There are three modes latest Start consumption from the latest offset earliest Start consumption from the earliest offset specific Start consumption from the specified offset , and specify the start offset of each partition at this time. The setting method is through offset.reset.specific=\"{0:111,1:123}\"","s":"consumer.bootstrap.servers [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#consumerbootstrapservers-string","p":104653},{"i":104666,"t":"Currently supports three formats json csv avro","s":"format.type [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#formattype-string","p":104653},{"i":104668,"t":"The csv format uses this parameter to set the separator and so on. For example, set the column delimiter to \\t , format.field-delimiter=\\\\t","s":"format.* [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#format-string","p":104653},{"i":104670,"t":"csv The schema of csv is a string of jsonArray , such as \"[{\\\"field\\\":\\\"name\\\",\\\"type\\\":\\\"string\\\"},{\\\"field\\\":\\\"age\\ \",\\\"type\\\":\\\"int\\\"}]\" . json The schema parameter of json is to provide a json string of the original data, and the schema can be automatically generated, but the original data with the most complete content needs to be provided, otherwise the fields will be lost. avro The schema parameter of avro is to provide a standard avro schema JSON string , such as {\\\"name\\\":\\\"test\\\",\\\"type\\\":\\\"record\\\",\\\"fields\\\":[{ \\\"name\\\":\\\"name\\\",\\\"type\\\":\\\"string\\\"},{\\\"name\\\":\\\"age\\\",\\\"type\\\":\\\"long\\\"} ,{\\\"name\\\":\\\"addrs\\\",\\\"type\\\":{\\\"name\\\":\\\"addrs\\\",\\\"type\\\":\\\"record\\\",\\\"fields\\\" :[{\\\"name\\\":\\\"province\\\",\\\"type\\\":\\\"string\\\"},{\\\"name\\\":\\\"city\\\",\\\"type\\\":\\\"string \\\"}]}}]} To learn more about how the Avro Schema JSON string should be defined, please refer to: https://avro.apache.org/docs/current/spec.html","s":"schema [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#schema-string","p":104653},{"i":104672,"t":"Extract timestamp using current configuration field for flink event time watermark","s":"rowtime.field [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#rowtimefield-string","p":104653},{"i":104674,"t":"Sets a built-in watermark strategy for rowtime.field attributes which are out-of-order by a bounded time interval. Emits watermarks which are the maximum observed timestamp minus the specified delay.","s":"watermark [long]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#watermark-long","p":104653},{"i":104676,"t":"The consumer's initial offset is only valid for new consumers. There are three modes latest Start consumption from the latest offset earliest Start consumption from the earliest offset specific Start consumption from the specified offset , and specify the start offset of each partition at this time. The setting method is through offset.reset.specific=\"{0:111,1:123}\"","s":"offset.reset [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#offsetreset-string","p":104653},{"i":104678,"t":"In addition to the above necessary parameters that must be specified by the Kafka consumer client, users can also specify multiple consumer client non-mandatory parameters, covering all consumer parameters specified in the official Kafka document. The way to specify parameters is to add the prefix consumer. to the original parameter name. For example, the way to specify auto.offset.reset is: consumer.auto.offset.reset = latest . If these non-essential parameters are not specified, they will use the default values given in the official Kafka documentation.","s":"consumer.* [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#consumer-string","p":104653},{"i":104680,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#common-options-string","p":104653},{"i":104682,"t":"Spark Flink kafkaStream { topics = \"seatunnel\" consumer.bootstrap.servers = \"localhost:9092\" consumer.group.id = \"seatunnel_group\" } KafkaTableStream { consumer.bootstrap.servers = \"127.0.0.1:9092\" consumer.group.id = \"seatunnel5\" topics = test result_table_name = test format.type = csv schema = \"[{\\\"field\\\":\\\"name\\\",\\\"type\\\":\\\"string\\\"},{\\\"field\\\":\\\"age\\\",\\\"type\\\":\\\"int\\\"}]\" format.field-delimiter = \";\" format.allow-comments = \"true\" format.ignore-parse-errors = \"true\" }","s":"Examples","u":"/docs/2.2.0-beta/connector/source/Kafka","h":"#examples","p":104653},{"i":104684,"t":"Kudu source connector","s":"Kudu","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"","p":104683},{"i":104686,"t":"Read data from Kudu. tip Engine Supported and plugin name Spark: Kudu Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"#description","p":104683},{"i":104688,"t":"name type required default value kudu_master string yes - kudu_table string yes -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"#options","p":104683},{"i":104690,"t":"Kudu Master","s":"kudu_master [string]","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"#kudu_master-string","p":104683},{"i":104692,"t":"Kudu Table","s":"kudu_table [string]","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"#kudu_table-string","p":104683},{"i":104694,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"#common-options-string","p":104683},{"i":104696,"t":"kudu { kudu_master = \"master:7051\" kudu_table = \"impala::test_db.test_table\" result_table_name = \"kudu_result_table\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Kudu","h":"#example","p":104683},{"i":104698,"t":"MongoDb source connector","s":"MongoDb","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"","p":104697},{"i":104700,"t":"Read data from MongoDB. tip Engine Supported and plugin name Spark: MongoDb Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#description","p":104697},{"i":104702,"t":"name type required default value readconfig.uri string yes - readconfig.database string yes - readconfig.collection string yes - readconfig.* string no - schema string no - common-options string yes -","s":"Options","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#options","p":104697},{"i":104704,"t":"MongoDB uri","s":"readconfig.uri [string]","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#readconfiguri-string","p":104697},{"i":104706,"t":"MongoDB database","s":"readconfig.database [string]","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#readconfigdatabase-string","p":104697},{"i":104708,"t":"MongoDB collection","s":"readconfig.collection [string]","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#readconfigcollection-string","p":104697},{"i":104710,"t":"More other parameters can be configured here, see MongoDB Configuration for details, see the Input Configuration section. The way to specify parameters is to prefix the original parameter name readconfig. For example, the way to set spark.mongodb.input.partitioner is readconfig.spark.mongodb.input.partitioner=\"MongoPaginateBySizePartitioner\" . If you do not specify these optional parameters, the default values of the official MongoDB documentation will be used.","s":"readconfig.* [string]","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#readconfig-string","p":104697},{"i":104712,"t":"Because MongoDB does not have the concept of schema, when spark reads MongoDB , it will sample MongoDB data and infer the schema . In fact, this process will be slow and may be inaccurate. This parameter can be manually specified. Avoid these problems. schema is a json string, such as {\\\"name\\\":\\\"string\\\",\\\"age\\\":\\\"integer\\\",\\\"addrs\\\":{\\\"country\\\":\\\"string\\ \",\\\"city\\\":\\\"string\\\"}}","s":"schema [string]","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#schema-string","p":104697},{"i":104714,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#common-options-string","p":104697},{"i":104716,"t":"mongodb { readconfig.uri = \"mongodb://username:password@127.0.0.1:27017/mypost\" readconfig.database = \"mydatabase\" readconfig.collection = \"mycollection\" readconfig.spark.mongodb.input.partitioner = \"MongoPaginateBySizePartitioner\" schema=\"{\\\"name\\\":\\\"string\\\",\\\"age\\\":\\\"integer\\\",\\\"addrs\\\":{\\\"country\\\":\\\"string\\\",\\\"city\\\":\\\"string\\\"}}\" result_table_name = \"mongodb_result_table\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/MongoDB","h":"#example","p":104697},{"i":104718,"t":"Neo4j source connector","s":"Neo4j","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"","p":104717},{"i":104720,"t":"Read data from Neo4j. Neo4j Connector for Apache Spark allows you to read data from Neo4j in 3 different ways: by node labels, by relationship name, and by direct Cypher query. The Options required of yes* means that you must specify one way of (query labels relationship) for detail neo4j config message please visit neo4j doc tip Engine Supported and plugin name Spark: Neo4j Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#description","p":104717},{"i":104722,"t":"name type required default value result_table_name string yes - authentication.type string no - authentication.basic.username string no - authentication.basic.password string no - url string yes - query string yes* - labels string yes* - relationship string yes* - schema.flatten.limit string no - schema.strategy string no - pushdown.filters.enabled string no - pushdown.columns.enabled string no - partitions string no - query.count string no - relationship.nodes.map string no - relationship.source.labels string Yes - relationship.target.labels string Yes -","s":"Options","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#options","p":104717},{"i":104724,"t":"result table name","s":"result.table.name [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#resulttablename-string","p":104717},{"i":104726,"t":"authentication type","s":"authentication.type [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#authenticationtype-string","p":104717},{"i":104728,"t":"username","s":"authentication.basic.username [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#authenticationbasicusername-string","p":104717},{"i":104730,"t":"password","s":"authentication.basic.password [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#authenticationbasicpassword-string","p":104717},{"i":104732,"t":"url","s":"url [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#url-string","p":104717},{"i":104734,"t":"Cypher query to read the data.You must specify one option from [query, labels OR relationship]","s":"query [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#query-string","p":104717},{"i":104736,"t":"List of node labels separated by : The first label will be the primary label. You must specify one option from [query, labels OR relationship]","s":"labels [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#labels-string","p":104717},{"i":104738,"t":"Name of a relationship. You must specify one option from [query, labels OR relationship]","s":"relationship [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#relationship-string","p":104717},{"i":104740,"t":"Number of records to be used to create the Schema (only if APOC are not installed)","s":"schema.flatten.limit [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#schemaflattenlimit-string","p":104717},{"i":104742,"t":"Strategy used by the connector in order to compute the Schema definition for the Dataset. Possibile values are string, sample. When string it coerces all the properties to String otherwise it will try to sample the Neo4j’s dataset.","s":"schema.strategy [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#schemastrategy-string","p":104717},{"i":104744,"t":"Enable or disable the Push Down Filters support","s":"pushdown.filters.enabled [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#pushdownfiltersenabled-string","p":104717},{"i":104746,"t":"Enable or disable the Push Down Column support","s":"pushdown.columns.enabled [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#pushdowncolumnsenabled-string","p":104717},{"i":104748,"t":"This defines the parallelization level while pulling data from Neo4j. Note: as more parallelization does not mean more performances so please tune wisely in according to your Neo4j installation.","s":"partitions [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#partitions-string","p":104717},{"i":104750,"t":"Query count, used only in combination with query option, it’s a query that returns a count field like the following: MATCH (p:Person)-[r:BOUGHT]->(pr:Product) WHERE pr.name = 'An Awesome Product' RETURN count(p) AS count or a simple number that represents the amount of records returned by query. Consider that the number passed by this value represent the volume of the data pulled of Neo4j, so please use it carefully.","s":"query.count [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#querycount-string","p":104717},{"i":104752,"t":"If true return source and target nodes as Map<String, String>, otherwise we flatten the properties by returning every single node property as column prefixed by source or target","s":"relationship.nodes.map [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#relationshipnodesmap-string","p":104717},{"i":104754,"t":"List of source node Labels separated by :","s":"relationship.source.labels [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#relationshipsourcelabels-string","p":104717},{"i":104756,"t":"List of target node Labels separated by :","s":"relationship.target.labels [string]","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#relationshiptargetlabels-string","p":104717},{"i":104758,"t":"Neo4j { result_table_name = \"test\" authentication.type = \"basic\" authentication.basic.username = \"test\" authentication.basic.password = \"test\" url = \"bolt://localhost:7687\" labels = \"Person\" #query = \"MATCH (n1)-[r]->(n2) RETURN r, n1, n2 \" } The returned table is a data table in which both fields are strings <id> <labels> name born 1 [Person] Keanu Reeves 1964 2 [Person] Carrie-Anne Moss 1967 3 [Person] Laurence Fishburne 1961 4 [Person] Hugo Weaving 1960 5 [Person] Andy Wachowski 1967 6 [Person] Lana Wachowski 1965 7 [Person] Joel Silver 1952 8 [Person] Emil Eifrem 1978","s":"Example","u":"/docs/2.2.0-beta/connector/source/neo4j","h":"#example","p":104717},{"i":104760,"t":"Phoenix source connector","s":"Phoenix","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"","p":104759},{"i":104762,"t":"Read external data source data through Phoenix , compatible with Kerberos authentication tip Engine Supported and plugin name Spark: Phoenix Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#description","p":104759},{"i":104764,"t":"name type required default value zk-connect string yes - table string yes columns string no - tenantId string no - predicate string no -","s":"Options","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#options","p":104759},{"i":104766,"t":"Connection string, configuration example: host1:2181,host2:2181,host3:2181 [/znode]","s":"zk-connect [string]","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#zk-connect-string","p":104759},{"i":104768,"t":"Source data table name","s":"table [string]","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#table-string","p":104759},{"i":104770,"t":"Read column name configuration. Read all columns set to [] , optional configuration item, default is [] .","s":"columns [string-list]","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#columns-string-list","p":104759},{"i":104772,"t":"Tenant ID, optional configuration item","s":"tenant-id [string]","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#tenant-id-string","p":104759},{"i":104774,"t":"Conditional filter string configuration, optional configuration items","s":"predicate [string]","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#predicate-string","p":104759},{"i":104776,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#common-options-string","p":104759},{"i":104778,"t":"Phoenix { zk-connect = \"host1:2181,host2:2181,host3:2181\" table = \"table22\" result_table_name = \"tmp1\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Phoenix","h":"#example","p":104759},{"i":104780,"t":"Redis source connector","s":"Redis","u":"/docs/2.2.0-beta/connector/source/Redis","h":"","p":104779},{"i":104782,"t":"Read data from Redis. tip Engine Supported and plugin name Spark: Redis Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#description","p":104779},{"i":104784,"t":"name type required default value host string no \"localhost\" port int no 6379 auth string no db_num int no 0 keys_or_key_pattern string yes partition_num int no 3 data_type string no \"KV\" timeout int no 2000 common-options string yes is_self_achieved boolean no false","s":"Options","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#options","p":104779},{"i":104786,"t":"Redis server address, default \"localhost\"","s":"host [string]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#host-string","p":104779},{"i":104788,"t":"Redis service port, default 6379","s":"port [int]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#port-int","p":104779},{"i":104790,"t":"Redis authentication password","s":"auth [string]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#auth-string","p":104779},{"i":104792,"t":"Redis database index ID. It is connected to db 0 by default","s":"db_num [int]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#db_num-int","p":104779},{"i":104794,"t":"Redis Key, support fuzzy matching","s":"keys_or_key_pattern [string]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#keys_or_key_pattern-string","p":104779},{"i":104796,"t":"Number of Redis shards. The default is 3","s":"partition_num [int]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#partition_num-int","p":104779},{"i":104798,"t":"Redis data type eg: KV HASH LIST SET ZSET","s":"data_type [string]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#data_type-string","p":104779},{"i":104800,"t":"Redis timeout","s":"timeout [int]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#timeout-int","p":104779},{"i":104802,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#common-options-string","p":104779},{"i":104804,"t":"If a redis access by a self-achieved redis proxy, which is not support redis function of \"info Replication\"","s":"is_self_achieved [boolean]","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#is_self_achieved-boolean","p":104779},{"i":104806,"t":"redis { host = \"localhost\" port = 6379 auth = \"myPassword\" db_num = 1 keys_or_key_pattern = \"*\" partition_num = 20 data_type = \"HASH\" result_table_name = \"hash_result_table\" is_self_achieved = false } The returned table is a data table in which both fields are strings raw_key raw_message keys xxx my_keys xxx keys_mine xxx","s":"Example","u":"/docs/2.2.0-beta/connector/source/Redis","h":"#example","p":104779},{"i":104808,"t":"Socket source connector","s":"Socket","u":"/docs/2.2.0-beta/connector/source/Socket","h":"","p":104807},{"i":104810,"t":"SocketStream is mainly used to receive Socket data and is used to quickly verify Spark streaming computing. tip Engine Supported and plugin name Spark: SocketStream Flink: SocketStream","s":"Description","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#description","p":104807},{"i":104812,"t":"Spark Flink name type required default value host string no localhost port number no 9999 common-options string yes - host [string]​ socket server hostname port [number]​ socket server port common options [string]​ Source plugin common parameters, please refer to Source Plugin for details name type required default value host string no localhost port int no 9999 common-options string no - host [string]​ socket server hostname port [int]​ socket server port common options [string]​ Source plugin common parameters, please refer to Source Plugin for details","s":"Options","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#options","p":104807},{"i":104814,"t":"socket server hostname","s":"host [string]","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#host-string","p":104807},{"i":104816,"t":"socket server port","s":"port [number]","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#port-number","p":104807},{"i":104818,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#common-options-string","p":104807},{"i":104820,"t":"socket server hostname","s":"host [string]","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#host-string-1","p":104807},{"i":104822,"t":"socket server port","s":"port [int]","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#port-int","p":104807},{"i":104824,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#common-options-string-1","p":104807},{"i":104826,"t":"Spark Flink source { SocketStream { port = 9999 } } source { SocketStream{ result_table_name = \"socket\" field_name = \"info\" } }","s":"Examples","u":"/docs/2.2.0-beta/connector/source/Socket","h":"#examples","p":104807},{"i":104828,"t":"Webhook source connector","s":"WebhookStream","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"","p":104827},{"i":104830,"t":"Provide http interface to push data，only supports post requests. tip Engine Supported and plugin name Spark: WebhookStream Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"#description","p":104827},{"i":104832,"t":"name type required default value port int no 9999 path string no /","s":"Options","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"#options","p":104827},{"i":104834,"t":"Port for push requests, default 9999.","s":"port[int]","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"#portint","p":104827},{"i":104836,"t":"Push request path, default \"/\".","s":"path[string]","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"#pathstring","p":104827},{"i":104838,"t":"Source plugin common parameters, please refer to Source Plugin for details.","s":"common options [string]","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"#common-options-string","p":104827},{"i":104840,"t":"WebhookStream { result_table_name = \"request_body\" }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Webhook","h":"#example","p":104827},{"i":104842,"t":"TiDB source connector","s":"Tidb","u":"/docs/2.2.0-beta/connector/source/Tidb","h":"","p":104841},{"i":104844,"t":"Read data from Tidb. tip Engine Supported and plugin name Spark: Tidb Flink","s":"Description","u":"/docs/2.2.0-beta/connector/source/Tidb","h":"#description","p":104841},{"i":104846,"t":"name type required default value spark.tispark.pd.addresses string yes - spark.sql.extensions string yes org.apache.spark.sql.TiExtensions spark.tispark.pd.addresses [string]​ your pd servers spark.sql.extensions [string]​ default value : org.apache.spark.sql.TiExtensions","s":"Env Options","u":"/docs/2.2.0-beta/connector/source/Tidb","h":"#env-options","p":104841},{"i":104848,"t":"name type required default value database string yes - pre_sql string yes - database [string]​ Tidb database pre_sql [string]​ sql script common options [string]​ Source Plugin common parameters, refer to Source Plugin for details","s":"Options","u":"/docs/2.2.0-beta/connector/source/Tidb","h":"#options","p":104841},{"i":104850,"t":"env { spark.tispark.pd.addresses = \"192.168.0.1:2379\" spark.sql.extensions = \"org.apache.spark.sql.TiExtensions\" } source { tidb { database = \"test\" pre_sql = \"select * from table1\" } }","s":"Example","u":"/docs/2.2.0-beta/connector/source/Tidb","h":"#example","p":104841},{"i":104852,"t":"There are two parent modules for Spark plugins: seatunnel-connectors-spark seatunnel-transforms-spark Once you want to contribute a new plugin, you need to:","s":"Contribute Spark Plugins","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"","p":104851},{"i":104854,"t":"Create your plugin module under the corresponding parent plugin module. For example, if you want to add a new Spark connector plugin, you need to create a new module under the seatunnel-connectors-spark module. <project