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use SeaTunnel to write data to Hive?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-use-seatunnel-to-write-data-to-hive","p":207802},{"i":207821,"t":"How does SeaTunnel write multiple instances of ClickHouse to achieve load balancing?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-does-seatunnel-write-multiple-instances-of-clickhouse-to-achieve-load-balancing","p":207802},{"i":207823,"t":"How to solve OOM when SeaTunnel consumes Kafka?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-solve-oom-when-seatunnel-consumes-kafka","p":207802},{"i":207825,"t":"How to solve the Error Exception in thread \"main\" java.lang.NoSuchFieldError: INSTANCE?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-solve-the-error-exception-in-thread-main-javalangnosuchfielderror-instance","p":207802},{"i":207827,"t":"The default JDK of my Spark cluster is JDK7. 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整理 | 测试工程师 冯秀兰 对于百亿级批数据的导入，传统的 JDBC 方式在一些海量数据同步场景下的表现并不尽如人意。为了提供更快的写入速度，Apache SeaTunnel(Incubating) 在刚刚发布的 2.1.1 版本中提供了 ClickhouseFile-Connector 的支持，以实现 Bulk load 数据写入。 Bulk load 指把海量数据同步到目标 DB 中，目前 SeaTunnel 已实现数据同步到 ClickHouse 中。 在 Apache SeaTunnel(Incubating) 4 月 Meetup 上，Apache SeaTunnel(Incubating) Contributor 范佳分享了《基于 SeaTunnel 的 ClickHouse bulk load 实现》，详细讲解了 ClickHouseFile 高效处理海量数据的具体实现原理和流程。 感谢本文整理志愿者 测试工程师 冯秀兰 对 Apache SeaTunnel(Incubating) 项目的支持！ 本次演讲主要包含七个部分： ClickHouse Sink 现状 ClickHouse Sink 弱场景 ClickHouseFile 插件介绍 ClickHouseFile 核心技术点 ClickHouseFile 插件的实现解析 插件能力对比 后期优化方向 ​范 佳白鲸开源 高级工程师","s":"百亿级数据同步，如何基于 SeaTunnel 的 ClickHouse 实现？","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205252,"t":"现阶段，SeaTunnel 把数据同步到 ClickHouse 的流程是：只要是 SeaTunnel 支持的数据源，都可以把数据抽取出来，抽取出来之后，经过转换（也可以不转换），直接把源数据写入 ClickHouse sink connector 中，再通过 JDBC 写入到 ClickHouse 的服务器中。 但是，通过传统的 JDBC 写入到 ClickHouse 服务器中会存在一些问题。 首先，现阶段使用的工具是 ClickHouse 提供的驱动，实现方式是通过 HTTP，然而 HTTP 在某些场景下，实现效率不高。其次是海量数据，如果有重复数据或者一次性写入大量数据，使用传统的方式是生成对应的插入语句，通过 HTTP 发送到 ClickHouse 服务器端，在服务器端来进行逐条或分批次解析、执行，无法实现数据压缩。 最后就是我们通常会遇到的问题，数据量过大可能导致 SeaTunnel 端 OOM，或者服务器端因为写入数据量过大，频率过高，导致服务器端挂掉。 于是我们思考，是否有比 HTTP 更快的发送方式？如果可以在 SeaTunnel 端做数据预处理或数据压缩，那么网络带宽压力会降低，传输速率也会提高。","s":"01 ClickHouse Sink 现状","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205254,"t":"如果使用 HTTP 传输协议，当数据量过大，批处理以微批的形式发送请求，HTTP 可能处理不过来； 太多的 insert 请求，服务器压力大。假设带宽可以承受大量的请求，但服务器端不一定能承载。线上的服务器不仅需要数据插入，更重要的是查询数据为其他业务团队使用。若因为插入数据过多导致服务器集群宕机，是得不偿失的。","s":"02 ClickHouse Sink 的弱场景","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205256,"t":"针对这些 ClickHouse 的弱场景，我们想，有没有一种方式，既能在 Spark 端就能完成数据压缩，还可以在数据写入时不增加 Server 的资源负载，并且能快速写入海量数据？于是我们开发了 ClickHouseFile 插件来满足这些需求。 ClickHouseFile 插件的关键技术是 ClickHouse -local。ClickHouse-local 模式可以让用户能够对本地文件执行快速处理，而无需部署和配置 ClickHouse 服务器。ClickHouse-local 使用与 ClickHouse Server 相同的核心，因此它支持大多数功能以及相同的格式和表引擎。 因为有这 2 个特点,这意味着用户可以直接处理本地文件，而无需在 ClickHouse 服务器端做处理。由于是相同的格式，我们在远端或者 SeaTunnel 端进行的操作所产生的数据和服务器端是无缝兼容的，可以使用 ClickHouse local 来进行数据写入。ClickHouse local 是实现 ClickHouseFile 的核心技术点，因为有了这个插件，现阶段才能够实现 ClickHouse file 连接器。 ClickHouse local 核心使用方式： 第一行：将数据通过 Linux 管道传递给 ClickHouse-local 程序的 test_table 表。 第二至五行：创建一个 result_table 表用于接收数据。 第六行：将数据从 test_table 到 result_table 表。 第七行：定义数据处理的磁盘路径。 通过调用 Clickhouse-local 组件，实现在 Apache SeaTunnel(Incubating) 端完成数据文件的生成，以及数据压缩等一系列操作。再通过和 Server 进行通信，将生成的数据直接发送到 Clickhouse 的不同节点，再将数据文件提供给节点查询。 原阶段和现阶段实现方式对比： 原来是 Spark 把数据包括 insert 语句，发送给服务器端，服务器端做 SQL 的解析，表的数据文件生成、压缩，生成对应的文件、建立对应索引。若使用 ClickHouse local 技术，则由 SeaTunnel 端做数据文件的生成、文件压缩，以及索引的创建，最终产出就是给服务器端使用的文件或文件夹，同步给服务器后，服务器就只需对数据查询，不需要做额外的操作。","s":"03 ClickHouse File 核心技术点","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205258,"t":"以上流程可以促使数据同步更加高效，得益于我们对其中的三点优化。 第一，数据实际上师从管道传输到 ClickHouseFile，在长度和内存上会有限制。为此，我们将 ClickHouse connector，也就是 sink 端收到的数据通过 MMAP 技术写入临时文件，再由 ClickHouse local 读取临时文件的数据，生成我们的目标 local file，以达到增量读取数据的效果，解决 OM 的问题。 第二，支持分片。因为如果在集群中使用，如果只生成一个文件或文件夹，实际上文件只分发到一个节点上，会大大降低查询的性能。因此，我们进行了分片支持，用户可以在配置文件夹中设置分片的 key，算法会将数据分为多个 log file，写入到不同的集群节点中，大幅提升读取性能。 第三个重要的优化是文件传输，目前 SeaTunnel 支持两种文件传输方式，一种是 SCP，其特点是安全、通用、无需额外配置；另一种是 RSYNC，其有点事快速高效，支持断点续传，但需要额外配置，用户可以根据需要选择适合自己的方式。","s":"04 核心技术点","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205260,"t":"概括而言，ClickHouseFile 的总体实现流程如下： 缓存数据，缓存到 ClickHouse sink 端； 调用本地的 ClickHouse-local 生成文件； 将数据发送到 ClickHouse 服务端； 执行 ATTACH 命令 通过以上四个步骤，生成的数据达到可查询的状态。","s":"05 插件实现解析","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205262,"t":"从数据传输角度来说，ClickHouseFile 更适用于海量数据，优势在于不需要额外的配置，通用性强，而 ClickHouseFile 配置比较复杂，目前支持的 engine 较少； 就环境复杂度来说，ClickHouse 更适合环境复杂度高的情况，不需要额外配置就能直接运行； 在通用性上，ClickHouse 由于是 SeaTunnel 官方支持的 JDBC diver，基本上支持所有的 engine 的数据写入，ClickHouseFile 支持的 engine 相对较少；从服务器压力方面来说，ClickHouseFile 的优势在海量数据传输时就体现出来了，不会对服务器造成太大的压力。 但这二者并不是竞争关系，需要根据使用场景来选择。","s":"06 插件能力对比","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205264,"t":"目前虽然 SeaTunnel 支持 ClickHouseFile 插件，但是还有很多地方需要优化，主要包括： Rsync 支持； Exactly-Once 支持； 支持 Zero Copy 传输数据文件； 更多 Engine 的支持","s":"07 后续计划","u":"/zh-CN/blog/2022/05/10/ClickHouse","h":"","p":205249},{"i":205266,"t":"在Apache SeaTunnel(Incubating) 4 月Meetup上，孩子王大数据专家、OLAP平台架构师 袁洪军 为我们带来了《Apache SeaTunnel (Incubating)在孩子王的应用实践》。 本次演讲主要包含五个部分： 孩子王引入Apache SeaTunnel (Incubating)的背景介绍 大数据处理主流工具对比分析 Apache SeaTunnel (Incubating)的落地实践 Apache SeaTunnel (Incubating)改造中的常见问题 对孩子王未来发展方向的预测展望 袁洪军 孩子王 大数据专家、OLAP 平台架构师。多年大数据平台研发管理经验，在数据资产、血缘图谱、数据治理、OLAP 等领域有着丰富的研究经验。","s":"SeaTunnel 在孩子王的选型过程及应用改造实践","u":"/zh-CN/blog/2022/05/01/_Kidswant","h":"","p":205265},{"i":205268,"t":"目前孩子王的OLAP平台主要包含元数据层、任务层、存储层、SQL层、调度层、服务层以及监控层七部分，本次分享主要关注任务层中的离线任务。 其实孩子王内部有一套完整的采集推送系统，但由于一些历史遗留问题，公司现有的平台无法快速支持OLAP平台上线，因此当时公司只能选择放弃自身的平台，转而着手研发新的系统。 当时摆在OLAP面前的有三个选择： 1、给予采集推送系统做二次研发； 2、完全自研； 3、参与开源项目。","s":"01 背景介绍","u":"/zh-CN/blog/2022/05/01/_Kidswant","h":"#01-背景介绍","p":205265},{"i":205270,"t":"而这三项选择却各有优劣。若采基于采集推送做二次研发，其优点是有前人的经验，能够避免重复踩坑。但缺点是代码量大，研读时间、研读周期较长，而且抽象代码较少，与业务绑定的定制化功能较多，这也导致了其二开的难度较大。 若完全自研，其优点第一是开发过程自主可控，第二是可以通过Spark等一些引擎做贴合我们自身的架构，但缺点是可能会遭遇一些未知的问题。 最后如果使用开源框架，其优点一是抽象代码较多，二是经过其他大厂或公司的验证，框架在性能和稳定方面能够得到保障。因此孩子王在OLAP数据同步初期，我们主要研究了DATAX、Sqoop和SeaTunnel这三个开源数据同步工具。 从脑图我们可以看到，Sqoop的主要功能是针对RDB的数据同步，其实现方式是基于MAP/REDUCE。Sqoop拥有丰富的参数和命令行可以去执行各种操作。Sqoop的优点在于它首先贴合Hadoop生态，并已经支持大部分RDB到HIVE任意源的转换，拥有完整的命令集和API的分布式数据同步工具。 但其缺点是Sqoop只支持RDB的数据同步，并且对于数据文件有一定的限制，以及还没有数据清洗的概念。 DataX的主要功能是任意源的数据同步，通过配置化文件+多线程的方式实现，主要分为三个流程：Reader、Framework和Writer，其中Framework主要起到通信和留空的作用。 DataX的优点是它采用了插件式的开发，拥有自己的流控和数据管控，在社区活跃度上，DataX的官网上提供了许多不同源的数据推送。但DataX的缺点在于它基于内存，对数据量可能存在限制。 Apache SeaTunnel (Incubating)做的也是任意源的数据同步，实现流程分为source、transform和sink三步，基于配置文件、Spark或Flink实现。其优点是目前官网2.1.0有非常多的插件和源的推送，基于插件式的思想也使其非常容易扩展，拥抱Spark和Flink的同时也做到了分布式的架构。要说Apache SeaTunnel (Incubating)唯一的缺点可能是目前缺少IP的调用，UI界面需要自己做管控。 综上所述，Sqoop虽然是分布式，但是仅支持RDB和HIVE、Hbase之间的数据同步且扩展能力差，不利于二开。DataX扩展性好，整体性稳定，但由于是单机版，无法分布式集群部署，且数据抽取能力和机器性能有强依赖关系。而SeaTunnel和DataX类似并弥补了DataX非分布式的问题，对于实时流也做了很好的支持，虽然是新产品，但社区活跃度高。基于是否支持分布式、是否需要单独机器部署等诸多因素的考量，最后我们选择了SeaTunnel。","s":"02 大数据处理主流工具对比分析","u":"/zh-CN/blog/2022/05/01/_Kidswant","h":"#02-大数据处理主流工具对比分析","p":205265},{"i":205272,"t":"在Apache SeaTunnel (Incubating)的官网我们可以看到Apache SeaTunnel (Incubating)的基础流程包括source、transform和sink三部分。根据官网的指南，Apache SeaTunnel (Incubating)的启动需要配置脚本，但经过我们的研究发现，Apache SeaTunnel (Incubating)的最终执行是依赖config文件的spark-submit提交的一个Application应用。 这种初始化方式虽然简单，但存在必须依赖Config文件的问题，每次运行任务后都会生成再进行清除，虽然可以在调度脚本中动态生成，但也产生了两个问题。1、频繁的磁盘操作是否有意义；2、是否存在更为高效的方式支持Apache SeaTunnel (Incubating)的运行。 基于以上考量，在最终的设计方案中，我们增加了一个统一配置模板平台模块。调度时只需要发起一个提交命令，由Apache SeaTunnel (Incubating)自身去统一配置模板平台中拉取配置信息，再去装载和初始化参数。 上图展示的便是孩子王OLAP的业务流程，主要分为三块。数据从Parquet，即Hive，通过Parquet表的方式到KYLIN和CK source的整体流程。 这是我们建模的页面，主要通过拖拉拽的方式生成最终模型，每个表之间通过一些交易操作，右侧是针对Apache SeaTunnel (Incubating)的微处理。 因此我们最终提交的命令如上，其中标红的首先是【-conf customconfig/jars】，指用户可以再统一配置模板平台进行处理，或者建模时单独指定。最后标红的【421 $start_time $end_time $taskType】Unicode，属于唯一编码。 下方图左就是我们最终调度脚本提交的38个命令，下方图右是针对Apache SeaTunnel (Incubating)做的改造，可以看到一个较为特殊的名为WaterdropContext的工具类。可以首先判断Unicode是否存在，再通过Unicode_code来获取不同模板的配置信息，避免了config文件的操作。 在最后的reportMeta则是用于在任务执行完成后上报一些信息，这也会在Apache SeaTunnel (Incubating)中完成。 在最终完成的config文件如上，值得注意的是在transform方面，孩子王做了一些改造。首先是针对手机或者身份证号等做脱敏处理，如果用户指定字段，就按照字段做，如果不指定字段就扫描所有字段，然后根据模式匹配，进行脱敏加密。 第二transform还支持自定义处理，如上文说道OLAP建模的时候说到。加入了HideStr，可以保留一串字符的前十个字段，加密后方的所有字符，在数据安全上有所保障。 然后，在sink端，我们为了支持任务的幂等性，我们加入了pre_sql，这主要完成的任务是数据的删除，或分区的删除，因为任务在生产过程中不可能只运行一次，一旦出现重跑或补数等操作，就需要这一部分为数据的不同和正确性做考量。 在图右方的一个Clickhouse的Sink端，这里我们加入了一个is_senseless_mode，它组成了一个读写分离的无感模式，用户在查询和补数的时候不感知整体区域，而是用到CK的分区转换，即名为MOVE PARTITION TO TABLE的命令进行操作的。 此处特别说明KYLIN的Sink端，KYLIN是一个非常特殊的源，拥有自己一整套数据录入的逻辑，而且，他有自己的监控页面，因此我们给予KYLIN的改造只是简单地调用其API操作，在使用KYLIN时也只是简单的API调用和不断轮询的状态，所以KYLIN这块的资源在统一模板配置平台就被限制地很小。","s":"03 Apache SeaTunnel (Incubating)的落地实践","u":"/zh-CN/blog/2022/05/01/_Kidswant","h":"#03-apache-seatunnel-incubating的落地实践","p":205265},{"i":205274,"t":"1、OOM&Too many Parts 问题通常会出现在Hive到Hive的过程中，即使我们通过了自动资源的分配，但也存在数据突然间变大的情况，比如在举办了多次活动之后。这样的问题其实只能通过手动动态地调参，调整数据同步批量时间来避免。未来我们可能尽力去完成对于数据量的掌握，做到精细的控制。 2、字段、类型不一致问题 模型上线后，任务依赖的上游表或者字段，用户都会做一些修改，这些修改若无法感知，可能导致任务的失败。目前解决方法是依托血缘+快照的方式进行提前感知来避免错误。 3、自定义数据源&自定义分隔符 如财务部门需要单独使用的分割符，或是jar信息，现在用户可以自己在统一配置模板平台指定加载额外jar信息以及分割符信息。 4、数据倾斜问题 这可能因为用户自己设置了并行度，但无法做到尽善尽美。这一块我们暂时还没有完成处理，后续的思路可能在Source模块中添加post处理，对数据进行打散，完成倾斜。 5、KYLIN全局字典锁问题 随着业务发展，一个cube无法满足用户使用，就能需要建立多个cube，如果多个cube之间用了相同的字段，就会遇到KYLIN全局字典锁的问题。目前解决的思路是把两个或多个任务之间的调度时间进行隔开，如果无法隔开，可以做一个分布式锁的控制。KYLIN的sink端必须要拿到锁才能运行。 05 对孩子王未来发展方向的预测展望 多源数据同步，未来可能针对RDB源进行处理 基于实时Flink的实现 接管已有采集调度平台（主要解决分库分表的问题） 数据质量校验，像一些空值、整个数据的空置率、主时间的判断等 我的分享就到这里，希望以后可以和社区多多交流，共同进步，谢谢！","s":"04 Apache SeaTunnel (Incubating)改造中的常见问题","u":"/zh-CN/blog/2022/05/01/_Kidswant","h":"#04-apache-seatunnel-incubating改造中的常见问题","p":205265},{"i":205277,"t":"Apache SeaTunnel (Incubating) 与 Apache Inlong (Incubating) 的5月联合Meetup中，第二位分享的嘉宾是来自白鲸开源的高级工程师李宗文。在使用Apache SeaTunnel (Incubating) 的过程中，他发现了 Apache SeaTunnel (Incubating) 存在的四大问题：Connector实现次数多、参数不统一、难以支持多个版本的引擎以及引擎升级难的问题。为了解决以上的难题，李宗文将目标放在将Apache SeaTunnel (Incubating)与计算引擎进行解耦，重构其中Source与Sink API，实现改良了开发体验。 本次演讲主要包含四个部分： Apache SeaTunnel (Incubating)重构的背景和动机 Apache SeaTunnel (Incubating)重构的目标 Apache SeaTunnel (Incubating)重构整体的设计 Apache SeaTunnel (Incubating) Source API的设计 Apache SeaTunnel (Incubating) Sink API的设计 李宗文 白鲸开源 高级工程师 Apache SeaTunnel(Incubating) & Flink Contributor, Flink CDC & Debezium Contributor","s":"Apache SeaTunnel 与计算引擎的解耦之道，重构API我们做了些什么","u":"/zh-CN/blog/2022/05/31/engine","h":"","p":205276},{"i":205280,"t":"用过Apache SeaTunnel (Incubating) 的小伙伴或者开发者应该知道，目前Apache SeaTunnel (Incubating) 与引擎完全耦合，完全基于Spark、Flink开发，其中的配置文件参数都基于Flink、Spark引擎。从贡献者和用户的角度出发，我们能发现一些问题。 从贡献者的角度：反复实现Connector，没有收获感；潜在贡献者由于引擎版本不一致无法贡献社区； 从用户的角度：目前很多公司采用Lambda架构，离线作业使用Spark，实时作业使用Flink， 使用中就会发现SeaTunnel 的Connector可能Spark有，但是Flink没有，以及两个引擎对于同一存储引擎的Connector的参数也不统一，有较高的使用成本，脱离了SeaTunnel简单易用的初衷；还有用户提问说目前支不支持Flink的1.14版本，按照目前SeaTunnel的架构，想要支持Flink的1.14就必须抛弃之前的版本，因此这也会对之前版本的用户造成很大的问题。 因此，我们不管是做引擎升级或者支持更多的版本的用户都很困难。 另外Spark和Flink都采用了Chandy-lamport算法实现的Checkpoint容错机制，也在内部进行了DataSet与DataStream的统一，以此为前提我们认为解耦是可行的。","s":"01 Apache SeaTunnel(Incubating)与引擎耦合","u":"/zh-CN/blog/2022/05/31/engine","h":"#01-apache-seatunnelincubating与引擎耦合","p":205276},{"i":205282,"t":"因此为了解决以上提出的问题，我们有了以下的目标： Connector只实现一次：针对参数不统一、Connector多次实现的问题，我们希望实现一个统一的Source 与Sink API; 支持多个版本的Spark与Flink引擎：在Source与Sink API上再加入翻译层去支持多个版本与Spark和Flink引擎，解耦后这个代价会小很多。 明确Source的分片并行逻辑和Sink的提交逻辑：我们必须提供一个良好的API去支持Connector开发； 支持实时场景下的数据库整库同步：这个是目前很多用户提到需要CDC支持衍生的需求。我之前参与过Flink CDC社区，当时有许多用户提出在CDC的场景中，如果直接使用Flink CDC的话会导致每一个表都持有一个链接，当遇到需要整库同步需求时，千张表就有千个链接，该情况无论是对于数据库还是DBA都是不能接受的，如果要解决这个问题，最简单的方式就是引入Canal、Debezium等组件，使用其拉取增量数据到Kafka等MQ做中间存储，再使用Flink SQL进行同步，这实际已经违背了Flink CDC最早减少链路的想法，但是Flink CDC的定位只是一个Connector，无法做全链路的需求，所以该proposal在Flink CDC社区中没有被提出，我们借着本次重构，将proposa提交到了SeaTunnel社区中。 支持元信息的自动发现与存储：这一部分用户应该有所体验，如Kafka这类存储引擎，没有记录数据结构的功能，但我们在读取数据时又必须是结构化的，导致每次读取一个topic之前，用户都必须定义topic的结构化数据类型，我们希望做到用户只需要完成一次配置，减少重复的操作。 可能也有同学有疑惑为什么我们不直接使用Apache Beam，Beam的Source分为BOUNDED与UNBOUNDED，也就是需要实现两遍，并且有些Source与Sink的特性也不支持，具体所需的特性在后面会提到；","s":"02 Apache SeaTunnel(Incubating)与引擎解耦","u":"/zh-CN/blog/2022/05/31/engine","h":"#02-apache-seatunnelincubating与引擎解耦","p":205276},{"i":205284,"t":"Apache SeaTunnel(Incubating) API总体结构的设计如上图； Source & Sink API：数据集成的核心API之一，明确Source的分片并行逻辑和Sink的提交逻辑，用于实现Connector； Engine API： Translation: 翻译层，用于将SeaTunnel的Souce与Sink API翻译成引擎内部可以运行的Connector； Execution：执行逻辑，用于定义Source、Transform、Sink等操作在引擎内部的执行逻辑； Table API： Table SPI：主要用于以SPI的方式暴露Source与Sink接口，并明确Connector的必填与可选参数等； DataType：SeaTunnel的数据结构，用于隔离引擎，声明Table Schema等； Catalog：用于获取Table Scheme、Options等； Catalog Storage: 用于存储用户定义Kafka等非结构化引擎的Table Scheme等； 从上图是我们现在设想的执行流程： 从配置文件或UI等方式获取任务参数； 通过参数从Catalog中解析得到Table Schema、Option等信息； 以SPI方式拉起SeaTunnel的Connector，并注入Table信息等； 将SeaTunnel的Connector翻译为引擎内部的Connector； 执行引擎的作业逻辑，图中的多表分发目前只存在CDC整库同步场景下，其他Connector都是单表，不需要分发逻辑； 从以上可以看出，最难的部分是如何将Apache SeaTunnel(Incubating) 的Source和Sink翻译成引擎内部的Source和Sink。 当下许多用户不仅把Apache SeaTunnel (Incubating) 当做一个数据集成方向的工具，也当做数仓方向的工具，会使用很多Spark和Flink的SQL，我们目前希望能够保留这样的SQL能力，让用户实现无缝升级。 根据我们的调研，如上图，是对Source与Sink的理想执行逻辑，由于SeaTunnel以WaterDrop孵化，所以图上的术语偏向Spark； 理想情况下，在Driver上可以运行Source和Sink的协调器，然后Worker上运行Source的Reader和Sink的Writer。在Source协调器方面，我们希望它能支持几个能力。 一、是数据的分片逻辑，可以将分片动态添加到Reader中。 二、是可以支持Reader的协调。SourceReader用于读取数据，然后将数据发送到引擎中流转，最终流转到Source Writer中进行数据写入，同时Writer可以支持二阶段事务提交，并由Sink的协调器支持Iceberg等Connector的聚合提交需求；","s":"03 Apache SeaTunnel(Incubating)重构整体的设计","u":"/zh-CN/blog/2022/05/31/engine","h":"#03-apache-seatunnelincubating重构整体的设计","p":205276},{"i":205286,"t":"通过我们的调研，发现Source所需要的以下特性： 统一离线和实时API：Source只实现一次，同时支持离线和实时； 能够支持并行读取：比如Kafka每一个分区都生成一个的读取器，并行的执行； 支持动态添加分片：比如Kafka定于一个topic正则，由于业务量的需求，需要新增一个topic，该Source API可以支持我们动态添加到作业中。 支持协调读取器的工作：这个目前只发现在CDC这种Connector需要支持。CDC目前都是基于Netfilx的DBlog并行算法去支持，该情况在全量同步和增量同步两个阶段的切换时需要协调读取器的工作。 支持单个读取器处理多张表：即由前面提到的支持实时场景下的数据库整库同步需求； 对应以上需求，我们做出了基础的API，如上图，目前代码以提交到Apache SeaTunnel(Incubating)的社区中api-draft分支，感兴趣的可以查看代码详细了解。","s":"04 Source API","u":"/zh-CN/blog/2022/05/31/engine","h":"#04-source-api","p":205276},{"i":205288,"t":"Flink与Spark都在后面统一了DataSet与DataStream API，即能够支持前两个特性，那么对于剩下的3个特性： 如何支持动态添加分片？ 如何支持协调读取器？ 如何支持单个读取器处理多张表？ 带着问题，进入目前的设计。 我们发现除了CDC之外，其他Connector是不需要协调器的，针对不需要协调器的，我们会有一个支持并行的Source，并进行引擎翻译。 如上图中左边是一个分片的enumerator，可以列举source需要哪些分片，有哪些分片，实时进行分片的枚举，随后将每个分片分发到真正的数据读取模块SourceReader中。对于离线与实时作业的区分使用Boundedness标记，Connector可以在分片中标记是否有停止的Offset，如Kafka可以支持实时，同时也可以支持离线。ParallelSource可以在引擎设置任意并行度，以支持并行读取。 在需要协调器的场景，如上图，需要在Reader和Enumerator之间进行Event传输， Enumerator通过Reader发送的Event进行协调工作。Coordinated Source需要在引擎层面保证单并行度，以保证数据的一致性；当然这也不能良好的使用引擎的内存管理机制，但是取舍是必要的； 对于最后一个问题，我们如何支持单个读取器处理多张表。这会涉及到Table API层，通过Catalog读取到了所有需要的表后，有些表可能属于一个作业，可以通过一个链接去读取，有些可能需要分开，这个依赖于Source是怎么实现的。基于这是一个特殊需求，我们想要减少普通开发者的难度，在Table API这一层，我们会提供一个SupportMultipleTable接口，用于声明Source支持多表的读取。Source在实现时，要根据多张表实现对应的反序列化器。针对衍生的多表数据如何分离，Flink将采用Side Output机制，Spark预想使用Filter或Partition机制。","s":"如何适配Spark和Flink引擎","u":"/zh-CN/blog/2022/05/31/engine","h":"#如何适配spark和flink引擎","p":205276},{"i":205290,"t":"目前Sink所需的特性并不是很多，经过调研目前发现有三个需求： 幂等写入，这个不需要写代码，主要看存储引擎是否能支持。 分布式事务，主流是二阶段提交，如Kafka都是可以支持分布式事务的。 聚合提交，对于Iceberg、hoodie等存储引擎而言，我们不希望有小文件问题，于是期望将这些文件聚合成一个文件，再进行提交。 基于以上三个需求，我们有对应的三个API，分别是SinkWriter、SinkCommitter、SinkAggregated Committer。SinkWriter是作为基础写入，可能是幂等写入，也可能不是。SinkCommitter支持二阶段提交。SinkAggregatedCommitter支持聚合提交。 理想状态下，AggregatedCommitter单并行的在Driver中运行，Writer与Committer运行在Worker中，可能有多个并行度，每个并行度都有自己的预提交工作，然后把自己提交的信息发送给Aggregated Committer再进行聚合。 目前Spark和Flink的高版本都支持在Driver(Job Manager)运行AggregatedCommitter，worker(Job Manager)运行writer和Committer。 但是对于Flink低版本，无法支持AggregatedCommitter在JM中运行，我们也进行翻译适配的设计。Writer与Committer会作为前置的算子，使用Flink的ProcessFunction进行包裹，支持并发的预提交与写入工作，基于Flink的Checkpoint机制实现二阶段提交，这也是目前Flink的很多Connector的2PC实现方式。这个ProcessFunction会将预提交信息发送到下游的Aggregated Committer中，Aggregated Committer可以采用SinkFunction或Process Function等算子包裹，当然，我们需要保证AggregatedCommitter只会启动一个，即单并行度，否则聚合提交的逻辑就会出现问题。 感谢各位的观看，如果大家对具体实现感兴趣，可以去 Apache SeaTunnel (Incubating) 的社区查看api-draft分支代码，谢谢大家。","s":"05 Sink API","u":"/zh-CN/blog/2022/05/31/engine","h":"#05-sink-api","p":205276},{"i":205292,"t":"ClickHouse 是面向 OLAP 的分布式列式 DBMS。我们部门目前已经把所有数据分析相关的日志数据存储至 ClickHouse 这个优秀的数据仓库之中，当前日数据量达到了 300 亿。 之前介绍的有关数据处理入库的经验都是基于实时数据流，数据存储在 Kafka 中，我们使用 Java 或者 Golang 将数据从 Kafka 中读取、解析、清洗之后写入 ClickHouse 中，这样可以实现数据的快速接入。然而在很多同学的使用场景中，数据都不是实时的，可能需要将 HDFS 或者是 Hive 中的数据导入 ClickHouse。有的同学通过编写 Spark 程序来实现数据的导入，那么是否有更简单、高效的方法呢。 