xmlns=\"http://maven.apache.org/POM/4.0.0\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd\"> <parent> <groupId>org.apache.seatunnel</groupId> <artifactId>seatunnel-connectors-spark</artifactId> <version>${revision}</version> </parent> <modelVersion>4.0.0</modelVersion> <artifactId>seatunnel-connector-spark-hello</artifactId> <dependencies> <dependency> <groupId>org.apache.seatunnel</groupId> <artifactId>seatunnel-api-spark</artifactId> <version>${project.version}</version> </dependency> </dependencies> </project>","s":"Create plugin module","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"#create-plugin-module","p":104851},{"i":104856,"t":"You need to implement the Connector service provider interface. e.g. BaseSource/BaseSink. Conveniently, there are some abstract class can help you easy to create your plugin. If you want to create a source connector, you can implement with SparkBatchSource/SparkStreamingSource. If you want to create a sink connector, you can implement with SparkBatchSink/SparkStreamingSink. The methods defined in SparkBatchSource are some lifecycle methods. will be executed by SeaTunnel engine. The execution order of the lifecycle methods is: checkConfig -> prepare -> getData -> close. import java.util.Date; @AutoService(BaseSparkSource.class) public class Hello extends SparkBatchSource { @Override public Dataset<Row> getData(SparkEnvironment env) { // do your logic here to generate data Dataset<Row> dataset = null; return dataset; } @Override public CheckResult checkConfig() { return super.checkConfig(); } @Override public void prepare(SparkEnvironment env) { super.prepare(env); } @Override public void close() throws Exception { super.close(); } @Override public String getPluginName() { return \"hello\"; } } The getPluginName method is used to identify the plugin name. The @AutoService is used to generate the META-INF/services/org.apache.seatunnel.BaseSparkSource file automatically. Since this process cannot work on scala, if you use slala to implement your plugin, you need to add a service provider to the META-INF/services file. The file name should be org.apache.seatunnel.spark.BaseSparkSource or org.apache.seatunnel.spark.BaseSparkSink, dependents on the plugin type. The content of the file should be the fully qualified class name of your implementation.","s":"Add plugin implementation","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"#add-plugin-implementation","p":104851},{"i":104858,"t":"You need to add your plugin to the seatunnel-connectors-spark-dist module, then the plugin will in distribution. <dependency> <groupId>org.apache.seatunnel</groupId> <artifactId>seatunnel-connector-spark-hello</artifactId> <version>${project.version}</version> </dependency> After you using mvn package to make a distribution, you can find the plugin in your ${distribution}/connectors/spark.","s":"Add plugin to the distribution","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"#add-plugin-to-the-distribution","p":104851},{"i":104860,"t":"SeaTunnel use plugin-mapping.properties file to locate the name of the jar package, the file is under module seatunnel-connectors, the key/value rule in properties is : engineName.pluginType.pluginName=artifactId. eg: spark.source.hello=seatunnel-connector-spark-hello. So that SeaTunnel can find plugin jar according to user's config file.","s":"Add information to plugin-mapping.properties file","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"#add-information-to-plugin-mappingproperties-file","p":104851},{"i":104862,"t":"The steps to contribute a Flink plugin is similar to the steps to contribute a Spark plugin. Different from Spark, you need to add your plugin in Flink plugin modules.","s":"Contribute Flink Plugins","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"","p":104851},{"i":104864,"t":"Once you add a new plugin, it is recommended to add e2e tests for it. We have a seatunnel-e2e module to help you to do this. For example, if you want to add an e2e test for your flink connector, you can create a new test in seatunnel-flink-e2e module. And extend the FlinkContainer class in the test. public class HellpSourceIT extends FlinkContainer { @Test public void testHellpSource() throws IOException, InterruptedException { Container.ExecResult execResult = executeSeaTunnelFlinkJob(\"/hello/hellosource.conf\"); Assertions.assertEquals(0, execResult.getExitCode()); // do some other assertion here } } Once your class implements the FlinkContainer interface, it will auto create a Flink cluster in Docker, and you just need to execute the executeSeaTunnelFlinkJob method with your SeaTunnel configuration file, it will submit the SeaTunnel job. In most times, you need to start a third-part datasource in your test, for example, if you add a clickhouse connectors, you may need to start a Clickhouse database in your test. You can use GenericContainer to start a container. It should be noted that your e2e test class should be named ending with IT. By default, we will not execute the test if the class name ending with IT. You can add -DskipIT=false to execute the e2e test, it will rely on a Docker environment.","s":"Add e2e tests for your plugin","u":"/docs/2.2.0-beta/contribution/contribute-plugin","h":"","p":104851},{"i":104867,"t":"You have to pay attention to the following open-source software protocols which Apache projects support when you intend to add a new feature to the SeaTunnel (or other Apache projects), which functions refers to other open-source software references. ASF 3RD PARTY LICENSE POLICY If the 3rd party software is not present at the above policy, we could't that accept your code.","s":"ASF 3RD PARTY LICENSE POLICY","u":"/docs/2.2.0-beta/contribution/new-license","h":"#asf-3rd-party-license-policy","p":104865},{"i":104869,"t":"Moreover, when we intend to refer a new software ( not limited to 3rd party jar, text, CSS, js, pics, icons, audios etc and modifications based on 3rd party files) to our project, we need to use them legally in addition to the permission of ASF. Refer to the following article: COMMUNITY-LED DEVELOPMENT \"THE APACHE WAY\" For example, we should contain the NOTICE file (most of open-source project has NOTICE file, generally under root directory) of ZooKeeper in our project when we are using ZooKeeper. As the Apache explains, \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work. We are not going to dive into every 3rd party open-source license policy in here, you may look up them if interested.","s":"How to Legally Use 3rd Party Open-source Software in the SeaTunnel","u":"/docs/2.2.0-beta/contribution/new-license","h":"#how-to-legally-use-3rd-party-open-source-software-in-the-seatunnel","p":104865},{"i":104871,"t":"In general, we would have our License-check scripts to our project. SeaTunnel-License-Check is provided by SkyWalking which differ a bit from other open-source projects. All in all, we are trying to make sure avoiding the license issues at the first time. We need to follow the following steps when we need to add new jars or external resources: Add the name and the version of the jar file in the known-dependencies.txt Add relevant maven repository address under 'seatunnel-dist/release-docs/LICENSE' directory Append relevant NOTICE files under 'seatunnel-dist/release-docs/NOTICE' directory and make sure they are no different to the original repository Add relevant source code protocols under 'seatunnel-dist/release-docs/licenses' directory and the file name should be named as license+filename.txt. Eg: license-zk.txt","s":"SeaTunnel-License Check Rules","u":"/docs/2.2.0-beta/contribution/new-license","h":"#seatunnel-license-check-rules","p":104865},{"i":104873,"t":"COMMUNITY-LED DEVELOPMENT \"THE APACHE WAY\" ASF 3RD PARTY LICENSE POLICY","s":"References","u":"/docs/2.2.0-beta/contribution/new-license","h":"#references","p":104865},{"i":104875,"t":"In this section, we are going to show you how to set up your development environment for SeaTunnel, and then run a simple example in your JetBrains IntelliJ IDEA. You can develop or test SeaTunnel code in any development environment that you like, but here we use JetBrains IDEA as an example to teach you to step by step environment.","s":"Set Up Develop Environment","u":"/docs/2.2.0-beta/contribution/setup","h":"","p":104874},{"i":104877,"t":"Before we start talking about how to set up the environment, we need to do some preparation work. Make sure you already have installed the following software: Git installed. Java ( JDK8/JDK11 are supported by now) installed and JAVA_HOME set. Scala (only scala 2.11.12 supported by now) installed. JetBrains IDEA installed.","s":"Prepare","u":"/docs/2.2.0-beta/contribution/setup","h":"#prepare","p":104874},{"i":104880,"t":"First of all, you need to clone the SeaTunnel source code from GitHub. git clone git@github.com:apache/incubator-seatunnel.git","s":"Clone the Source Code","u":"/docs/2.2.0-beta/contribution/setup","h":"#clone-the-source-code","p":104874},{"i":104882,"t":"After cloning the source code, you should run the ./mvnw command to install the subproject to the maven local repository. Otherwise, your code could not start in JetBrains IntelliJ IDEA correctly. ./mvnw install -Dmaven.test.skip","s":"Install Subproject Locally","u":"/docs/2.2.0-beta/contribution/setup","h":"#install-subproject-locally","p":104874},{"i":104884,"t":"After you install the maven, you can use the follow command to compile and package. mvn clean package -pl seatunnel-dist -am -Dmaven.test.skip=true","s":"Building SeaTunnel from source","u":"/docs/2.2.0-beta/contribution/setup","h":"#building-seatunnel-from-source","p":104874},{"i":104886,"t":"Now, you can open your JetBrains IntelliJ IDEA and explore the source code, but allow building Scala code in IDEA, you should also install JetBrains IntelliJ IDEA's Scala plugin. See install plugins for IDEA if you want to.","s":"Install JetBrains IDEA Scala Plugin","u":"/docs/2.2.0-beta/contribution/setup","h":"#install-jetbrains-idea-scala-plugin","p":104874},{"i":104888,"t":"Before running the following example, you should also install JetBrains IntelliJ IDEA's Lombok plugin. See install plugins for IDEA if you want to.","s":"Install JetBrains IDEA Lombok Plugin","u":"/docs/2.2.0-beta/contribution/setup","h":"#install-jetbrains-idea-lombok-plugin","p":104874},{"i":104890,"t":"Before coding, you should also install JetBrains IntelliJ IDEA's CheckStyle-IDEA plugin. See install plugins for IDEA if you want to. Next, you should go to Preferences -> Editor -> Code style -> Scheme -> Import Scheme -> CheckStyle Configration and import tools/checkstyle/checkStyle.xml If you want to change to automatically formatting, these configurations are also required.","s":"Install JetBrains IDEA CheckStyle-IDEA Plugin","u":"/docs/2.2.0-beta/contribution/setup","h":"#install-jetbrains-idea-checkstyle-idea-plugin","p":104874},{"i":104892,"t":"After all the above things are done, you just finish the environment setup and can run an example we provide to you out of box. All examples are in module seatunnel-examples, you could pick one you are interested in, running or debugging it in IDEA as you wish. Here we use seatunnel-examples/seatunnel-flink-examples/src/main/java/org/apache/seatunnel/example/flink/LocalFlinkExample.java as an example, when you run it successfully you could see the output as below: +I[Ricky Huo, 71] +I[Gary, 12] +I[Ricky Huo, 93] ... ... +I[Ricky Huo, 83]","s":"Run Simple Example","u":"/docs/2.2.0-beta/contribution/setup","h":"#run-simple-example","p":104874},{"i":104894,"t":"All our examples use simple source and sink to make it less dependent and easy to run. You can change the example configuration in resources/examples. You could change your configuration as below, if you want to use PostgreSQL as the source and sink to console. env { execution.parallelism = 1 } source { JdbcSource { driver = org.postgresql.Driver url = \"jdbc:postgresql://host:port/database\" username = postgres query = \"select * from test\" } } sink { ConsoleSink {} }","s":"What's More","u":"/docs/2.2.0-beta/contribution/setup","h":"#whats-more","p":104874},{"i":104896,"t":"This section will show you how to submit your SeaTunnel application in all kinds of cluster engine. If you still not installation SeaTunnel you could go to see quick start about how to prepare and change SeaTunnel configuration firstly.","s":"Deployment","u":"/docs/2.2.0-beta/deployment","h":"","p":104895},{"i":104899,"t":"Local mode only support Spark engine for now. ./bin/start-seatunnel-spark.sh \\ --master local[4] \\ --deploy-mode client \\ --config ./config/application.conf","s":"Local Mode(Spark Only)","u":"/docs/2.2.0-beta/deployment","h":"#local-modespark-only","p":104895},{"i":104901,"t":"Spark Flink # client mode ./bin/start-seatunnel-spark.sh \\ --master spark://ip:7077 \\ --deploy-mode client \\ --config ./config/application.conf # cluster mode ./bin/start-seatunnel-spark.sh \\ --master spark://ip:7077 \\ --deploy-mode cluster \\ --config ./config/application.conf bin/start-seatunnel-flink.sh \\ --config config-path # -p 2 specifies that the parallelism of flink job is 2. You can also specify more parameters, use flink run -h to view bin/start-seatunnel-flink.sh \\ -p 2 \\ --config config-path","s":"Standalone Cluster","u":"/docs/2.2.0-beta/deployment","h":"#standalone-cluster","p":104895},{"i":104903,"t":"Spark Flink # client mode ./bin/start-seatunnel-spark.sh \\ --master yarn \\ --deploy-mode client \\ --config ./config/application.conf # cluster mode ./bin/start-seatunnel-spark.sh \\ --master yarn \\ --deploy-mode cluster \\ --config ./config/application.conf bin/start-seatunnel-flink.sh \\ -m yarn-cluster \\ --config config-path # -ynm seatunnel specifies the name displayed in the yarn webUI as seatunnel, you can also specify more parameters, use flink run -h to view bin/start-seatunnel-flink.sh \\ -m yarn-cluster \\ -ynm seatunnel \\ --config config-path","s":"Yarn Cluster","u":"/docs/2.2.0-beta/deployment","h":"#yarn-cluster","p":104895},{"i":104905,"t":"Mesos cluster deployment only support Spark engine for now. # cluster mode ./bin/start-seatunnel-spark.sh \\ --master mesos://ip:7077 \\ --deploy-mode cluster \\ --config ./config/application.conf","s":"Mesos Cluster(Spark Only)","u":"/docs/2.2.0-beta/deployment","h":"#mesos-clusterspark-only","p":104895},{"i":104907,"t":"(This section is about engine instead of SeaTunnel itself, it is background knowledge for user who not understand engine cluster type). Both Spark and Flink could be run in different kind of cluster and any scale. This guide only show the basic usage of SeaTunnel which build above engine Spark or Flink, if you want to scale your engine cluster see Spark or Flink document.","s":"Run Your Engine in Scaling","u":"/docs/2.2.0-beta/deployment","h":"#run-your-engine-in-scaling","p":104895},{"i":104910,"t":"Now SeaTunnel uses computing engines such as spark and flink to complete resource scheduling and node communication, so we can focus on the ease of use of data synchronization and the development of high-performance components. But this is only temporary.","s":"Why I should install computing engine like Spark or Flink","u":"/docs/2.2.0-beta/faq","h":"#why-i-should-install-computing-engine-like-spark-or-flink","p":104908},{"i":104912,"t":"I encounter a problem when using SeaTunnel and I cannot solve it by myself. What should I do? Firstly search in Issue list or mailing list to see if someone has already asked the same question and got the answer. If you still cannot find the answer, you can contact community members for help in these ways .","s":"I have a question, but I can not solve it by myself","u":"/docs/2.2.0-beta/faq","h":"#i-have-a-question-but-i-can-not-solve-it-by-myself","p":104908},{"i":104914,"t":"If you want to know how to declare a variable in SeaTunnel's configuration, and then dynamically replace the value of the variable at runtime? Since v1.2.4 SeaTunnel supports variables substitution in the configuration. This feature is often used for timing or non-timing offline processing to replace variables such as time and date. The usage is as follows: Configure the variable name in the configuration, here is an example of sql transform (actually anywhere in the configuration file the value in 'key = value' can use the variable substitution): ... transform { sql { sql = \"select * from user_view where city ='\"${city}\"' and dt = '\"${date}\"'\" } } ... Taking Spark Local mode as an example, the startup command is as follows: ./bin/start-seatunnel-spark.sh \\ -c ./config/your_app.conf \\ -e client \\ -m local[2] \\ -i city=shanghai \\ -i date=20190319 You can use the parameter -i or --variable followed with key=value to specify the value of the variable, where the key needs to be same as the variable name in the configuration.","s":"how to declare variable","u":"/docs/2.2.0-beta/faq","h":"#how-to-declare-variable","p":104908},{"i":104916,"t":"When a configured text is very long and you want to wrap it, you can use three double quotes to indicate it: var = \"\"\" whatever you want \"\"\"","s":"How to write a configuration item into multi-line text in the configuration file?","u":"/docs/2.2.0-beta/faq","h":"#how-to-write-a-configuration-item-into-multi-line-text-in-the-configuration-file","p":104908},{"i":104918,"t":"It is a little troublesome to do variable substitution in multi-line text, because the variable cannot be included in three double quotation marks: var = \"\"\" your string 1 \"\"\"${you_var}\"\"\" your string 2\"\"\" refer to: lightbend/config#456","s":"How to implement variable substitution for multi-line text?","u":"/docs/2.2.0-beta/faq","h":"#how-to-implement-variable-substitution-for-multi-line-text","p":104908},{"i":104920,"t":"Of course, please see the screenshot below:","s":"Is SeaTunnel supportted in Azkaban, Oozie, DolphinScheduler?","u":"/docs/2.2.0-beta/faq","h":"#is-seatunnel-supportted-in-azkaban-oozie-dolphinscheduler","p":104908},{"i":104922,"t":"env { ... } source { hdfs { ... } elasticsearch { ... } jdbc {...} } transform { sql { sql = \"\"\" select .... from hdfs_table join es_table on hdfs_table.uid = es_table.uid where ...\"\"\" } } sink { elasticsearch { ... } }","s":"Does SeaTunnel have a case of configuring multiple sources, such as configuring  elasticsearch and hdfs in source at the same time?","u":"/docs/2.2.0-beta/faq","h":"#does-seatunnel-have-a-case-of-configuring-multiple-sources-such-as-configuring--elasticsearch-and-hdfs-in-source-at-the-same-time","p":104908},{"i":104924,"t":"There is hbase input plugin, download it from here: https://github.com/garyelephant/waterdrop-input-hbase","s":"Are there any HBase plugins?","u":"/docs/2.2.0-beta/faq","h":"#are-there-any-hbase-plugins","p":104908},{"i":104926,"t":"env { spark.sql.catalogImplementation = \"hive\" spark.hadoop.hive.exec.dynamic.partition = \"true\" spark.hadoop.hive.exec.dynamic.partition.mode = \"nonstrict\" } source { sql = \"insert into ...\" } sink { // The data has been written to hive through the sql source, this is just a placeholder, it does not actually work. stdout { limit = 1 } } In addition, SeaTunnel has implemented Hive output plugin after 1.5.7 in 1.x branch; in 2.x branch, the Hive plugin of the Spark engine has been supported after version 2.0.5: https://github.com/apache/incubator-seatunnel/issues/910.","s":"How to use SeaTunnel to write data to Hive?","u":"/docs/2.2.0-beta/faq","h":"#how-to-use-seatunnel-to-write-data-to-hive","p":104908},{"i":104928,"t":"Write distributed tables directly (not recommended) By adding a proxy or domain name (DNS) in front of multiple instances of ClickHouse: { output { clickhouse { host = \"ck-proxy.xx.xx:8123\" # Local table table = \"table_name\" } } } Configure multiple instances in the configuration: { output { clickhouse { host = \"ck1:8123,ck2:8123,ck3:8123\" # Local table table = \"table_name\" } } } Use cluster mode: { output { clickhouse { # Configure only one host host = \"ck1:8123\" cluster = \"clickhouse_cluster_name\" # Local table table = \"table_name\" } } }","s":"How does SeaTunnel write multiple instances of ClickHouse to achieve load balancing?","u":"/docs/2.2.0-beta/faq","h":"#how-does-seatunnel-write-multiple-instances-of-clickhouse-to-achieve-load-balancing","p":104908},{"i":104930,"t":"In most cases, OOM is caused by the fact that there is no rate limit for consumption. The solution is as follows: Regarding the current limit of Spark consumption of Kafka: Suppose the number of partitions of Kafka Topic 1 you consume with KafkaStream = N. Assuming that the production speed of the message producer (Producer) of Topic 1 is K messages/second, it is required that The speed of write message to the partition is uniform. Suppose that after testing, it is found that the processing capacity of Spark Executor per core per second is M per second. The following conclusions can be drawn: If you want to make spark's consumption of Topic 1 keep up with its production speed, then you need spark.executor.cores * spark.executor.instances >= K / M When data delay occurs, if you want the consumption speed not to be too fast, resulting in spark executor OOM, then you need to configure spark.streaming.kafka.maxRatePerPartition <= (spark.executor.cores spark.executor.instances) M / N In general, both M and N are determined, and the conclusion can be drawn from 2: The size of spark.streaming.kafka.maxRatePerPartition is positively correlated with the size of spark.executor.cores * spark.executor.instances, and it can be increased while increasing the resource maxRatePerPartition to speed up consumption.","s":"How to solve OOM when SeaTunnel consumes Kafka?","u":"/docs/2.2.0-beta/faq","h":"#how-to-solve-oom-when-seatunnel-consumes-kafka","p":104908},{"i":104932,"t":"The reason is that the version of httpclient.jar that comes with the CDH version of Spark is lower, and The httpclient version where ClickHouse JDBC is based on is 4.5.2, and the package version conflicts. The solution is to replace the jar package that comes with CDH with httpclient-4.5.2 version.","s":"How to solve the Error Exception in thread \"main\" java.lang.NoSuchFieldError: INSTANCE?","u":"/docs/2.2.0-beta/faq","h":"#how-to-solve-the-error-exception-in-thread-main-javalangnosuchfielderror-instance","p":104908},{"i":104934,"t":"In SeaTunnel's config file, specify the following configuration: spark { ... spark.executorEnv.JAVA_HOME=\"/your/java_8_home/directory\" spark.yarn.appMasterEnv.JAVA_HOME=\"/your/java_8_home/directory\" ... }","s":"The default JDK of my Spark cluster is JDK7. After I install JDK8, how can I specify the SeaTunnel starts with JDK8?","u":"/docs/2.2.0-beta/faq","h":"#the-default-jdk-of-my-spark-cluster-is-jdk7-after-i-install-jdk8-how-can-i-specify-the-seatunnel-starts-with-jdk8","p":104908},{"i":104936,"t":"For example, if you want to set the JDK version to JDK8, there are two cases: The Yarn cluster has deployed JDK8, but the default JDK is not JDK8. you should only add 2 configurations to the SeaTunnel config file: env { ... spark.executorEnv.JAVA_HOME=\"/your/java_8_home/directory\" spark.yarn.appMasterEnv.JAVA_HOME=\"/your/java_8_home/directory\" ... } Yarn cluster does not deploy JDK8. At this time, when you start SeaTunnel attached with JDK8.For detailed operations, see the link below: https://www.cnblogs.com/jasondan/p/spark-specific-jdk-version.html","s":"How to specify a different JDK version for SeaTunnel on Yarn?","u":"/docs/2.2.0-beta/faq","h":"#how-to-specify-a-different-jdk-version-for-seatunnel-on-yarn","p":104908},{"i":104938,"t":"If you run in local mode, you need to modify the start-seatunnel.sh startup script after spark-submit, add a parameter --driver-memory 4g . Under normal circumstances, the local mode is not used in the production environment. Therefore, this parameter generally does not need to be set during On Yarn. See: Application Properties for details .","s":"What should I do if OOM always appears when running SeaTunnel in Spark local[*] mode?","u":"/docs/2.2.0-beta/faq","h":"#what-should-i-do-if-oom-always-appears-when-running-seatunnel-in-spark-local-mode","p":104908},{"i":104940,"t":"Place the Jar package under the specified structure of the plugins directory: cd SeaTunnel mkdir -p plugins/my_plugins/lib cp third-part.jar plugins/my_plugins/lib my_plugins can be any string.","s":"Where can the self-written plugins or third-party jdbc.jar be placed to be loaded by SeaTunnel?","u":"/docs/2.2.0-beta/faq","h":"#where-can-the-self-written-plugins-or-third-party-jdbcjar-be-placed-to-be-loaded-by-seatunnel","p":104908},{"i":104942,"t":"There are 3 ways to configure Logging related parameters (such as Log Level): [Not recommended] Change the default $SPARK_HOME/conf/log4j.properties This will affect all programs submitted via $SPARK_HOME/bin/spark-submit [Not recommended] Modify logging related parameters directly in the Spark code of SeaTunnel This is equivalent to writing dead, and each change needs to be recompiled [Recommended] Use the following methods to change the logging configuration in the SeaTunnel configuration file(It only takes effect after SeaTunnel >= 1.5.5 ): env { spark.driver.extraJavaOptions = \"-Dlog4j.configuration=file:<file path>/log4j.properties\" spark.executor.extraJavaOptions = \"-Dlog4j.configuration=file:<file path>/log4j.properties\" } source { ... } transform { ... } sink { ... } The contents of the log4j configuration file for reference are as follows: $ cat log4j.properties log4j.rootLogger=ERROR, console # set the log level for these components log4j.logger.org=ERROR log4j.logger.org.apache.spark=ERROR log4j.logger.org.spark-project=ERROR log4j.logger.org.apache.hadoop=ERROR log4j.logger.io.netty=ERROR log4j.logger.org.apache.zookeeper=ERROR # add a ConsoleAppender to the logger stdout to write to the console log4j.appender.console=org.apache.log4j.ConsoleAppender log4j.appender.console.layout=org.apache.log4j.PatternLayout # use a simple message format log4j.appender.console.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n How to configure logging related parameters in SeaTunnel-v2(Spark, Flink)? Currently, it cannot be set directly. The user needs to modify the SeaTunnel startup script.The relevant parameters are specified in the task submission command. For specific parameters, please refer to the official document: Spark official documentation: http://spark.apache.org/docs/latest/configuration.html#configuring-logging Flink official documentation: https://ci.apache.org/projects/flink/flink-docs-stable/monitoring/logging.html Reference: https://stackoverflow.com/questions/27781187/how-to-stop-info-messages-displaying-on-spark-console http://spark.apache.org/docs/latest/configuration.html#configuring-logging https://medium.com/@iacomini.riccardo/spark-logging-configuration-in-yarn-faf5ba5fdb01 https://stackoverflow.com/questions/27781187/how-to-stop-info-messages-displaying-on-spark-console","s":"How to configure logging related parameters in SeaTunnel-v1(Spark)?","u":"/docs/2.2.0-beta/faq","h":"#how-to-configure-logging-related-parameters-in-seatunnel-v1spark","p":104908},{"i":104944,"t":"In SeaTunnel, the data type will not be actively converted. After the Input reads the data, the corresponding Schema. When writing ClickHouse, the field type needs to be strictly matched, and the mismatch needs to be done. Data conversion, data conversion can be achieved through the following two plug-ins: Filter Convert plugin Filter Sql plugin Detailed data type conversion reference: ClickHouse Data Type Check List Refer to issue:#488 #382","s":"Error when writing to ClickHouse: ClassCastException","u":"/docs/2.2.0-beta/faq","h":"#error-when-writing-to-clickhouse-classcastexception","p":104908},{"i":104946,"t":"Please refer to: #590","s":"How does SeaTunnel access kerberos-authenticated HDFS, YARN, Hive and other resources?","u":"/docs/2.2.0-beta/faq","h":"#how-does-seatunnel-access-kerberos-authenticated-hdfs-yarn-hive-and-other-resources","p":104908},{"i":104948,"t":"There is a high probability that there are multiple different versions of the corresponding Jar package class loaded in the Java classpath, because of the conflict of the load order, instead of really missing this Jar, please modify this SeaTunnel startup command, add the following parameters to the spark-submit submission place, and debug in detail through the output log. spark-submit --verbose ... --conf 'spark.driver.extraJavaOptions=-verbose:class' --conf 'spark.executor.extraJavaOptions=-verbose:class' ...","s":"How to troubleshoot NoClassDefFoundError, ClassNotFoundException and other issues?","u":"/docs/2.2.0-beta/faq","h":"#how-to-troubleshoot-noclassdeffounderror-classnotfoundexception-and-other-issues","p":104908},{"i":104950,"t":"Just configure hdfs-site.xml properly, refer to: https://www.cnblogs.com/suanec/p/7828139.html There is an article on how to modify the spark code to complete the configuration(SeaTunnel does not need to do this): https://www.jianshu.com/p/3e84c4c97610","s":"How to use SeaTunnel to synchronize data across HDFS clusters?","u":"/docs/2.2.0-beta/faq","h":"#how-to-use-seatunnel-to-synchronize-data-across-hdfs-clusters","p":104908},{"i":104952,"t":"SeaTunnel has a completely abstract and structured code implementation, and many people have chosen SeaTunnel As a way to learn Spark, you can learn the source code from the main program entry: Seatunnel.java","s":"I want to learn the source code of SeaTunnel, where should I start?","u":"/docs/2.2.0-beta/faq","h":"#i-want-to-learn-the-source-code-of-seatunnel-where-should-i-start","p":104908},{"i":104954,"t":"The plug-in developed by the developer has nothing to do with the SeaTunnel project and does not need to write your plug-in code The plugin can be completely independent with SeaTunnel project, in which you can use java, Scala, maven, sbt, gradle, whatever you want. This is also the way we recommend developers to develop plugins.","s":"When SeaTunnel developers develop their own plugins, do they need to understand the SeaTunnel code? Should these code integrated into the SeaTunnel project?","u":"/docs/2.2.0-beta/faq","h":"#when-seatunnel-developers-develop-their-own-plugins-do-they-need-to-understand-the-seatunnel-code-should-these-code-integrated-into-the-seatunnel-project","p":104908},{"i":104956,"t":"Run mvn install first. Because in the seatunnel-config/seatunnel-config-base subproject, package com.typesafe.config has been relocated to org.apache.seatunnel.shade.com.typesafe.config and install to maven local repository in subproject seatunnel-config/seatunnel-config-shade .","s":"Import project, compiler has exception \"class not found org.apache.seatunnel.shade.com.typesafe.config.Config\"","u":"/docs/2.2.0-beta/faq","h":"#import-project-compiler-has-exception-class-not-found-orgapacheseatunnelshadecomtypesafeconfigconfig","p":104908},{"i":104958,"t":"SeaTunnel is a very easy-to-use ultra-high-performance distributed data integration platform that supports real-time synchronization of massive data. It can synchronize tens of billions of data stably and efficiently every day, and has been used in the production of nearly 100 companies.","s":"About Seatunnel","u":"/docs/2.2.0-beta/intro/about","h":"","p":104957},{"i":104960,"t":"Mass data synchronization Mass data integration ETL with massive data Mass data aggregation Multi-source data processing","s":"Use Scenarios","u":"/docs/2.2.0-beta/intro/about","h":"#use-scenarios","p":104957},{"i":104962,"t":"Easy to use, flexible configuration, low code development Real-time streaming Offline multi-source data analysis High-performance, massive data processing capabilities Modular and plug-in mechanism, easy to extend Support data processing and aggregation by SQL Support Spark structured streaming Support Spark 2.x","s":"Features","u":"/docs/2.2.0-beta/intro/about","h":"#features","p":104957},{"i":104964,"t":"Source[Data Source Input] -> Transform[Data Processing] -> Sink[Result Output] The data processing pipeline is constituted by multiple filters to meet a variety of data processing needs. If you are accustomed to SQL, you can also directly construct a data processing pipeline by SQL, which is simple and efficient. Currently, the filter list supported by SeaTunnel is still being expanded. Furthermore, you can develop your own data processing plug-in, because the whole system is easy to expand.","s":"Workflow","u":"/docs/2.2.0-beta/intro/about","h":"#workflow","p":104957},{"i":104966,"t":"Input plugin Fake, File, Hdfs, Kafka, Druid, InfluxDB, S3, Socket, self-developed Input plugin Filter plugin Add, Checksum, Convert, Date, Drop, Grok, Json, Kv, Lowercase, Remove, Rename, Repartition, Replace, Sample, Split, Sql, Table, Truncate, Uppercase, Uuid, Self-developed Filter plugin Output plugin Elasticsearch, File, Hdfs, Jdbc, Kafka, Druid, InfluxDB, Mysql, S3, Stdout, self-developed Output plugin","s":"Connector","u":"/docs/2.2.0-beta/intro/about","h":"#connector","p":104957},{"i":104968,"t":"SeaTunnel have lots of users which you can find more information in users","s":"Who Use SeaTunnel","u":"/docs/2.2.0-beta/intro/about","h":"#who-use-seatunnel","p":104957},{"i":104970,"t":"SeaTunnel enriches the CNCF CLOUD NATIVE Landscape.","s":"Landscapes","u":"/docs/2.2.0-beta/intro/about","h":"#landscapes","p":104957},{"i":104972,"t":"You can see Quick Start for the next step.","s":"What's More","u":"/docs/2.2.0-beta/intro/about","h":"#whats-more","p":104957},{"i":104974,"t":"SeaTunnel was formerly named WaterDrop","s":"History of Seatunnel","u":"/docs/2.2.0-beta/intro/history","h":"","p":104973},{"i":104976,"t":"This project renamed to SeaTunnel since Oct 12th, 2021.","s":"Rename to SeaTunnel","u":"/docs/2.2.0-beta/intro/history","h":"#rename-to-seatunnel","p":104973},{"i":104978,"t":"SeaTunnel joined the Apache Software Foundation’s Incubator program in Dec 9th, 2021.","s":"Enter Apache Software Foundation’s Incubator","u":"/docs/2.2.0-beta/intro/history","h":"#enter-apache-software-foundations-incubator","p":104973},{"i":104980,"t":"SeaTunnel will do its best to solve the problems that may be encountered in the synchronization of massive data: Data loss and duplication Task accumulation and delay Low throughput Long cycle to be applied in the production environment Lack of application running status monitoring","s":"Why SeaTunnel","u":"/docs/2.2.0-beta/intro/why","h":"","p":104979},{"i":104982,"t":"WIP","s":"Set Up with Docker","u":"/docs/2.2.0-beta/start-v2/docker","h":"","p":104981},{"i":104984,"t":"This section provides a quick guide to using SeaTunnel with Kubernetes.","s":"Set Up with Kubernetes","u":"/docs/2.2.0-beta/start-v2/kubernetes","h":"","p":104983},{"i":104986,"t":"We assume that you have a local installations of the following: docker kubernetes helm So that the kubectl and helm commands are available on your local system. For kubernetes minikube is our choice, at the time of writing this we are using version v1.23.3. You can start a cluster with the following command: minikube start --kubernetes-version=v1.23.3","s":"Prerequisites","u":"/docs/2.2.0-beta/start-v2/kubernetes","h":"#prerequisites","p":104983},{"i":104989,"t":"To run the image with SeaTunnel, first create a Dockerfile: Flink FROM flink:1.13 ENV SEATUNNEL_VERSION=\"2.2.0-beta\" ENV SEATUNNEL_HOME = \"/opt/seatunnel\" RUN mkdir -p $SEATUNNEL_HOME RUN wget https://archive.apache.org/dist/incubator/seatunnel/${SEATUNNEL_VERSION}/apache-seatunnel-incubating-${SEATUNNEL_VERSION}-bin.tar.gz RUN tar -xzvf apache-seatunnel-incubating-${SEATUNNEL_VERSION}-bin.tar.gz RUN cp -r apache-seatunnel-incubating-${SEATUNNEL_VERSION}/* $SEATUNNEL_HOME/ RUN rm -rf apache-seatunnel-incubating-${SEATUNNEL_VERSION}* RUN rm -rf $SEATUNNEL_HOME/connectors/seatunnel Then run the following commands to build the image: docker build -t seatunnel:2.2.0-beta-flink-1.13 -f Dockerfile . Image seatunnel:2.2.0-beta-flink-1.13 need to be present in the host (minikube) so that the deployment can take place. Load image to minikube via: minikube image load seatunnel:2.2.0-beta-flink-1.13","s":"SeaTunnel docker image","u":"/docs/2.2.0-beta/start-v2/kubernetes","h":"#seatunnel-docker-image","p":104983},{"i":104991,"t":"Flink The steps below provide a quick walk-through on setting up the Flink Kubernetes Operator. Install the certificate manager on your Kubernetes cluster to enable adding the webhook component (only needed once per Kubernetes cluster): kubectl create -f https://github.com/jetstack/cert-manager/releases/download/v1.7.1/cert-manager.yaml Now you can deploy the latest stable Flink Kubernetes Operator version using the included Helm chart: helm repo add flink-operator-repo https://downloads.apache.org/flink/flink-kubernetes-operator-0.1.0/ helm install flink-kubernetes-operator flink-operator-repo/flink-kubernetes-operator You may verify your installation via kubectl: kubectl get pods NAME READY STATUS RESTARTS AGE flink-kubernetes-operator-5f466b8549-mgchb 1/1 Running 3 (23h ago) 16d","s":"Deploying the operator","u":"/docs/2.2.0-beta/start-v2/kubernetes","h":"#deploying-the-operator","p":104983},{"i":104993,"t":"Run Application:: SeaTunnel already providers out-of-the-box configurations. Flink In this guide we are going to use flink.streaming.conf: env { execution.parallelism = 1 } source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { sql = \"select name,age from fake\" } } sink { ConsoleSink {} } This configuration need to be present when we are going to deploy the application (SeaTunnel) to Flink cluster (on Kubernetes), we also need to configure a Pod to Use a PersistentVolume for Storage. Create /mnt/data on your Node. Open a shell to the single Node in your cluster. How you open a shell depends on how you set up your cluster. For example, in our case weare using Minikube, you can open a shell to your Node by entering minikube ssh. In your shell on that Node, create a /mnt/data directory: minikube ssh # This assumes that your Node uses \"sudo\" to run commands # as the superuser sudo mkdir /mnt/data Copy application (SeaTunnel) configuration files to your Node. minikube cp flink.streaming.conf /mnt/data/flink.streaming.conf Once the Flink Kubernetes Operator is running as seen in the previous steps you are ready to submit a Flink (SeaTunnel) job: Create seatunnel-flink.yaml FlinkDeployment manifest: apiVersion: flink.apache.org/v1alpha1 kind: FlinkDeployment metadata: namespace: default name: seatunnel-flink-streaming-example spec: image: seatunnel:2.2.0-beta-flink-1.13 flinkVersion: v1_14 flinkConfiguration: taskmanager.numberOfTaskSlots: \"2\" serviceAccount: flink jobManager: replicas: 1 resource: memory: \"2048m\" cpu: 1 taskManager: resource: memory: \"2048m\" cpu: 2 podTemplate: spec: containers: - name: flink-main-container volumeMounts: - mountPath: /data name: config-volume volumes: - name: config-volume hostPath: path: \"/mnt/data\" type: Directory job: jarURI: local:///opt/seatunnel/lib/seatunnel-flink-starter.jar entryClass: org.apache.seatunnel.core.starter.flink.SeatunnelFlink args: [\"--config\", \"/data/flink.streaming.conf\"] parallelism: 2 upgradeMode: stateless Run the example application: kubectl apply -f seatunnel-flink.yaml See The Output Flink You may follow the logs of your job, after a successful startup (which can take on the order of a minute in a fresh environment, seconds afterwards) you can: kubectl logs -f deploy/seatunnel-flink-streaming-example To expose the Flink Dashboard you may add a port-forward rule: kubectl port-forward svc/seatunnel-flink-streaming-example-rest 8081 Now the Flink Dashboard is accessible at localhost:8081. Or launch minikube dashboard for a web-based Kubernetes user interface. The content printed in the TaskManager Stdout log: kubectl logs \\ -l 'app in (seatunnel-flink-streaming-example), component in (taskmanager)' \\ --tail=-1 \\ -f looks like the below (your content may be different since we use FakeSourceStream to automatically generate random stream data): +I[Kid Xiong, 1650316786086] +I[Ricky Huo, 1650316787089] +I[Ricky Huo, 1650316788089] +I[Ricky Huo, 1650316789090] +I[Kid Xiong, 1650316790090] +I[Kid Xiong, 1650316791091] +I[Kid Xiong, 1650316792092] To stop your job and delete your FlinkDeployment you can simply: kubectl delete -f seatunnel-flink.yaml Happy SeaTunneling!","s":"Run SeaTunnel Application","u":"/docs/2.2.0-beta/start-v2/kubernetes","h":"#run-seatunnel-application","p":104983},{"i":104995,"t":"For now, you are already taking a quick look at SeaTunnel, you could see connector to find all source and sink SeaTunnel supported. Or see deployment if you want to submit your application in another kind of your engine cluster.","s":"What's More","u":"/docs/2.2.0-beta/start-v2/kubernetes","h":"#whats-more","p":104983},{"i":104997,"t":"Let's take an application that randomly generates data in memory, processes it through SQL, and finally outputs it to the console as an example.","s":"Set Up with Locally","u":"/docs/2.2.0-beta/start-v2/local","h":"","p":104996},{"i":104999,"t":"Before you getting start the local run, you need to make sure you already have installed the following software which SeaTunnel required: Java (Java 8 or 11, other versions greater than Java 8 can theoretically work as well) installed and JAVA_HOME set. Download the engine, you can choose and download one of them from below as your favour, you could see more information about why we need engine in SeaTunnel Spark: Please download Spark first(required version >= 2 and version < 3.x ). For more information you could see Getting Started: standalone Flink: Please download Flink first(required version >= 1.12.0 and version < 1.14.x ). For more information you could see Getting Started: standalone","s":"Step 1: Prepare the environment","u":"/docs/2.2.0-beta/start-v2/local","h":"#step-1-prepare-the-environment","p":104996},{"i":105001,"t":"Enter the seatunnel download page and download the latest version of distribute package seatunnel-<version>-bin.tar.gz Or you can download it by terminal export version=\"2.2.0-beta\" wget \"https://archive.apache.org/dist/incubator/seatunnel/${version}/apache-seatunnel-incubating-${version}-bin.tar.gz\" tar -xzvf \"apache-seatunnel-incubating-${version}-bin.tar.gz\"","s":"Step 2: Download SeaTunnel","u":"/docs/2.2.0-beta/start-v2/local","h":"#step-2-download-seatunnel","p":104996},{"i":105003,"t":"Since 2.2.0-beta, the binary package does not provide connector dependencies by default, so when using it for the first time, we need to execute the following command to install the connector: (Of course, you can also manually download the connector from [Apache Maven Repository](https://repo.maven.apache.org/maven2/org/apache/seatunnel/ to download, then manually move to the seatunnel subdirectory under the connectors directory). sh bin/install_plugin.sh 2.2.0-beta If you need to specify the version of the connector, take 2.2.0-beta as an example, we need to execute sh bin/install_plugin.sh 2.2.0-beta Usually we don't need all the connector plugins, so you can specify the plugins you need by configuring config/plugin_config, for example, you only need the connector-console plugin, then you can modify plugin.properties as --seatunnel-connectors-- connector-console --end-- If we want our sample application to work properly, we need to add the following plugins --seatunnel-connectors-- connector-fake connector-console --end-- You can find all supported connectors and corresponding plugin_config configuration names under ${SEATUNNEL_HOME}/connectors/plugins-mapping.properties. tip If you want to install the connector plugin by manually downloading the connector, you need to pay special attention to the following The connectors directory contains the following subdirectories, if they do not exist, you need to create them manually flink flink-sql seatunnel spark If you want to install the V2 connector plugin manually, you only need to download the V2 connector plugin you need and put them in the seatunnel directory","s":"Step 3: Install connectors plugin","u":"/docs/2.2.0-beta/start-v2/local","h":"#step-3-install-connectors-plugin","p":104996},{"i":105005,"t":"Configure SeaTunnel: Change the setting in config/seatunnel-env.sh, it is base on the path your engine install at prepare step two. Change SPARK_HOME if you using Spark as your engine, or change FLINK_HOME if you're using Flink. Edit config/seatunnel.streaming.conf.template, which determines the way and logic of data input, processing, and output after seatunnel is started. The following is an example of the configuration file, which is the same as the example application mentioned above. env { # You can set flink configuration here execution.parallelism = 1 job.mode = \"STREAMING\" #execution.checkpoint.interval = 10000 #execution.checkpoint.data-uri = \"hdfs://localhost:9000/checkpoint\" # For Spark #spark.app.name = \"SeaTunnel\" #spark.executor.instances = 2 #spark.executor.cores = 1 #spark.executor.memory = \"1g\" #spark.master = local } source { FakeSource { result_table_name = \"fake\" row.num = 16 schema = { fields { name = \"string\" age = \"int\" } } } } transform { sql { sql = \"select name,age from fake\" } } sink { Console {} } More information about config please check config concept","s":"Step 4: Configure SeaTunnel Application","u":"/docs/2.2.0-beta/start-v2/local","h":"#step-4-configure-seatunnel-application","p":104996},{"i":105007,"t":"You could start the application by the following commands Spark Flink cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-spark-connector-v2.sh \\ --master local[4] \\ --deploy-mode client \\ --config ./config/seatunnel.streaming.conf.template cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-flink-connector-v2.sh \\ --config ./config/seatunnel.streaming.conf.template See The Output: When you run the command, you could see its output in your console or in Flink/Spark UI, You can think this is a sign that the command ran successfully or not. The SeaTunnel console will prints some logs as below: fields : name, age types : STRING, INT row=1 : elWaB, 1984352560 row=2 : uAtnp, 762961563 row=3 : TQEIB, 2042675010 row=4 : DcFjo, 593971283 row=5 : SenEb, 2099913608 row=6 : DHjkg, 1928005856 row=7 : eScCM, 526029657 row=8 : sgOeE, 600878991 row=9 : gwdvw, 1951126920 row=10 : nSiKE, 488708928 row=11 : xubpl, 1420202810 row=12 : rHZqb, 331185742 row=13 : rciGD, 1112878259 row=14 : qLhdI, 1457046294 row=15 : ZTkRx, 1240668386 row=16 : SGZCr, 94186144 If use Flink, The content printed in the TaskManager Stdout log of flink WebUI.","s":"Step 5: Run SeaTunnel Application","u":"/docs/2.2.0-beta/start-v2/local","h":"#step-5-run-seatunnel-application","p":104996},{"i":105009,"t":"Our local quick start is using one of the build-in example in directory config, and we provider more than one out-of-box example you could and feel free to have a try and make your hands dirty. All you have to do is change the started command option value in running application to the configuration you want to run, we use batch template in config as examples: Spark Flink cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-spark-connector-v2.sh \\ --master local[4] \\ --deploy-mode client \\ --config ./config/spark.batch.conf.template cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-flink-connector-v2.sh \\ --config ./config/flink.batch.conf.template","s":"Explore More Build-in Examples","u":"/docs/2.2.0-beta/start-v2/local","h":"#explore-more-build-in-examples","p":104996},{"i":105011,"t":"For now, you are already take a quick look about SeaTunnel, you could see connector to find all source and sink SeaTunnel supported. Or see deployment if you want to submit your application in other kind of your engine cluster.","s":"What's More","u":"/docs/2.2.0-beta/start-v2/local","h":"#whats-more","p":104996},{"i":105013,"t":"WIP","s":"Set Up with Docker","u":"/docs/2.2.0-beta/start/docker","h":"","p":105012},{"i":105015,"t":"This