目前开源社区上有一款工具 Seatunnel，项目地址 https://github.com/apache/incubator-seatunnel，可以快速地将 HDFS 中的数据导入 ClickHouse。","s":"如何快速地把 HDFS 中的数据导入 ClickHouse","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"","p":205291},{"i":205294,"t":"假设我们的日志存储在 HDFS 中，我们需要将日志进行解析并筛选出我们关心的字段，将对应的字段写入 ClickHouse 的表中。","s":"HDFS To ClickHouse","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"#hdfs-to-clickhouse","p":205291},{"i":205296,"t":"我们在 HDFS 中存储的日志格式如下， 是很常见的 Nginx 日志 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":"/zh-CN/blog/hdfs-to-clickhouse","h":"#log-sample","p":205291},{"i":205298,"t":"我们的 ClickHouse 建表语句如下，我们的表按日进行分区 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":"/zh-CN/blog/hdfs-to-clickhouse","h":"#clickhouse-schema","p":205291},{"i":205300,"t":"接下来会给大家详细介绍，我们如何通过 Seatunnel 满足上述需求，将 HDFS 中的数据写入 ClickHouse 中。","s":"Seatunnel with ClickHouse","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"#seatunnel-with-clickhouse","p":205291},{"i":205302,"t":"Seatunnel 是一个非常易用，高性能，能够应对海量数据的实时数据处理产品，它构建在Spark之上。Seatunnel 拥有着非常丰富的插件，支持从 Kafka、HDFS、Kudu 中读取数据，进行各种各样的数据处理，并将结果写入 ClickHouse、Elasticsearch 或者 Kafka 中。","s":"Seatunnel","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"#seatunnel","p":205291},{"i":205304,"t":"首先我们需要安装 Seatunnel，安装十分简单，无需配置系统环境变量 准备 Spark 环境 安装 Seatunnel 配置 Seatunnel 以下是简易步骤，具体安装可以参照 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 # 指定Spark安装路径 SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.2.0-bin-hadoop2.7}","s":"Prerequisites","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"#prerequisites","p":205291},{"i":205306,"t":"我们仅需要编写一个 seatunnel Pipeline 的配置文件即可完成数据的导入。 配置文件包括四个部分，分别是 Spark、Input、filter 和 Output。 Spark​ 这一部分是 Spark 的相关配置，主要配置 Spark 执行时所需的资源大小。 spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } Input​ 这一部分定义数据源，如下是从 HDFS 文件中读取 text 格式数据的配置案例。 input { hdfs { path = \"hdfs://nomanode:8020/rowlog/accesslog\" table_name = \"access_log\" format = \"text\" } } Filter​ 在 Filter 部分，这里我们配置一系列的转化，包括正则解析将日志进行拆分、时间转换将 HTTPDATE 转化为 ClickHouse 支持的日期格式、对 Number 类型的字段进行类型转换以及通过 SQL 进行字段筛减等 filter { # 使用正则解析原始日志 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}' } # 将\"dd/MMM/yyyy:HH:mm:ss Z\"格式的数据转换为 # \"yyyy/MM/dd HH:mm:ss\"格式的数据 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\" } # 使用SQL筛选关注的字段，并对字段进行处理 # 甚至可以通过过滤条件过滤掉不关心的数据 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​ 最后我们将处理好的结构化数据写入 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":"/zh-CN/blog/hdfs-to-clickhouse","h":"#seatunnel-pipeline","p":205291},{"i":205308,"t":"我们将上述四部分配置组合成为我们的配置文件 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 { # 使用正则解析原始日志 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}' } # 将\"dd/MMM/yyyy:HH:mm:ss Z\"格式的数据转换为 # \"yyyy/MM/dd HH:mm:ss\"格式的数据 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\" } # 使用SQL筛选关注的字段，并对字段进行处理 # 甚至可以通过过滤条件过滤掉不关心的数据 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\" } } 执行命令，指定配置文件，运行 Seatunnel，即可将数据写入 ClickHouse。这里我们以本地模式为例。 ./bin/start-seatunnel.sh --config config/batch.conf -e client -m 'local[2]'","s":"Running seatunnel","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"#running-seatunnel","p":205291},{"i":205310,"t":"在这篇文章中，我们介绍了如何使用 Seatunnel 将 HDFS 中的 Nginx 日志文件导入 ClickHouse 中。仅通过一个配置文件便可快速完成数据的导入，无需编写任何代码。除了支持 HDFS 数据源之外，Seatunnel 同样支持将数据从 Kafka 中实时读取处理写入 ClickHouse 中。我们的下一篇文章将会介绍，如何将 Hive 中的数据快速导入 ClickHouse 中。 当然，Seatunnel 不仅仅是 ClickHouse 数据写入的工具，在 Elasticsearch 以及 Kafka等 数据源的写入上同样可以扮演相当重要的角色。 希望了解 Seatunnel 和 ClickHouse、Elasticsearch、Kafka 结合使用的更多功能和案例，可以直接进入官网 https://seatunnel.apache.org/ -- Power by InterestingLab","s":"Conclusion","u":"/zh-CN/blog/hdfs-to-clickhouse","h":"#conclusion","p":205291},{"i":205313,"t":"ClickHouse是面向OLAP的分布式列式DBMS。我们部门目前已经把所有数据分析相关的日志数据存储至ClickHouse这个优秀的数据仓库之中，当前日数据量达到了300亿。 在之前的文章 如何快速地把HDFS中的数据导入ClickHouse 中我们提到过使用 Seatunnel https://github.com/apache/incubator-seatunnel 对HDFS中的数据经过很简单的操作就可以将数据写入ClickHouse。HDFS中的数据一般是非结构化的数据，那么针对存储在Hive中的结构化数据，我们应该怎么操作呢？","s":"如何快速地把 Hive 中的数据导入 ClickHouse","u":"/zh-CN/blog/hive-to-clickhouse","h":"","p":205312},{"i":205315,"t":"假定我们的数据已经存储在Hive中，我们需要读取Hive表中的数据并筛选出我们关心的字段，或者对字段进行转换，最后将对应的字段写入ClickHouse的表中。","s":"Hive to ClickHouse","u":"/zh-CN/blog/hive-to-clickhouse","h":"#hive-to-clickhouse","p":205312},{"i":205317,"t":"我们在Hive中存储的数据表结构如下，存储的是很常见的Nginx日志 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":"/zh-CN/blog/hive-to-clickhouse","h":"#hive-schema","p":205312},{"i":205319,"t":"我们的ClickHouse建表语句如下，我们的表按日进行分区 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":"/zh-CN/blog/hive-to-clickhouse","h":"#clickhouse-schema","p":205312},{"i":205321,"t":"接下来会给大家介绍，我们如何通过 Seatunnel 将Hive中的数据写入ClickHouse中。","s":"Seatunnel with ClickHouse","u":"/zh-CN/blog/hive-to-clickhouse","h":"#seatunnel-with-clickhouse","p":205312},{"i":205323,"t":"Seatunnel 是一个非常易用，高性能，能够应对海量数据的实时数据处理产品，它构建在Spark之上。Seatunnel 拥有着非常丰富的插件，支持从Kafka、HDFS、Kudu中读取数据，进行各种各样的数据处理，并将结果写入ClickHouse、Elasticsearch或者Kafka中。 Seatunnel的环境准备以及安装步骤这里就不一一赘述了，具体安装步骤可以参考上一篇文章或者访问 Seatunnel Docs","s":"Seatunnel","u":"/zh-CN/blog/hive-to-clickhouse","h":"#seatunnel","p":205312},{"i":205325,"t":"我们仅需要编写一个Seatunnel Pipeline的配置文件即可完成数据的导入。 配置文件包括四个部分，分别是Spark、Input、filter和Output。 Spark​ 这一部分是Spark的相关配置，主要配置Spark执行时所需的资源大小。 spark { // 这个配置必需填写 spark.sql.catalogImplementation = \"hive\" spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } Input​ 这一部分定义数据源，如下是从Hive文件中读取text格式数据的配置案例。 input { hive { pre_sql = \"select * from access.nginx_msg_detail\" table_name = \"access_log\" } } 看，很简单的一个配置就可以从Hive中读取数据了。其中pre_sql是从Hive中读取数据SQL，table_name是将读取后的数据，注册成为Spark中临时表的表名，可为任意字段。 需要注意的是，必须保证hive的metastore是在服务状态。 在Cluster、Client、Local模式下运行时，必须把hive-site.xml文件置于提交任务节点的$HADOOP_CONF目录下 Filter​ 在Filter部分，这里我们配置一系列的转化，我们这里把不需要的minute和hour字段丢弃。当然我们也可以在读取Hive的时候通过pre_sql不读取这些字段 filter { remove { source_field = [\"minute\", \"hour\"] } } Output​ 最后我们将处理好的结构化数据写入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":"/zh-CN/blog/hive-to-clickhouse","h":"#seatunnel-pipeline","p":205312},{"i":205327,"t":"我们将上述四部分配置组合成为我们的配置文件config/batch.conf。 vim config/batch.conf spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" // 这个配置必需填写 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\" } } 执行命令，指定配置文件，运行 Seatunnel，即可将数据写入ClickHouse。这里我们以本地模式为例。 ./bin/start-seatunnel.sh --config config/batch.conf -e client -m 'local[2]'","s":"Running Seatunnel","u":"/zh-CN/blog/hive-to-clickhouse","h":"#running-seatunnel","p":205312},{"i":205329,"t":"在这篇文章中，我们介绍了如何使用 Seatunnel 将Hive中的数据导入ClickHouse中。仅仅通过一个配置文件便可快速完成数据的导入，无需编写任何代码，十分简单。 希望了解 Seatunnel 与ClickHouse、Elasticsearch、Kafka、Hadoop结合使用的更多功能和案例，可以直接进入官网 https://seatunnel.apache.org/ -- Power by InterestingLab","s":"Conclusion","u":"/zh-CN/blog/hive-to-clickhouse","h":"#conclusion","p":205312},{"i":205331,"t":"Amazon Aurora DSQL是亚马逊云科技于2024年12月推出的分布式SQL数据库，专为构建扩展性无限、高可用且免基础设施管理的应用程序设计，具有可用性高、无服务器模式架构、兼容性强、容错能力和安全级别高等特点。 由于Aurora DSQL的认证机制与IAM集成， 访问Aurora DSQL数据库需要通过IAM的身份来生成token 进行访问，而token 默认只有15分钟有效期，因此目前一些主流的数据同步工具暂不支持将其他数据库的数据迁移到Aurora DSQL。 基于这种情况，本文作者基于数据同步工具Apache SeaTunnel开发了一个专门针对Aurora DSQL的sink Connector，以满足从其他数据库迁移数据到Aurora DSQL需求。","s":"亚马逊云科技架构师的分享：基于SeaTunnel迁移数据到Amazon Aurora DSQL","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"","p":205330},{"i":205333,"t":"SeaTunnel是一个非常易用、多模态、超高性能的分布式数据集成平台，专注于数据集成和数据同步，主要旨在解决数据集成领域的常见问题。","s":"SeaTunnel 介绍","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#seatunnel-介绍","p":205330},{"i":205335,"t":"丰富且可扩展的Connector： 目前，SeaTunnel 支持超过 190 个Connector且数量还在增加，像主流数据库MySQL 、Oracle、SQLServer、PostgreSQL等都已经提供了Connector支持。插件式设计让用户可以轻松开发自己的Connector并将其集成到SeaTunnel项目中。 批流集成：基于SeaTunnel Connector API开发的Connector完美兼容离线同步、实时同步、全量同步、增量同步等场景。 它们大大降低了管理数据集成任务的难度。 分布式快照：支持分布式快照算法，保证数据一致性。 多引擎支持：SeaTunnel默认使用SeaTunnel引擎（Zeta）进行数据同步。 SeaTunnel还支持使用Flink或Spark作为Connector的执行引擎，以适应企业现有的技术组件。 SeaTunnel 支持 Spark 和 Flink 的多个版本。 JDBC复用、数据库日志多表解析：SeaTunnel支持多表或全库同步，解决了过度JDBC连接的问题； 支持多表或全库日志读取解析，解决了CDC多表同步场景下需要处理日志重复读取解析的问题。 高吞吐量、低延迟：SeaTunnel支持并行读写，提供稳定可靠、高吞吐量、低延迟的数据同步能力。 完善的实时监控：SeaTunnel支持数据同步过程中每一步的详细监控信息，让用户轻松了解同步任务读写的数据数量、数据大小、QPS等信息。","s":"SeaTunnel 相关特性","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#seatunnel-相关特性","p":205330},{"i":205337,"t":"图一 Seatunnel工作流图 SeaTunnel的工作流程如上图所示，用户配置作业信息并选择提交作业的执行引擎。Source Connector负责并行读取源端数据并将数据发送到下游Transform或直接发送到Sink，Sink将数据写入目的地。","s":"SeaTunnel 工作流程","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#seatunnel-工作流程","p":205330},{"i":205339,"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-${version}-bin.tar.gz\" 从源码构建成功后，所有的Connector插件和一些必要的依赖（例如：mysql驱动）都包含在二进制包中。您可以直接使用Connector插件，而无需单独安装它们。","s":"从源码构建SeaTunnel","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#从源码构建seatunnel","p":205330},{"i":205341,"t":"env { parallelism = 1 job.mode = \"STREAMING\" checkpoint.interval = 6000 checkpoint.timeout = 1200000 } source { MySQL-CDC { username = \"user name\" password = \"password\" table-names = [\"db.table1\"] url = \"jdbc:mysql://dbhost:3306/db?useSSL=false&allowPublicKeyRetrieval=true&serverTimezone=UTC&connectTimeout=120000&socketTimeout=120000&autoReconnect=true&failOverReadOnly=false&maxReconnects=10\" table-names-config = [ { table = \"db.table1\" primaryKeys = [\"id\"] } ] } } transform { } sink { Jdbc { url=\"jdbc:postgresql://<dsql_endpoint>:5432/postgres\" dialect=\"dsql\" driver = \"org.postgresql.Driver\" username = \"admin\" access_key_id = \"ACCESSKEYIDEXAMPLE\" secret_access_key = \"SECRETACCESSKEYEXAMPLE\" region = \"us-east-1\" database = \"postgres\" generate_sink_sql = true primary_keys = [\"id\"] max_retries=\"3\" batch_size =1000 } }","s":"使用Seatunnel同步MySQL数据到Aurora DSQL 配置示例","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#使用seatunnel同步mysql数据到aurora-dsql-配置示例","p":205330},{"i":205343,"t":"将上面的配置保存为mysql-to-dsql.conf 文件(请注意需要将示例中的值替换为真实的参数)，存放在apache-seatunnel-${version} 的config 目录下，执行以下命令: cd \"apache-seatunnel-${version}\" ./bin/seatunnel.sh --config ./config/mysql-to-dsql.conf -m local 图二 数据同步日志信息 命令执行成功后，您可以通过新产生的日志观察任务执行情况，如果出现错误，也可以根据异常信息进行定位，比如数据库连接超时、表不存在情况。而正常情况下，数据会成功写入目标 Aurora DSQL，如上图所示。","s":"运行数据同步任务","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#运行数据同步任务","p":205330},{"i":205345,"t":"Aurora DSQL是一款高度安全、易扩展、无服务器基础设施的分布式数据库，它的认证方式与IAM身份结合，因此目前缺少合适的工具可以将数据同步到Aurora DSQL中，尤其是在实时数据同步方面。SeaTunnel 是一款非常优秀数据集成和数据同步工具，目前支持多种数据源的数据同步，并且基于SeaTunnel 也可以非常灵活地实现自定义的数据同步需求，比如全量同步/增量实时同步。基于这种灵活性，本文作者开发了一种专门针对于Aurora DSQL 的Sink Connector， 以满足对于Aurora DSQL 数据同步需求。","s":"总结","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#总结","p":205330},{"i":205347,"t":"SeaTunnel 部署：https://seatunnel.apache.org/zh-CN/docs/start-v2/locally/deployment 开发新的SeaTunnel Connector： https://github.com/apache/seatunnel/blob/dev/seatunnel-connectors-v2/README.zh.md 在Aurora DSQL 中生成身份验证令牌：https://docs.aws.amazon.com/aurora-dsql/latest/userguide/SECTION_authentication-token.html *前述特定亚马逊云科技生成式人工智能相关的服务目前在亚马逊云科技海外区域可用。亚马逊云科技中国区域相关云服务由西云数据和光环新网运营，具体信息以中国区域官网为准。","s":"参考文档","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#参考文档","p":205330},{"i":205349,"t":"谭志强，亚马逊云科技迁移解决方案架构师，主要负责企业级客户的上云或跨云迁移工作，具有十几年 IT 专业服务经验，历任程序设计师、项目经理、技术顾问、解决方案架构师。","s":"本篇作者","u":"/zh-CN/blog/migrate-data-to-amazon-aurora-dsql","h":"#本篇作者","p":205330},{"i":205351,"t":"说到数据写入 Elasticsearch，最先想到的肯定是Logstash。Logstash因为其简单上手、可扩展、可伸缩等优点被广大用户接受。但是尺有所短，寸有所长，Logstash肯定也有它无法适用的应用场景，比如： 海量数据ETL 海量数据聚合 多源数据处理 为了满足这些场景，很多同学都会选择Spark，借助Spark算子进行数据处理，最后将处理结果写入Elasticsearch。 我们部门之前利用Spark对Nginx日志进行分析，统计我们的Web服务访问情况，将Nginx日志每分钟聚合一次最后将结果写入Elasticsearch，然后利用Kibana配置实时监控Dashboard。Elasticsearch和Kibana都很方便、实用，但是随着类似需求越来越多，如何快速通过Spark将数据写入Elasticsearch成为了我们的一大问题。 今天给大家推荐一款能够实现数据快速写入的黑科技 Seatunnel https://github.com/apache/incubator-seatunnel 一个非常易用，高性能，能够应对海量数据的实时数据处理产品，它构建在Spark之上，简单易用，灵活配置，无需开发。","s":"如何使用 Spark 快速将数据写入 Elasticsearch","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"","p":205350},{"i":205353,"t":"和Logstash一样，Seatunnel同样支持多种类型的数据输入，这里我们以最常见的Kakfa作为输入源为例，讲解如何使用 Seatunnel 将数据快速写入Elasticsearch","s":"Kafka to Elasticsearch","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#kafka-to-elasticsearch","p":205350},{"i":205355,"t":"原始日志格式如下: 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":"/zh-CN/blog/spark-execute-elasticsearch","h":"#log-sample","p":205350},{"i":205357,"t":"我们想要统计，一分钟每个域名的访问情况，聚合完的数据有以下字段: domain String hostname String status int datetime String count int","s":"Elasticsearch Document","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#elasticsearch-document","p":205350},{"i":205359,"t":"接下来会给大家详细介绍，我们如何通过 Seatunnel 读取Kafka中的数据，对数据进行解析以及聚合，最后将处理结果写入Elasticsearch中。","s":"Seatunnel with Elasticsearch","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#seatunnel-with-elasticsearch","p":205350},{"i":205361,"t":"Seatunnel 同样拥有着非常丰富的插件，支持从Kafka、HDFS、Hive中读取数据，进行各种各样的数据处理，并将结果写入Elasticsearch、Kudu或者Kafka中。","s":"Seatunnel","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#seatunnel","p":205350},{"i":205363,"t":"首先我们需要安装seatunnel，安装十分简单，无需配置系统环境变量 准备Spark环境 安装 Seatunnel 配置 Seatunnel 以下是简易步骤，具体安装可以参照 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 # 指定Spark安装路径 SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.2.0-bin-hadoop2.7}","s":"Prerequisites","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#prerequisites","p":205350},{"i":205365,"t":"与Logstash一样，我们仅需要编写一个Seatunnel Pipeline的配置文件即可完成数据的导入，相信了解Logstash的朋友可以很快入手 Seatunnel 配置。 配置文件包括四个部分，分别是Spark、Input、filter和Output。 Spark​ 这一部分是Spark的相关配置，主要配置Spark执行时所需的资源大小。 spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" spark.streaming.batchDuration = 5 } Input​ 这一部分定义数据源，如下是从Kafka中读取数据的配置案例， kafkaStream { topics = \"seatunnel-es\" consumer.bootstrap.servers = \"localhost:9092\" consumer.group.id = \"seatunnel_es_group\" consumer.rebalance.max.retries = 100 } Filter​ 在Filter部分，这里我们配置一系列的转化，包括正则解析将日志进行拆分、时间转换将HTTPDATE转化为Elasticsearch支持的日期格式、对Number类型的字段进行类型转换以及通过SQL进行数据聚合 filter { # 使用正则解析原始日志 # 最开始数据都在raw_message字段中 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}' } # 将\"dd/MMM/yyyy:HH:mm:ss Z\"格式的数据转换为 # Elasticsearch中支持的格式 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\" } ## 利用SQL对数据进行聚合 sql { table_name = \"access_log\" sql = \"select domain, hostname, int(status), datetime, count(*) from access_log group by domain, hostname, status, datetime\" } } Output​ 最后我们将处理好的结构化数据写入Elasticsearch。 output { elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-${now}\" es.batch.size.entries = 100000 index_time_format = \"yyyy.MM.dd\" } }","s":"Seatunnel Pipeline","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#seatunnel-pipeline","p":205350},{"i":205367,"t":"我们将上述四部分配置组合成为我们的配置文件 config/batch.conf。 vim config/batch.conf spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" spark.streaming.batchDuration = 5 } input { kafkaStream { topics = \"seatunnel-es\" consumer.bootstrap.servers = \"localhost:9092\" consumer.group.id = \"seatunnel_es_group\" consumer.rebalance.max.retries = 100 } } filter { # 使用正则解析原始日志 # 最开始数据都在raw_message字段中 grok { source_field = \"raw_message\" pattern = '%{IP:hostname}\\\\s%{NOTSPACE:domain}\\\\s%{IP:remote_addr}\\\\s%{NUMBER:request_time}s\\\\s\\\"%{DATA:upstream_ip}\\\"\\\\s\\\\[%{HTTPDATE:timestamp}\\\\]\\\\s\\\"%{NOTSPACE:method}\\\\s%{DATA:url}\\\\s%{NOTSPACE:http_ver}\\\"\\\\s%{NUMBER:status}\\\\s%{NUMBER:body_bytes_send}\\\\s%{DATA:referer}\\\\s%{NOTSPACE:cookie_info}\\\\s\\\"%{DATA:user_agent}' } # 将\"dd/MMM/yyyy:HH:mm:ss Z\"格式的数据转换为 # Elasticsearch中支持的格式 date { source_field = \"timestamp\" target_field = \"datetime\" source_time_format = \"dd/MMM/yyyy:HH:mm:ss Z\" target_time_format = \"yyyy-MM-dd'T'HH:mm:00.SSS+08:00\" } ## 利用SQL对数据进行聚合 sql { table_name = \"access_log\" sql = \"select domain, hostname, status, datetime, count(*) from access_log group by domain, hostname, status, datetime\" } } output { elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-${now}\" es.batch.size.entries = 100000 index_time_format = \"yyyy.MM.dd\" } } 执行命令，指定配置文件，运行 Seatunnel，即可将数据写入Elasticsearch。这里我们以本地模式为例。 ./bin/start-seatunnel.sh --config config/batch.conf -e client -m 'local[2]' 最后，写入Elasticsearch中的数据如下，再配上Kibana就可以实现Web服务的实时监控了^_^. \"_source\": { \"domain\": \"elasticsearch.cn\", \"hostname\": \"localhost\", \"status\": \"200\", \"datetime\": \"2018-11-26T21:54:00.000+08:00\", \"count\": 26 }","s":"Running Seatunnel","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#running-seatunnel","p":205350},{"i":205369,"t":"在这篇文章中，我们介绍了如何通过 Seatunnel 将Kafka中的数据写入Elasticsearch中。仅仅通过一个配置文件便可快速运行一个Spark Application，完成数据的处理、写入，无需编写任何代码，十分简单。 当数据处理过程中有遇到Logstash无法支持的场景或者Logstah性能无法达到预期的情况下，都可以尝试使用 Seatunnel 解决问题。 希望了解 Seatunnel 与Elasticsearch、Kafka、Hadoop结合使用的更多功能和案例，可以直接进入官网 https://seatunnel.apache.org/ 我们近期会再发布一篇《如何用Spark和Elasticsearch做交互式数据分析》，敬请期待.","s":"Conclusion","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#conclusion","p":205350},{"i":205371,"t":"邮件列表 : dev@seatunnel.apache.org. 