section provides a quick guide to using SeaTunnel with Kubernetes.","s":"Set Up with Kubernetes","u":"/docs/2.2.0-beta/start/kubernetes","h":"","p":105014},{"i":105017,"t":"We assume that you have a local installations of the following: Docker Kubernetes Helm So that the kubectl and helm commands are available on your local system. For Kubernetes minikube is our choice, at the time of writing this we are using version v1.23.3. You can start a cluster with the following command: minikube start --kubernetes-version=v1.23.3","s":"Prerequisites","u":"/docs/2.2.0-beta/start/kubernetes","h":"#prerequisites","p":105014},{"i":105020,"t":"To run the image with SeaTunnel, first create a Dockerfile: Flink FROM flink:1.13 ENV SEATUNNEL_VERSION=\"2.2.0-beta\" ENV SEATUNNEL_HOME = \"/opt/seatunnel\" RUN mkdir -p $SEATUNNEL_HOME RUN wget https://archive.apache.org/dist/incubator/seatunnel/${SEATUNNEL_VERSION}/apache-seatunnel-incubating-${SEATUNNEL_VERSION}-bin.tar.gz RUN tar -xzvf apache-seatunnel-incubating-${SEATUNNEL_VERSION}-bin.tar.gz RUN cp -r apache-seatunnel-incubating-${SEATUNNEL_VERSION}/* $SEATUNNEL_HOME/ RUN rm -rf apache-seatunnel-incubating-${SEATUNNEL_VERSION}* RUN rm -rf $SEATUNNEL_HOME/connectors/flink Then run the following commands to build the image: docker build -t seatunnel:2.2.0-beta-flink-1.13 -f Dockerfile . Image seatunnel:2.2.0-beta-flink-1.13 need to be present in the host (minikube) so that the deployment can take place. Load image to minikube via: minikube image load seatunnel:2.2.0-beta-flink-1.13","s":"SeaTunnel docker image","u":"/docs/2.2.0-beta/start/kubernetes","h":"#seatunnel-docker-image","p":105014},{"i":105022,"t":"Flink The steps below provide a quick walk-through on setting up the Flink Kubernetes Operator. Install the certificate manager on your Kubernetes cluster to enable adding the webhook component (only needed once per Kubernetes cluster): kubectl create -f https://github.com/jetstack/cert-manager/releases/download/v1.7.1/cert-manager.yaml Now you can deploy the latest stable Flink Kubernetes Operator version using the included Helm chart: helm repo add flink-operator-repo https://downloads.apache.org/flink/flink-kubernetes-operator-0.1.0/ helm install flink-kubernetes-operator flink-operator-repo/flink-kubernetes-operator You may verify your installation via kubectl: kubectl get pods NAME READY STATUS RESTARTS AGE flink-kubernetes-operator-5f466b8549-mgchb 1/1 Running 3 (23h ago) 16d","s":"Deploying the operator","u":"/docs/2.2.0-beta/start/kubernetes","h":"#deploying-the-operator","p":105014},{"i":105024,"t":"Run Application:: SeaTunnel already providers out-of-the-box configurations. Flink In this guide we are going to use flink.streaming.conf: env { execution.parallelism = 1 } source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { sql = \"select name,age from fake\" } } sink { ConsoleSink {} } This configuration need to be present when we are going to deploy the application (SeaTunnel) to Flink cluster (on Kubernetes), we also need to configure a Pod to Use a PersistentVolume for Storage. Create /mnt/data on your Node. Open a shell to the single Node in your cluster. How you open a shell depends on how you set up your cluster. For example, in our case weare using Minikube, you can open a shell to your Node by entering minikube ssh. In your shell on that Node, create a /mnt/data directory: minikube ssh # This assumes that your Node uses \"sudo\" to run commands # as the superuser sudo mkdir /mnt/data Copy application (SeaTunnel) configuration files to your Node. minikube cp flink.streaming.conf /mnt/data/flink.streaming.conf Once the Flink Kubernetes Operator is running as seen in the previous steps you are ready to submit a Flink (SeaTunnel) job: Create seatunnel-flink.yaml FlinkDeployment manifest: apiVersion: flink.apache.org/v1alpha1 kind: FlinkDeployment metadata: namespace: default name: seatunnel-flink-streaming-example spec: image: seatunnel:2.2.0-beta-flink-1.13 flinkVersion: v1_14 flinkConfiguration: taskmanager.numberOfTaskSlots: \"2\" serviceAccount: flink jobManager: replicas: 1 resource: memory: \"2048m\" cpu: 1 taskManager: resource: memory: \"2048m\" cpu: 2 podTemplate: spec: containers: - name: flink-main-container volumeMounts: - mountPath: /data name: config-volume volumes: - name: config-volume hostPath: path: \"/mnt/data\" type: Directory job: jarURI: local:///opt/seatunnel/lib/seatunnel-core-flink.jar entryClass: org.apache.seatunnel.core.flink.SeatunnelFlink args: [\"--config\", \"/data/flink.streaming.conf\"] parallelism: 2 upgradeMode: stateless Run the example application: kubectl apply -f seatunnel-flink.yaml See The Output Flink You may follow the logs of your job, after a successful startup (which can take on the order of a minute in a fresh environment, seconds afterwards) you can: kubectl logs -f deploy/seatunnel-flink-streaming-example To expose the Flink Dashboard you may add a port-forward rule: kubectl port-forward svc/seatunnel-flink-streaming-example-rest 8081 Now the Flink Dashboard is accessible at localhost:8081. Or launch minikube dashboard for a web-based Kubernetes user interface. The content printed in the TaskManager Stdout log: kubectl logs \\ -l 'app in (seatunnel-flink-streaming-example), component in (taskmanager)' \\ --tail=-1 \\ -f looks like the below (your content may be different since we use FakeSourceStream to automatically generate random stream data): +I[Kid Xiong, 1650316786086] +I[Ricky Huo, 1650316787089] +I[Ricky Huo, 1650316788089] +I[Ricky Huo, 1650316789090] +I[Kid Xiong, 1650316790090] +I[Kid Xiong, 1650316791091] +I[Kid Xiong, 1650316792092] To stop your job and delete your FlinkDeployment you can simply: kubectl delete -f seatunnel-flink.yaml Happy SeaTunneling!","s":"Run SeaTunnel Application","u":"/docs/2.2.0-beta/start/kubernetes","h":"#run-seatunnel-application","p":105014},{"i":105026,"t":"For now, you are already taking a quick look at SeaTunnel, you could see connector to find all source and sink SeaTunnel supported. Or see deployment if you want to submit your application in another kind of your engine cluster.","s":"What's More","u":"/docs/2.2.0-beta/start/kubernetes","h":"#whats-more","p":105014},{"i":105028,"t":"Let's take an application that randomly generates data in memory, processes it through SQL, and finally outputs it to the console as an example.","s":"Set Up with Locally","u":"/docs/2.2.0-beta/start/local","h":"","p":105027},{"i":105030,"t":"Before you getting start the local run, you need to make sure you already have installed the following software which SeaTunnel required: Java (Java 8 or 11, other versions greater than Java 8 can theoretically work as well) installed and JAVA_HOME set. Download the engine, you can choose and download one of them from below as your favour, you could see more information about why we need engine in SeaTunnel Spark: Please download Spark first(required version >= 2 and version < 3.x ). For more information you could see Getting Started: standalone Flink: Please download Flink first(required version >= 1.12.0 and version < 1.14.x ). For more information you could see Getting Started: standalone","s":"Step 1: Prepare the environment","u":"/docs/2.2.0-beta/start/local","h":"#step-1-prepare-the-environment","p":105027},{"i":105032,"t":"Enter the seatunnel download page and download the latest version of distribute package seatunnel-<version>-bin.tar.gz Or you can download it by terminal export version=\"2.2.0-beta\" wget \"https://archive.apache.org/dist/incubator/seatunnel/${version}/apache-seatunnel-incubating-${version}-bin.tar.gz\" tar -xzvf \"apache-seatunnel-incubating-${version}-bin.tar.gz\"","s":"Step 2: Download SeaTunnel","u":"/docs/2.2.0-beta/start/local","h":"#step-2-download-seatunnel","p":105027},{"i":105034,"t":"Since 2.2.0-beta, the binary package does not provide connector dependencies by default, so when using it for the first time, we need to execute the following command to install the connector: (Of course, you can also manually download the connector from [Apache Maven Repository](https://repo. maven.apache.org/maven2/org/apache/seatunnel/ to download, then manually move to the corresponding subdirectory of the connectors directory, for example, flink plugins should be placed in the flink directory, spark plugins should be placed in the spark directory). sh bin/install_plugin.sh 2.2.0-beta If you need to specify the version of the connector, take 2.2.0-beta as an example, we need to execute sh bin/install_plugin.sh 2.2.0-beta Usually we don't need all the connector plugins, so you can specify the plugins you need by configuring config/plugin_config, for example, you only need the flink-console plugin, then you can modify plugin.properties as --flink-connectors-- seatunnel-connector-flink-console --end-- If we want our sample application to work properly, we need to add the following plugins Spark Flink --spark-connectors-- seatunnel-connector-spark-fake seatunnel-connector-spark-console --end-- --flink-connectors-- seatunnel-connector-flink-fake seatunnel-connector-flink-console --end-- You can find all supported connectors and corresponding plugin_config configuration names under ${SEATUNNEL_HOME}/connectors/plugins-mapping.properties. tip If you want to install the connector plugin by manually downloading the connector, you need to pay special attention to the following The connectors directory contains the following subdirectories, if they do not exist, you need to create them manually flink flink-sql seatunnel spark If you want to manually install the connector plugin of the flink engine, you need to download the connector plugin of the flink engine you need, and then put them in the flink directory. Similarly, if you want to manually install the connector plugin of the spark engine, you need to download the connector plugin of the spark engine you need, and then put them in the spark directory","s":"Step 3: Install connectors plugin","u":"/docs/2.2.0-beta/start/local","h":"#step-3-install-connectors-plugin","p":105027},{"i":105036,"t":"Configure SeaTunnel: Change the setting in config/seatunnel-env.sh, it is base on the path your engine install at prepare step two. Change SPARK_HOME if you using Spark as your engine, or change FLINK_HOME if you're using Flink. Edit config/flink(spark).streaming.conf.template, which determines the way and logic of data input, processing, and output after seatunnel is started. The following is an example of the configuration file, which is the same as the example application mentioned above. ###### ###### This config file is a demonstration of streaming processing in SeaTunnel config ###### env { # You can set flink configuration here execution.parallelism = 1 # For Spark #spark.app.name = \"SeaTunnel\" #spark.executor.instances = 2 #spark.executor.cores = 1 #spark.executor.memory = \"1g\" #spark.master = local } source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } } transform { sql { sql = \"select name,age from fake\" } } sink { ConsoleSink {} } More information about config please check config concept","s":"Step 4: Configure SeaTunnel Application","u":"/docs/2.2.0-beta/start/local","h":"#step-4-configure-seatunnel-application","p":105027},{"i":105038,"t":"You could start the application by the following commands Spark Flink cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-spark.sh \\ --master local[4] \\ --deploy-mode client \\ --config ./config/spark.streaming.conf.template cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-flink.sh \\ --config ./config/flink.streaming.conf.template See The Output: When you run the command, you could see its output in your console or in Flink UI, You can think this is a sign that the command ran successfully or not. Spark Flink The SeaTunnel console will prints some logs as below: Hello World, SeaTunnel Hello World, SeaTunnel Hello World, SeaTunnel ... Hello World, SeaTunnel The content printed in the TaskManager Stdout log of flink WebUI, is two columned record just like below(your content maybe different cause we use fake source to create data random): apache, 15 seatunnel, 30 incubator, 20 ... topLevel, 20","s":"Step 5: Run SeaTunnel Application","u":"/docs/2.2.0-beta/start/local","h":"#step-5-run-seatunnel-application","p":105027},{"i":105040,"t":"Our local quick start is using one of the build-in example in directory config, and we provider more than one out-of-box example you could and feel free to have a try and make your hands dirty. All you have to do is change the started command option value in running application to the configuration you want to run, we use batch template in config as examples: Spark Flink cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-spark.sh \\ --master local[4] \\ --deploy-mode client \\ --config ./config/spark.batch.conf.template cd \"apache-seatunnel-incubating-${version}\" ./bin/start-seatunnel-flink.sh \\ --config ./config/flink.batch.conf.template","s":"Explore More Build-in Examples","u":"/docs/2.2.0-beta/start/local","h":"#explore-more-build-in-examples","p":105027},{"i":105042,"t":"For now, you are already take a quick look about SeaTunnel, you could see connector to find all source and sink SeaTunnel supported. Or see deployment if you want to submit your application in other kind of your engine cluster.","s":"What's More","u":"/docs/2.2.0-beta/start/local","h":"#whats-more","p":105027},{"i":105044,"t":"Json transform plugin","s":"Json","u":"/docs/2.2.0-beta/transform/json","h":"","p":105043},{"i":105046,"t":"Json analysis of the specified fields of the original data set tip This transform ONLY supported by Spark.","s":"Description","u":"/docs/2.2.0-beta/transform/json","h":"#description","p":105043},{"i":105048,"t":"name type required default value source_field string no raw_message target_field string no root schema_dir string no - schema_file string no - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/transform/json","h":"#options","p":105043},{"i":105050,"t":"Source field, if not configured, the default is raw_message","s":"source_field [string]","u":"/docs/2.2.0-beta/transform/json","h":"#source_field-string","p":105043},{"i":105052,"t":"The target field, if it is not configured, the default is __root__ , and the result of Json parsing will be uniformly placed at the top of the Dataframe","s":"target_field [string]","u":"/docs/2.2.0-beta/transform/json","h":"#target_field-string","p":105043},{"i":105054,"t":"Style directory, if not configured, the default is $seatunnelRoot/plugins/json/files/schemas/","s":"schema_dir [string]","u":"/docs/2.2.0-beta/transform/json","h":"#schema_dir-string","p":105043},{"i":105056,"t":"The style file name, if it is not configured, the default is empty, that is, the structure is not specified, and the system derives it by itself according to the input of the data source.","s":"schema_file [string]","u":"/docs/2.2.0-beta/transform/json","h":"#schema_file-string","p":105043},{"i":105058,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/json","h":"#common-options-string","p":105043},{"i":105060,"t":"json schema usage scenarios The multiple data sources of a single task may contain different styles of json data. For example, the topicA style from Kafka is { \"A\": \"a_val\", \"B\": \"b_val\" } The style from topicB is { \"C\": \"c_val\", \"D\": \"d_val\" } When running Transform , you need to fuse the data of topicA and topicB into a wide table for calculation. You can specify a schema whose content style is: { \"A\": \"a_val\", \"B\": \"b_val\", \"C\": \"c_val\", \"D\": \"d_val\" } Then the fusion output result of topicA and topicB is: +-----+-----+-----+-----+ |A |B |C |D | +-----+-----+-----+-----+ |a_val|b_val|null |null | |null |null |c_val|d_val| +-----+-----+-----+-----+","s":"Schema Use cases","u":"/docs/2.2.0-beta/transform/json","h":"#schema-use-cases","p":105043},{"i":105063,"t":"json { source_field = \"message\" } Source +----------------------------+ |message | +----------------------------+ |{\"name\": \"ricky\", \"age\": 24}| |{\"name\": \"gary\", \"age\": 28} | +----------------------------+ Sink +----------------------------+---+-----+ |message |age|name | +----------------------------+---+-----+ |{\"name\": \"gary\", \"age\": 28} |28 |gary | |{\"name\": \"ricky\", \"age\": 23}|23 |ricky| +----------------------------+---+-----+","s":"Do not use target_field","u":"/docs/2.2.0-beta/transform/json","h":"#do-not-use-target_field","p":105043},{"i":105065,"t":"json { source_field = \"message\" target_field = \"info\" } Souce +----------------------------+ |message | +----------------------------+ |{\"name\": \"ricky\", \"age\": 24}| |{\"name\": \"gary\", \"age\": 28} | +----------------------------+ Sink +----------------------------+----------+ |message |info | +----------------------------+----------+ |{\"name\": \"gary\", \"age\": 28} |[28,gary] | |{\"name\": \"ricky\", \"age\": 23}|[23,ricky]| +----------------------------+----------+ The results of json processing support select * from where info.age = 23 such SQL statements","s":"Use target_field","u":"/docs/2.2.0-beta/transform/json","h":"#use-target_field","p":105043},{"i":105067,"t":"json { source_field = \"message\" schema_file = \"demo.json\" } Schema Place the following content in ~/seatunnel/plugins/json/files/schemas/demo.json of Driver Node: { \"name\": \"demo\", \"age\": 24, \"city\": \"LA\" } Source +----------------------------+ |message | +----------------------------+ |{\"name\": \"ricky\", \"age\": 24}| |{\"name\": \"gary\", \"age\": 28} | +----------------------------+ Sink +----------------------------+---+-----+-----+ |message |age|name |city | +----------------------------+---+-----+-----+ |{\"name\": \"gary\", \"age\": 28} |28 |gary |null | |{\"name\": \"ricky\", \"age\": 23}|23 |ricky|null | +----------------------------+---+-----+-----+ If you use cluster mode for deployment, make sure that the json schemas directory is packaged in plugins.tar.gz","s":"Use schema_file","u":"/docs/2.2.0-beta/transform/json","h":"#use-schema_file","p":105043},{"i":105069,"t":"Common parameters of transform plugins tip This transform both supported by engine Spark and Flink.","s":"Common Options","u":"/docs/2.2.0-beta/transform/common-options","h":"","p":105068},{"i":105071,"t":"Spark Flink name type required default value source_table_name string no - result_table_name string no - name type required default value source_table_name string no - result_table_name string no - field_name string no - field_name [string]​ When the data is obtained from the upper-level plugin, you can specify the name of the obtained field, which is convenient for use in subsequent sql plugins.","s":"Transform Plugin common parameters","u":"/docs/2.2.0-beta/transform/common-options","h":"#transform-plugin-common-parameters","p":105068},{"i":105073,"t":"When the data is obtained from the upper-level plugin, you can specify the name of the obtained field, which is convenient for use in subsequent sql plugins.","s":"field_name [string]","u":"/docs/2.2.0-beta/transform/common-options","h":"#field_name-string","p":105068},{"i":105075,"t":"When source_table_name is not specified, the current plug-in processes the data set (dataset) output by the previous plug-in in the configuration file; When source_table_name is specified, the current plugin is processing the data set corresponding to this parameter.","s":"source_table_name [string]","u":"/docs/2.2.0-beta/transform/common-options","h":"#source_table_name-string","p":105068},{"i":105077,"t":"When result_table_name is not specified, the data processed by this plugin will not be registered as a data set that can be directly accessed by other plugins, or called a temporary table (table); When result_table_name is specified, the data processed by this plugin will be registered as a data set (dataset) that can be directly accessed by other plugins, or called a temporary table (table) . The dataset registered here can be directly accessed by other plugins by specifying source_table_name .","s":"result_table_name [string]","u":"/docs/2.2.0-beta/transform/common-options","h":"#result_table_name-string","p":105068},{"i":105079,"t":"Spark Flink split { source_table_name = \"source_view_table\" source_field = \"message\" delimiter = \"&\" fields = [\"field1\", \"field2\"] result_table_name = \"result_view_table\" } The Split plugin will process the data in the temporary table source_view_table and register the processing result as a temporary table named result_view_table. This temporary table can be used by any subsequent Filter or Output plugin by specifying source_table_name . split { source_field = \"message\" delimiter = \"&\" fields = [\"field1\", \"field2\"] } Note: If source_table_name is not configured, output the processing result of the last Transform plugin in the configuration file source { FakeSourceStream { result_table_name = \"fake_1\" field_name = \"name,age\" } FakeSourceStream { result_table_name = \"fake_2\" field_name = \"name,age\" } } transform { sql { source_table_name = \"fake_1\" sql = \"select name from fake_1\" result_table_name = \"fake_name\" } } If source_table_name is not specified, the sql plugin will process the data of fake_2 , and if it is set to fake_1 , it will process the data of fake_1 .","s":"Examples","u":"/docs/2.2.0-beta/transform/common-options","h":"#examples","p":105068},{"i":105081,"t":"NULL rate transform plugin","s":"NullRate","u":"/docs/2.2.0-beta/transform/nullRate","h":"","p":105080},{"i":105083,"t":"When there is a large amount of data, the final result will always be greatly affected by the problem of data null value. Therefore, early null value detection is particularly important. For this reason, this function came into being tip This transform ONLY supported by Spark.","s":"Description","u":"/docs/2.2.0-beta/transform/nullRate","h":"#description","p":105080},{"i":105085,"t":"name type required default value fields string_list yes - rates double_list yes - throw_exception_enable boolean no - save_to_table_name string no -","s":"Options","u":"/docs/2.2.0-beta/transform/nullRate","h":"#options","p":105080},{"i":105087,"t":"Which fields do you want to monitor .","s":"field [string_list]","u":"/docs/2.2.0-beta/transform/nullRate","h":"#field-string_list","p":105080},{"i":105089,"t":"It is consistent with the number of fields. Double type indicates the set null rate value .","s":"rates [double_list]","u":"/docs/2.2.0-beta/transform/nullRate","h":"#rates-double_list","p":105080},{"i":105091,"t":"Whether to throw an exception when it is greater than the set value. The default value is false .","s":"throw_exception_enable [boolean]","u":"/docs/2.2.0-beta/transform/nullRate","h":"#throw_exception_enable-boolean","p":105080},{"i":105093,"t":"Whether the current verification value is output to the table. It is not output by defaul .","s":"save_to_table_name [string]","u":"/docs/2.2.0-beta/transform/nullRate","h":"#save_to_table_name-string","p":105080},{"i":105095,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/nullRate","h":"#common-options-string","p":105080},{"i":105097,"t":"nullRate { fields = [\"msg\", \"name\"] rates = [10.0,3.45] save_to_table_name = \"tmp\" throw_exception_enable = true } } Use NullRate in transform's Dataset. transform { NullRate { fields = [\"msg\", \"name\"] rates = [10.0,3.45] save_to_table_name = \"tmp\" throw_exception_enable = true } }","s":"Examples","u":"/docs/2.2.0-beta/transform/nullRate","h":"#examples","p":105080},{"i":105099,"t":"NULL default value transform plugin","s":"Nulltf","u":"/docs/2.2.0-beta/transform/nulltf","h":"","p":105098},{"i":105101,"t":"set default value for null field tip This transform only supported by engine Spark.","s":"Description","u":"/docs/2.2.0-beta/transform/nulltf","h":"#description","p":105098},{"i":105103,"t":"name type required default value fields array no -","s":"Options","u":"/docs/2.2.0-beta/transform/nulltf","h":"#options","p":105098},{"i":105105,"t":"A list of fields whose default value will be set. The default value of the field can be set in the form of \"field:value\", If no set, the default value will be set according to the field type.","s":"fields [list]","u":"/docs/2.2.0-beta/transform/nulltf","h":"#fields-list","p":105098},{"i":105107,"t":"the configuration nulltf { fields { name: \"\", price: 0, num: 100, flag: false, dt_timestamp: \"2022-05-18 13:51:40.603\", dt_date: \"2022-05-19\" } } before use nulltf transform +-----+-----+----+-----+--------------------+----------+ | name|price| num| flag| dt_timestamp| dt_date| +-----+-----+----+-----+--------------------+----------+ |名称1| 22.5| 100|false|2022-05-20 14:34:...|2022-05-20| | null| 22.5| 100|false|2022-05-20 14:35:...|2022-05-20| |名称1| null| 100|false|2022-05-20 14:35:...|2022-05-20| |名称1| 22.5|null|false|2022-05-20 14:36:...|2022-05-20| |名称1| 22.5| 100| null|2022-05-20 14:36:...|2022-05-20| |名称1| 22.5| 100|false| null|2022-05-20| |名称1| 22.5| 100|false|2022-05-20 14:37:...| null| +-----+-----+----+-----+--------------------+----------+ after use nulltf transform +-----+-----+----+-----+--------------------+----------+ | name|price| num| flag| dt_timestamp| dt_date| +-----+-----+----+-----+--------------------+----------+ |名称1| 22.5|100|false|2022-05-20 14:34:...|2022-05-20| | | 22.5|100|false|2022-05-20 14:35:...|2022-05-20| |名称1| 0.0|100|false|2022-05-20 14:35:...|2022-05-20| |名称1| 22.5|100|false|2022-05-20 14:36:...|2022-05-20| |名称1| 22.5|100|false|2022-05-20 14:36:...|2022-05-20| |名称1| 22.5|100|false|2022-05-18 13:51:...|2022-05-20| |名称1| 22.5|100|false|2022-05-20 14:37:...|2022-05-19| +-----+-----+---+-----+--------------------+----------+","s":"Examples","u":"/docs/2.2.0-beta/transform/nulltf","h":"#examples","p":105098},{"i":105109,"t":"Replace transform plugin","s":"Replace","u":"/docs/2.2.0-beta/transform/replace","h":"","p":105108},{"i":105111,"t":"Examines string value in a given field and replaces substring of the string value that matches the given string literal or regexes with the given replacement. tip This transform ONLY supported by Spark.","s":"Description","u":"/docs/2.2.0-beta/transform/replace","h":"#description","p":105108},{"i":105113,"t":"name type required default value source_field string no raw_message fields string yes - pattern string yes - replacement string yes - is_regex boolean no false replace_first boolean no false","s":"Options","u":"/docs/2.2.0-beta/transform/replace","h":"#options","p":105108},{"i":105115,"t":"Source field, if not configured, the default is raw_message","s":"source_field [string]","u":"/docs/2.2.0-beta/transform/replace","h":"#source_field-string","p":105108},{"i":105117,"t":"The name of the field to replaced.","s":"field [string]","u":"/docs/2.2.0-beta/transform/replace","h":"#field-string","p":105108},{"i":105119,"t":"The string to match.","s":"pattern [string]","u":"/docs/2.2.0-beta/transform/replace","h":"#pattern-string","p":105108},{"i":105121,"t":"The replacement pattern (is_regex is true) or string literal (is_regex is false).","s":"replacement [string]","u":"/docs/2.2.0-beta/transform/replace","h":"#replacement-string","p":105108},{"i":105123,"t":"Whether or not to interpret the pattern as a regex (true) or string literal (false).","s":"is_regex [boolean]","u":"/docs/2.2.0-beta/transform/replace","h":"#is_regex-boolean","p":105108},{"i":105125,"t":"Whether or not to skip any matches beyond the first match.","s":"replace_first [boolean]","u":"/docs/2.2.0-beta/transform/replace","h":"#replace_first-boolean","p":105108},{"i":105127,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/replace","h":"#common-options-string","p":105108},{"i":105129,"t":"the word a will be replaced by b at message field values. replace { source_field = \"message\" fields = \"_replaced\" pattern = \"a\" replacement = \"b\" } Use Replace as udf in sql. Replace { fields = \"_replaced\" pattern = \"([^ ]*) ([^ ]*)\" replacement = \"$2\" isRegex = true replaceFirst = true } # Use the split function (confirm that the fake table exists) sql { sql = \"select * from (select raw_message, replace(raw_message) as info_row from fake) t1\" }","s":"Examples","u":"/docs/2.2.0-beta/transform/replace","h":"#examples","p":105108},{"i":105131,"t":"Split transform plugin","s":"Split","u":"/docs/2.2.0-beta/transform/split","h":"","p":105130},{"i":105133,"t":"A string cutting function is defined, which is used to split the specified field in the Sql plugin. tip This transform both supported by engine Spark and Flink.","s":"Description","u":"/docs/2.2.0-beta/transform/split","h":"#description","p":105130},{"i":105135,"t":"Spark Flink name type required default value separator string no \" \" fields array yes - source_field string no raw_message target_field string no root common-options string no - separator [string]​ Separator, the input string is separated according to the separator. The default separator is a space (\" \") . Note: If you use some special characters in the separator, you need to escape it. e.g. \"\\|\" source_field [string]​ The source field of the string before being split, if not configured, the default is raw_message target_field [string]​ target_field can specify the location where multiple split fields are added to the Event. If it is not configured, the default is _root_ , that is, all split fields will be added to the top level of the Event. If a specific field is specified, the divided field will be added to the next level of this field. name type required default value separator string no , fields array yes - common-options string no - separator [string]​ The specified delimiter, the default is ,","s":"Options","u":"/docs/2.2.0-beta/transform/split","h":"#options","p":105130},{"i":105137,"t":"Separator, the input string is separated according to the separator. The default separator is a space (\" \") . Note: If you use some special characters in the separator, you need to escape it. e.g. \"\\|\"","s":"separator [string]","u":"/docs/2.2.0-beta/transform/split","h":"#separator-string","p":105130},{"i":105139,"t":"The source field of the string before being split, if not configured, the default is raw_message","s":"source_field [string]","u":"/docs/2.2.0-beta/transform/split","h":"#source_field-string","p":105130},{"i":105141,"t":"target_field can specify the location where multiple split fields are added to the Event. If it is not configured, the default is _root_ , that is, all split fields will be added to the top level of the Event. If a specific field is specified, the divided field will be added to the next level of this field.","s":"target_field [string]","u":"/docs/2.2.0-beta/transform/split","h":"#target_field-string","p":105130},{"i":105143,"t":"The specified delimiter, the default is ,","s":"separator [string]","u":"/docs/2.2.0-beta/transform/split","h":"#separator-string-1","p":105130},{"i":105145,"t":"In the split field name list, specify the field names of each character string after splitting in order. If the length of the fields is greater than the length of the separation result, the extra fields are assigned null characters.","s":"fields [list]","u":"/docs/2.2.0-beta/transform/split","h":"#fields-list","p":105130},{"i":105147,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/split","h":"#common-options-string","p":105130},{"i":105149,"t":"Spark Flink Split the message field in the source data according to &, you can use field1 or field2 as the key to get the corresponding value split { source_field = \"message\" separator = \"&\" fields = [\"field1\", \"field2\"] } Split the message field in the source data according to , , the split field is info , you can use info.field1 or info.field2 as the key to get the corresponding value split { source_field = \"message\" target_field = \"info\" separator = \",\" fields = [\"field1\", \"field2\"] } Use Split as udf in sql. # This just created a udf called split Split{ separator = \"#\" fields = [\"name\",\"age\"] } # Use the split function (confirm that the fake table exists) sql { sql = \"select * from (select raw_message,split(raw_message) as info_row from fake) t1\" }","s":"Examples","u":"/docs/2.2.0-beta/transform/split","h":"#examples","p":105130},{"i":105151,"t":"Sql transform plugin","s":"Sql","u":"/docs/2.2.0-beta/transform/sql","h":"","p":105150},{"i":105153,"t":"Use SQL to process data and support engine's UDF function. tip This transform both supported by engine Spark and Flink.","s":"Description","u":"/docs/2.2.0-beta/transform/sql","h":"#description","p":105150},{"i":105155,"t":"name type required default value sql string yes - common-options string no -","s":"Options","u":"/docs/2.2.0-beta/transform/sql","h":"#options","p":105150},{"i":105157,"t":"SQL statement, the table name used in SQL configured in the Source or Transform plugin","s":"sql [string]","u":"/docs/2.2.0-beta/transform/sql","h":"#sql-string","p":105150},{"i":105159,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/sql","h":"#common-options-string","p":105150},{"i":105162,"t":"Use the SQL plugin for field deletion. Only the username and address fields are reserved, and the remaining fields will be discarded. user_info is the result_table_name configured by the previous plugin sql { sql = \"select username, address from user_info\", }","s":"Simple Select","u":"/docs/2.2.0-beta/transform/sql","h":"#simple-select","p":105150},{"i":105164,"t":"Use SQL plugin for data processing, use substring functions to intercept the telephone field sql { sql = \"select substring(telephone, 0, 10) from user_info\", }","s":"Use UDF","u":"/docs/2.2.0-beta/transform/sql","h":"#use-udf","p":105150},{"i":105166,"t":"Use SQL plugin for data aggregation, use avg functions to perform aggregation operations on the original data set, and take out the average value of the age field sql { sql = \"select avg(age) from user_info\", table_name = \"user_info\" }","s":"Use UDAF","u":"/docs/2.2.0-beta/transform/sql","h":"#use-udaf","p":105150},{"i":105168,"t":"UDF transform plugin","s":"udf","u":"/docs/2.2.0-beta/transform/udf","h":"","p":105167},{"i":105170,"t":"Supports using UDF in data integration by the transform. Need to specify the function name and class name and put UDF jars in Flink's classpath or import them via 'Flink run -c xxx.jar' tip This transform ONLY supported by Flink.","s":"Description","u":"/docs/2.2.0-beta/transform/udf","h":"#description","p":105167},{"i":105172,"t":"name type required default value function string yes -","s":"Options","u":"/docs/2.2.0-beta/transform/udf","h":"#options","p":105167},{"i":105174,"t":"A config prefix, use like function.test=\"xxx.Test\".","s":"function [string]","u":"/docs/2.2.0-beta/transform/udf","h":"#function-string","p":105167},{"i":105176,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/udf","h":"#common-options-string","p":105167},{"i":105178,"t":"Use udf in sql. udf { function.test_1 = \"com.example.udf.flink.TestUDF\" function.test_2 = \"com.example.udf.flink.TestUDTF\" } # Use the specify function (confirm that the fake table exists) sql { sql = \"select test_1(name), age from fake\" }","s":"Examples","u":"/docs/2.2.0-beta/transform/udf","h":"#examples","p":105167},{"i":105180,"t":"UUID transform plugin","s":"UUID","u":"/docs/2.2.0-beta/transform/uuid","h":"","p":105179},{"i":105182,"t":"Generate a universally unique identifier on a specified field. tip This transform ONLY supported by Spark.","s":"Description","u":"/docs/2.2.0-beta/transform/uuid","h":"#description","p":105179},{"i":105184,"t":"name type required default value fields string yes - prefix string no - secure boolean no false","s":"Options","u":"/docs/2.2.0-beta/transform/uuid","h":"#options","p":105179},{"i":105186,"t":"The name of the field to generate.","s":"field [string]","u":"/docs/2.2.0-beta/transform/uuid","h":"#field-string","p":105179},{"i":105188,"t":"The prefix string constant to prepend to each generated UUID.","s":"prefix [string]","u":"/docs/2.2.0-beta/transform/uuid","h":"#prefix-string","p":105179},{"i":105190,"t":"the cryptographically secure algorithm can be comparatively slow The nonSecure algorithm uses a secure random seed but is otherwise deterministic","s":"secure [boolean]","u":"/docs/2.2.0-beta/transform/uuid","h":"#secure-boolean","p":105179},{"i":105192,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/docs/2.2.0-beta/transform/uuid","h":"#common-options-string","p":105179},{"i":105194,"t":"UUID { fields = \"u\" prefix = \"uuid-\" secure = true } } Use UUID as udf in sql. UUID { fields = \"u\" prefix = \"uuid-\" secure = true } # Use the uuid function (confirm that the fake table exists) sql { sql = \"select * from (select raw_message, UUID() as info_row from fake) t1\" }","s":"Examples","u":"/docs/2.2.0-beta/transform/uuid","h":"#examples","p":105179},{"i":105196,"t":"DataSource Version: 1.0.0 Supported DataSources List all datasource supported Apache SeaTunnel Web for now. 📄️ JDBC-MySQL DataSource Previous Define And Run SeaTunnel Job Next JDBC-MySQL DataSource","s":"Supported DataSources","u":"/seatunnel_web/1.0.0/category/datasource","h":"","p":105195},{"i":105198,"t":"SinkNodes Version: 1.0.0 Supported SinkNodes List all sink nodes supported Apache SeaTunnel Web for now. 📄️ JDBC-MySQL Sink Node Previous JDBC-MySQL Source Node Next JDBC-MySQL Sink Node","s":"Supported SinkNodes","u":"/seatunnel_web/1.0.0/category/sink_node","h":"","p":105197},{"i":105200,"t":"About SeaTunnel Web Version: 1.0.0 About SeaTunnel Web SeaTunnel Web is a web project that provides visual management of jobs, scheduling, running and monitoring capabilities. It is developed based on the SeaTunnel Connector API and the SeaTunnel Zeta Engine. It is a web project that can be deployed independently. It is also a sub-project of SeaTunnel. For more information, please refer to SeaTunnel Web. Edit this page Next Deployment of Apache SeaTunnel Web","s":"About SeaTunnel Web","u":"/seatunnel_web/1.0.0/about","h":"","p":105199},{"i":105202,"t":"SourceNodes Version: 1.0.0 Supported SourceNodes List all source nodes supported Apache SeaTunnel Web for now. 📄️ JDBC-MySQL Source Node Previous JDBC-MySQL DataSource Next JDBC-MySQL Source Node","s":"Supported SourceNodes","u":"/seatunnel_web/1.0.0/category/source_node","h":"","p":105201},{"i":105204,"t":"DataSource JDBC-MySQL DataSource Version: 1.0.0 JDBC-MySQL DataSource Edit this page Previous DataSource Next SourceNodes","s":"JDBC-MySQL DataSource","u":"/seatunnel_web/1.0.0/datasources/jdbc-mysql","h":"","p":105203},{"i":105206,"t":"Deployment of Apache SeaTunnel Web Version: 1.0.0 On this page Deployment of Apache SeaTunnel Web Apache SeaTunnel offers two methods for submitting tasks: SeaTunnel Web: This is a visual method to create and submit seatunnel tasks. Command Line: SeaTunnel also provides a command line method for task submission. Deployment of Apache SeaTunnel Server​ Since SeaTunnel Web uses SeaTunnel Java client to submit tasks, a SeaTunnel server is required to run these tasks. Therefore, the first step is to deploy a SeaTunnel server. If you haven't yet started the SeaTunnel server, you can refer to the following steps for deployment and service start-up. Download and deploy the SeaTunnel installation package to start the Zeta service. Obtain the SeaTunnel server package (version 2.3.3 or higher, as SeaTunnel Web supports only 2.3.3 and above versions of the SeaTunnel Zeta engine) from https://seatunnel.apache.org/download. Download Connector Plugins: This process also involves automatic download and installation of third-party dependencies, such as seatunnel-hadoop3-3.1.4-uber-2.3.3-optional.jar. Follow the instructions on https://seatunnel.apache.org/docs/2.3.3/start-v2/locally/deployment to download the connector plugins. Repackaging: After downloading the connector plugins, run tar -zcvf apache-seatunnel-2.3.3.tar.gz apache-seatunnel-2.3.3 to repackage, creating a complete installation package apache-seatunnel-2.3.3.tar.gz for deploying the SeaTunnel Zeta engine server. Cluster Deployment: Copy apache-seatunnel-2.3.3.tar.gz to other server nodes for SeaTunnel Zeta cluster deployment. For details on deployment and starting the Zeta service, refer to https://seatunnel.apache.org/docs/seatunnel-engine/deployment. To start the Zeta service, use: $SEATUNNEL_HOME/bin/seatunnel-cluster.sh -d 2. Download and Unzip SeaTunnel Web​ Download installation package apache-seatunnel-web-1.0.0-bin.tar.gz from SeaTunnel Download Page or directly from https://www.apache.org/dyn/closer.lua/seatunnel/seatunnel-web/1.0.0/apache-seatunnel-web-1.0.0-bin.tar.gz. unzip tar -zxvf apache-seatunnel-web-1.0.0-bin.tar.gz to get a directory named apache-seatunnel-web-1.0.0-bin. 3. Deploy SeaTunnel Zeta Client on SeaTunnel Web​ If you're using SeaTunnel Web, you need to deploy a SeaTunnel Zeta client on the SeaTunnel Web node. (This step can be skipped if you're running both SeaTunnel Zeta service and SeaTunnel Web on the same node.) Copy apache-seatunnel-2.3.3.tar.gz to the SeaTunnel Web node and unzip it in the same path as the SeaTunnel Zeta server node. Set Environment Variables: On the SeaTunnel Web node, set SEATUNNEL_HOME as an environment variable, similar to the SeaTunnel Zeta server node. export SEATUNNEL_HOME=${seatunnel install path} export PATH=$PATH:$SEATUNNEL_HOME/bin Run $SEATUNNEL_HOME/bin/seatunnel.sh --config $SEATUNNEL_HOME/config/v2.batch.config.template. Successful completion of this job indicates that the Zeta client is deployed successfully. 4. Initialize Database for SeaTunnel Web Service​ Modify apache-seatunnel-web-1.0.0-bin/script/seatunnel_server_env.sh to set the database address, port, username, and password. Example: export HOSTNAME=\"localhost\" export PORT=\"3306\" export USERNAME=\"root\" export PASSWORD=\"123456\" Install MySQL Client: On the current node, install mysql-client using sudo apt install mysql-client. Execute sh apache-seatunnel-web-1.0.0-bin/script/init_sql.sh. A successful run without errors indicates successful initialization. (Alternatively, manually copy /script/seatunnel_server_mysql.sql to the database and execute the script.) 5. Download DataSource Plugins​ Download the download_datasource.sh script and place it in the apache-seatunnel-web-1.0.0-bin/bin directory. Run the command sh download_datasource.sh. This will automatically download the necessary JAR packages for various data source connections required for web operation to the libs directory. After downloading, ensure that the MySQL driver is included in the libs directory. If not, manually download the MySQL driver to this directory, as SeaTunnel Web cannot start without it. 6. Configure the Application and Start SeaTunnel Web Backend Service​ Modify apache-seatunnel-web-1.0.0-bin/conf/application.yml with SeaTunnel database connection information (same as in script/seatunnel_server_env.sh). url: jdbc:mysql://localhost:3306/seatunnel?useSSL=false&useUnicode=true&characterEncoding=utf-8&allowMultiQueries=true&allowPublicKeyRetrieval=true username: xxx password: xxx Copy hazelcast-client.yaml from apache-seatunnel-2.3.3/config to apache-seatunnel-web-1.0.0-bin/conf/ and configure it, especially the cluster members' IP and port. Refer to SeaTunnel Engine Client Configuration. hazelcast-client: cluster-name: seatunnel properties: hazelcast.logging.type: log4j2 network: cluster-members: - ip:5801 Move plugin-mapping.properties from apache-seatunnel-2.3.3/connectors to the apache-seatunnel-web-1.0.0-bin/conf/ directory. 7. Start SeaTunnel Web​ Go to the apache-seatunnel-web-1.0.0-bin directory: cd apache-seatunnel-web-1.0.0-bin Start the SeaTunnel Web backend service: sh bin/seatunnel-backend-daemon.sh start Visit UI http://127.0.0.1:8801/ui/ and log in with the default credentials (username: admin, password: admin). A successful login screen indicates successful deployment. ![Login UI](https://github.com/apache/dolphinscheduler/assets/15833811/39505fec-64bd-4e94-9f60-de505b4bb0cb) Edit this page Previous About SeaTunnel Web Next Define And Run SeaTunnel Job Deployment of Apache SeaTunnel Server 2. Download and Unzip SeaTunnel Web 3. Deploy SeaTunnel Zeta Client on SeaTunnel Web 4. Initialize Database for SeaTunnel Web Service 5. Download DataSource Plugins 6. Configure the Application and Start SeaTunnel Web Backend Service 7. Start SeaTunnel Web","s":"Deployment of Apache SeaTunnel Web","u":"/seatunnel_web/1.0.0/deploy","h":"","p":105205},{"i":105208,"t":"Define And Run SeaTunnel Job Version: 1.0.0 Define And Run SeaTunnel Job Edit this page Previous Deployment of Apache SeaTunnel Web Next DataSource","s":"Define And Run SeaTunnel Job","u":"/seatunnel_web/1.0.0/how_to_use","h":"","p":105207},{"i":105210,"t":"SinkNodes JDBC-MySQL Sink Node Version: 1.0.0 JDBC-MySQL Sink Node Edit this page Previous SinkNodes","s":"JDBC-MySQL Sink Node","u":"/seatunnel_web/1.0.0/sinknodes/jdbc-mysql","h":"","p":105209},{"i":105212,"t":"SourceNodes JDBC-MySQL Source Node Version: 1.0.0 JDBC-MySQL Source Node Edit this page Previous SourceNodes Next SinkNodes","s":"JDBC-MySQL Source Node","u":"/seatunnel_web/1.0.0/sourcenodes/jdbc-mysql","h":"","p":105211},{"i":105214,"t":"This is unreleased documentation for Apache SeaTunnel Next version. For up-to-date documentation, see the latest version (1.0.0). About SeaTunnel Web Version: Next About SeaTunnel Web SeaTunnel Web is a web project that provides visual management of jobs, scheduling, running and monitoring capabilities. It is developed based on the SeaTunnel Connector API and the SeaTunnel Zeta Engine. It is a web project that can be deployed independently. It is also a sub-project of SeaTunnel. For more information, please refer to SeaTunnel Web Edit this page","s":"About SeaTunnel Web","u":"/seatunnel_web/next/about","h":"","p":105213},{"i":105216,"t":"This is unreleased documentation for Apache SeaTunnel Next version. For up-to-date documentation, see the latest version (1.0.0). Version: Next On this page JDBC-MySQL DataSource Step 1: Click Create​ Step 2: Select the corresponding data source​ Step 3: Fill in the data source information and set jdbc parameters​ Step 4: Test Connect,Click OK to create​ Step 5: Created successfully​ Edit this page Step 1: Click Create Step 2: Select the corresponding data source Step 3: Fill in the data source information and set jdbc parameters Step 4: Test Connect,Click OK to create Step 5: Created successfully","s":"JDBC-MySQL DataSource","u":"/seatunnel_web/next/datasources/jdbc-mysql","h":"","p":105215},{"i":105218,"t":"This is unreleased documentation for Apache SeaTunnel Next version. For up-to-date documentation, see the latest version (1.0.0). Version: Next On this page Deploy 1 Preparing the Apache SeaTunnel environment​ Because SeaTunnel Web uses the SeaTunnel Java Client to submit jobs, running SeaTunnel Web requires preparing a SeaTunnel Zeta Engine service first. Based on the usage requirements of SeaTunnel Zeta Engine, the SeaTunnel Client node that submits the job must have the same operating system and installation directory structure as the SeaTunnel Server node that runs the job. Therefore, if you want to run SeaTunnel Web in IDEA, you must install and run a SeaTunnel Zeta Engine Server on the same machine as the IDEA. Don't worry, the next steps will tell you how to correctly install SeaTunnel Zeta Engine Server in different situations. 2 Run SeaTunnel Web In Server​ To run SeaTunnel Web on the server, you need to first have a SeaTunnel Zeta Engine Server environment. If you do not already have one, you can refer to the following steps for deployment. 