发送任意内容至 dev-subscribe@seatunnel.apache.org， 按照回复订阅邮件列表。 Slack: 发送 Request to join SeaTunnel slack 邮件到邮件列表 (dev@seatunnel.apache.org), 我们会邀请你加入（在此之前请确认已经注册Slack）. bilibili B站 视频","s":"Contract us","u":"/zh-CN/blog/spark-execute-elasticsearch","h":"#contract-us","p":205350},{"i":205373,"t":"前言​ StructuredStreaming是Spark 2.0以后新开放的一个模块，相比SparkStreaming，它有一些比较突出的优点： 一、它能做到更低的延迟; 二、可以做实时的聚合，例如实时计算每天每个商品的销售总额； 三、可以做流与流之间的关联，例如计算广告的点击率，需要将广告的曝光记录和点击记录关联。 以上几点如果使用SparkStreaming来实现可能会比较麻烦或者说是很难实现，但是使用StructuredStreaming实现起来会比较轻松。","s":"如何支持的 Spark StructuredStreaming","u":"/zh-CN/blog/spark-structured-streaming","h":"","p":205372},{"i":205375,"t":"StructuredStreaming是Spark 2.0以后新开放的一个模块，相比SparkStreaming，它有一些比较突出的优点： 一、它能做到更低的延迟; 二、可以做实时的聚合，例如实时计算每天每个商品的销售总额； 三、可以做流与流之间的关联，例如计算广告的点击率，需要将广告的曝光记录和点击记录关联。 以上几点如果使用SparkStreaming来实现可能会比较麻烦或者说是很难实现，但是使用StructuredStreaming实现起来会比较轻松。","s":"前言","u":"/zh-CN/blog/spark-structured-streaming","h":"#前言","p":205372},{"i":205377,"t":"可能你没有详细研究过StructuredStreaming，但是发现StructuredStreaming能很好的解决你的需求，如何快速利用StructuredStreaming来解决你的需求？目前社区有一款工具 Seatunnel，项目地址：https://github.com/apache/incubator-seatunnel , 可以高效低成本的帮助你利用StructuredStreaming来完成你的需求。","s":"如何使用StructuredStreaming","u":"/zh-CN/blog/spark-structured-streaming","h":"#如何使用structuredstreaming","p":205372},{"i":205379,"t":"Seatunnel 是一个非常易用，高性能，能够应对海量数据的实时数据处理产品，它构建在Spark之上。Seatunnel 拥有着非常丰富的插件，支持从Kafka、HDFS、Kudu中读取数据，进行各种各样的数据处理，并将结果写入ClickHouse、Elasticsearch或者Kafka中","s":"Seatunnel","u":"/zh-CN/blog/spark-structured-streaming","h":"#seatunnel","p":205372},{"i":205381,"t":"首先我们需要安装 Seatunnel，安装十分简单，无需配置系统环境变量 准备Spark环境 安装 Seatunnel 配置 Seatunnel 以下是简易步骤，具体安装可以参照 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 # 指定Spark安装路径 SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.2.0-bin-hadoop2.7}","s":"准备工作","u":"/zh-CN/blog/spark-structured-streaming","h":"#准备工作","p":205372},{"i":205383,"t":"我们仅需要编写一个 Seatunnel Pipeline的配置文件即可完成数据的导入。 配置文件包括四个部分，分别是Spark、Input、filter和Output。 Spark​ 这一部分是Spark的相关配置，主要配置Spark执行时所需的资源大小。 spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } Input​ 下面是一个从kafka读取数据的例子 kafkaStream { topics = \"seatunnel\" consumer.bootstrap.servers = \"localhost:9092\" schema = \"{\\\"name\\\":\\\"string\\\",\\\"age\\\":\\\"integer\\\",\\\"addrs\\\":{\\\"country\\\":\\\"string\\\",\\\"city\\\":\\\"string\\\"}}\" } 通过上面的配置就可以读取kafka里的数据了 ，topics是要订阅的kafka的topic，同时订阅多个topic可以以逗号隔开，consumer.bootstrap.servers就是Kafka的服务器列表，schema是可选项，因为StructuredStreaming从kafka读取到的值(官方固定字段value)是binary类型的，详见http://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html 但是如果你确定你kafka里的数据是json字符串的话，你可以指定schema，input插件将按照你指定的schema解析 Filter​ 下面是一个简单的filter例子 filter{ sql{ table_name = \"student\" sql = \"select name,age from student\" } } table_name是注册成的临时表名，以便于在下面的sql使用 Output​ 处理好的数据往外输出，假设我们的输出也是kafka output{ kafka { topic = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" streaming_output_mode = \"update\" checkpointLocation = \"/your/path\" } } topic 是你要输出的topic， producer.bootstrap.servers是kafka集群列表，streaming_output_mode是StructuredStreaming的一个输出模式参数，有三种类型append|update|complete，具体使用参见文档http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-modes checkpointLocation是StructuredStreaming的checkpoint路径，如果配置了的话，这个目录会存储程序的运行信息，比如程序退出再启动的话会接着上次的offset进行消费。","s":"Seatunnel Pipeline","u":"/zh-CN/blog/spark-structured-streaming","h":"#seatunnel-pipeline","p":205372},{"i":205385,"t":"以上就是一个简单的例子，接下来我们就来介绍的稍微复杂一些的业务场景 场景一：实时聚合场景​ 假设现在有一个商城，上面有10种商品，现在需要实时求每天每种商品的销售额，甚至是求每种商品的购买人数（不要求十分精确）。 这么做的巨大的优势就是海量数据可以在实时处理的时候，完成聚合，再也不需要先将数据写入数据仓库，再跑离线的定时任务进行聚合， 操作起来还是很方便的。 kafka的数据如下 {\"good_id\":\"abc\",\"price\":300,\"user_id\":123456,\"time\":1553216320} 那我们该怎么利用 Seatunnel 来完成这个需求呢，当然还是只需要配置就好了。 #spark里的配置根据业务需求配置 spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } #配置input input { kafkaStream { topics = \"good_topic\" consumer.bootstrap.servers = \"localhost:9092\" schema = \"{\\\"good_id\\\":\\\"string\\\",\\\"price\\\":\\\"integer\\\",\\\"user_id\\\":\\\"Long\\\",\\\"time\\\":\\\"Long\\\"}\" } } #配置filter filter { #在程序做聚合的时候，内部会去存储程序从启动开始的聚合状态，久而久之会导致OOM,如果设置了watermark，程序自动的会去清理watermark之外的状态 #这里表示使用ts字段设置watermark，界限为1天 Watermark { time_field = \"time\" time_type = \"UNIX\" #UNIX表示时间字段为10为的时间戳，还有其他的类型详细可以查看插件文档 time_pattern = \"yyyy-MM-dd\" #这里之所以要把ts对其到天是因为求每天的销售额，如果是求每小时的销售额可以对其到小时`yyyy-MM-dd HH` delay_threshold = \"1 day\" watermark_field = \"ts\" #设置watermark之后会新增一个字段，`ts`就是这个字段的名字 } #之所以要group by ts是要让watermark生效，approx_count_distinct是一个估值，并不是精确的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\" } } #接下来我们选择将结果实时输出到Kafka output{ kafka { topic = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" streaming_output_mode = \"update\" checkpointLocation = \"/your/path\" } } 如上配置完成，启动 Seatunnel，就可以获取你想要的结果了。 场景二：多个流关联场景​ 假设你在某个平台投放了广告，现在要实时计算出每个广告的CTR(点击率)，数据分别来自两个topic，一个是广告曝光日志，一个是广告点击日志, 此时我们就需要把两个流数据关联到一起做计算，而 Seatunnel 最近也支持了此功能，让我们一起看一下该怎么做： 点击topic数据格式 {\"ad_id\":\"abc\",\"click_time\":1553216320,\"user_id\":12345} 曝光topic数据格式 {\"ad_id\":\"abc\",\"show_time\":1553216220,\"user_id\":12345} #spark里的配置根据业务需求配置 spark { spark.app.name = \"seatunnel\" spark.executor.instances = 2 spark.executor.cores = 1 spark.executor.memory = \"1g\" } #配置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 { #左关联右表必须设置watermark #右关左右表必须设置watermark #http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#inner-joins-with-optional-watermarking Watermark { source_table_name = \"click_table\" #这里可以指定为某个临时表添加watermark，不指定的话就是为input中的第一个 time_field = \"time\" time_type = \"UNIX\" delay_threshold = \"3 hours\" watermark_field = \"ts\" result_table_name = \"click_table_watermark\" #添加完watermark之后可以注册成临时表，方便后续在sql中使用 } 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 \" } } #接下来我们选择将结果实时输出到Kafka output { kafka { topic = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" streaming_output_mode = \"append\" #流关联只支持append模式 checkpointLocation = \"/your/path\" } } 通过配置，到这里流关联的案例也完成了。","s":"场景分析","u":"/zh-CN/blog/spark-structured-streaming","h":"#场景分析","p":205372},{"i":205387,"t":"通过配置能很快的利用StructuredStreaming做实时数据处理，但是还是需要对StructuredStreaming的一些概念了解，比如其中的watermark机制，还有程序的输出模式。 最后，Seatunnel 当然还支持spark streaming和spark 批处理。 如果你对这两个也感兴趣的话，可以阅读我们以前发布的文章《如何快速地将Hive中的数据导入ClickHouse》、 《优秀的数据工程师，怎么用Spark在TiDB上做OLAP分析》、 《如何使用Spark快速将数据写入Elasticsearch》 希望了解 Seatunnel 和 HBase, ClickHouse、Elasticsearch、Kafka、MySQL 等数据源结合使用的更多功能和案例，可以直接进入官网 https://seatunnel.apache.org/","s":"结语","u":"/zh-CN/blog/spark-structured-streaming","h":"#结语","p":205372},{"i":205389,"t":"邮件列表 : dev@seatunnel.apache.org. 发送任意内容至 dev-subscribe@seatunnel.apache.org， 按照回复订阅邮件列表。 Slack: 发送 Request to join SeaTunnel slack 邮件到邮件列表 (dev@seatunnel.apache.org), 我们会邀请你加入（在此之前请确认已经注册Slack）. bilibili B站 视频","s":"联系我们","u":"/zh-CN/blog/spark-structured-streaming","h":"#联系我们","p":205372},{"i":205391,"t":"TiDB 是一款定位于在线事务处理/在线分析处理的融合型数据库产品，实现了一键水平伸缩，强一致性的多副本数据安全，分布式事务，实时 OLAP 等重要特性。 TiSpark 是 PingCAP 为解决用户复杂 OLAP 需求而推出的产品。它借助 Spark 平台，同时融合 TiKV 分布式集群的优势。 直接使用 TiSpark 完成 OLAP 操作需要了解 Spark，还需要一些开发工作。那么，有没有一些开箱即用的工具能帮我们更快速地使用 TiSpark 在 TiDB 上完成 OLAP 分析呢？ 目前开源社区上有一款工具 Seatunnel，项目地址 https://github.com/apache/incubator-seatunnel ，可以基于Spark，在 TiSpark 的基础上快速实现 TiDB 数据读取和 OLAP 分析。","s":"怎么用 Spark 在 TiDB 上做 OLAP 分析","u":"/zh-CN/blog/spark-execute-tidb","h":"","p":205390},{"i":205393,"t":"在我们线上有这么一个需求，从 TiDB 中读取某一天的网站访问数据，统计每个域名以及服务返回状态码的访问次数，最后将统计结果写入 TiDB 另外一个表中。 我们来看看 Seatunnel 是如何实现这么一个功能的。","s":"使用 Seatunnel 操作TiDB","u":"/zh-CN/blog/spark-execute-tidb","h":"#使用-seatunnel-操作tidb","p":205390},{"i":205395,"t":"Seatunnel 是一个非常易用，高性能，能够应对海量数据的实时数据处理产品，它构建在 Spark 之上。Seatunnel 拥有着非常丰富的插件，支持从 TiDB、Kafka、HDFS、Kudu 中读取数据，进行各种各样的数据处理，然后将结果写入 TiDB、ClickHouse、Elasticsearch 或者 Kafka 中。 准备工作​ 1. TiDB 表结构介绍​ Input（存储访问日志的表） 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（存储结果数据的表） 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. 安装 Seatunnel​ 有了 TiDB 输入和输出表之后， 我们需要安装 Seatunnel，安装十分简单，无需配置系统环境变量 准备 Spark环境 安装 Seatunnel 配置 Seatunnel 以下是简易步骤，具体安装可以参照 Quick Start # 下载安装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 # 下载安装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 # 指定Spark安装路径 SPARK_HOME=${SPARK_HOME:-/usr/local/spark-2.1.0-bin-hadoop2.7}","s":"Seatunnel","u":"/zh-CN/blog/spark-execute-tidb","h":"#seatunnel","p":205390},{"i":205397,"t":"我们仅需要编写一个 Seatunnel 配置文件即可完成数据的读取、处理、写入。 Seatunnel 配置文件由四个部分组成，分别是 Spark、Input、Filter 和 Output。Input 部分用于指定数据的输入源，Filter 部分用于定义各种各样的数据处理、聚合，Output 部分负责将处理之后的数据写入指定的数据库或者消息队列。 整个处理流程为 Input -> Filter -> Output，整个流程组成了 Seatunnel 的 处理流程（Pipeline）。 以下是一个具体配置，此配置来源于线上实际应用，但是为了演示有所简化。 Input (TiDB)​ 这里部分配置定义输入源，如下是从 TiDB 一张表中读取数据。 input { tidb { database = \"nginx\" pre_sql = \"select * from nginx.access_log\" table_name = \"spark_nginx_input\" } } Filter​ 在Filter部分，这里我们配置一系列的转化, 大部分数据分析的需求，都是在Filter完成的。Seatunnel 提供了丰富的插件，足以满足各种数据分析需求。这里我们通过 SQL 插件完成数据的聚合操作。 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)​ 最后， 我们将处理后的结果写入TiDB另外一张表中。TiDB Output是通过JDBC实现的 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​ 这一部分是 Spark 的相关配置，主要配置 Spark 执行时所需的资源大小以及其他 Spark 配置。 我们的 TiDB Input 插件是基于 TiSpark 实现的，而 TiSpark 依赖于 TiKV 集群和 Placement Driver (PD)。因此我们需要指定 PD 节点信息以及 TiSpark 相关配置spark.tispark.pd.addresses和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\" } 运行 Seatunnel​ 我们将上述四部分配置组合成我们最终的配置文件 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\" } } 执行命令，指定配置文件，运行 Seatunnel ，即可实现我们的数据处理逻辑。 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 如果是本机测试验证逻辑，用本地模式（Local）就可以了，一般生产环境下，都是使用yarn-client或者yarn-cluster模式。 检查结果​ 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":"实现 Seatunnel 处理流程","u":"/zh-CN/blog/spark-execute-tidb","h":"#实现-seatunnel-处理流程","p":205390},{"i":205399,"t":"在这篇文章中，我们介绍了如何使用 Seatunnel 从 TiDB 中读取数据，做简单的数据处理之后写入 TiDB 另外一个表中。仅通过一个配置文件便可快速完成数据的导入，无需编写任何代码。 除了支持 TiDB 数据源之外，Seatunnel 同样支持Elasticsearch, Kafka, Kudu, ClickHouse等数据源。 于此同时，我们正在研发一个重要功能，就是在 Seatunnel 中，利用 TiDB 的事务特性，实现从 Kafka 到 TiDB 流式数据处理，并且支持端（Kafka）到端（TiDB）的 Exactly-Once 数据一致性。 希望了解 Seatunnel 和 TiDB，ClickHouse、Elasticsearch、Kafka结合使用的更多功能和案例，可以直接进入官网 https://seatunnel.apache.org/","s":"总结","u":"/zh-CN/blog/spark-execute-tidb","h":"#总结","p":205390},{"i":205401,"t":"邮件列表 : dev@seatunnel.apache.org. 发送任意内容至 dev-subscribe@seatunnel.apache.org， 按照回复订阅邮件列表。 Slack: 发送 Request to join SeaTunnel slack 邮件到邮件列表 (dev@seatunnel.apache.org), 我们会邀请你加入（在此之前请确认已经注册Slack）. bilibili B站 视频 -- Power by InterestingLab","s":"联系我们","u":"/zh-CN/blog/spark-execute-tidb","h":"#联系我们","p":205390},{"i":205403,"t":"可管理，可调用，可计算，可变现的数据资源才能成为资产，信息系统的互联互通使得多源和多维度的数据集成需求巨大，这就对数据处理和集成的工具提出了严苛的要求。 智能化时代，在“智慧城市”、“智慧治理”、“产品智能化”等的趋势下，企业大多面临如何实现高效数据推送，提高平台质量，以及保障数据安全的挑战。选对数据集成工具和平台，数据才能发挥出做大的作用。 Apache SeaTunnel (Incubating) 作为下一代高性能、分布式、海量数据集成框架，致力于让数据同步更简单，更高效，加快分布式数据处理能力在生产环境落地。 在 Apache SeaTunnel(Incubating) Meetup（2022 年 4 月 16日），Apache SeaTunnel(Incubating) 社区将邀请了 Apache SeaTunnel(Incubating)的资深用户，分享 Apache SeaTunnel(Incubating)在智能化生产环境中落地的最佳实践。此外，还会有贡献者现场进行 Apache SeaTunnel(Incubating)的源码解析，让你对 Apache SeaTunnel(Incubating)有一个更加全面而深入的了解。 无论你是对 Apache SeaTunnel(Incubating)抱有兴趣的初学者，还是在日常的生产实践中遭遇了复杂棘手的部署问题，都可以来到这里，与我们的讲师近距离沟通，得到你想要的答案。","s":"智能化时代的数据集成技术革新","u":"/zh-CN/blog/智能化时代的数据集成技术革新","h":"","p":205402},{"i":205405,"t":"Apache SeaTunnel (Incubating) Meetup | 4 月线上直播报名通道已开启，赶快预约吧！ 时间：2022-4-16 14:00-17:00 形式：线上直播 点击链接或扫码预约报名（免费）： 扫码预约报名 扫码进直播群","s":"01 报 名 通 道","u":"/zh-CN/blog/智能化时代的数据集成技术革新","h":"#01-报-名-通-道","p":205402},{"i":205407,"t":"行业案例详解 特色功能分析 一线企业踩坑心得 开源社区实战攻略 行业技术专家面对面 Q&A 惊喜礼品送不停","s":"02 活 动 亮 点","u":"/zh-CN/blog/智能化时代的数据集成技术革新","h":"#02-活-动-亮-点","p":205402},{"i":205409,"t":"活动当天，将有来自孩子王、oppo 的工程师现场分享来自厂商的一线前沿实践经验，还有来自白鲸开源的高级工程师对 Apache SeaTunnel(Incubating)的重要功能更新进行“硬核”讲解，干货满满。 袁洪军 孩子王 大数据专家、OLAP 平台架构师 多年大数据平台研发管理经验，在数据资产、血缘图谱、数据治理、OLAP 等领域有着丰富的研究经验 演讲时间：14:00-14:40 演讲题目：Apache SeaTunnel(Incubating) 在孩子王的应用实践 演讲概要： 如何实现高效数据推送？如何提高平台质量？如何保障数据安全？孩子王又对 Apache SeaTunnel(Incubating)做了哪些改造？ 范佳 白鲸开源 高级工程师 Apache SeaTunnel Contributor 演讲时间： 14:40-15:20 演讲题目： 基于 Apache SeaTunnel(Incubating)的 Clickhouse Bulk Load 实现 演讲概要： 通过扩展 Apache SeaTunnel(Incubating)的 Connector实现 Clickhouse的 bulk load 数据同步功能。 王子超 oppo 高级后端工程师 演讲时间： 15:50-16:30 演讲题目： oppo智能推荐样本中心基于 Apache SeaTunnel(Incubating)的技术革新 演讲概要： 介绍 oppo 智能推荐机器学习样本流程的演进及 Apache SeaTunnel(Incubating) 在其中发挥的作用。 除了精彩的演讲之外，现场还设置了多个抽奖环节，参与抽奖有机会获得 Apache SeaTunnel(Incubating) 精美定制礼品，敬请期待~","s":"03 活 动 议 程","u":"/zh-CN/blog/智能化时代的数据集成技术革新","h":"#03-活-动-议-程","p":205402},{"i":205412,"t":"在大规模数据集成场景中，吞吐瓶颈往往不在数据通道本身，而在“元数据路径”上：启动时的 Connector/Jar 加载、运行中的状态管理与恢复、以及初始化阶段对外部系统（如数据库、Hive Metastore）的 Schema/分区查询。任务量一旦上到千级、万级，这些“看似轻量”的动作会被放大成集群级别的压力。 Apache SeaTunnel Engine（Zeta）把一部分高频、可复用且昂贵的元数据下沉到引擎侧进行缓存，并配合分布式存储与自动清理策略，让海量同步任务可以更稳定地并行运行。","s":"深度拆解 Apache SeaTunnel 元数据缓存：支撑数万同步任务并行运行","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"","p":205411},{"i":205414,"t":"以“万级小作业”并行启动为例，常见的元数据瓶颈主要来自三类： 类加载与依赖隔离：每个作业独立创建 ClassLoader，会反复加载同一批 Connector 依赖，快速抬升 JVM Metaspace 压力。 状态与恢复信息：Checkpoint、任务状态、历史作业信息等若缺少分层存储与自动清理，会带来内存与 IO 的双重负担。 外部目录/Schema 查询：作业初始化阶段对源端数据库或 Metastore 的频繁请求，容易造成连接拥塞与元数据服务抖动。 下面从三条主线拆解 SeaTunnel 的“元数据缓存”思路与可落地的配置建议。","s":"为什么“元数据”会成为瓶颈","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#为什么元数据会成为瓶颈","p":205411},{"i":205416,"t":"当大量作业复用相同的 Source/Sink Connector 时，持续创建和销毁类加载器会带来明显的 Metaspace 抖动，甚至触发溢出。SeaTunnel Engine 提供 classloader-cache-mode，用于复用作业之间的 ClassLoader，减少重复加载和频繁回收的开销。 在 seatunnel.yaml 中开启（该配置默认开启，若你曾手动关闭可重新启用）： seatunnel: engine: classloader-cache-mode: true 适用场景： 作业规模大、启动频繁，且 Connector 类型相对有限（复用率高）。 JVM Metaspace 频繁增长，或出现与类加载相关的内存告警。 注意点： 如果集群长期运行且 Connector 类型非常分散，缓存会增加常驻的类元数据占用；建议结合监控观察 Metaspace 曲线，再决定是否开启或调整作业结构。","s":"一：ClassLoader 缓存，降低 Metaspace 压力","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#一classloader-缓存降低-metaspace-压力","p":205411},{"i":205418,"t":"SeaTunnel Engine 的容错语义基于 Chandy–Lamport Checkpoint 思想。为了兼顾性能与可靠性，它在引擎内部使用 Hazelcast 的分布式数据结构（如 IMap）承载一部分运行态信息，并通过外部存储（共享/分布式存储）完成故障恢复所需的数据落盘。 你通常需要关心三组配置：","s":"二：分布式状态与持久化，保证可恢复与可运营","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#二分布式状态与持久化保证可恢复与可运营","p":205411},{"i":205420,"t":"seatunnel: engine: checkpoint: interval: 300000 timeout: 10000 说明：如果在作业配置文件 env 中配置了 checkpoint.interval/checkpoint.timeout，会优先以作业配置为准。","s":"1) Checkpoint 触发参数","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#1-checkpoint-触发参数","p":205411},{"i":205422,"t":"当集群节点数大于 1 时，建议至少配置 backup-count，以降低单点故障导致的内存态信息丢失风险；对于需要“全停全启后自动恢复”的场景，可进一步配置 IMap 外部持久化。 相关细节可参考文档： /docs/seatunnel-engine/deployment /docs/seatunnel-engine/checkpoint-storage","s":"2) IMap 备份与持久化（建议用于生产集群）","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#2-imap-备份与持久化建议用于生产集群","p":205411},{"i":205424,"t":"SeaTunnel 将已完成作业的状态、计数器、错误日志等信息存放在 IMap 中。作业越多，累积越快。建议按需配置 history-job-expire-minutes，让过期信息自动淘汰，避免内存长期膨胀（默认 1440 分钟，即 1 天）。 seatunnel: engine: history-job-expire-minutes: 1440","s":"3) 历史作业信息的自动清理","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#3-历史作业信息的自动清理","p":205411},{"i":205426,"t":"大量作业并行启动时，对外部系统的元数据请求（表结构、分区信息、约束信息等）很容易成为“隐形风暴”。SeaTunnel 在 Connector/Catalog 侧引入缓存与复用思路，尽量把高频查询前置到引擎侧，减少重复的网络往返与服务端解析开销。 JDBC 场景：初始化阶段会读取表结构、字段类型、主键等信息，用于校验与分片规划。建议在高并发启动时避免每个作业对同一张表重复拉取全量元数据（可通过作业编排层做批次启动/预热）。 Hive 场景：Hive Metastore 往往是共享服务且相对敏感，建议尽量复用 Catalog 实例与已加载的 Database/Table/Partition 信息，并在大规模分区表同步中关注 Metastore 的 QPS 与响应时间。","s":"三：Catalog/Schema 元数据缓存，减少源端压力","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#三catalogschema-元数据缓存减少源端压力","p":205411},{"i":205428,"t":"Flink 的设计重心是长生命周期的流作业与复杂算子状态；Spark 更偏向批处理与作业级 Context 管理。在“万级独立小任务并发”这个目标下，SeaTunnel Engine 的策略更强调把可复用的启动与运行元数据沉到引擎层：减少重复加载、减少重复查询、并对历史信息进行可控的生命周期管理，从而提升并发启动与稳定性。","s":"与 Flink / Spark 的差异：面向“海量小作业”的轻量化","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#与-flink--spark-的差异面向海量小作业的轻量化","p":205411},{"i":205430,"t":"启用合理备份：生产集群建议 backup-count >= 1，并评估是否需要 IMap 外部持久化以支持全停全启自动恢复。 收敛 Connector 类型：尽量在同一集群里控制 Connector 组合的离散程度，让 classloader-cache-mode 的收益最大化。 关注“元数据指标”：除了 JVM 指标，建议关注 Checkpoint 延迟/失败率、Hazelcast 内存使用、IMap 大小与增长速率、历史作业累积速度等。 配置过期策略：根据排障与审计需求设置 history-job-expire-minutes，避免“为了可观测性而撑爆内存”。","s":"生产落地建议","u":"/zh-CN/blog/seatunnel-metadata-cache","h":"#生产落地建议","p":205411},{"i":205433,"t":"SeaTunnel 在唯品会的实践 2022年2月18日 · 阅读需 17 分钟 分享嘉宾：唯品会 资深大数据工程师 王玉 讲稿整理：张德通 导读: 唯品会早在1.0版本时就引用了SeaTunnel，我们使用SeaTunnel进行一些Hive到ClickHouse之间数据交互的工作。 今天的介绍会围绕下面几点展开： ClickHouse数据导入的需求和痛点； ClickHouse出仓入仓工具选型； Hive to ClickHouse； ClickHouse to Hive； SeaTunnel与唯品会数据平台的集成； 未来展望； ClickHouse数据导入的需求和痛点 1.唯品会数据OLAP架构​ 图中是唯品会OLAP架构，我们负责的模块是图中的数据服务和计算引擎两大部分。底层依赖的数据仓库分为离线数仓、实时数仓和湖仓。计算引擎方面，我们使用Presto、Kylin和Clickhouse。虽然Clickhouse是一个存储一体的OLAP数据库，我们为了利用Clickhouse的优秀计算性能而将它归入了计算引擎部分。