2.1 Deploy SeaTunnel Zeta Engine Server In Server Node​ You have two ways to get the SeaTunnel installer package. Build from source code or download from the SeaTunnel website. The SeaTunnel version used here is only for writing this document to show you the process used, and does not necessarily represent the correct version. SeaTunnel Web and SeaTunnel Engine have strict version dependencies, and you can confirm the specific version mapping through xxx Support SeaTunnel Version SeaTunnel 2.3.3 Only 2.1.1 Build from source code​ Get the source package from https://seatunnel.apache.org/download or https://github.com/apache/seatunnel.git Build installer package use maven command ./mvnw -U -T 1C clean install -DskipTests -D\"maven.test.skip\"=true -D\"maven.javadoc.skip\"=true -D\"checkstyle.skip\"=true -D\"license.skipAddThirdParty\" Then you can get the installer package in ${Your_code_dir}/seatunnel-dist/target, For example:apache-seatunnel-2.3.3-SNAPSHOT-bin.tar.gz 2.1.2 Download installer package​ The other way to get SeaTunnel Zeta Engine Server installer package is download the installer package from https://seatunnel.apache.org/download and install plugins online. Download and install connector plugin(Some third-party dependency packages will also be automatically downloaded and installed during this process, such as hadoop jar). You can get the step from https://seatunnel.apache.org/docs/2.3.3/start-v2/locally/deployment. After completing the previous step, you will receive an installation package that can be used to install SeaTunnel Zeta Engine Server on the server. Run tar -zcvf apache-seatunnel-2.3.3-SNAPSHOT-bin.tar.gz apache-seatunnel-2.3.3-SNAPSHOT 2.1.3 Deploy SeaTunnel Zeta Server​ After 3.1.1 or 3.1.2 you can get an installer package apache-seatunnel-2.3.3-SNAPSHOT-bin.tar.gz, Then you can copy it to you server node and deploy reference https://seatunnel.apache.org/docs/seatunnel-engine/deployment. 2.1.4 Deploy SeaTunnel Zeta Client In SeaTunnel Web Run Node​ If you use SeaTunnel Web, you need deploy a SeaTunnel Zeta Client in the SeaTunnel Web run Node. If you run SeaTunnel Zeta Server and SeaTunnel Web in same node, you can skip this step. Copy apache-seatunnel-2.3.3-SNAPSHOT-bin.tar.gz to the SeaTunnel Web node and unzip it in the same path of SeaTunnel Zeta Server node. Set SEATUNNEL_HOME to environment variable like SeaTunnel Zeta Server node. Config hazelcast-client.yaml reference https://seatunnel.apache.org/docs/seatunnel-engine/deployment#6-config-seatunnel-engine-client Run $SEATUNNEL_HOME/bin/seatunnel.sh --config $SEATUNNEL_HOME/config/v2.batch.config.template, If this job run finished, it indicates successful client deployment. 2.2 Download and Install SeaTunnel Web​ Download seatunnel web from https://seatunnel.apache.org/download Copy the apache-seatunnel-web-bin-${project.version}.tar.gz to your server node and unzip it. tar -zxvf apache-seatunnel-web-bin-${project.version}.tar.gz 2.3 Init database​ Edit apache-seatunnel-web-bin-${project.version}/script/seatunnel_server_env.sh file, Complete the installed database address, port, username, and password. Here is an example: export HOSTNAME=\"localhost\" export PORT=\"3306\" export USERNAME=\"root\" export PASSWORD=\"123456\" Run init shell sh apache-seatunnel-web-bin-${project.version}/script/init_sql.sh If there are no errors during operation, it indicates successful initialization. 2.4 Config application and Run SeaTunnel Web Backend Server​ Edit apache-seatunnel-web-bin-${project.version}/conf/application.yml Fill in the database connection information in the file. Copy $SEATUNNEL_HOME/config/hazelcast-client.yaml to apache-seatunnel-web-bin-${project.version}/conf/ Copy apache-seatunnel-2.3.3-SNAPSHOT/connectors/plugin-mapping.properties file to apache-seatunnel-web-bin-${project.version}/conf/ dir. 2.5 Start SeaTunnel Web​ cd apache-seatunnel-web-${project.version} sh bin/seatunnel-backend-daemon.sh start Accessing in a browser http://127.0.0.1:8801/ui/ Okay, the default username and password are admin/admin. Edit this page 1 Preparing the Apache SeaTunnel environment 2 Run SeaTunnel Web In Server","s":"Deploy","u":"/seatunnel_web/next/deploy","h":"","p":105217},{"i":105220,"t":"This is unreleased documentation for Apache SeaTunnel Next version. For up-to-date documentation, see the latest version (1.0.0). Version: Next Define And Run SeaTunnel Job Edit this page","s":"Define And Run SeaTunnel Job","u":"/seatunnel_web/next/how_to_use","h":"","p":105219},{"i":105222,"t":"This is unreleased documentation for Apache SeaTunnel Next version. For up-to-date documentation, see the latest version (1.0.0). Version: Next JDBC-MySQL Sink Node Edit this page","s":"JDBC-MySQL Sink Node","u":"/seatunnel_web/next/sinknodes/jdbc-mysql","h":"","p":105221},{"i":105224,"t":"This is unreleased documentation for Apache SeaTunnel Next version. For up-to-date documentation, see the latest version (1.0.0). Version: Next JDBC-MySQL Source Node Edit this page","s":"JDBC-MySQL Source Node","u":"/seatunnel_web/next/sourcenodes/jdbc-mysql","h":"","p":105223},{"i":105226,"t":"Contribution Guide Code Review Guidance Code Review Guidance In order to continuously improve the quality of Apache SeaTunnel code, we have compiled a code review guide. We hope that code reviewers strictly adhere to this guidance, especially in terms of document and e2e inspections. Is the title of PR in compliance with regulations and the correct expression of meaning Is there any issue link related to bugs in the description of PR, and is there a design document link for major modifications. Check if documents have been added/changed, and if the document description is correct. A good example is https://github.com/apache/seatunnel/pull/4590 Check whether to add e2e and the correctness of e2e test (function coverage and result data validation, including whether to cover all supported data types, check whether the columns between the source and target should be aligned, the number of rows should be aligned, and the data in each row should also be aligned). A good example is https://github.com/apache/seatunnel/tree/dev/seatunnel-e2e/seatunnel-connector-v2-e2e/connector-clickhouse-e2e Check if there are any incompatible changes (especially modifications to parameters, with special attention paid. If incompatible changes are indeed needed, they need to be discussed by email) Check CI results, license updates, etc Enumerator update, check if the split snapshot and restore are correct, including whether the split allocation strategy is stable Reader update, check split snapshot and checkpoint lock range and various end conditions in pollnext Sink update, check for two-stage submission of XXXCommitter (if any) Writer updates, checking data refresh frequency, interval, memory like batch size, etc After passing the above functional checks, please review the code style (ensuring that the functionality is based on the code style), and the style is related to personal preferences. You can also refer to this: https://shardingsphere.apache.org/community/cn/involved/conduct/code/ Edit this page Previous SeaTunnel Improvement Proposal (STIP) Next Become Apache SeaTunnel Committer","s":"Code Review Guidance","u":"/community/contribution_guide/code-review","h":"","p":105225},{"i":105228,"t":"Contribution Guide Become Apache SeaTunnel Committer On this page Become Apache SeaTunnel Committer SeaTunnel Project Management Committee (PMC) is responsible for assessing the contributions of candidates. Like many Apache projects, SeaTunnel welcome all contributions, including code contributions, blog entries, guides for new users, public speeches, and enhancement of the project in various ways. Becoming a committer To get started contributing to SeaTunnel, learn how to contribute – anyone can submit patches, documentation and examples to the project. The PMC regularly adds new committers from the active contributors, based on their contributions to SeaTunnel. The qualifications for new committers include: Sustained contributions to SeaTunnel: Committers should have a history of major contributions to SeaTunnel. An ideal committer will have contributed broadly throughout the project, and have contributed at least one major component where they have taken an “ownership” role. An ownership role means that existing contributors feel that they should run patches for this component by this person. Quality of contributions: Committers more than any other community member should submit simple, well-tested, and well-designed patches. In addition, they should show sufficient expertise to be able to review patches, including making sure they fit within SeaTunnel’s engineering practices (testability, documentation, API stability, code style, etc). The committership is collectively responsible for the software quality and maintainability of SeaTunnel. Note that contributions to critical parts of SeaTunnel, like its core and SQL modules, will be held to a higher standard when assessing quality. Contributors to these areas will face more review of their changes. Community involvement: Committers should have a constructive and friendly attitude in all community interactions. They should also be active on the dev and user list and help mentor newer contributors and users. In design discussions, committers should maintain a professional and diplomatic approach, even in the face of disagreement. Committer​ Nominate new committer​ In SeaTunnel, new committer nomination could only be officially started by existing PMC members. If a new committer feels that he/she is qualified, he/she should contact any existing PMC member and discuss. If this is agreed among some members of the PMC, the process will kick off. The following steps are recommended (to be initiated only by an existing PMC member): Send an email titled [DISCUSS] Promote xxx as new committer to private@seatunnel.apache.org. List the important contributions of the candidate, so you could gather support from other PMC members for your proposal. Keep the discussion open for more than 3 days but no more than 1 week, unless there is any express objection or concern. If the PMC generally agrees to the proposal, send an email titled [VOTE] Promote xxx as new committer to private@seatunnel.apache.org. Keep the voting process open for more than 3 days, but no more than 1 week. Consider the result as Consensus Approval if there are three +1 votes and +1 votes > -1 votes. Send an email titled [RESULT][VOTE] Promote xxx as new committer to private@seatunnel.apache.org, and list the voting details, including who the voters are. Invite new committer​ The PMC member who starts the promotion is responsible for sending an invitation to the new committer and guiding him/her to set up the ASF env. The PMC member should send an email using the following template to the new committer: To: <invitee name>@gmail.com Cc: private@seatunnel.apache.org Subject: Invitation to become SeaTunnel committer: <invitee name> Hello <invitee name>, The SeaTunnel Project Management Committee (PMC) hereby offers you committer privileges to the project. These privileges are offered on the understanding that you'll use them reasonably and with common sense. We like to work on trust rather than unnecessary constraints. Being a committer enables you to more easily make changes without needing to go through the patch submission process. Being a committer does not require you to participate any more than you already do. It does tend to make one even more committed. You will probably find that you spend more time here. Of course, you can decline and instead remain as a contributor, participating as you do now. A. This personal invitation is a chance for you to accept or decline in private. Either way, please let us know in reply to the [private@SeaTunnel.apache.org] address only. B. If you accept, the next step is to register an iCLA: 1. Details of the iCLA and the forms are found through this link: http://www.apache.org/licenses/#clas 2. Instructions for its completion and return to the Secretary of the ASF are found at http://www.apache.org/licenses/#submitting 3. When you transmit the completed iCLA, request to notify the Apache SeaTunnel and choose a unique Apache id. Look to see if your preferred id is already taken at http://people.apache.org/committer-index.html This will allow the Secretary to notify the PMC when your iCLA has been recorded. When recording of your iCLA is noticed, you will receive a follow-up message with the next steps for establishing you as a committer. Invitation acceptance process​ The new committer should reply to private@seatunnel.apache.org (choose reply all), and express his/her intention to accept the invitation. Then, this invitation will be treated as accepted by the project's PMC. Of course, the new committer may also choose to decline the invitation. Once the invitation has been accepted, the new committer has to take the following steps: Subscribe to dev@seatunnel.apache.org. Usually this is already done. Choose a Apache ID that is not on the apache committers list page. Download the ICLA (If the new committer contributes to the project as a day job, CCLA is expected). After filling in the icla.pdf (or ccla.pdf) with the correct information, print, sign it by hand, scan it as an PDF, and send it as an attachment to secretary@apache.org. (If electronic signature is preferred, please follow the steps on this page) The PMC will wait for the Apache secretary to confirm the ICLA (or CCLA) filed. The new committer and PMC will receive the following email: Dear XXX, This message acknowledges receipt of your ICLA, which has been filed in the Apache Software Foundation records. Your account has been requested for you and you should receive email with next steps within the next few days (can take up to a week). Please refer to https://www.apache.org/foundation/how-it-works.html#developers for more information about roles at Apache. In the unlikely event that the account has not yet been requested, the PMC member should contact the project V.P.. The V.P. could request through the Apache Account Submission Helper Form. After several days, the new committer will receive an email confirming creation of the account, titled Welcome to the Apache Software Foundation (ASF)!. Congratulations! The new committer now has an official Apache ID. The PMC member should add the new committer to the official committer list through roster. Set up the Apache ID and dev env​ Go to Apache Account Utility Platform, create your password, set up your personal mailbox (Forwarding email address) and GitHub account(Your GitHub Username). An organizational invite will be sent to you via email shortly thereafter (within 2 hours). If you would like to use the xxx@apache.org email service, please refer to here. Gmail is recommended, because this forwarding mode is not easy to find in most mailbox service settings. Follow the authorized GitHub 2FA wiki to enable two-factor authorization (2FA) on Github. When you set 2FA to \"off\", it will be delisted by the corresponding Apache committer write permission group until you set it up again. (NOTE: Treat your recovery codes with the same level of attention as you would your password!) Use GitBox Account Linking Utility to obtain write permission of the SeaTunnel project. Follow this doc to update the website. If you would like to show up publicly in the Apache GitHub org, you need to go to the Apache GitHub org people page, search for yourself, and choose Organization visibility to Public. Committer rights, duties, and responsibilities​ The SeaTunnel project doesn't require continuing contributions from you after you have become a committer, but we truly hope that you will continue to play a part in our community! As a committer, you could Review and merge the pull request to the master branch in the Apache repo. A pull request often contains multiple commits. Those commits must be squashed and merged into a single commit with explanatory comments. It is recommended for new committers to request recheck of the pull request from senior committers. Create and push codes to the new branch in the Apache repo. Follow the release process to prepare a new release. Remember to confirm with the committer team that it is the right time to create the release. The PMC hopes that the new committer will take part in the release process as well as release voting, even though their vote will be regarded as +1 no binding. Being familiar with the release process is key to being promoted to the role of PMC member. Project Management Committee​ The Project Management Committee (PMC) member does not have any special rights in code contributions. They simply oversee the project and make sure that it follows the Apache requirements. Its functions include: Binding voting for releases and license checks; New committer and PMC member recognition; Identification of branding issues and brand protection; and Responding to questions raised by the ASF board, and taking necessary actions. The V.P. and chair of the PMC is the secretary, who is responsible for initializing the board report. In most cases, a new PMC member is nominated from the committer team. But it is also possible to become a PMC member directly, so long as the PMC agrees to the nomination and is confident that the candidate is ready. For instance, this can be demonstrated by the fact that he/she has been an Apache member, an Apache officer, or a PMC member of another project. The new PMC voting process should also follow the [DISCUSS], [VOTE] and [RESULT][VOTE] procedures using a private mail list, just like the voting process for new committers. Once the invitation is accepted, a PMC member should add the new member to the official PMC list through roster. Edit this page Previous Code Review Guidance Next Become Apache Release Manager Committer Nominate new committer Invite new committer Invitation acceptance process Set up the Apache ID and dev env Committer rights, duties, and responsibilities Project Management Committee","s":"Become Apache SeaTunnel Committer","u":"/community/contribution_guide/committer","h":"","p":105227},{"i":105230,"t":"Contribution Guide Participate in Contributing On this page Participate in Contributing This guide documents the best way to make various types of contribution to Apache SeaTunnel, including what is required before submitting a code change. Contributing to SeaTunnel doesn't just mean writing code. Helping new users on the mailing list, testing releases, and improving documentation are also welcome. In fact, proposing significant code changes usually requires first gaining experience and credibility within the community by helping in other ways. This is also a guide to becoming an effective contributor. So, this guide organizes contributions in order that they should probably be considered by new contributors who intend to get involved long-term. Build some track record of helping others, rather than just open pull requests. Contributing by helping other users​ A great way to contribute to SeaTunnel is to help answer user questions on the dev@seatunnel.apache.org mailing list or on StackOverflow. There are always many new SeaTunnel users; taking a few minutes to help answer a question is a very valuable community service. Contributors should subscribe to this list and follow it in order to keep up to date on what's happening in SeaTunnel. Answering questions is an excellent and visible way to help the community, which also demonstrates your expertise. See the Mailing Lists guide for guidelines about how to effectively participate in discussions on the mailing list, as well as forums like ISSUE. Contributing by testing releases​ SeaTunnel's release process is community-oriented, and members of the community can vote on new releases on the dev@seatunnel.apache.org mailing list. SeaTunnel users are invited to subscribe to this list to receive announcements, and test their workloads on newer release and provide feedback on any performance or correctness issues found in the newer release. Contributing by reviewing changes​ Changes to SeaTunnel source code are proposed, reviewed and committed via GitHub pull requests (described later). Anyone can view and comment on active changes here. Reviewing others' changes is a good way to learn how the change process works and gain exposure to activity in various parts of the code. You can help by reviewing the changes and asking questions or pointing out issues -- as simple as typos or small issues of style. Contributing documentation changes​ To propose a change to release documentation (that is, docs that appear under docs edit the Markdown source files in SeaTunnel's docs directory, whose README file shows how to build the documentation locally to test your changes. The process to propose a doc change is otherwise the same as the process for proposing code changes below. To propose a change to the rest of the documentation (that is, docs that do not appear under docs , similarly, edit the Markdown in the website and open a pull request. Contributing bug reports​ Ideally, bug reports are accompanied by a proposed code change to fix the bug. This isn't always possible, as those who discover a bug may not have the experience to fix it. A bug may be reported by creating a ISSUE but without creating a pull request (see below). Bug reports are only useful however if they include enough information to understand, isolate and ideally reproduce the bug. Simply encountering an error does not mean a bug should be reported; as below, search ISSUE and search and inquire on the SeaTunnel user / dev mailing lists first. Unreproducible bugs, or simple error reports, may be closed. It's very helpful if the bug report has a description about how the bug was introduced, by which commit, so that reviewers can easily understand the bug. It also helps committers to decide how far the bug fix should be backported, when the pull request is merged. The pull request to fix the bug should narrow down the problem to the root cause. Performance regression is also one kind of bug. The pull request to fix a performance regression must provide a benchmark to prove the problem is indeed fixed. Note that, data correctness/data loss bugs are very serious. Make sure the corresponding bug report ISSUE ticket is labeled as correctness or data-loss. If the bug report doesn't get enough attention, please send an email to dev@seatunnel.apache.org, to draw more attentions. It is possible to propose new features as well. These are generally not helpful unless accompanied by detail, such as a design document and/or code change. Large new contributions should consider be discussed on the mailing list first. Feature requests may be rejected, or closed after a long period of inactivity. For significant new features or architectural changes, consider opening a SeaTunnel Improvement Proposal (STIP) before starting implementation. STIPs provide a structured way to gain community consensus and preserve design decisions. Contributing to ISSUE maintenance​ Given the sheer volume of issues raised in the Apache SeaTunnel ISSUE, inevitably some issues are duplicates, or become obsolete and eventually fixed otherwise, or can't be reproduced, or could benefit from more detail, and so on. It's useful to help identify these issues and resolve them, either by advancing the discussion or even resolving the ISSUE. Most contributors are able to directly resolve ISSUEs. Use judgment in determining whether you are quite confident the issue should be resolved, although changes can be easily undone. If in doubt, just leave a comment on the ISSUE. When resolving ISSUEs, observe a few useful conventions: Resolve as Fixed if there's a change you can point to that resolved the issue Set Fix Version(s), if and only if the resolution is Fixed Set Assignee to the person who most contributed to the resolution, which is usually the person who opened the PR that resolved the issue. In case several people contributed, prefer to assign to the more 'junior', non-committer contributor For issues that can't be reproduced against master as reported, resolve as Cannot Reproduce Fixed is reasonable too, if it's clear what other previous pull request resolved it. Link to it. If the issue is the same as or a