基于OLAP组件之上，我们提供了SQL类数据服务和非SQL的唯品会自主分析，为不同智能服务。例如非SQL服务是为BI和商务提供更贴近业务的数据分析的服务。在数据服务至上抽象了多个数据应用。 2.需求​ 我们通过Presto Connector和Spark组件，把底层的Hive、Kudu、Alluxio组件打通。大数据组件之间可以互相导入导出数据，可以根据数据分析的需求和场景任意利用合适的组件分析数据。但我们引入Clickhouse时，它是一个数据孤岛，数据的导入和导出比较困难。Hive和Clickhouse之间需要做很多工作才能实现导入导出。我们的第一个数据导入导出需求就是提升导入导出效率，把Clickhouse纳入大数据体系中。 第二个需求是Presto跑SQL比较慢，图中是一个慢SQL的例子。图中的SQL where条件设置了日期、时间范围和具体过滤条件，这类SQL使用由于Presto使用分区粒度下推，运行比较慢。即使用Hive的Bucket表和分桶等其他方式优化后也是几秒的返回时间、不能满足业务要求。这种情况下，我们需要利用Clickhouse做离线的OLAP计算加速。 我们的实时数据是通过Kafka、Flink SQL方式写入到Clickhouse中。但分析时只用实时数据是不够的，需要用Hive维度表和已经ETL计算号的T+1实时表一起在Clickhouse中做加速运输。这需要把Hive的数据导入到Clickhouse中，这就是我们的第三个需求。 3.痛点​ 首先，我们引入一项数据组件时要考虑其性能。Hive表粒度是五分钟，是否有组件可以支撑五分钟内完成一个短小ETL流程并把ETL结果导入到Clickhouse中？第二，我们需要保证数据质量，数据的准确性需要有保障。Hive和Clickhouse的数据条数需要保障一致性，如果数据质量出问题能否通过重跑等机制修复数据？第三，数据导入需要支持的数据类型是否完备？不同数据库之间的数据类型和一些机制不同，我们有HiperLogLog和BitMap这类在某一存储引擎中利用得比较多得数据类型，是否可以正确传输和识别，且可以较好地使用。 ClickHouse和Hive出仓入仓工具的选型 基于数据业务上的痛点，我们对数据出仓入仓工具进行了对比和选择。我们主要在开源工具中进行选择，没有考虑商业出入仓工具，主要对比DataX、SeaTunnel和编写Spark程序并用jdbc插入ClickHouse这三个方案中取舍。 SeaTunnel和Spark依赖唯品会自己的Yarn集群，可以直接实现分布式读取和写入。DataX是非分布式的，且Reader、Writer之间的启动过程耗时时间长，性能普通，SeaTunnel和Spark处理数据的性能可以达到DataX的数倍。 十亿以上的数据可以平稳地在SeaTunnel和Spark中运行，DataX在数据量大以后性能压力大，处理十亿以上数据吃力。 在读写插件扩展性方面，SeaTunnel支持了多种数据源，支持用户开发插件。SeaTunnel支持了数据导入Redis。 稳定性上，SeaTunnel和DataX由于是自成体系的工具，稳定性会更好。Spark的稳定性方面需要关注代码质量。 我们的曝光表数据量每天在几十亿级，我们有5min内完成数据处理的性能要求，我们我们存在数据导入导出到Redis的需求，我们需要导入导出工具可以接入到数据平台上进行任务调度。 出于数据量级、性能、可扩展性、平台兼容性几方面的考虑，我们选择了SeaTunnel作为我们的数仓导入导出工具。 Hive数据导入到ClickHouse 下面将介绍我们对SeaTunnel的使用。 图中是一张Hive表，它是我们三级的商品维度表，包含品类商品、维度品类和用户人群信息。表的主键是一个三级品类ct_third_id，下面的value是两个uid的位图，是用户id的bitmap类型，我们要把这个Hive表导入到Clickhouse。 SeaTunnel安装简单，官网文档有介绍如何安装。下图中是SeaTunnel的配置，配置中env、source和sink是必不可少的。env部分，图中的例子是Spark配置，配置了包括并发度等，可以调整这些参数。source部分是数据来源，这里配置了Hive数据源，包括一条Hive Select语句，Spark运行source配置中的SQL把数据读出，此处支持UDF进行简单ETL；sink部分配置了Clickhouse，可以看到output_type=rowbinary，rowbinary是唯品会自研加速方案；pre_sql和check_sql是自研的用于数据校验的功能，后面也会详细介绍；clickhouse.socket_timeout和bulk_size都是可以根据实际情况进行调整的。 运行SeaTunnel，执行sh脚本文件、配置conf文件地址和yarn信息，后即可。 运行过程中会产生Spark日志，运行成功和运行中错误都可以在日志中查看。 为了更贴合业务，唯品会对SeaTunnel做了一些改进。我们的ETL任务都是需要重跑的，我们支持了pre_sql和check_sql实现数据的重跑和对数。主要流程是在数据准备好后，执行pre_sql进行预处理，在Clickhouse中执行删除旧分区数据、存放到某一目录下在失败时恢复该分区、rename这类操作。check_sql会检验，校验通过后整个流程结束；如果检验不通过，根据配置进行重跑，重跑不通过则报警到对应负责人。 唯品会基于1.0版本SeaTunnel增加了RowBinary做加速，也让HuperLogLog和BinaryBitmap的二进制文件能更容易地从Hive导入到Clickhouse。我们在ClickHouse-jdbc、bulk_size、Hive-source几处进行了修改。使用CK-jdbc的extended api，以rowbinary方式将数据写入CK，bulk_size引入了以rowbinary方式写入CK的控制逻辑，Hive-source RDD以HashPartitioner进行分区将数据打散，防止数据倾斜。 我们还让SeaTunnel支持了多类型，为了圈人群的功能，需要在Clickhouse、Preso、Spark中实现对应的方法。我们在Clickhouse-jdbc中增加支持Batch特性的Callback、HttpEntity、RowBinaryStream，在Clickhouse-jdbc和Clickhouse-sink代码中增加了bitmap类型映射，在Presto和Spark中实现了Clickhouse的Hyperloglog和Bitmap的function的UDF。 前面的配置中，Clickhouse-sink部分可以指定表名，这里有写入本地表和分布式表的差异。写入分布式表的性能比写入本地表差对Clickhouse集群的压力会更大，但在计算曝光表、流量表，ABTest等场景中需要两表Join，两张表量级均在几十亿。这时我们希望Join key落在本机，Join成本更小。我们建表时在Clickhouse的分布式表分布规则中配置murmurHash64规则，然后在Seatunnel的sink里直接配置分布式表，把写入规则交给Clickhouse，利用了分布式表的特性进行写入。写入本地表对Clickhouse的压力会更小，写入的性能也会更好。我们在Seatunnel里，根据sink的本地表，去Clickhouse的System.cluster表里获取表的分布信息和机器分布host。然后根据均分规则写入这些host。把数据分布式写入的事情放到Seatunnel里来做。 针对本地表和分布式表的写入，我们未来的改造方向是在Seatunnel实现一致性哈希，直接按照一定规则写如Clickhouse、不依赖Clickhouse自身做数据分发，改善Clickhouse高CPU负载问题。 ClickHouse数据导入到Hive 我们有圈人群的需求，每天唯品会为供应商圈20万个人群，比如80后、高富帅、白富美的人群集合。这些在Clickhouse中的Bitmap人群信息需要导出到Hive表，在Hive中与其他ETL任务进行配合，最后推到PIKA交给外部媒体使用。我们使SeaTunnel将Clickhouse Bitmap人群数据反推到Hive。 图中是SeaTunnel配置，我们把source配置为Clickhouse、sink配置为Hive，数据校验也配置在Hive内。 由于我们接入SeaTunnel较早，我们对一些模块间进行了加工，包括新增plugin-spark-sink-hive模块、plugin-spark-source-ClickHouse模块，重写Spark Row相关方法，使其能封装经过Schem映射后的Clickhouse数据，重新构造StructField并生成最终需要落地Hive的DataFrame。最新版本已经有了很多source和sink组件，在SeaTunnel使用上更方便。现在也可以在SeaTunnel中直接集成Flink connector。 SeaTunnel与唯品会数据平台的集成 各个公司都有自己的调度系统，例如白鲸、宙斯。唯品会的调度工具是数坊，调度工具中集成了数据传输工具。下面是调度系统架构图，其中包含各类数据的出入仓。 SeaTunnel任务类型集成到平台中，图中是数坊的定时任务截图，可以看到选中的部分，是一个配置好的SeaTunnel任务，负责人、最近一次耗时，前后依赖任务的血缘信息，消耗的资源信息。下面展示了历史运行实例信息。 我们把SeaTunnel集成到了调度系统中，数坊调度Master会根据任务类型把任务分配到对应的Agent上，根据Agent负载情况分配到合适的机器上运行，管控器把前台的任务调度配置和信息拉取到后生成SeaTunnel cluster，在类似于k8s pod、cgroup隔离的虚拟环境内进行执行。运行结果会由调度平台的数据质量监控判断任务是否完成、是否运行成功，失败时进行重跑和告警。 SeaTunnel本身是一个工具化的组件，是为了进行数据血缘，数据质量，历史记录，高警监控，还包括资源分配这些信息的管控。我们把SeaTunnel集成到平台中，可以利用平台优势利用好SeaTunnel。 圈存人群中利用了SeaTunnel进行处理。我们通过打点数据，把圈存人群按照路径和使用情况分为不同的人，或称千人千面，把用户打上标签，圈出的某一类人群推送给用户、分析师和供应商。 流量进入Kafka，通过Flink入仓，再通过ETL形成用户标签表，用户标签表生成后，我们通过Presto实现了的BitMap方法，把数据打成Hive中的宽表。用户通过在人群系统页面中框选词条创建任务，提交腾群，生成SQL查询Clickhouse BitMap。Clickhouse的BitMap查询速度非常快，由天生优势，我们需要把Hive的BitMap表通过SeaTunnel导入到Clickhouse中。圈完人群后我们需要把表落地，形成Clickhouse的一个分区或一条记录，再把生成的结果BitMap表通过SeaTunnel存储到Hive中去。最后同步工具会将Hive的BitMap人群结果同步给外部媒体仓库Pika。每天圈20w个人群左右。 整个过程中SeaTunnel负责把数据从Hive导出到Clickhouse，Clickhouse的ETL流程完成后SeaTunnel把数据从Clickhouse导出到Hive。 为了完成这样的需求，我们在Presto和Spark端现ClickHouse的Hyperloglog和BitMap的function的UDF；我们还开发Seatunnel接口，使得用户在ClickHouse里使用Bitmap方法圈出来的人群，可以直接通过Seatunnel写入Hive表，无需中间落地步骤。用户也可以在Hive里通过spark圈人群或者反解人群bitmap，调用SeaTunnel接口，使数据直接传输到ClickHouse的结果表，而无需中间落地。 后续工作 后续我们会进一步改善Clickhouse写入数据时CPU负载高的问题，下一步会在SeaTunnel中实现Clickhouse数据源和读取端的CK-local模式，读写分离，减轻Clickhouse压力。未来我们也会增加更多sink支持，如数据推送到Pika和相应的数据检查。 标签： 唯品会 ClickHouse","s":"ClickHouse数据导入的需求和痛点","u":"/zh-CN/user_cases","h":"","p":205432},{"i":205435,"t":"2022 2022年2月18日 - SeaTunnel 在唯品会的实践","s":"历史博文","u":"/zh-CN/user_cases/archive","h":"","p":205434},{"i":205437,"t":"标签 C ClickHouse1 唯 唯品会1","s":"标签","u":"/zh-CN/user_cases/tags","h":"","p":205436},{"i":205439,"t":"SeaTunnel 在唯品会的实践 2022年2月18日 · 阅读需 17 分钟 分享嘉宾：唯品会 资深大数据工程师 王玉 讲稿整理：张德通 导读: 唯品会早在1.0版本时就引用了SeaTunnel，我们使用SeaTunnel进行一些Hive到ClickHouse之间数据交互的工作。 今天的介绍会围绕下面几点展开： ClickHouse数据导入的需求和痛点； ClickHouse出仓入仓工具选型； Hive to ClickHouse； ClickHouse to Hive； SeaTunnel与唯品会数据平台的集成； 未来展望； ClickHouse数据导入的需求和痛点 1.唯品会数据OLAP架构​ 图中是唯品会OLAP架构，我们负责的模块是图中的数据服务和计算引擎两大部分。底层依赖的数据仓库分为离线数仓、实时数仓和湖仓。计算引擎方面，我们使用Presto、Kylin和Clickhouse。虽然Clickhouse是一个存储一体的OLAP数据库，我们为了利用Clickhouse的优秀计算性能而将它归入了计算引擎部分。基于OLAP组件之上，我们提供了SQL类数据服务和非SQL的唯品会自主分析，为不同智能服务。例如非SQL服务是为BI和商务提供更贴近业务的数据分析的服务。在数据服务至上抽象了多个数据应用。 2.需求​ 我们通过Presto Connector和Spark组件，把底层的Hive、Kudu、Alluxio组件打通。大数据组件之间可以互相导入导出数据，可以根据数据分析的需求和场景任意利用合适的组件分析数据。但我们引入Clickhouse时，它是一个数据孤岛，数据的导入和导出比较困难。Hive和Clickhouse之间需要做很多工作才能实现导入导出。我们的第一个数据导入导出需求就是提升导入导出效率，把Clickhouse纳入大数据体系中。 第二个需求是Presto跑SQL比较慢，图中是一个慢SQL的例子。图中的SQL where条件设置了日期、时间范围和具体过滤条件，这类SQL使用由于Presto使用分区粒度下推，运行比较慢。即使用Hive的Bucket表和分桶等其他方式优化后也是几秒的返回时间、不能满足业务要求。这种情况下，我们需要利用Clickhouse做离线的OLAP计算加速。 我们的实时数据是通过Kafka、Flink SQL方式写入到Clickhouse中。但分析时只用实时数据是不够的，需要用Hive维度表和已经ETL计算号的T+1实时表一起在Clickhouse中做加速运输。这需要把Hive的数据导入到Clickhouse中，这就是我们的第三个需求。 3.痛点​ 首先，我们引入一项数据组件时要考虑其性能。Hive表粒度是五分钟，是否有组件可以支撑五分钟内完成一个短小ETL流程并把ETL结果导入到Clickhouse中？第二，我们需要保证数据质量，数据的准确性需要有保障。Hive和Clickhouse的数据条数需要保障一致性，如果数据质量出问题能否通过重跑等机制修复数据？第三，数据导入需要支持的数据类型是否完备？不同数据库之间的数据类型和一些机制不同，我们有HiperLogLog和BitMap这类在某一存储引擎中利用得比较多得数据类型，是否可以正确传输和识别，且可以较好地使用。 ClickHouse和Hive出仓入仓工具的选型 基于数据业务上的痛点，我们对数据出仓入仓工具进行了对比和选择。我们主要在开源工具中进行选择，没有考虑商业出入仓工具，主要对比DataX、SeaTunnel和编写Spark程序并用jdbc插入ClickHouse这三个方案中取舍。 SeaTunnel和Spark依赖唯品会自己的Yarn集群，可以直接实现分布式读取和写入。DataX是非分布式的，且Reader、Writer之间的启动过程耗时时间长，性能普通，SeaTunnel和Spark处理数据的性能可以达到DataX的数倍。 十亿以上的数据可以平稳地在SeaTunnel和Spark中运行，DataX在数据量大以后性能压力大，处理十亿以上数据吃力。 在读写插件扩展性方面，SeaTunnel支持了多种数据源，支持用户开发插件。SeaTunnel支持了数据导入Redis。 稳定性上，SeaTunnel和DataX由于是自成体系的工具，稳定性会更好。Spark的稳定性方面需要关注代码质量。 我们的曝光表数据量每天在几十亿级，我们有5min内完成数据处理的性能要求，我们我们存在数据导入导出到Redis的需求，我们需要导入导出工具可以接入到数据平台上进行任务调度。 出于数据量级、性能、可扩展性、平台兼容性几方面的考虑，我们选择了SeaTunnel作为我们的数仓导入导出工具。 Hive数据导入到ClickHouse 下面将介绍我们对SeaTunnel的使用。 图中是一张Hive表，它是我们三级的商品维度表，包含品类商品、维度品类和用户人群信息。表的主键是一个三级品类ct_third_id，下面的value是两个uid的位图，是用户id的bitmap类型，我们要把这个Hive表导入到Clickhouse。 SeaTunnel安装简单，官网文档有介绍如何安装。下图中是SeaTunnel的配置，配置中env、source和sink是必不可少的。env部分，图中的例子是Spark配置，配置了包括并发度等，可以调整这些参数。source部分是数据来源，这里配置了Hive数据源，包括一条Hive Select语句，Spark运行source配置中的SQL把数据读出，此处支持UDF进行简单ETL；sink部分配置了Clickhouse，可以看到output_type=rowbinary，rowbinary是唯品会自研加速方案；pre_sql和check_sql是自研的用于数据校验的功能，后面也会详细介绍；clickhouse.socket_timeout和bulk_size都是可以根据实际情况进行调整的。 运行SeaTunnel，执行sh脚本文件、配置conf文件地址和yarn信息，后即可。 运行过程中会产生Spark日志，运行成功和运行中错误都可以在日志中查看。 为了更贴合业务，唯品会对SeaTunnel做了一些改进。我们的ETL任务都是需要重跑的，我们支持了pre_sql和check_sql实现数据的重跑和对数。主要流程是在数据准备好后，执行pre_sql进行预处理，在Clickhouse中执行删除旧分区数据、存放到某一目录下在失败时恢复该分区、rename这类操作。check_sql会检验，校验通过后整个流程结束；如果检验不通过，根据配置进行重跑，重跑不通过则报警到对应负责人。 唯品会基于1.0版本SeaTunnel增加了RowBinary做加速，也让HuperLogLog和BinaryBitmap的二进制文件能更容易地从Hive导入到Clickhouse。我们在ClickHouse-jdbc、bulk_size、Hive-source几处进行了修改。使用CK-jdbc的extended api，以rowbinary方式将数据写入CK，bulk_size引入了以rowbinary方式写入CK的控制逻辑，Hive-source RDD以HashPartitioner进行分区将数据打散，防止数据倾斜。 我们还让SeaTunnel支持了多类型，为了圈人群的功能，需要在Clickhouse、Preso、Spark中实现对应的方法。我们在Clickhouse-jdbc中增加支持Batch特性的Callback、HttpEntity、RowBinaryStream，在Clickhouse-jdbc和Clickhouse-sink代码中增加了bitmap类型映射，在Presto和Spark中实现了Clickhouse的Hyperloglog和Bitmap的function的UDF。 前面的配置中，Clickhouse-sink部分可以指定表名，这里有写入本地表和分布式表的差异。写入分布式表的性能比写入本地表差对Clickhouse集群的压力会更大，但在计算曝光表、流量表，ABTest等场景中需要两表Join，两张表量级均在几十亿。这时我们希望Join key落在本机，Join成本更小。我们建表时在Clickhouse的分布式表分布规则中配置murmurHash64规则，然后在Seatunnel的sink里直接配置分布式表，把写入规则交给Clickhouse，利用了分布式表的特性进行写入。写入本地表对Clickhouse的压力会更小，写入的性能也会更好。我们在Seatunnel里，根据sink的本地表，去Clickhouse的System.cluster表里获取表的分布信息和机器分布host。然后根据均分规则写入这些host。把数据分布式写入的事情放到Seatunnel里来做。 针对本地表和分布式表的写入，我们未来的改造方向是在Seatunnel实现一致性哈希，直接按照一定规则写如Clickhouse、不依赖Clickhouse自身做数据分发，改善Clickhouse高CPU负载问题。 ClickHouse数据导入到Hive 我们有圈人群的需求，每天唯品会为供应商圈20万个人群，比如80后、高富帅、白富美的人群集合。这些在Clickhouse中的Bitmap人群信息需要导出到Hive表，在Hive中与其他ETL任务进行配合，最后推到PIKA交给外部媒体使用。我们使SeaTunnel将Clickhouse Bitmap人群数据反推到Hive。 图中是SeaTunnel配置，我们把source配置为Clickhouse、sink配置为Hive，数据校验也配置在Hive内。 由于我们接入SeaTunnel较早，我们对一些模块间进行了加工，包括新增plugin-spark-sink-hive模块、plugin-spark-source-ClickHouse模块，重写Spark Row相关方法，使其能封装经过Schem映射后的Clickhouse数据，重新构造StructField并生成最终需要落地Hive的DataFrame。最新版本已经有了很多source和sink组件，在SeaTunnel使用上更方便。现在也可以在SeaTunnel中直接集成Flink connector。 SeaTunnel与唯品会数据平台的集成 各个公司都有自己的调度系统，例如白鲸、宙斯。唯品会的调度工具是数坊，调度工具中集成了数据传输工具。下面是调度系统架构图，其中包含各类数据的出入仓。 SeaTunnel任务类型集成到平台中，图中是数坊的定时任务截图，可以看到选中的部分，是一个配置好的SeaTunnel任务，负责人、最近一次耗时，前后依赖任务的血缘信息，消耗的资源信息。下面展示了历史运行实例信息。 我们把SeaTunnel集成到了调度系统中，数坊调度Master会根据任务类型把任务分配到对应的Agent上，根据Agent负载情况分配到合适的机器上运行，管控器把前台的任务调度配置和信息拉取到后生成SeaTunnel cluster，在类似于k8s pod、cgroup隔离的虚拟环境内进行执行。运行结果会由调度平台的数据质量监控判断任务是否完成、是否运行成功，失败时进行重跑和告警。 SeaTunnel本身是一个工具化的组件，是为了进行数据血缘，数据质量，历史记录，高警监控，还包括资源分配这些信息的管控。我们把SeaTunnel集成到平台中，可以利用平台优势利用好SeaTunnel。 圈存人群中利用了SeaTunnel进行处理。我们通过打点数据，把圈存人群按照路径和使用情况分为不同的人，或称千人千面，把用户打上标签，圈出的某一类人群推送给用户、分析师和供应商。 流量进入Kafka，通过Flink入仓，再通过ETL形成用户标签表，用户标签表生成后，我们通过Presto实现了的BitMap方法，把数据打成Hive中的宽表。用户通过在人群系统页面中框选词条创建任务，提交腾群，生成SQL查询Clickhouse BitMap。Clickhouse的BitMap查询速度非常快，由天生优势，我们需要把Hive的BitMap表通过SeaTunnel导入到Clickhouse中。圈完人群后我们需要把表落地，形成Clickhouse的一个分区或一条记录，再把生成的结果BitMap表通过SeaTunnel存储到Hive中去。最后同步工具会将Hive的BitMap人群结果同步给外部媒体仓库Pika。每天圈20w个人群左右。 整个过程中SeaTunnel负责把数据从Hive导出到Clickhouse，Clickhouse的ETL流程完成后SeaTunnel把数据从Clickhouse导出到Hive。 为了完成这样的需求，我们在Presto和Spark端现ClickHouse的Hyperloglog和BitMap的function的UDF；我们还开发Seatunnel接口，使得用户在ClickHouse里使用Bitmap方法圈出来的人群，可以直接通过Seatunnel写入Hive表，无需中间落地步骤。用户也可以在Hive里通过spark圈人群或者反解人群bitmap，调用SeaTunnel接口，使数据直接传输到ClickHouse的结果表，而无需中间落地。 后续工作 后续我们会进一步改善Clickhouse写入数据时CPU负载高的问题，下一步会在SeaTunnel中实现Clickhouse数据源和读取端的CK-local模式，读写分离，减轻Clickhouse压力。未来我们也会增加更多sink支持，如数据推送到Pika和相应的数据检查。 标签： 唯品会 ClickHouse","s":"SeaTunnel 在唯品会的实践","u":"/zh-CN/user_cases/SeaTunnel 在唯品会的实践","h":"","p":205438},{"i":205441,"t":"1 篇博文 含有标签「唯品会」 查看所有标签 SeaTunnel 在唯品会的实践 2022年2月18日 · 阅读需 17 分钟 分享嘉宾：唯品会 资深大数据工程师 王玉 讲稿整理：张德通 导读: 唯品会早在1.0版本时就引用了SeaTunnel，我们使用SeaTunnel进行一些Hive到ClickHouse之间数据交互的工作。 今天的介绍会围绕下面几点展开： ClickHouse数据导入的需求和痛点； ClickHouse出仓入仓工具选型； Hive to ClickHouse； ClickHouse to Hive； SeaTunnel与唯品会数据平台的集成； 未来展望； ClickHouse数据导入的需求和痛点 1.唯品会数据OLAP架构​ 图中是唯品会OLAP架构，我们负责的模块是图中的数据服务和计算引擎两大部分。底层依赖的数据仓库分为离线数仓、实时数仓和湖仓。计算引擎方面，我们使用Presto、Kylin和Clickhouse。虽然Clickhouse是一个存储一体的OLAP数据库，我们为了利用Clickhouse的优秀计算性能而将它归入了计算引擎部分。基于OLAP组件之上，我们提供了SQL类数据服务和非SQL的唯品会自主分析，为不同智能服务。例如非SQL服务是为BI和商务提供更贴近业务的数据分析的服务。在数据服务至上抽象了多个数据应用。 2.需求​ 我们通过Presto Connector和Spark组件，把底层的Hive、Kudu、Alluxio组件打通。大数据组件之间可以互相导入导出数据，可以根据数据分析的需求和场景任意利用合适的组件分析数据。但我们引入Clickhouse时，它是一个数据孤岛，数据的导入和导出比较困难。Hive和Clickhouse之间需要做很多工作才能实现导入导出。我们的第一个数据导入导出需求就是提升导入导出效率，把Clickhouse纳入大数据体系中。 第二个需求是Presto跑SQL比较慢，图中是一个慢SQL的例子。图中的SQL where条件设置了日期、时间范围和具体过滤条件，这类SQL使用由于Presto使用分区粒度下推，运行比较慢。即使用Hive的Bucket表和分桶等其他方式优化后也是几秒的返回时间、不能满足业务要求。这种情况下，我们需要利用Clickhouse做离线的OLAP计算加速。 我们的实时数据是通过Kafka、Flink SQL方式写入到Clickhouse中。但分析时只用实时数据是不够的，需要用Hive维度表和已经ETL计算号的T+1实时表一起在Clickhouse中做加速运输。这需要把Hive的数据导入到Clickhouse中，这就是我们的第三个需求。 3.痛点​ 首先，我们引入一项数据组件时要考虑其性能。Hive表粒度是五分钟，是否有组件可以支撑五分钟内完成一个短小ETL流程并把ETL结果导入到Clickhouse中？第二，我们需要保证数据质量，数据的准确性需要有保障。Hive和Clickhouse的数据条数需要保障一致性，如果数据质量出问题能否通过重跑等机制修复数据？第三，数据导入需要支持的数据类型是否完备？不同数据库之间的数据类型和一些机制不同，我们有HiperLogLog和BitMap这类在某一存储引擎中利用得比较多得数据类型，是否可以正确传输和识别，且可以较好地使用。 ClickHouse和Hive出仓入仓工具的选型 基于数据业务上的痛点，我们对数据出仓入仓工具进行了对比和选择。我们主要在开源工具中进行选择，没有考虑商业出入仓工具，主要对比DataX、SeaTunnel和编写Spark程序并用jdbc插入ClickHouse这三个方案中取舍。 SeaTunnel和Spark依赖唯品会自己的Yarn集群，可以直接实现分布式读取和写入。DataX是非分布式的，且Reader、Writer之间的启动过程耗时时间长，性能普通，SeaTunnel和Spark处理数据的性能可以达到DataX的数倍。 十亿以上的数据可以平稳地在SeaTunnel和Spark中运行，DataX在数据量大以后性能压力大，处理十亿以上数据吃力。 在读写插件扩展性方面，SeaTunnel支持了多种数据源，支持用户开发插件。SeaTunnel支持了数据导入Redis。 稳定性上，SeaTunnel和DataX由于是自成体系的工具，稳定性会更好。Spark的稳定性方面需要关注代码质量。 我们的曝光表数据量每天在几十亿级，我们有5min内完成数据处理的性能要求，我们我们存在数据导入导出到Redis的需求，我们需要导入导出工具可以接入到数据平台上进行任务调度。 出于数据量级、性能、可扩展性、平台兼容性几方面的考虑，我们选择了SeaTunnel作为我们的数仓导入导出工具。 Hive数据导入到ClickHouse 下面将介绍我们对SeaTunnel的使用。 图中是一张Hive表，它是我们三级的商品维度表，包含品类商品、维度品类和用户人群信息。表的主键是一个三级品类ct_third_id，下面的value是两个uid的位图，是用户id的bitmap类型，我们要把这个Hive表导入到Clickhouse。 SeaTunnel安装简单，官网文档有介绍如何安装。下图中是SeaTunnel的配置，配置中env、source和sink是必不可少的。env部分，图中的例子是Spark配置，配置了包括并发度等，可以调整这些参数。source部分是数据来源，这里配置了Hive数据源，包括一条Hive Select语句，Spark运行source配置中的SQL把数据读出，此处支持UDF进行简单ETL；sink部分配置了Clickhouse，可以看到output_type=rowbinary，rowbinary是唯品会自研加速方案；pre_sql和check_sql是自研的用于数据校验的功能，后面也会详细介绍；clickhouse.socket_timeout和bulk_size都是可以根据实际情况进行调整的。 运行SeaTunnel，执行sh脚本文件、配置conf文件地址和yarn信息，后即可。 运行过程中会产生Spark日志，运行成功和运行中错误都可以在日志中查看。 为了更贴合业务，唯品会对SeaTunnel做了一些改进。我们的ETL任务都是需要重跑的，我们支持了pre_sql和check_sql实现数据的重跑和对数。主要流程是在数据准备好后，执行pre_sql进行预处理，在Clickhouse中执行删除旧分区数据、存放到某一目录下在失败时恢复该分区、rename这类操作。check_sql会检验，校验通过后整个流程结束；如果检验不通过，根据配置进行重跑，重跑不通过则报警到对应负责人。 唯品会基于1.0版本SeaTunnel增加了RowBinary做加速，也让HuperLogLog和BinaryBitmap的二进制文件能更容易地从Hive导入到Clickhouse。我们在ClickHouse-jdbc、bulk_size、Hive-source几处进行了修改。使用CK-jdbc的extended api，以rowbinary方式将数据写入CK，bulk_size引入了以rowbinary方式写入CK的控制逻辑，Hive-source RDD以HashPartitioner进行分区将数据打散，防止数据倾斜。 我们还让SeaTunnel支持了多类型，为了圈人群的功能，需要在Clickhouse、Preso、Spark中实现对应的方法。我们在Clickhouse-jdbc中增加支持Batch特性的Callback、HttpEntity、RowBinaryStream，在Clickhouse-jdbc和Clickhouse-sink代码中增加了bitmap类型映射，在Presto和Spark中实现了Clickhouse的Hyperloglog和Bitmap的function的UDF。 前面的配置中，Clickhouse-sink部分可以指定表名，这里有写入本地表和分布式表的差异。写入分布式表的性能比写入本地表差对Clickhouse集群的压力会更大，但在计算曝光表、流量表，ABTest等场景中需要两表Join，两张表量级均在几十亿。这时我们希望Join key落在本机，Join成本更小。我们建表时在Clickhouse的分布式表分布规则中配置murmurHash64规则，然后在Seatunnel的sink里直接配置分布式表，把写入规则交给Clickhouse，利用了分布式表的特性进行写入。写入本地表对Clickhouse的压力会更小，写入的性能也会更好。我们在Seatunnel里，根据sink的本地表，去Clickhouse的System.cluster表里获取表的分布信息和机器分布host。然后根据均分规则写入这些host。把数据分布式写入的事情放到Seatunnel里来做。 针对本地表和分布式表的写入，我们未来的改造方向是在Seatunnel实现一致性哈希，直接按照一定规则写如Clickhouse、不依赖Clickhouse自身做数据分发，改善Clickhouse高CPU负载问题。 ClickHouse数据导入到Hive 我们有圈人群的需求，每天唯品会为供应商圈20万个人群，比如80后、高富帅、白富美的人群集合。这些在Clickhouse中的Bitmap人群信息需要导出到Hive表，在Hive中与其他ETL任务进行配合，最后推到PIKA交给外部媒体使用。我们使SeaTunnel将Clickhouse Bitmap人群数据反推到Hive。 图中是SeaTunnel配置，我们把source配置为Clickhouse、sink配置为Hive，数据校验也配置在Hive内。 由于我们接入SeaTunnel较早，我们对一些模块间进行了加工，包括新增plugin-spark-sink-hive模块、plugin-spark-source-ClickHouse模块，重写Spark Row相关方法，使其能封装经过Schem映射后的Clickhouse数据，重新构造StructField并生成最终需要落地Hive的DataFrame。最新版本已经有了很多source和sink组件，在SeaTunnel使用上更方便。现在也可以在SeaTunnel中直接集成Flink connector。 SeaTunnel与唯品会数据平台的集成 各个公司都有自己的调度系统，例如白鲸、宙斯。唯品会的调度工具是数坊，调度工具中集成了数据传输工具。下面是调度系统架构图，其中包含各类数据的出入仓。 SeaTunnel任务类型集成到平台中，图中是数坊的定时任务截图，可以看到选中的部分，是一个配置好的SeaTunnel任务，负责人、最近一次耗时，前后依赖任务的血缘信息，消耗的资源信息。下面展示了历史运行实例信息。 我们把SeaTunnel集成到了调度系统中，数坊调度Master会根据任务类型把任务分配到对应的Agent上，根据Agent负载情况分配到合适的机器上运行，管控器把前台的任务调度配置和信息拉取到后生成SeaTunnel cluster，在类似于k8s pod、cgroup隔离的虚拟环境内进行执行。运行结果会由调度平台的数据质量监控判断任务是否完成、是否运行成功，失败时进行重跑和告警。 SeaTunnel本身是一个工具化的组件，是为了进行数据血缘，数据质量，历史记录，高警监控，还包括资源分配这些信息的管控。我们把SeaTunnel集成到平台中，可以利用平台优势利用好SeaTunnel。 圈存人群中利用了SeaTunnel进行处理。我们通过打点数据，把圈存人群按照路径和使用情况分为不同的人，或称千人千面，把用户打上标签，圈出的某一类人群推送给用户、分析师和供应商。 流量进入Kafka，通过Flink入仓，再通过ETL形成用户标签表，用户标签表生成后，我们通过Presto实现了的BitMap方法，把数据打成Hive中的宽表。用户通过在人群系统页面中框选词条创建任务，提交腾群，生成SQL查询Clickhouse BitMap。Clickhouse的BitMap查询速度非常快，由天生优势，我们需要把Hive的BitMap表通过SeaTunnel导入到Clickhouse中。圈完人群后我们需要把表落地，形成Clickhouse的一个分区或一条记录，再把生成的结果BitMap表通过SeaTunnel存储到Hive中去。最后同步工具会将Hive的BitMap人群结果同步给外部媒体仓库Pika。每天圈20w个人群左右。 整个过程中SeaTunnel负责把数据从Hive导出到Clickhouse，Clickhouse的ETL流程完成后SeaTunnel把数据从Clickhouse导出到Hive。 为了完成这样的需求，我们在Presto和Spark端现ClickHouse的Hyperloglog和BitMap的function的UDF；我们还开发Seatunnel接口，使得用户在ClickHouse里使用Bitmap方法圈出来的人群，可以直接通过Seatunnel写入Hive表，无需中间落地步骤。用户也可以在Hive里通过spark圈人群或者反解人群bitmap，调用SeaTunnel接口，使数据直接传输到ClickHouse的结果表，而无需中间落地。 后续工作 后续我们会进一步改善Clickhouse写入数据时CPU负载高的问题，下一步会在SeaTunnel中实现Clickhouse数据源和读取端的CK-local模式，读写分离，减轻Clickhouse压力。未来我们也会增加更多sink支持，如数据推送到Pika和相应的数据检查。 标签： 唯品会 ClickHouse","s":"1 篇博文 含有标签「唯品会」","u":"/zh-CN/user_cases/tags/唯品会","h":"","p":205440},{"i":205443,"t":"1 篇博文 含有标签「ClickHouse」 查看所有标签 SeaTunnel 在唯品会的实践 2022年2月18日 · 阅读需 17 分钟 分享嘉宾：唯品会 资深大数据工程师 王玉 讲稿整理：张德通 导读: 唯品会早在1.0版本时就引用了SeaTunnel，我们使用SeaTunnel进行一些Hive到ClickHouse之间数据交互的工作。 今天的介绍会围绕下面几点展开： ClickHouse数据导入的需求和痛点； ClickHouse出仓入仓工具选型； Hive to ClickHouse； ClickHouse to Hive； SeaTunnel与唯品会数据平台的集成； 未来展望； ClickHouse数据导入的需求和痛点 1.唯品会数据OLAP架构​ 图中是唯品会OLAP架构，我们负责的模块是图中的数据服务和计算引擎两大部分。底层依赖的数据仓库分为离线数仓、实时数仓和湖仓。计算引擎方面，我们使用Presto、Kylin和Clickhouse。虽然Clickhouse是一个存储一体的OLAP数据库，我们为了利用Clickhouse的优秀计算性能而将它归入了计算引擎部分。基于OLAP组件之上，我们提供了SQL类数据服务和非SQL的唯品会自主分析，为不同智能服务。例如非SQL服务是为BI和商务提供更贴近业务的数据分析的服务。在数据服务至上抽象了多个数据应用。 2.需求​ 我们通过Presto Connector和Spark组件，把底层的Hive、Kudu、Alluxio组件打通。大数据组件之间可以互相导入导出数据，可以根据数据分析的需求和场景任意利用合适的组件分析数据。但我们引入Clickhouse时，它是一个数据孤岛，数据的导入和导出比较困难。Hive和Clickhouse之间需要做很多工作才能实现导入导出。我们的第一个数据导入导出需求就是提升导入导出效率，把Clickhouse纳入大数据体系中。 第二个需求是Presto跑SQL比较慢，图中是一个慢SQL的例子。图中的SQL where条件设置了日期、时间范围和具体过滤条件，这类SQL使用由于Presto使用分区粒度下推，运行比较慢。即使用Hive的Bucket表和分桶等其他方式优化后也是几秒的返回时间、不能满足业务要求。这种情况下，我们需要利用Clickhouse做离线的OLAP计算加速。 我们的实时数据是通过Kafka、Flink SQL方式写入到Clickhouse中。但分析时只用实时数据是不够的，需要用Hive维度表和已经ETL计算号的T+1实时表一起在Clickhouse中做加速运输。这需要把Hive的数据导入到Clickhouse中，这就是我们的第三个需求。 3.痛点​ 首先，我们引入一项数据组件时要考虑其性能。Hive表粒度是五分钟，是否有组件可以支撑五分钟内完成一个短小ETL流程并把ETL结果导入到Clickhouse中？第二，我们需要保证数据质量，数据的准确性需要有保障。Hive和Clickhouse的数据条数需要保障一致性，如果数据质量出问题能否通过重跑等机制修复数据？第三，数据导入需要支持的数据类型是否完备？不同数据库之间的数据类型和一些机制不同，我们有HiperLogLog和BitMap这类在某一存储引擎中利用得比较多得数据类型，是否可以正确传输和识别，且可以较好地使用。 ClickHouse和Hive出仓入仓工具的选型 基于数据业务上的痛点，我们对数据出仓入仓工具进行了对比和选择。我们主要在开源工具中进行选择，没有考虑商业出入仓工具，主要对比DataX、SeaTunnel和编写Spark程序并用jdbc插入ClickHouse这三个方案中取舍。 SeaTunnel和Spark依赖唯品会自己的Yarn集群，可以直接实现分布式读取和写入。DataX是非分布式的，且Reader、Writer之间的启动过程耗时时间长，性能普通，SeaTunnel和Spark处理数据的性能可以达到DataX的数倍。 十亿以上的数据可以平稳地在SeaTunnel和Spark中运行，DataX在数据量大以后性能压力大，处理十亿以上数据吃力。 在读写插件扩展性方面，SeaTunnel支持了多种数据源，支持用户开发插件。SeaTunnel支持了数据导入Redis。 稳定性上，SeaTunnel和DataX由于是自成体系的工具，稳定性会更好。Spark的稳定性方面需要关注代码质量。 我们的曝光表数据量每天在几十亿级，我们有5min内完成数据处理的性能要求，我们我们存在数据导入导出到Redis的需求，我们需要导入导出工具可以接入到数据平台上进行任务调度。 出于数据量级、性能、可扩展性、平台兼容性几方面的考虑，我们选择了SeaTunnel作为我们的数仓导入导出工具。 Hive数据导入到ClickHouse 下面将介绍我们对SeaTunnel的使用。 图中是一张Hive表，它是我们三级的商品维度表，包含品类商品、维度品类和用户人群信息。表的主键是一个三级品类ct_third_id，下面的value是两个uid的位图，是用户id的bitmap类型，我们要把这个Hive表导入到Clickhouse。 SeaTunnel安装简单，官网文档有介绍如何安装。下图中是SeaTunnel的配置，配置中env、source和sink是必不可少的。env部分，图中的例子是Spark配置，配置了包括并发度等，可以调整这些参数。source部分是数据来源，这里配置了Hive数据源，包括一条Hive Select语句，Spark运行source配置中的SQL把数据读出，此处支持UDF进行简单ETL；sink部分配置了Clickhouse，可以看到output_type=rowbinary，rowbinary是唯品会自研加速方案；pre_sql和check_sql是自研的用于数据校验的功能，后面也会详细介绍；clickhouse.socket_timeout和bulk_size都是可以根据实际情况进行调整的。 运行SeaTunnel，执行sh脚本文件、配置conf文件地址和yarn信息，后即可。 运行过程中会产生Spark日志，运行成功和运行中错误都可以在日志中查看。 为了更贴合业务，唯品会对SeaTunnel做了一些改进。我们的ETL任务都是需要重跑的，我们支持了pre_sql和check_sql实现数据的重跑和对数。主要流程是在数据准备好后，执行pre_sql进行预处理，在Clickhouse中执行删除旧分区数据、存放到某一目录下在失败时恢复该分区、rename这类操作。check_sql会检验，校验通过后整个流程结束；如果检验不通过，根据配置进行重跑，重跑不通过则报警到对应负责人。 唯品会基于1.0版本SeaTunnel增加了RowBinary做加速，也让HuperLogLog和BinaryBitmap的二进制文件能更容易地从Hive导入到Clickhouse。我们在ClickHouse-jdbc、bulk_size、Hive-source几处进行了修改。使用CK-jdbc的extended api，以rowbinary方式将数据写入CK，bulk_size引入了以rowbinary方式写入CK的控制逻辑，Hive-source RDD以HashPartitioner进行分区将数据打散，防止数据倾斜。 我们还让SeaTunnel支持了多类型，为了圈人群的功能，需要在Clickhouse、Preso、Spark中实现对应的方法。我们在Clickhouse-jdbc中增加支持Batch特性的Callback、HttpEntity、RowBinaryStream，在Clickhouse-jdbc和Clickhouse-sink代码中增加了bitmap类型映射，在Presto和Spark中实现了Clickhouse的Hyperloglog和Bitmap的function的UDF。 前面的配置中，Clickhouse-sink部分可以指定表名，这里有写入本地表和分布式表的差异。写入分布式表的性能比写入本地表差对Clickhouse集群的压力会更大，但在计算曝光表、流量表，ABTest等场景中需要两表Join，两张表量级均在几十亿。这时我们希望Join key落在本机，Join成本更小。我们建表时在Clickhouse的分布式表分布规则中配置murmurHash64规则，然后在Seatunnel的sink里直接配置分布式表，把写入规则交给Clickhouse，利用了分布式表的特性进行写入。写入本地表对Clickhouse的压力会更小，写入的性能也会更好。我们在Seatunnel里，根据sink的本地表，去Clickhouse的System.cluster表里获取表的分布信息和机器分布host。然后根据均分规则写入这些host。把数据分布式写入的事情放到Seatunnel里来做。 针对本地表和分布式表的写入，我们未来的改造方向是在Seatunnel实现一致性哈希，直接按照一定规则写如Clickhouse、不依赖Clickhouse自身做数据分发，改善Clickhouse高CPU负载问题。 ClickHouse数据导入到Hive 我们有圈人群的需求，每天唯品会为供应商圈20万个人群，比如80后、高富帅、白富美的人群集合。这些在Clickhouse中的Bitmap人群信息需要导出到Hive表，在Hive中与其他ETL任务进行配合，最后推到PIKA交给外部媒体使用。我们使SeaTunnel将Clickhouse Bitmap人群数据反推到Hive。 图中是SeaTunnel配置，我们把source配置为Clickhouse、sink配置为Hive，数据校验也配置在Hive内。 由于我们接入SeaTunnel较早，我们对一些模块间进行了加工，包括新增plugin-spark-sink-hive模块、plugin-spark-source-ClickHouse模块，重写Spark Row相关方法，使其能封装经过Schem映射后的Clickhouse数据，重新构造StructField并生成最终需要落地Hive的DataFrame。最新版本已经有了很多source和sink组件，在SeaTunnel使用上更方便。现在也可以在SeaTunnel中直接集成Flink connector。 SeaTunnel与唯品会数据平台的集成 各个公司都有自己的调度系统，例如白鲸、宙斯。唯品会的调度工具是数坊，调度工具中集成了数据传输工具。下面是调度系统架构图，其中包含各类数据的出入仓。 SeaTunnel任务类型集成到平台中，图中是数坊的定时任务截图，可以看到选中的部分，是一个配置好的SeaTunnel任务，负责人、最近一次耗时，前后依赖任务的血缘信息，消耗的资源信息。下面展示了历史运行实例信息。 我们把SeaTunnel集成到了调度系统中，数坊调度Master会根据任务类型把任务分配到对应的Agent上，根据Agent负载情况分配到合适的机器上运行，管控器把前台的任务调度配置和信息拉取到后生成SeaTunnel cluster，在类似于k8s pod、cgroup隔离的虚拟环境内进行执行。运行结果会由调度平台的数据质量监控判断任务是否完成、是否运行成功，失败时进行重跑和告警。 SeaTunnel本身是一个工具化的组件，是为了进行数据血缘，数据质量，历史记录，高警监控，还包括资源分配这些信息的管控。我们把SeaTunnel集成到平台中，可以利用平台优势利用好SeaTunnel。 圈存人群中利用了SeaTunnel进行处理。我们通过打点数据，把圈存人群按照路径和使用情况分为不同的人，或称千人千面，把用户打上标签，圈出的某一类人群推送给用户、分析师和供应商。 流量进入Kafka，通过Flink入仓，再通过ETL形成用户标签表，用户标签表生成后，我们通过Presto实现了的BitMap方法，把数据打成Hive中的宽表。用户通过在人群系统页面中框选词条创建任务，提交腾群，生成SQL查询Clickhouse BitMap。Clickhouse的BitMap查询速度非常快，由天生优势，我们需要把Hive的BitMap表通过SeaTunnel导入到Clickhouse中。圈完人群后我们需要把表落地，形成Clickhouse的一个分区或一条记录，再把生成的结果BitMap表通过SeaTunnel存储到Hive中去。最后同步工具会将Hive的BitMap人群结果同步给外部媒体仓库Pika。每天圈20w个人群左右。 整个过程中SeaTunnel负责把数据从Hive导出到Clickhouse，Clickhouse的ETL流程完成后SeaTunnel把数据从Clickhouse导出到Hive。 为了完成这样的需求，我们在Presto和Spark端现ClickHouse的Hyperloglog和BitMap的function的UDF；我们还开发Seatunnel接口，使得用户在ClickHouse里使用Bitmap方法圈出来的人群，可以直接通过Seatunnel写入Hive表，无需中间落地步骤。用户也可以在Hive里通过spark圈人群或者反解人群bitmap，调用SeaTunnel接口，使数据直接传输到ClickHouse的结果表，而无需中间落地。 后续工作 后续我们会进一步改善Clickhouse写入数据时CPU负载高的问题，下一步会在SeaTunnel中实现Clickhouse数据源和读取端的CK-local模式，读写分离，减轻Clickhouse压力。未来我们也会增加更多sink支持，如数据推送到Pika和相应的数据检查。 标签： 唯品会 ClickHouse","s":"1 篇博文 含有标签「ClickHouse」","u":"/zh-CN/user_cases/tags/click-house","h":"","p":205442},{"i":205448,"t":"Since https://github.com/apache/incubator-seatunnel/issues/1608 We Added Connector V2 Features. Connector V2 is a connector defined based on the Seatunnel Connector API interface. Unlike Connector V1, Connector V2 supports the following features. Multi Engine Support SeaTunnel Connector API is an engine independent API. The connectors developed based on this API can run in multiple engines. Currently, Flink and Spark are supported, and we will support other engines in the future. Multi Engine Version Support Decoupling the connector from the engine through the translation layer solves the problem that most connectors need to modify the code in order to support a new version of the underlying engine. Unified Batch And Stream Connector V2 can perform batch processing or streaming processing. We do not need to develop connectors for batch and stream separately. Multiplexing JDBC/Log connection. Connector V2 supports JDBC resource reuse and sharing database log parsing.","s":"Differences Between Connector V2 And Connector v1","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#differences-between-connector-v2-and-connector-v1","p":205446},{"i":205450,"t":"Source connectors have some common core features, and each source connector supports them to varying degrees.","s":"Source Connector Features","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#source-connector-features","p":205446},{"i":205452,"t":"If each piece of data in the data source will only be sent downstream by the source once, we think this source connector supports exactly once. In SeaTunnel, we can save the read Split and its offset(The position of the read data in split at that time, such as line number, byte size, offset, etc) as StateSnapshot when checkpoint. If the task restarted, we will get the last StateSnapshot and then locate the Split and offset read last time and continue to send data downstream. For example File, Kafka.","s":"exactly-once","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#exactly-once","p":205446},{"i":205454,"t":"If the source connector supports selective reading of certain columns or redefine columns order or supports the data format read through schema params, we think it supports schema projection. For example JDBCSource can use sql define read columns, KafkaSource can use schema params to define the read schema.","s":"schema projection","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#schema-projection","p":205446},{"i":205456,"t":"Batch Job Mode, The data read is bounded and the job will stop when all data read complete.","s":"batch","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#batch","p":205446},{"i":205458,"t":"Streaming Job Mode, The data read is unbounded and the job never stop.","s":"stream","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#stream","p":205446},{"i":205460,"t":"Parallelism Source Connector support config parallelism, every parallelism will create a task to read the data. In the Parallelism Source Connector, the source will be split into multiple splits, and then the enumerator will allocate the splits to the SourceReader for processing.","s":"parallelism","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#parallelism","p":205446},{"i":205462,"t":"User can config the split rule.","s":"support user-defined split","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#support-user-defined-split","p":205446},{"i":205464,"t":"Sink connectors have some common core features, and each sink connector supports them to varying degrees.","s":"Sink Connector Features","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#sink-connector-features","p":205446},{"i":205466,"t":"When any piece of data flows into a distributed system, if the system processes any piece of data accurately only once in the whole processing process and the processing results are correct, it is considered that the system meets the exact once consistency. For sink connector, the sink connector supports exactly-once if any piece of data only write into target once. There are generally two ways to achieve this: The target database supports key deduplication. For example MySQL, Kudu. The target support XA Transaction(This transaction can be used across sessions. Even if the program that created the transaction has ended, the newly started program only needs to know the ID of the last transaction to resubmit or roll back the transaction). Then we can use Two-phase Commit to ensure exactly-once. For example File, MySQL.","s":"exactly-once","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#exactly-once-1","p":205446},{"i":205468,"t":"If a sink connector supports the fields and their types or redefine columns order written in the configuration, we think it supports schema projection.","s":"schema projection","u":"/zh-CN/docs/2.3.0-beta/concept/connector-v2-features","h":"#schema-projection-1","p":205446},{"i":205470,"t":"Clickhouse sink connector","s":"Clickhouse","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"","p":205469},{"i":205472,"t":"Used to write data to Clickhouse.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#description","p":205469},{"i":205474,"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 提示 Write data to Clickhouse can also be done using JDBC","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#key-features","p":205469},{"i":205476,"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 no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#options","p":205469},{"i":205478,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#host-string","p":205469},{"i":205480,"t":"The ClickHouse database","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#database-string","p":205469},{"i":205482,"t":"The table name","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#table-string","p":205469},{"i":205484,"t":"ClickHouse user username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#username-string","p":205469},{"i":205486,"t":"ClickHouse user password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#password-string","p":205469},{"i":205488,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#fields-array","p":205469},{"i":205490,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#clickhouse-string","p":205469},{"i":205492,"t":"The number of rows written through Clickhouse-jdbc each time, the default is 20000 .","s":"bulk_size [number]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#bulk_size-number","p":205469},{"i":205494,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#split_mode-boolean","p":205469},{"i":205496,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#sharding_key-string","p":205469},{"i":205498,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#common-options","p":205469},{"i":205500,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#examples","p":205469},{"i":205503,"t":"Add ClickHouse Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#220-beta-2022-09-26","p":205469},{"i":205505,"t":"[Improve] Clickhouse Support Int128,Int256 Type (3067)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Clickhouse","h":"#230-beta-2022-10-20","p":205469},{"i":205507,"t":"Common parameters of sink connectors name type required default value source_table_name string no - parallelism int no -","s":"Common Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/common-options","h":"","p":205506},{"i":205509,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/common-options","h":"#source_table_name-string","p":205506},{"i":205511,"t":"When parallelism is not specified, the parallelism in env is used by default. When parallelism is specified, it will override the parallelism in env.","s":"parallelism [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/common-options","h":"#parallelism-int","p":205506},{"i":205513,"t":"source { FakeSourceStream { parallelism = 2 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 { parallelism = 3 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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/common-options","h":"#examples","p":205506},{"i":205516,"t":"Assert sink connector","s":"Assert","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"","p":205515},{"i":205518,"t":"A flink sink plugin which can assert illegal data by user defined rules","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#description","p":205515},{"i":205520,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#key-features","p":205515},{"i":205522,"t":"name type required default value rules ConfigMap yes - rules.field_rules string yes - rules.field_rules.field_name string yes - rules.field_rules.field_type string no - rules.field_rules.field_value ConfigList no - rules.field_rules.field_value.rule_type string no - rules.field_rules.field_value.rule_value double no - rules.row_rules string yes - rules.row_rules.rule_type string no - rules.row_rules.rule_value string no - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#options","p":205515},{"i":205524,"t":"Rule definition of user's available data. Each rule represents one field validation or row num validation.","s":"rules [ConfigMap]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#rules-configmap","p":205515},{"i":205526,"t":"field rules for field validation","s":"field_rules [ConfigList]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#field_rules-configlist","p":205515},{"i":205528,"t":"field name（string）","s":"field_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#field_name-string","p":205515},{"i":205530,"t":"field type (string), e.g. string,boolean,byte,short,int,long,float,double,char,void,BigInteger,BigDecimal,Instant","s":"field_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#field_type-string","p":205515},{"i":205532,"t":"A list value rule define the data value validation","s":"field_value [ConfigList]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#field_value-configlist","p":205515},{"i":205534,"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 MIN_ROW define the minimun number of rows MAX_ROW define the maximum number of rows","s":"rule_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#rule_type-string","p":205515},{"i":205536,"t":"the value related to rule type","s":"rule_value [double]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#rule_value-double","p":205515},{"i":205538,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#common-options","p":205515},{"i":205540,"t":"the whole config obey with hocon style Assert { rules = { row_rules = [ { rule_type = MAX_ROW rule_value = 10 }, { rule_type = MIN_ROW rule_value = 5 } ], field_rules = [{ field_name = name field_type = string field_value = [ { rule_type = NOT_NULL }, { rule_type = MIN_LENGTH rule_value = 5 }, { rule_type = MAX_LENGTH rule_value = 10 } ] }, { field_name = age field_type = int field_value = [ { rule_type = NOT_NULL }, { rule_type = MIN rule_value = 32767 }, { rule_type = MAX rule_value = 2147483647 } ] } ] } }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#example","p":205515},{"i":205543,"t":"Add Assert Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#220-beta-2022-09-26","p":205515},{"i":205545,"t":"[Improve] 1.Support check the number of rows (2844) (3031): check rows not empty check minimum number of rows check maximum number of rows [Improve] 2.Support direct define of data values(row) (2844) (3031) [Improve] 3.Support setting parallelism as 1 (2844) (3031)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Assert","h":"#230-beta-2022-10-20","p":205515},{"i":205547,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"","p":205546},{"i":205549,"t":"Before you read on, you can find config file examples here and in distribute package's config directory.","s":"Example","u":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#example","p":205546},{"i":205551,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#config-file-structure","p":205546},{"i":205553,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#env","p":205546},{"i":205555,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#source","p":205546},{"i":205557,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#transform","p":205546},{"i":205559,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#sink","p":205546},{"i":205561,"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":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#other","p":205546},{"i":205563,"t":"If you want to know the details of this format configuration, Please see HOCON.","s":"What's More","u":"/zh-CN/docs/2.3.0-beta/concept/config","h":"#whats-more","p":205546},{"i":205565,"t":"Clickhouse file sink connector","s":"ClickhouseFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"","p":205564},{"i":205567,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#description","p":205564},{"i":205569,"t":"exactly-once schema projection 提示 Write data to Clickhouse can also be done using JDBC","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#key-features","p":205564},{"i":205571,"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 no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#options","p":205564},{"i":205573,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#host-string","p":205564},{"i":205575,"t":"The ClickHouse database","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#database-string","p":205564},{"i":205577,"t":"The table name","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#table-string","p":205564},{"i":205579,"t":"ClickHouse user username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#username-string","p":205564},{"i":205581,"t":"ClickHouse user password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#password-string","p":205564},{"i":205583,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#sharding_key-string","p":205564},{"i":205585,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#clickhouse_local_path-string","p":205564},{"i":205587,"t":"Specifies the method used to transfer files, the default is scp, optional scp and rsync","s":"copy_method [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#copy_method-string","p":205564},{"i":205589,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_free_password-boolean","p":205564},{"i":205591,"t":"Used to save the addresses and corresponding passwords of all clickhouse servers","s":"node_pass [list]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_pass-list","p":205564},{"i":205593,"t":"The address corresponding to the clickhouse server","s":"node_pass.node_address [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_passnode_address-string","p":205564},{"i":205595,"t":"The username corresponding to the clickhouse server, default root user.","s":"node_pass.username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_passusername-string","p":205564},{"i":205597,"t":"The password corresponding to the clickhouse server.","s":"node_pass.password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#node_passpassword-string","p":205564},{"i":205599,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#common-options","p":205564},{"i":205601,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#examples","p":205564},{"i":205604,"t":"Support write data to ClickHouse File and move to ClickHouse data dir","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/ClickhouseFile","h":"#220-beta-2022-09-26","p":205564},{"i":205606,"t":"Feishu sink connector","s":"Feishu","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"","p":205605},{"i":205608,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#description","p":205605},{"i":205610,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#key-features","p":205605},{"i":205612,"t":"name type required default value url String Yes - headers Map No - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#options","p":205605},{"i":205614,"t":"Feishu webhook url","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#url-string","p":205605},{"i":205616,"t":"Http request headers","s":"headers [Map]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#headers-map","p":205605},{"i":205618,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#common-options","p":205605},{"i":205620,"t":"simple: Feishu { url = \"https://www.feishu.cn/flow/api/trigger-webhook/108bb8f208d9b2378c8c7aedad715c19\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#example","p":205605},{"i":205623,"t":"Add Feishu Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Feishu","h":"#220-beta-2022-09-26","p":205605},{"i":205625,"t":"Ftp file sink connector","s":"FtpFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"","p":205624},{"i":205627,"t":"Output data to Ftp .","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#description","p":205624},{"i":205629,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#key-features","p":205624},{"i":205631,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#options","p":205624},{"i":205633,"t":"The target ftp host is required","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#host-string","p":205624},{"i":205635,"t":"The target ftp port is required","s":"port [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#port-int","p":205624},{"i":205637,"t":"The target ftp username is required","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#username-string","p":205624},{"i":205639,"t":"The target ftp password is required","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#password-string","p":205624},{"i":205641,"t":"The target dir path is required.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#path-string","p":205624},{"i":205643,"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 ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#file_name_expression-string","p":205624},{"i":205645,"t":"We supported as the following file types: text json csv orc parquet Please note that, The final file name will end with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#file_format-string","p":205624},{"i":205647,"t":"When the format in the file_name_expression parameter is xxxx-${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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#filename_time_format-string","p":205624},{"i":205649,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#field_delimiter-string","p":205624},{"i":205651,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#row_delimiter-string","p":205624},{"i":205653,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#partition_by-array","p":205624},{"i":205655,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#partition_dir_expression-string","p":205624},{"i":205657,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#is_partition_field_write_in_file-boolean","p":205624},{"i":205659,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#sink_columns-array","p":205624},{"i":205661,"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 ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#is_enable_transaction-boolean","p":205624},{"i":205663,"t":"Sink plugin common parameters, please refer to Sink Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#common-options","p":205624},{"i":205665,"t":"For text file format FtpFile { host = \"xxx.xxx.xxx.xxx\" port = 21 username = \"username\" password = \"password\" path = \"/data/ftp\" field_delimiter = \"\\t\" row_delimiter = \"\\n\" partition_by = [\"age\"] partition_dir_expression = \"${k0}=${v0}\" is_partition_field_write_in_file = true file_name_expression = \"${transactionId}_${now}\" file_format = \"text\" sink_columns = [\"name\",\"age\"] filename_time_format = \"yyyy.MM.dd\" is_enable_transaction = true }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#example","p":205624},{"i":205668,"t":"Add Ftp File Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#220-beta-2022-09-26","p":205624},{"i":205670,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [BugFix] Fix filesystem get error (3117) [BugFix] Solved the bug of can not parse '\\t' as delimiter from config file (3083)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/FtpFile","h":"#230-beta-2022-10-20","p":205624},{"i":205673,"t":"Output data to Elasticsearch.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#description","p":205671},{"i":205675,"t":"exactly-once schema projection 提示 Engine Supported supported ElasticSearch version is >= 2.x and < 8.x","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#key-features","p":205671},{"i":205677,"t":"name type required default value hosts array yes - index string yes - index_type string no username string no password string no max_retry_size int no 3 max_batch_size int no 10 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#options","p":205671},{"i":205679,"t":"Elasticsearch cluster http address, the format is host:port , allowing multiple hosts to be specified. Such as [\"host1:9200\", \"host2:9200\"].","s":"hosts [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#hosts-array","p":205671},{"i":205681,"t":"Elasticsearch index name.Index support contains variables of field name,such as seatunnel_${age},and the field must appear at seatunnel row. If not, we will treat it as a normal index.","s":"index [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#index-string","p":205671},{"i":205683,"t":"Elasticsearch index type, it is recommended not to specify in elasticsearch 6 and above","s":"index_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#index_type-string","p":205671},{"i":205685,"t":"x-pack username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#username-string","p":205671},{"i":205687,"t":"x-pack password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#password-string","p":205671},{"i":205689,"t":"one bulk request max try size","s":"max_retry_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#max_retry_size-int","p":205671},{"i":205691,"t":"batch bulk doc max size","s":"max_batch_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#max_batch_size-int","p":205671},{"i":205693,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#common-options","p":205671},{"i":205695,"t":"Elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel-${age}\" }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#examples","p":205671},{"i":205698,"t":"Add Elasticsearch Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Elasticsearch","h":"#220-beta-2022-09-26","p":205671},{"i":205701,"t":"Spark Flink Spark V2 Flink V2 bin/start-seatunnel-spark.sh bin/start-seatunnel-flink.sh ```bash bin/start-seatunnel-spark-connector-v2.sh ``` ```bash bin/start-seatunnel-flink-connector-v2.sh ```","s":"Command Entrypoint","u":"/zh-CN/docs/2.3.0-beta/command/usage","h":"#command-entrypoint","p":205699},{"i":205703,"t":"Spark Flink Spark V2 Flink V2 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 ```bash bin/start-seatunnel-spark-connector-v2.sh \\ -c config-path \\ -m master \\ -e deploy-mode \\ -i city=beijing \\ -n spark-test ``` - Use `-m` or `--master` to specify the cluster manager - Use `-e` or `--deploy-mode` to specify the deployment mode - Use `-n` or `--name` to specify the app name 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) ```bash bin/start-seatunnel-flink-connector-v2.sh \\ -c config-path \\ -i key=value \\ -r run-application \\ -n flink-test \\ [other params] ``` - Use `-r` or `--run-mode` to specify the flink job run mode, you can use `run-application` or `run` (default value) - Use `-n` or `--name` to specify the app name 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":"/zh-CN/docs/2.3.0-beta/command/usage","h":"#options","p":205699},{"i":205705,"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":"/zh-CN/docs/2.3.0-beta/command/usage","h":"#example","p":205699},{"i":205707,"t":"Http sink connector","s":"Http","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"","p":205706},{"i":205709,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#description","p":205706},{"i":205711,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#key-features","p":205706},{"i":205713,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#options","p":205706},{"i":205715,"t":"http request url","s":"url [String]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#url-string","p":205706},{"i":205717,"t":"http headers","s":"headers [Map]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#headers-map","p":205706},{"i":205719,"t":"http params","s":"params [Map]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#params-map","p":205706},{"i":205721,"t":"The max retry times if request http return to IOException","s":"retry [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#retry-int","p":205706},{"i":205723,"t":"The retry-backoff times(millis) multiplier if request http failed","s":"retry_backoff_multiplier_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#retry_backoff_multiplier_ms-int","p":205706},{"i":205725,"t":"The maximum retry-backoff times(millis) if request http failed","s":"retry_backoff_max_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#retry_backoff_max_ms-int","p":205706},{"i":205727,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#common-options","p":205706},{"i":205729,"t":"simple: Http { url = \"http://localhost/test/webhook\" headers { token = \"9e32e859ef044462a257e1fc76730066\" } }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#example","p":205706},{"i":205732,"t":"Add Http Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Http","h":"#220-beta-2022-09-26","p":205706},{"i":205734,"t":"Hive sink connector","s":"Hive","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"","p":205733},{"i":205736,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#description","p":205733},{"i":205738,"t":"exactly-once By default, we use 2PC commit to ensure exactly-once schema projection file format text parquet orc","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#key-features","p":205733},{"i":205740,"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\" common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#options","p":205733},{"i":205742,"t":"Target Hive table name eg: db1.table1","s":"table_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#table_name-string","p":205733},{"i":205744,"t":"Hive metastore uri","s":"metastore_uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#metastore_uri-string","p":205733},{"i":205746,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#partition_by-array","p":205733},{"i":205748,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#sink_columns-array","p":205733},{"i":205750,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#is_enable_transaction-boolean","p":205733},{"i":205752,"t":"Storage mode, we need support overwrite and append. append is now supported. Streaming Job not support overwrite.","s":"save_mode [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#save_mode-string","p":205733},{"i":205754,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#common-options","p":205733},{"i":205756,"t":"Hive { table_name = \"default.seatunnel_orc\" metastore_uri = \"thrift://namenode001:9083\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#example","p":205733},{"i":205758,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#example-1","p":205733},{"i":205761,"t":"Add Hive Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#220-beta-2022-09-26","p":205733},{"i":205763,"t":"[Improve] Hive Sink supports automatic partition repair (3133)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Hive","h":"#230-beta-2022-10-20","p":205733},{"i":205765,"t":"JDBC sink connector","s":"JDBC","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"","p":205764},{"i":205767,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#description","p":205764},{"i":205769,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#key-features","p":205764},{"i":205771,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#options","p":205764},{"i":205773,"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 any driver yourself like MySQL JDBC Driver, e.g. copy mysql-connector-java-xxx.jar to $SEATNUNNEL_HOME/lib for Standalone.","s":"driver [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#driver-string","p":205764},{"i":205775,"t":"userName","s":"user [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#user-string","p":205764},{"i":205777,"t":"password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#password-string","p":205764},{"i":205779,"t":"The URL of the JDBC connection. Refer to a case: jdbc:postgresql://localhost/test","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#url-string","p":205764},{"i":205781,"t":"Query statement","s":"query [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#query-string","p":205764},{"i":205783,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#connection_check_timeout_sec-int","p":205764},{"i":205785,"t":"The number of retries to submit failed (executeBatch)","s":"max_retries[int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#max_retriesint","p":205764},{"i":205787,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#batch_sizeint","p":205764},{"i":205789,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#batch_interval_msint","p":205764},{"i":205791,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#is_exactly_onceboolean","p":205764},{"i":205793,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#xa_data_source_class_namestring","p":205764},{"i":205795,"t":"The number of retries for transaction commit failures","s":"max_commit_attempts[int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#max_commit_attemptsint","p":205764},{"i":205797,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#transaction_timeout_secint","p":205764},{"i":205799,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#common-options","p":205764},{"i":205801,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#tips","p":205764},{"i":205803,"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 Phoenix org.apache.phoenix.queryserver.client.Driver jdbc:phoenix:thin:url=http://localhost:8765;serialization=PROTOBUF / https://mvnrepository.com/artifact/com.aliyun.phoenix/ali-phoenix-shaded-thin-client SQL Server com.microsoft.sqlserver.jdbc.SQLServerDriver jdbc:microsoft:sqlserver://localhost:1433 com.microsoft.sqlserver.jdbc.SQLServerXADataSource https://mvnrepository.com/artifact/com.microsoft.sqlserver/mssql-jdbc Oracle oracle.jdbc.OracleDriver jdbc:oracle:thin:@localhost:1521/xepdb1 oracle.jdbc.xa.OracleXADataSource https://mvnrepository.com/artifact/com.oracle.database.jdbc/ojdbc8 GBase8a com.gbase.jdbc.Driver jdbc:gbase://e2e_gbase8aDb:5258/test / https://www.gbase8.cn/wp-content/uploads/2020/10/gbase-connector-java-8.3.81.53-build55.5.7-bin_min_mix.jar StarRocks com.mysql.cj.jdbc.Driver jdbc:mysql://localhost:3306/test / https://mvnrepository.com/artifact/mysql/mysql-connector-java","s":"appendix","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#appendix","p":205764},{"i":205805,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#example","p":205764},{"i":205808,"t":"Add Console Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#220-beta-2022-09-26","p":205764},{"i":205810,"t":"[BugFix] Fix JDBC split exception (2904) [Feature] Support Phoenix JDBC Source (2499) [Feature] Support SQL Server JDBC Source (2646) [Feature] Support Oracle JDBC Source (2550) [Feature] Support StarRocks JDBC Source (3060)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Jdbc","h":"#230-beta-2022-10-20","p":205764},{"i":205812,"t":"Kafka sink connector Description​ Write Rows to a Kafka topic.","s":"Kafka","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"","p":205811},{"i":205815,"t":"exactly-once By default, we will use 2pc to guarantee the message is sent to kafka exactly once. schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#key-features","p":205811},{"i":205817,"t":"name type required default value topic string yes - bootstrap.servers string yes - kafka.* kafka producer config no - semantic string no NON partition_key string no - partition int no - assign_partitions list no - transaction_prefix string no - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#options","p":205811},{"i":205819,"t":"Kafka Topic.","s":"topic [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#topic-string","p":205811},{"i":205821,"t":"Kafka Brokers List.","s":"bootstrap.servers [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#bootstrapservers-string","p":205811},{"i":205823,"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 kafka. to the original parameter name. For example, the way to specify request.timeout.ms is: kafka.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":"kafka.* [kafka producer config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#kafka-kafka-producer-config","p":205811},{"i":205825,"t":"Semantics that can be chosen EXACTLY_ONCE/AT_LEAST_ONCE/NON, default NON. In EXACTLY_ONCE, producer will write all messages in a Kafka transaction that will be committed to Kafka on a checkpoint. In AT_LEAST_ONCE, producer will wait for all outstanding messages in the Kafka buffers to be acknowledged by the Kafka producer on a checkpoint. NON does not provide any guarantees: messages may be lost in case of issues on the Kafka broker and messages may be duplicated.","s":"semantic [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#semantic-string","p":205811},{"i":205827,"t":"Configure which field is used as the key of the kafka message. 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: name age data Jack 16 data-example1 Mary 23 data-example2 If name is set as the key, then the hash value of the name column will determine which partition the message is sent to. If the field name does not exist in the upstream data, the configured parameter will be used as the key.","s":"partition_key [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#partition_key-string","p":205811},{"i":205829,"t":"We can specify the partition, all messages will be sent to this partition.","s":"partition [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#partition-int","p":205811},{"i":205831,"t":"We can decide which partition to send based on the content of the message. The function of this parameter is to distribute information. For example, there are five partitions in total, and the assign_partitions field in config is as follows: assign_partitions = [\"shoe\", \"clothing\"] Then the message containing \"shoe\" will be sent to partition zero ,because \"shoe\" is subscripted as zero in assign_partitions, and the message containing \"clothing\" will be sent to partition one.For other messages, the hash algorithm will be used to divide them into the remaining partitions. This function by MessageContentPartitioner class implements org.apache.kafka.clients.producer.Partitioner interface.If we need custom partitions, we need to implement this interface as well.","s":"assign_partitions [list]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#assign_partitions-list","p":205811},{"i":205833,"t":"If semantic is specified as EXACTLY_ONCE, the producer will write all messages in a Kafka transaction. Kafka distinguishes different transactions by different transactionId. This parameter is prefix of kafka transactionId, make sure different job use different prefix.","s":"transaction_prefix [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#transaction_prefix-string","p":205811},{"i":205835,"t":"Sink plugin common parameters, please refer to Sink Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#common-options","p":205811},{"i":205837,"t":"sink { kafka { topic = \"seatunnel\" bootstrap.servers = \"localhost:9092\" partition = 3 kafka.request.timeout.ms = 60000 semantics = EXACTLY_ONCE } }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#examples","p":205811},{"i":205840,"t":"Add Kafka Sink Connector","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kafka","h":"#230-beta-2022-10-20","p":205811},{"i":205842,"t":"Kudu sink connector","s":"Kudu","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"","p":205841},{"i":205844,"t":"Write data to Kudu. The tested kudu version is 1.11.1.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#description","p":205841},{"i":205846,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#key-features","p":205841},{"i":205848,"t":"name type required default value kudu_master string yes - kudu_table string yes - save_mode string yes - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#options","p":205841},{"i":205850,"t":"kudu_master The address of kudu master,such as '192.168.88.110:7051'.","s":"kudu_master [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#kudu_master-string","p":205841},{"i":205852,"t":"kudu_table The name of kudu table..","s":"kudu_table [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#kudu_table-string","p":205841},{"i":205854,"t":"Storage mode, we need support overwrite and append. append is now supported.","s":"save_mode [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#save_mode-string","p":205841},{"i":205856,"t":"Sink plugin common parameters, please refer to Sink Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#common-options","p":205841},{"i":205858,"t":"kuduSink { kudu_master = \"192.168.88.110:7051\" kudu_table = \"studentlyhresultflink\" save_mode=\"append\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#example","p":205841},{"i":205861,"t":"Add Kudu Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#220-beta-2022-09-26","p":205841},{"i":205863,"t":"[Improve] Kudu Sink Connector Support to upsert row (2881)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Kudu","h":"#230-beta-2022-10-20","p":205841},{"i":205865,"t":"IoTDB sink connector","s":"IoTDB","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"","p":205864},{"i":205867,"t":"Used to write data to IoTDB.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#description","p":205864},{"i":205869,"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 提示 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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#key-features","p":205864},{"i":205871,"t":"name type required default value node_urls list yes - username string yes - password string yes - key_device string yes - key_timestamp string no processing time key_measurement_fields array no exclude device & timestamp storage_group string no - 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 - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#options","p":205864},{"i":205873,"t":"IoTDB cluster address, the format is [\"host:port\", ...]","s":"node_urls [list]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#node_urls-list","p":205864},{"i":205875,"t":"IoTDB user username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#username-string","p":205864},{"i":205877,"t":"IoTDB user password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#password-string","p":205864},{"i":205879,"t":"Specify field name of the IoTDB deviceId in SeaTunnelRow","s":"key_device [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#key_device-string","p":205864},{"i":205881,"t":"Specify field-name of the IoTDB timestamp in SeaTunnelRow. If not specified, use processing-time as timestamp","s":"key_timestamp [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#key_timestamp-string","p":205864},{"i":205883,"t":"Specify field-name of the IoTDB measurement list in SeaTunnelRow. If not specified, include all fields but exclude device & timestamp","s":"key_measurement_fields [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#key_measurement_fields-array","p":205864},{"i":205885,"t":"Specify device storage group(path prefix) example: deviceId = ${storage_group} + \".\" + ${key_device}","s":"storage_group [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#storage_group-string","p":205864},{"i":205887,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#batch_size-int","p":205864},{"i":205889,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#batch_interval_ms-int","p":205864},{"i":205891,"t":"The number of retries to flush failed","s":"max_retries [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#max_retries-int","p":205864},{"i":205893,"t":"Using as a multiplier for generating the next delay for backoff","s":"retry_backoff_multiplier_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#retry_backoff_multiplier_ms-int","p":205864},{"i":205895,"t":"The amount of time to wait before attempting to retry a request to IoTDB","s":"max_retry_backoff_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#max_retry_backoff_ms-int","p":205864},{"i":205897,"t":"Thrift init buffer size in IoTDB client","s":"default_thrift_buffer_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#default_thrift_buffer_size-int","p":205864},{"i":205899,"t":"Thrift max frame size in IoTDB client","s":"max_thrift_frame_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#max_thrift_frame_size-int","p":205864},{"i":205901,"t":"java.time.ZoneId in IoTDB client","s":"zone_id [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#zone_id-string","p":205864},{"i":205903,"t":"Enable rpc compression in IoTDB client","s":"enable_rpc_compression [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#enable_rpc_compression-boolean","p":205864},{"i":205905,"t":"The maximum time (in ms) to wait when connect IoTDB","s":"connection_timeout_in_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#connection_timeout_in_ms-int","p":205864},{"i":205907,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#common-options","p":205864},{"i":205910,"t":"Common options: sink { IoTDB { node_urls = [\"localhost:6667\"] username = \"root\" password = \"root\" batch_size = 1024 batch_interval_ms = 1000 } } When you assign key_device is device_name, for example: sink { IoTDB { ... key_device = \"device_name\" } } Upstream SeaTunnelRow data format is the following: device_name field_1 field_2 root.test_group.device_a 1001 1002 root.test_group.device_b 2001 2002 root.test_group.device_c 3001 3002 Output to IoTDB data format is the following: IoTDB> SELECT * FROM root.test_group.* align by device; +------------------------+------------------------+-----------+----------+ | Time| Device| field_1| field_2| +------------------------+------------------------+----------+-----------+ |2022-09-26T17:50:01.201Z|root.test_group.device_a| 1001| 1002| |2022-09-26T17:50:01.202Z|root.test_group.device_b| 2001| 2002| |2022-09-26T17:50:01.203Z|root.test_group.device_c| 3001| 3002| +------------------------+------------------------+----------+-----------+","s":"Case1","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#case1","p":205864},{"i":205912,"t":"When you assign key_device、key_timestamp、key_measurement_fields, for example: sink { IoTDB { ... key_device = \"device_name\" key_timestamp = \"ts\" key_measurement_fields = [\"temperature\", \"moisture\"] } } Upstream SeaTunnelRow data format is the following: ts device_name field_1 field_2 temperature moisture 1664035200001 root.test_group.device_a 1001 1002 36.1 100 1664035200001 root.test_group.device_b 2001 2002 36.2 101 1664035200001 root.test_group.device_c 3001 3002 36.3 102 Output to IoTDB data format is the following: IoTDB> SELECT * FROM root.test_group.