subset of another issue, resolved as Duplicate Make sure to link to the ISSUE it duplicates Prefer to resolve the issue that has less activity or discussion as the duplicate If the issue seems clearly obsolete and applies to issues or components that have changed radically since it was opened, resolve as Not a Problem If the issue doesn't make sense – not actionable, for example, a non-SeaTunnel issue, resolve as Invalid If it's a coherent issue, but there is a clear indication that there is not support or interest in acting on it, then resolve as Won't Fix Umbrellas are frequently marked Done if they are just container issues that don't correspond to an actionable change of their own Preparing to contribute code changes​ Choosing what to contribute​ Review can take hours or days of committer time. Everyone benefits if contributors focus on changes that are useful, clear, easy to evaluate, and already pass basic checks. Sometimes, a contributor will already have a particular new change or bug in mind. If seeking ideas, consult the list of starter tasks in ISSUE, or ask the dev@seatunnel.apache.org mailing list. Before proceeding, contributors should evaluate if the proposed change is likely to be relevant, new and actionable: Is it clear that code must change? Proposing a ISSUE and pull request is appropriate only when a clear problem or change has been identified. If simply having trouble using SeaTunnel, use the mailing lists first, rather than consider filing a ISSUE or proposing a change. When in doubt, email dev@seatunnel.apache.org first about the possible change Search the dev@seatunnel.apache.org mailing list for related discussions. Often, the problem has been discussed before, with a resolution that doesn't require a code change, or recording what kinds of changes will not be accepted as a resolution. Search ISSUE for existing issues: ISSUES Type SeaTunnel [search terms] at the top right search box. If a logically similar issue already exists, then contribute to the discussion on the existing ISSUE and pull request first, instead of creating a new one. Is the scope of the change matched to the contributor's level of experience? Anyone is qualified to suggest a typo fix, but refactoring core scheduling logic requires much more understanding of SeaTunnel. Some changes require building up experience first (see above). It's worth reemphasizing that changes to the core of SeaTunnel, or to highly complex and important modules are more difficult to make correctly. They will be subjected to more scrutiny and held to a higher standard of review than changes to less critical code. Error message guidelines​ Exceptions thrown in SeaTunnel should be associated with standardized and actionable error messages. Error messages should answer the following questions: What was the problem? Why did the problem happen? How can the problem be solved? When writing error messages, you should: Use active voice Avoid time-based statements, such as promises of future support Use the present tense to describe the error and provide suggestions Provide concrete examples if the resolution is unclear Avoid sounding accusatory, judgmental, or insulting Be direct Do not use programming jargon in user-facing errors Code review criteria​ Before considering how to contribute code, it's useful to understand how code is reviewed, and why changes may be rejected. See the detailed guide for code reviewers from Google's Engineering Practices documentation. Simply put, changes that have many or large positives, and few negative effects or risks, are much more likely to be merged, and merged quickly. Risky and less valuable changes are very unlikely to be merged, and may be rejected outright rather than receive iterations of review. Positives​ Fixes the root cause of a bug in existing functionality Adds functionality or fixes a problem needed by a large number of users Simple, targeted Easily tested; has tests Reduces complexity and lines of code Change has already been discussed and is known to committers Negatives, risks​ Band-aids a symptom of a bug only Introduces complex new functionality, especially an API that needs to be supported Adds complexity that only helps a niche use case Changes a public API or semantics (rarely allowed) Adds large dependencies Changes versions of existing dependencies Adds a large amount of code Makes lots of modifications in one \"big bang\" change Contributing code changes​ Please review the preceding section before proposing a code change. This section documents how to do so. When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so. Cloning the Apache SeaTunnel™ source code​ If you are interested in working with the newest under-development code or contributing to Apache SeaTunnel development, you can check out the master branch from Git: # Master development branch git clone git@github.com:apache/seatunnel.git Once you've downloaded SeaTunnel, you can find instructions for installing and building it on the documentation page ISSUE​ Generally, SeaTunnel uses ISSUE to track logical issues, including bugs and improvements, and uses GitHub pull requests to manage the review and merge of specific code changes. That is, ISSUEs are used to describe what should be fixed or changed, and high-level approaches, and pull requests describe how to implement that change in the project's source code. For example, major design decisions are discussed in ISSUE. Find the existing SeaTunnel ISSUE that the change pertains to. Do not create a new ISSUE if creating a change to address an existing issue in ISSUE; add to the existing discussion and work instead Look for existing pull requests that are linked from the ISSUE, to understand if someone is already working on the ISSUE If the change is new, then it usually needs a new ISSUE. However, trivial changes, where the what should change is virtually the same as the how it should change do not require a ISSUE. Example: Fix typos in Foo scaladoc If required, create a new ISSUE: Provide a descriptive Title. \"Update web UI\" or \"Problem in scheduler\" is not sufficient. \"Kafka Streaming support fails to handle empty queue in YARN cluster mode\" is good. Write a detailed Description. For bug reports, this should ideally include a short reproduction of the problem. For new features, it may include a design document. Set required fields: Issue Type. Generally, Bug, Improvement and New Feature are the only types used in SeaTunnel. Priority. Set to Major or below; higher priorities are generally reserved for committers to set. The main exception is correctness or data-loss issues, which can be flagged as Blockers. ISSUE tends to unfortunately conflate \"size\" and \"importance\" in its Priority field values. Their meaning is roughly: Blocker: pointless to release without this change as the release would be unusable to a large minority of users. Correctness and data loss issues should be considered Blockers for their target versions. Critical: a large minority of users are missing important functionality without this, and/or a workaround is difficult Major: a small minority of users are missing important functionality without this, and there is a workaround Minor: a niche use case is missing some support, but it does not affect usage or is easily worked around Trivial: a nice-to-have change but unlikely to be any problem in practice otherwise Component Affects Version. For Bugs, assign at least one version that is known to exhibit the problem or need the change Label. Not widely used, except for the following: correctness: a correctness issue data-loss: a data loss issue release-notes: the change's effects need mention in release notes. The ISSUE or pull request should include detail suitable for inclusion in release notes -- see \"Docs Text\" below. starter: small, simple change suitable for new contributors Docs Text: For issues that require an entry in the release notes, this should contain the information that the release manager should include in Release Notes. This should include a short summary of what behavior is impacted, and detail on what behavior changed. It can be provisionally filled out when the ISSUE is opened, but will likely need to be updated with final details when the issue is resolved. Do not set the following fields: Fix Version. This is assigned by committers only when resolved. Target Version. This is assigned by committers to indicate a PR has been accepted for possible fix by the target version. Do not include a patch file; pull requests are used to propose the actual change. If the change is a large change, consider inviting discussion on the issue at dev@seatunnel.apache.org first before proceeding to implement the change. For architectural changes or new API/SPI surface, a SeaTunnel Improvement Proposal (STIP) is strongly recommended. Pull request​ Fork the GitHub repository at seatunnel if you haven't already Clone your fork, create a new branch, push commits to the branch. Consider whether documentation or tests need to be added or updated as part of the change, and add them as needed. When you add tests, make sure the tests are self-descriptive. Also, you should consider writing a ISSUE ID in the tests when your pull request targets to fix a specific issue. In practice, usually it is added when a ISSUE type is a bug or a PR adds a couple of tests to an existing test class. See the examples below: Scala test(\"SeaTunnel-12345: a short description of the test\") { ... Java @Test public void testCase() { // SeaTunnel-12345: a short description of the test ... The review process​ Other reviewers, including committers, may comment on the changes and suggest modifications. Changes can be added by simply pushing more commits to the same branch. Lively, polite, rapid technical debate is encouraged from everyone in the community. The outcome may be a rejection of the entire change. Keep in mind that changes to more critical parts of SeaTunnel, like its core components, will be subjected to more review, and may require more testing and proof of its correctness than other changes. Reviewers can indicate that a change looks suitable for merging with a comment such as: \"I think this patch looks good\". SeaTunnel uses the LGTM convention for indicating the strongest level of technical sign-off on a patch: simply comment with the word \"LGTM\". It specifically means: \"I've looked at this thoroughly and take as much ownership as if I wrote the patch myself\". If you comment LGTM you will be expected to help with bugs or follow-up issues on the patch. Consistent, judicious use of LGTMs is a great way to gain credibility as a reviewer with the broader community. Sometimes, other changes will be merged which conflict with your pull request's changes. The PR can't be merged until the conflict is resolved. This can be resolved by, for example, adding a remote to keep up with upstream changes by git remote add upstream git@github.com:apache/seatunnel.git, running git fetch upstream followed by git rebase upstream/master and resolving the conflicts by hand, then pushing the result to your branch. Try to be responsive to the discussion rather than let days pass between replies Closing your pull request / ISSUE​ If a change is accepted, it will be merged and the pull request will automatically be closed, along with the associated ISSUE if any Note that in the rare case you are asked to open a pull request against a branch besides master, that you will actually have to close the pull request manually The ISSUE will be Assigned to the primary contributor to the change as a way of giving credit. If the ISSUE isn't closed and/or Assigned promptly, comment on the ISSUE. If your pull request is ultimately rejected, please close it promptly ... because committers can't close PRs directly Pull requests will be automatically closed by an automated process at Apache after about a week if a committer has made a comment like \"mind closing this PR?\" This means that the committer is specifically requesting that it be closed. If a pull request has gotten little or no attention, consider improving the description or the change itself and ping likely reviewers again after a few days. Consider proposing a change that's easier to include, like a smaller and/or less invasive change. If it has been reviewed but not taken up after weeks, after soliciting review from the most relevant reviewers, or, has met with neutral reactions, the outcome may be considered a \"soft no\". It is helpful to withdraw and close the PR in this case. If a pull request is closed because it is deemed not the right approach to resolve a ISSUE, then leave the ISSUE open. However if the review makes it clear that the issue identified in the ISSUE is not going to be resolved by any pull request (not a problem, won't fix) then also resolve the ISSUE. If in doubt​ If you're not sure about the right style for something, try to follow the style of the existing codebase. Look at whether there are other examples in the code that use your feature. Feel free to ask on the dev@seatunnel.apache.org list as well and/or ask committers. Code of conduct​ The Apache SeaTunnel project follows the Apache Software Foundation Code of Conduct. The code of conduct applies to all spaces managed by the Apache Software Foundation, including IRC, all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our communities. A code of conduct which is specific to in-person events (ie., conferences) is codified in the published ASF anti-harassment policy. We expect this code of conduct to be honored by everyone who participates in the Apache community formally or informally, or claims any affiliation with the Foundation, in any Foundation-related activities and especially when representing the ASF, in any role. This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. We expect it to be followed in spirit as much as in the letter, so that it can enrich all of us and the technical communities in which we participate. For more information and specific guidelines, refer to the Apache Software Foundation Code of Conduct . Acknowledgement: This document refers to Spark Edit this page Next Subscribe Mailing Lists Contributing by helping other users Contributing by testing releases Contributing by reviewing changes Contributing documentation changes Contributing bug reports Contributing to ISSUE maintenance Preparing to contribute code changes Choosing what to contribute Error message guidelines Code review criteria Contributing code changes Cloning the Apache SeaTunnel™ source code ISSUE Pull request The review process Closing your pull request / ISSUE If in doubt Code of conduct","s":"Participate in Contributing","u":"/community/contribution_guide/contribute","h":"","p":105229},{"i":105232,"t":"Contribution Guide Become Apache Release Manager On this page Become Apache Release Manager Release managers shepherd a release from an initial community consensus to getting the compiled code package to final distribution, and may be involved in publicizing the release to the project's community and the ASF in general. Tips: The Release Manager needs to have an Apache LDAP account, which means you need to become a Committer of the project before you can have an Apache LDAP account. All of the following words for tyrantlucifer will serve as an example of a userId in the document, and the release manager needs to use their own userId. An Apache release is a set of valid, signed, artifacts, voted on by the appropriate PMC and distributed on the official ASF release infrastructure. For a release, you go through the following steps: Has code that complies with the software licensing requirements Decides as a community to make a release, and designates a release manager The release manager prepares and signs the proposed release materials The PMC votes on whether to approve the release If the vote passes, the release manager copies the artifacts to the distribution infrastructure. The source code and build process must comply with the ASF legal and intellectual property requirements for a valid release, and the project must have the infrastructure in place to correctly sign the release artifacts. Environment Preparation​ If this is the first time for a publisher to publish, you need to install the necessary environment, including the signature tool GnuPG and Maven repository certification-related preparations. Install Git​ Use to clone project source code locally. Install GPG​ Used to generate a digital signature, leaving a trail of every action you take. Apache's maven and SVN repositories both use GPG signatures to verify the validity of files. Download GnuPG binary releases from the official GnuPG website. Note: The commands of 1. x and 2. x versions of GnuPG are slightly different. The following description takes 2.2.28 as an example. After the installation, the GPG command is added to the system environment variable and is available. # Check the version, which should be 2. x gpg --full-gen-key Configure GPG KEY​ Create the Key​ gpg --full-gen-key gpg (GnuPG) 2.0.22; Copyright (C) 2013 Free Software Foundation, Inc. This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law. Please select what kind of key you want: (1) RSA and RSA (default) (2) DSA and Elgamal (3) DSA (sign only) (4) RSA (sign only) Your selection? 1 RSA keys may be between 1024 and 4096 bits long. What keysize do you want? (3072) 4096 Requested keysize is 4096 bits Please specify how long the key should be valid. 0 = key does not expire <n> = key expires in n days <n>w = key expires in n weeks <n>m = key expires in n months <n>y = key expires in n years Key is valid for? (0) 0 Key does not expire at all Is this correct? (y/N) y As shown above, the options are: 1 4096 0 y In the process of configuring GPG, the key types selected are RSA and RSA (default), so the first option is 1, and the desired key size can be directly input the corresponding value. The third option is 0, indicating that the validity period of the key is never expired. Finally, input y to ensure that the above input is correct. You need a user ID to identify your key; the software constructs the user ID from the Real Name, Comment and Email Address in this form: \"Heinrich Heine (Der Dichter) <heinrichh@duesseldorf.de>\" Real name: tyrantlucifer Email address: tyrantlucifer@apache.org Comment: The key of Apache SeaTunnel You selected this USER-ID: “tyrantlucifer (The key of Apache SeaTunnel) <tyrantlucifer@apache.org>” Change (N)ame, (C)omment, (E)mail or (O)kay/(Q)uit? O You need a password to protect your private key. As shown above, you will need to specify your personal information and encryption password for this key. You will need to fill in the following information: Name Email (Apache Email) Comment for key Password (Very important, don't forget) We need to generate a lot of random bytes. It is a good idea to perform some other action (type on the keyboard, move the mouse, utilize the disks) during the prime generation; this gives the random number generator a better chance to gain enough entropy. gpg: key 09****85 marked as ultimately trusted public and secret key created and signed. pub 4096R/0983DF85 2022-12-28 keys fingerprint = AE** FC** EC** 60** 72** 56** 05** AE** 09** DF** uid tyrantlucifer (The key of Apache SeaTunnel) <tyrantlucifer@apache.org> sub 4***R/B7****46 2022-12-28 Note: gpg keys, which need to be remembered and used in subsequent releases. Verify Key​ gpg --list-keys /home/hadoop/.gnupg/pubring.gpg ------------------------------- pub 4096R/0983DF85 2022-12-28 uid tyrantlucifer (The key of Apache SeaTunnel) <tyrantlucifer@apache.org> sub 4***R/B7****46 2022-12-28 Tips: 0983DF85 is your public key abbreviation. Upload the key to the public server​ The public key is sent to the keyserver using the key id. $ gpg --keyserver keyserver.ubuntu.com --send-key 0983DF85 keyserver.ubuntu.com specifies the selected keyserver. This is recommended because Apache Nexus uses this keyserver for verification. Verify that the key is uploaded properly​ You can use the following two methods to verify whether the key is uploaded properly: Command line validation gpg --keyserver keyserver.ubuntu.com --search-keys 0983DF85 Website verification OpenPGP Keyserver (ubuntu.com) Tips: This screenshot is the key that has been uploaded before. It is normal that the key generated in the previous step is inconsistent with that in the previous step. Install SHASUM​ Used to generate signatures for files. SVN​ Used to pull the Apache Release SVN repository. Apache uses svn to host the release of the project. Pull the release and dev repositories to the local​ mkdir -p ~/seatunnel-release-prepare/dev mkdir -p ~/seatunnel-release-prepare/release cd ~/seatunnel-release-prepare/dev svn --username=${APACHE LDAP username} co https://dist.apache.org/repos/dist/dev/seatunnel cd ~/seatunnel-release-prepare/release svn --username=${APACHE LDAP username} co https://dist.apache.org/repos/dist/release/seatunnel Upload the key to the dev and release repositories​ Tips: You only need to do this when you release this project for the first time. cd ~/seatunnel-release-prepare/dev/seatunnel gpg -a --export ${GPG USERNAME} >> KEYS svn add KEYS svn --username=${APACHE LDAP USERNAME} commit -m \"Add ${APACHE LDAP USERNAME} GPG key\" Committer not have permission of release folder, You should find a PMC member to help you add this KEYS file to release folder. cd ~/seatunnel-release-prepare/release/seatunnel gpg -a --export ${GPG USERNAME} >> KEYS svn add KEYS svn --username=${APACHE LDAP USERNAME} commit -m \"Add ${APACHE LDAP USERNAME} GPG key\" MAVEN​ Used to compile the project. Configure maven​ Create a master password​ mvn --encrypt-master-password <apache password> Configure the master password​ Add the file ${user.home}/.m2/settings-security.xml to configure the password created in the previous step. <settingsSecurity> <master><!-- Fill in the password printed in the previous step --></master> </settingsSecurity> Encrypt the Apache LDAP password​ mvn --encrypt-password <apache password> Add new profile​ Edit the configuration file of your local maven environment, the general path is ~/.m2/setting.xml, and add the following xml file: <settings> <servers> <server> <id>apache.snapshots.https</id> <username> <!-- APACHE LDAP USERNAME --> </username> <password> <!-- APACHE LDAP ENCRYPTED PASSWORD, Password encrypted in the previous step --> </password> </server> <server> <id>apache.releases.https</id> <username> <!-- APACHE LDAP USERNAME --> </username> <password> <!-- APACHE LDAP ENCRYPTED PASSWORD, Password encrypted in the previous step --> </password> </server> <server> <id>gpg.passphrase</id> <passphrase><!-- GPG KEY PASSWORD --></passphrase> </server> </servers> </settings> Tips: If you get a 403 forbidden error during the maven release step, and you are absolutely sure that you used the correct password, when you are using your own laptop, you can skip password encryption and use the original password value. Project version preparation​ Branch preparation​ mkdir -p ~/seatunnel-release-prepare cd ~/seatunnel-release-prepare git clone git@github.com:apache/seatunnel.git cd seatunnel git checkout -b ${RELEASE.VERSION}-release Update release-note​ vim release-note.md git add release-note.md git commit -m \"[Release][${RELEASE.VERSION}][release-note] Add release-note\" git push Precompiled test​ mvn release:prepare -Prelease -Darguments=\"-DskipTests -Dskip.spotless=true\" -DdryRun=true -Dusername=${GITHUB USERNAME} Compile​ before this step, you need edit the .gitignore file, delete seatunnel-examples first. mvn release:clean mvn release:prepare -Prelease -Darguments=\"-DskipTests -Dskip.spotless=true\" -DpushChanges=false -Dusername=${GITHUB USERNAME} After this step, the code has changed, and it not match our project's code style requirement, so you need run this command to fix code style and commit change. ./mvnw spotless:apply git commit -am \"fix code style\" Submit source code​ git push git push origin --tags Update the connector change log​ generate the connector change log python ./tools/documents/update_connector_change_log.py push the change log to the remote repository git commit -am \"[Release][${RELEASE.VERSION}][connector-change-log] Update connector change log\" git push Deploy jar packages​ Upload jar packages mvn release:perform -Prelease -Darguments=\"-DskipTests -Dskip.spotless=true\" -Dusername=${GITHUB USERNAME} Note: During the execution of this command, ensure that the IP address remains constant; otherwise, multiple unusable repositories might appear in stagingRepositories. Close the stage repository https://repository.apache.org/#stagingRepositories Upload to SVN​ Upload source code and binary packages to the dev repository​ Copy source code and binary packages​ mkdir -p ~/seatunnel-release-prepare/dev/seatunnel/${RELEASE.VERSION} cp -f ~/seatunnel-release-prepare/seatunnel/seatunnel-dist/target/*.tar.gz ~/seatunnel-release-prepare/dev/seatunnel/${RELEASE.VERSION} cd ~/seatunnel-release-prepare/dev/seatunnel/${RELEASE.VERSION} Generate signature​ shasum -a 512 apache-seatunnel-${RELEASE.VERSION}-src.tar.gz >> apache-seatunnel-${RELEASE.VERSION}-src.tar.gz.sha512 shasum -b -a 512 apache-seatunnel-${RELEASE.VERSION}-bin.tar.gz >> apache-seatunnel-${RELEASE.VERSION}-bin.tar.gz.sha512 Generate GPG signature​ gpg --armor --detach-sig apache-seatunnel-${RELEASE.VERSION}-src.tar.gz gpg --armor --detach-sig apache-seatunnel-${RELEASE.VERSION}-bin.tar.gz Check file signature​ shasum -c apache-seatunnel-${RELEASE.VERSION}-src.tar.gz.sha512 shasum -c apache-seatunnel-${RELEASE.VERSION}-bin.tar.gz.sha512 Check digital signatures​ step1: Import (Release Manager does not need to do this step) curl https://dist.apache.org/repos/dist/dev/seatunnel/KEYS >> KEYS gpg --import KEYS gpg --edit-key \"${GPG username of releaser}\" > trust Please decide how far you trust this user to correctly verify other users' keys (by looking at passports, checking fingerprints from different sources, etc.) 1 = I don't know or won't say 2 = I do NOT trust 3 = I trust marginally 4 = I trust fully 5 = I trust ultimately m = back to the main menu Your decision? 5 > save step2: Check the gpg digital signature gpg --verify apache-seatunnel-${RELEASE.VERSION}-src.tar.gz.asc apache-seatunnel-${RELEASE.VERSION}-src.tar.gz gpg --verify apache-seatunnel-${RELEASE.VERSION}-bin.tar.gz.asc apache-seatunnel-${RELEASE.VERSION}-bin.tar.gz Commit​ Commit all files to the dev repository. cd .. svn add ${RELEASE.VERSION} svn --username=${APACHE LDAP USERNAME} commit -m \"release ${RELEASE.VERSION}\" Vote by mail​ dev@seatunnel.apache.org Voting​ Voting initiate​ [VOTE] Release Apache SeaTunnel 2.3.8 (RC1) Hello SeaTunnel Community, This is a call for vote to release Apache SeaTunnel version 2.3.8 (RC1) Release notes: https://github.com/apache/seatunnel/blob/2.3.8/release-note.md The release candidates: https://dist.apache.org/repos/dist/dev/seatunnel/2.3.8 Git tag for the release: https://github.com/apache/seatunnel/tree/2.3.8 Docker image https://hub.docker.com/layers/apache/seatunnel/2.3.8/images/sha256-7ba69f18989b73afb159884c2e8717ff548087c334794fd4a13439726ce974d3?context=explore Maven 2 staging repository: https://repository.apache.org/content/repositories/orgapacheseatunnel-1120/org/apache/seatunnel/ Release Commit ID: https://github.com/apache/seatunnel/commit/860463186a4ae954496c223dd2055e6fc195b8d2 Keys to verify the Release Candidate: https://downloads.apache.org/seatunnel/KEYS The vote will be open for at least 72 hours or until necessary numbers of votes are reached. Please vote accordingly: [ ] +1 approve [ ] +0 no opinion [ ] -1 disapprove with the reason Checklist for reference: [ ] Download links are valid. [ ] Checksums and PGP signatures are valid. [ ] Source code artifacts have correct names matching the current release. [ ] LICENSE and NOTICE files are correct for each SeaTunnel repo. [ ] All files have license headers if necessary. [ ] No compiled archives bundled in source archive. — Best Regards Naijie Liu Voting close​ [RESULT] [VOTE] Release Apache SeaTunnel 2.3.8 (RC1) Hi SeaTunnel community, This vote now closes since 72 hours have passed. There are 6 approving votes, 4 of which are binding: - Jia Fan (binding) - Guangdong Liu (binding) - hailin0 (binding) - David (binding) - Mohammad Arshad - User name 6 There are 2 (+0 no opinion) vote - User name 7 - User name 8 There are no disapproving votes The vote passes with 4 binding +1 votes and 2 non-binding +1 votes The vote thread: https://lists.apache.org/thread/xf18yy6nw03m38k4rjn1tk1bjw84rqlz I will publish the release and make an announcement once it is done. — Best Regards Naijie Liu Only the PMC membere has binding vote. How to get vote email thread link: open this page https://lists.apache.org/list.html?dev@seatunnel.apache.org, find the vote email thread. and click the link copy icon. Official Release​ Moving files​ Moving files from the dev repository to the release repository. This step only PMC member has permission. svn mv https://dist.apache.org/repos/dist/dev/seatunnel/${RELEASE.VERSION} https://dist.apache.org/repos/dist/release/seatunnel/ Generate website document​ https://github.com/apache/seatunnel-website?tab=readme-ov-file#39-add-a-new-version-for-documents Release Maven Repository​ Send notification email​ dev@seatunnel.apache.org announce@apache.org Please note that you should send emails in plain text mode, otherwise they will be rejected by Apache's email server. Hi all, We are glad to announce the release of Apache SeaTunnel 2.3.8. Once again I would like to express my thanks to your help. SeaTunnel: SeaTunnel is a distributed, high-performance data integration tool for the synchronization and transformation of massive data (offline & real-time). Apache SeaTunnel website: http://seatunnel.apache.org/ Downloads: https://seatunnel.apache.org/download/ Release Notes: https://github.com/apache/seatunnel/blob/2.3.8/release-note.md Documents: https://seatunnel.apache.org/docs/2.3.8/about/ Twitter: https://twitter.com/ASFSeaTunnel SeaTunnel Resources: - GitHub: https://github.com/apache/seatunnel - Issue: https://github.com/apache/seatunnel/issues - Mailing list: dev@seatunnel.apache.org - Apache SeaTunnel Team Something After Release​ Update github repo releases​ https://github.com/apache/seatunnel/releases Update project snapshot version​ some PR for refer https://github.com/apache/seatunnel/pull/7841 https://github.com/apache/seatunnel/pull/7435 https://github.com/apache/seatunnel/pull/7305 Edit this page Previous Become Apache SeaTunnel Committer Next Documentation Notice Environment Preparation Install Git Install GPG Install SHASUM SVN MAVEN Project version preparation Branch preparation Update release-note Precompiled test Compile Submit source code Update the connector change log Deploy jar packages Upload to SVN Upload source code and binary packages to the dev repository Vote by mail dev@seatunnel.apache.org Voting Official Release Moving files Generate website document Release Maven Repository Send notification email Something After Release Update github repo releases Update project snapshot version","s":"Become Apache Release Manager","u":"/community/contribution_guide/release","h":"","p":105231},{"i":105234,"t":"Contribution Guide SeaTunnel Improvement Proposal (STIP) On this page SeaTunnel Improvement Proposal (STIP) What is a STIP?​ A SeaTunnel Improvement Proposal (STIP) is the standard process for proposing significant new features, architectural changes, or major improvements to Apache SeaTunnel. A STIP is tracked as a GitHub Issue with the design label and a sequential number prefix in its title (e.g., [STIP-23] ...). STIPs serve as the single source of truth for why a feature is being built, how it should be designed, and what the expected outcomes are. They give the community visibility into planned work, preserve a permanent record of design decisions, and help new contributors understand the direction of the project. When Should I Create a STIP?​ Create a STIP when your proposal involves one or more of the following: A new core feature or significant change to existing behavior A change to the SeaTunnel API or Connector SPI A new engine integration or major engine-level change An architectural decision that affects multiple modules A change that requires community consensus before implementation begins You do not need a STIP for: Bug fixes Documentation updates Minor improvements or refactoring Adding a new connector (unless it requires changes to the API or SPI) STIP Lifecycle​ Each STIP goes through the following states: State Meaning Draft The proposal is being written; not yet ready for community discussion. Under Review The issue has been opened and is being discussed by the community on GitHub and/or the mailing list. Accepted The community has reached consensus and implementation may proceed. Rejected The community decided not to move forward with the proposal. The issue is closed with a clear explanation. Implemented The feature has been merged and the corresponding issue is closed. A STIP author should update the issue with the current state as it progresses. STIP Numbering​ STIPs are numbered sequentially in the order they are created. To find the current highest number and determine the next one, browse the full list: https://github.com/apache/seatunnel/issues?q=is%3Aissue+label%3Adesign+sort%3Acreated-asc Here are a few representative examples for reference: Number Title Issue Status STIP-1 Decoupling connectors from compute engines #1608 Implemented STIP-5 ST-Engine Design And Task Tracking #2272 Implemented STIP-12 CDC Connector Design #3175 Implemented STIP-15 Design of Dirty Data Collection #4587 Accepted STIP-21 Support Traffic Dyeing (Sampling) and Context-Aware Metrics #10305 Under Review The next new proposal should be numbered STIP-23. How to Submit a STIP​ Step 1 — Gauge interest on the mailing list​ Before opening a formal STIP, consider sending a short email to dev@seatunnel.apache.org describing the problem and your high-level approach. Early feedback helps you avoid investing time in a direction the community will not accept, and may reveal related efforts you were unaware of. Step 2 — Find the next available number​ Browse the full STIP list to confirm the highest existing number and increment by one. Step 3 — Open a GitHub Issue​ Go to apache/seatunnel Issues and create a new issue with: Title: [STIP-N] [Module] Brief description (e.g., [STIP-23] [Connector] Support multi-catalog source) Write the issue body following the STIP Content Template below. Step 4 — Drive the discussion to consensus​ Respond promptly to comments, update the proposal as the design evolves, and work toward one of the terminal states: Accepted or Rejected. Large or controversial proposals should be explicitly discussed on dev@seatunnel.apache.org before being marked Accepted. Step 5 — Link PRs back to the STIP​ When submitting implementation pull requests, reference the STIP issue number in the PR description (e.g., Closes #NNNN or Part of STIP-N). STIP Content Template​ Use the following structure when writing the body of your STIP GitHub Issue. Delete sections that are genuinely not applicable; do not leave them empty. ## Background <!-- Describe the problem or opportunity this proposal addresses. Include enough context for a reader unfamiliar with the area to understand why this matters. --> ## Motivation <!-- Explain why the current state is insufficient and why solving this problem is valuable to the project. --> ## Goals <!-- List the concrete outcomes this proposal aims to achieve. Use bullet points, each starting with a verb: - Support reading nested schemas from Kafka in the Kafka source. - ... --> ## Non-Goals <!-- Explicitly state what is out of scope for this proposal to prevent scope creep during review. --> ## Design <!-- Describe the proposed solution in enough detail that an experienced contributor could implement it. Include: - Key interfaces / APIs / configuration options being added or changed - Data flow or sequence diagrams where helpful - How the change interacts with existing components --> ## Compatibility, Deprecation, and Migration Plan <!-- Describe any backward-incompatible changes and how existing users should migrate. If fully backward-compatible, state that explicitly. --> ## Test Plan <!-- Describe how the change will be tested: unit tests, integration tests, performance benchmarks, etc. --> ## Alternatives Considered <!-- Describe other approaches you evaluated and explain why you chose the proposed design over them. --> ## References <!-- Links to related issues, PRs, papers, or prior art. --> Edit this page Previous Subscribe Mailing Lists Next Code Review Guidance What is a STIP? When Should I Create a STIP? STIP Lifecycle STIP Numbering How to Submit a STIP Step 1 — Gauge interest on the mailing list Step 2 — Find the next available number Step 3 — Open a GitHub Issue Step 4 — Drive the discussion to consensus Step 5 — Link PRs back to the STIP STIP Content Template","s":"SeaTunnel Improvement Proposal (STIP)","u":"/community/contribution_guide/STIP","h":"","p":105233},{"i":105236,"t":"Contribution Guide Subscribe Mailing Lists Mailing lists dev@seatunnel.apache.org (developer holds other general discussions) users@seatunnel.apache.org (user asks for help) commits@seatunnel.apache.org (focuses on the commit logs) Subscribe Mailing Lists It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community. In the process of using Seatunnel, if you have any questions or ideas, suggestions, you can participate in the Seatunnel community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows: Send an email without any contents or subject to LISTNAME-subscribe@seatunnel.apache.org. (replace LISTNAME with dev, commits,users...) If you send us an email with a code snippet, make sure that: you do not link to files in external services as such files can change, get deleted, or the link might break and thus make an archived email thread useless. you paste text instead of screenshots of text. you keep formatting when pasting code in order to keep the code readable. there are enough import statements to avoid ambiguities. if you are using some platform, please specify the version. Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from LISTNAME-help@seatunnel.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary. Receive a welcome email. After completing the above steps, you will receive a welcome email with the subject WELCOME to LISTNAME@seatunnel.apache.org, and you have successfully subscribed to the Apache Seatunnel mailing list. Unsubscribe Mailing Lists If you do not need to know what's going on with Seatunnel, you can unsubscribe from the mailing list. Unsubscribe from the mailing list steps are as follows: Send an email without any contents or subject to LISTNAME-unsubscribe@seatunnel.apache.org. (replace LISTNAME with dev, commits,users...) Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from LISTNAME-help@seatunnel.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary. Receive a goodbye email. After completing the above steps, you will receive a goodbye email with the subject GOODBYE from LISTNAME@seatunnel.apache.org, and you have successfully unsubscribed to the Apache Seatunnel mailing list, and you will not receive emails from dev@seatunnel.apache.org. Edit this page Previous Participate in Contributing Next SeaTunnel Improvement Proposal (STIP)","s":"Mailing lists","u":"/community/contribution_guide/subscribe","h":"","p":105235},{"i":105238,"t":"Submit Guide Documentation Notice On this page Documentation Notice Good documentation is critical for any type of software. Any contribution that can improve the Seatunnel documentation is welcome. Get the document project​ Documentation for the Seatunnel project is maintained in a separate git repository. First you need to fork the document project into your own github repository, and then clone the document to your local computer. git clone https://github.com/<your-github-user-name>/seatunnel-website Preview and generate static files​ This website is compiled using node, using Docusaurus framework components Download and install nodejs (version>12.5.0) Clone the code to the local git clone git@github.com:apache/seatunnel-website.git Run npm install to install the required dependent libraries. Run npm run start in the root directory, you can visit http://localhost:3000 to view the English mode preview of the site Run npm run start-zh in the root directory, you can visit http://localhost:3000 to view the Chinese mode preview of the site To generate static website resource files, run npm run build. The static resources of the build are in the build directory. Directory structure​ |-- community |-- docs // The next version of the document that will be released soon |-- download |-- faq // Q&A |-- i18n | `-- zh-CN //Internationalized Chinese | |-- code.json | |-- docusaurus-plugin-content-docs | |-- docusaurus-plugin-content-docs-community | |-- docusaurus-plugin-content-docs-download | |-- docusaurus-plugin-content-docs-faq | `-- docusaurus-theme-classic |-- resource // Original project files for architecture/timing diagram/flow chart, etc. |-- src | |-- components | |-- css | |-- js | |-- pages | | |-- home | | |-- index.jsx | | |-- team | | |-- user | | `-- versions | |-- styles |-- static // Picture static resource | |-- doc // document picture | |-- user // users picture | |-- home // homepage picture | |-- img // common picture |-- docusaurus.config.js Specification​ Directory naming convention​ Use all lowercase, separated by underscores. If there is a plural structure, use plural nomenclature, and do not use plural abbreviations Positive example: scripts / styles / components / images / utils / layouts / demo_styles / demo-scripts / img / doc Counter example: script / style / demoStyles / imgs / docs Vue and the naming convention of static resource files​ All lowercase, separated by a dash Positive example: render-dom.js / signup.css / index.html / company-logo.png Counter example: renderDom.js / UserManagement.html Resource Path​ Image resources are unified under static/{module name} css and other style files are placed in the src/css directory Page content modification​ Except for the homepage, team, user, Docs>All Version module page, all other pages can be directly jumped to the corresponding github resource modification page through the'Edit this page' button at the bottom Home page modification​ Visit the page https://seatunnel.apache.org Located in src/pages/home ├─home │ languages.json // Home page Chinese and English configuration │ index.less // homepage style Team page modification​ Visit the page https://seatunnel.apache.org/team Located in src/pages/team ├─team │ languages.json │ index.js │ index.less User list page modification​ Visit the page https://seatunnel.apache.org/user Located in `src/pages/user` └─versions data.json images.json index.js index.less languages.json version List page modification​ Visit the page https://seatunnel.apache.org/versions Located in `src/pages/versions` └─versions languages.json index.jsorchestrator/overview.md index.less Edit this page Previous Become Apache Release Manager Next Submit Code Get the document project Preview and generate static files Directory structure Specification Directory naming convention Vue and the naming convention of static resource files Resource Path Page content modification Home page modification Team page modification User list page modification version List page modification","s":"Documentation Notice","u":"/community/submit_guide/document","h":"","p":105237},{"i":105240,"t":"Submit Guide License Notice On this page License Notice As we know that Seatunnel is an open-source undergoing project at The Apache Software Foundation (ASF), which means that you have to follow the Apache way to become the Seatunnel contributor. Furthermore, Apache has extremely strict rules according to the License. This passage will explain the ASF license and how to avoid License risks at the early stage when you participate in Seatunnel. Note: This article only applies to the Apache projects. Licenses Could be Accepted to the Apache Project​ You have to pay attention to the following open-source software protocols which Apache projects support when you intend to add a new feature to the Seatunnel (or other Apache projects), which functions refers to other open-source software references. ASF 3RD PARTY LICENSE POLICY If the 3rd party software is not present at the above policy, we are sorry that your code can not pass the audit and we suggest searching for other substitute plans. Besides, when you demand new dependencies in the project, please email us about the reason and the outcome of the influence to dev@seatunnel.apache.org to discuss. Besides, you need at least 3 positive votes from the PPMC to finish the whole step. How to Legally Use 3rd Party Open-source Software in the Seatunnel​ Moreover, when we intend to refer a new software ( not limited to 3rd party jar, text, CSS, js, pics, icons, audios etc and modifications based on 3rd party files) to our project, we need to use them legally in addition to the permission of ASF. Refer to the following article: COMMUNITY-LED DEVELOPMENT \"THE APACHE WAY\" For example, we should contain the NOTICE file (every open-source project has NOTICE file, generally under root directory) of ZooKeeper in our project when we are using ZooKeeper. As the Apache explains, \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work. We are not going to dive into every 3rd party open-source license policy, you may look up them if interested. Seatunnel-License Check Rules​ TODO Edit this page Previous Submit Code Licenses Could be Accepted to the Apache Project How to Legally Use 3rd Party Open-source Software in the Seatunnel Seatunnel-License Check Rules","s":"License Notice","u":"/community/submit_guide/license","h":"","p":105239},{"i":105242,"t":"Submit Guide Submit Code Submit Code First from the remote repository https://github.com/apache/seatunnel.git fork a copy of the code into your own repository There are currently three branches in the remote repository: dev daily development branch Every day dev development branch, newly submitted code can pull request to this branch. 2.3.9-release release version branch The release version branch, there will be 3.0...and other version branches in the future. Take your fork repository named seatunnel as an example Clone your repository to your local git clone git@github.com:<YOUR_GITHUB_ID>/seatunnel.git Add remote repository address, named upstream git remote add upstream git@github.com:apache/seatunnel.git View repository git remote -v At this time, there will be two repositories: origin (your own repository) and upstream (remote repository) Get/Update remote repository code git fetch upstream Synchronize remote repository code to local repository git checkout origin/dev git merge --no-ff upstream/dev Create a new branch to modify the code git checkout -b feature-xxx After modifying the code locally, submit it to your own repository: git commit -m 'commit content' git push Submit changes to the remote repository On the GitHub page, click \"New pull request\". Select the modified local branch and the branch you want to merge with the past, click \"Create pull request\". Then the community Committers will do CodeReview, and then he will discuss some details (including design, implementation, performance, etc.) with you. When everyone on the team is satisfied with this modification, the commit will be merged into the dev branch Finally, congratulations, you have become an official contributor to Apache SeaTunnel! Edit this page Previous Documentation Notice Next License Notice","s":"Submit 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