* align by device; +------------------------+------------------------+--------------+-----------+ | Time| Device| temperature| moisture| +------------------------+------------------------+--------------+-----------+ |2022-09-25T00:00:00.001Z|root.test_group.device_a| 36.1| 100| |2022-09-25T00:00:00.001Z|root.test_group.device_b| 36.2| 101| |2022-09-25T00:00:00.001Z|root.test_group.device_c| 36.3| 102| +------------------------+------------------------+--------------+-----------+","s":"Case2","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#case2","p":205864},{"i":205915,"t":"Add IoTDB Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#220-beta-2022-09-26","p":205864},{"i":205917,"t":"[Improve] Improve IoTDB Sink Connector (2917) Support align by sql syntax Support sql split ignore case Support restore split offset to at-least-once Support read timestamp from RowRecord [BugFix] Fix IoTDB connector sink NPE (3080)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/IoTDB","h":"#230-beta-2022-10-20","p":205864},{"i":205919,"t":"Local file sink connector","s":"LocalFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"","p":205918},{"i":205921,"t":"Output data to local file.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#description","p":205918},{"i":205923,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#key-features","p":205918},{"i":205925,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#options","p":205918},{"i":205927,"t":"The target dir path is required.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#path-string","p":205918},{"i":205929,"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 ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#file_name_expression-string","p":205918},{"i":205931,"t":"We supported as the following file types: text csv parquet orc json Please note that, The final file name will ends with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#file_format-string","p":205918},{"i":205933,"t":"When the format in the file_name_expression parameter is xxxx-${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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#filename_time_format-string","p":205918},{"i":205935,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#field_delimiter-string","p":205918},{"i":205937,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#row_delimiter-string","p":205918},{"i":205939,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#partition_by-array","p":205918},{"i":205941,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#partition_dir_expression-string","p":205918},{"i":205943,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#is_partition_field_write_in_file-boolean","p":205918},{"i":205945,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#sink_columns-array","p":205918},{"i":205947,"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 ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#is_enable_transaction-boolean","p":205918},{"i":205949,"t":"Sink plugin common parameters, please refer to Sink Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#common-options","p":205918},{"i":205951,"t":"For text file format LocalFile { path = \"/tmp/hive/warehouse/test2\" field_delimiter = \"\\t\" row_delimiter = \"\\n\" partition_by = [\"age\"] partition_dir_expression = \"${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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#example","p":205918},{"i":205954,"t":"Add Local File Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#220-beta-2022-09-26","p":205918},{"i":205956,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [BugFix] Fix filesystem get error (3117) [BugFix] Solved the bug of can not parse '\\t' as delimiter from config file (3083)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/LocalFile","h":"#230-beta-2022-10-20","p":205918},{"i":205958,"t":"MongoDB sink connector","s":"MongoDb","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"","p":205957},{"i":205960,"t":"Write data to MongoDB","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#description","p":205957},{"i":205962,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#key-features","p":205957},{"i":205964,"t":"name type required default value uri string yes - database string yes - collection string yes - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#options","p":205957},{"i":205966,"t":"uri to write to mongoDB","s":"uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#uri-string","p":205957},{"i":205968,"t":"database to write to mongoDB","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#database-string","p":205957},{"i":205970,"t":"collection to write to mongoDB","s":"collection [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#collection-string","p":205957},{"i":205972,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#common-options","p":205957},{"i":205974,"t":"mongodb { uri = \"mongodb://username:password@127.0.0.1:27017/mypost?retryWrites=true&writeConcern=majority\" database = \"mydatabase\" collection = \"mycollection\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#example","p":205957},{"i":205977,"t":"Add MongoDB Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/MongoDB","h":"#220-beta-2022-09-26","p":205957},{"i":205979,"t":"Console sink connector","s":"Console","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"","p":205978},{"i":205981,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#description","p":205978},{"i":205983,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#key-features","p":205978},{"i":205985,"t":"name type required default value common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#options","p":205978},{"i":205987,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#common-options","p":205978},{"i":205989,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#example","p":205978},{"i":205992,"t":"Add Console Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#220-beta-2022-09-26","p":205978},{"i":205994,"t":"[Improve] Console sink support print subtask index (3000)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Console","h":"#230-beta-2022-10-20","p":205978},{"i":205996,"t":"HDFS file sink connector","s":"HdfsFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"","p":205995},{"i":205998,"t":"Output data to hdfs file","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#description","p":205995},{"i":206000,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#key-features","p":205995},{"i":206002,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#options","p":205995},{"i":206004,"t":"The hadoop cluster address that start with hdfs://, for example: hdfs://hadoopcluster","s":"fs.defaultFS [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#fsdefaultfs-string","p":205995},{"i":206006,"t":"The target dir path is required.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#path-string","p":205995},{"i":206008,"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 ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#file_name_expression-string","p":205995},{"i":206010,"t":"We supported as the following file types: text csv parquet orc json Please note that, The final file name will ends with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#file_format-string","p":205995},{"i":206012,"t":"When the format in the file_name_expression parameter is xxxx-${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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#filename_time_format-string","p":205995},{"i":206014,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#field_delimiter-string","p":205995},{"i":206016,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#row_delimiter-string","p":205995},{"i":206018,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#partition_by-array","p":205995},{"i":206020,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#partition_dir_expression-string","p":205995},{"i":206022,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#is_partition_field_write_in_file-boolean","p":205995},{"i":206024,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#sink_columns-array","p":205995},{"i":206026,"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 ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#is_enable_transaction-boolean","p":205995},{"i":206028,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#common-options","p":205995},{"i":206030,"t":"For text file format HdfsFile { fs.defaultFS = \"hdfs://hadoopcluster\" path = \"/tmp/hive/warehouse/test2\" field_delimiter = \"\\t\" row_delimiter = \"\\n\" partition_by = [\"age\"] partition_dir_expression = \"${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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#example","p":205995},{"i":206033,"t":"Add HDFS File Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#220-beta-2022-09-26","p":205995},{"i":206035,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [BugFix] Fix filesystem get error (3117) [BugFix] Solved the bug of can not parse '\\t' as delimiter from config file (3083)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/HdfsFile","h":"#230-beta-2022-10-20","p":205995},{"i":206037,"t":"Socket sink connector","s":"Socket","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"","p":206036},{"i":206039,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#description","p":206036},{"i":206041,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#key-features","p":206036},{"i":206043,"t":"name type required default value host String Yes - port Integer yes - max_retries Integer No 3 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#options","p":206036},{"i":206045,"t":"socket server host","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#host-string","p":206036},{"i":206047,"t":"socket server port","s":"port [integer]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#port-integer","p":206036},{"i":206049,"t":"The number of retries to send record failed","s":"max_retries [integer]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#max_retries-integer","p":206036},{"i":206051,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#common-options","p":206036},{"i":206053,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#example","p":206036},{"i":206056,"t":"Add Socket Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Socket","h":"#220-beta-2022-09-26","p":206036},{"i":206058,"t":"Oss file sink connector","s":"OssFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"","p":206057},{"i":206060,"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 only supports hadoop version 2.9.X+.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#description","p":206057},{"i":206062,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#key-features","p":206057},{"i":206064,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#options","p":206057},{"i":206066,"t":"The target dir path is required.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#path-string","p":206057},{"i":206068,"t":"The bucket address of oss file system, for example: oss://tyrantlucifer-image-bed","s":"bucket [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#bucket-string","p":206057},{"i":206070,"t":"The access key of oss file system.","s":"access_key [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#access_key-string","p":206057},{"i":206072,"t":"The access secret of oss file system.","s":"access_secret [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#access_secret-string","p":206057},{"i":206074,"t":"The endpoint of oss file system.","s":"endpoint [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#endpoint-string","p":206057},{"i":206076,"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 ${transactionId}_ in the head of the file.","s":"file_name_expression [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#file_name_expression-string","p":206057},{"i":206078,"t":"We supported as the following file types: text csv parquet orc json Please note that, The final file name will end with the file_format's suffix, the suffix of the text file is txt.","s":"file_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#file_format-string","p":206057},{"i":206080,"t":"When the format in the file_name_expression parameter is xxxx-${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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#filename_time_format-string","p":206057},{"i":206082,"t":"The separator between columns in a row of data. Only needed by text and csv file format.","s":"field_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#field_delimiter-string","p":206057},{"i":206084,"t":"The separator between rows in a file. Only needed by text and csv file format.","s":"row_delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#row_delimiter-string","p":206057},{"i":206086,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#partition_by-array","p":206057},{"i":206088,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#partition_dir_expression-string","p":206057},{"i":206090,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#is_partition_field_write_in_file-boolean","p":206057},{"i":206092,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#sink_columns-array","p":206057},{"i":206094,"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 ${transactionId}_ in the head of the file. Only support true now.","s":"is_enable_transaction [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#is_enable_transaction-boolean","p":206057},{"i":206096,"t":"Sink plugin common parameters, please refer to Sink Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#common-options","p":206057},{"i":206098,"t":"For text file format OssFile { path=\"/seatunnel/sink\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxx\" access_secret = \"xxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" field_delimiter = \"\\t\" row_delimiter = \"\\n\" partition_by = [\"age\"] partition_dir_expression = \"${k0}=${v0}\" is_partition_field_write_in_file = true file_name_expression = \"${transactionId}_${now}\" file_format = \"text\" sink_columns = [\"name\",\"age\"] filename_time_format = \"yyyy.MM.dd\" is_enable_transaction = true } For parquet file format OssFile { path = \"/seatunnel/sink\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxx\" access_secret = \"xxxxxxxxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" field_delimiter = \"\\t\" row_delimiter = \"\\n\" partition_by = [\"age\"] partition_dir_expression = \"${k0}=${v0}\" is_partition_field_write_in_file = true file_name_expression = \"${transactionId}_${now}\" file_format = \"parquet\" sink_columns = [\"name\",\"age\"] filename_time_format = \"yyyy.MM.dd\" is_enable_transaction = true } For orc file format OssFile { path=\"/seatunnel/sink\" bucket = \"oss://tyrantlucifer-image-bed\" access_key = \"xxxxxxxxxxx\" access_secret = \"xxxxxxxxxxx\" endpoint = \"oss-cn-beijing.aliyuncs.com\" field_delimiter = \"\\t\" row_delimiter = \"\\n\" partition_by = [\"age\"] partition_dir_expression = \"${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 }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#example","p":206057},{"i":206101,"t":"Add OSS Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#220-beta-2022-09-26","p":206057},{"i":206103,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [BugFix] Fix filesystem get error (3117) [BugFix] Solved the bug of can not parse '\\t' as delimiter from config file (3083)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/OssFile","h":"#230-beta-2022-10-20","p":206057},{"i":206105,"t":"Clickhouse source connector","s":"Clickhouse","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"","p":206104},{"i":206107,"t":"Used to read data from Clickhouse.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#description","p":206104},{"i":206109,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split 提示 Reading data from Clickhouse can also be done using JDBC","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#key-features","p":206104},{"i":206111,"t":"name type required default value host string yes - database string yes - sql string yes - username string yes - password string yes - schema config No - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#options","p":206104},{"i":206113,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#host-string","p":206104},{"i":206115,"t":"The ClickHouse database","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#database-string","p":206104},{"i":206117,"t":"The query sql used to search data though Clickhouse server","s":"sql [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#sql-string","p":206104},{"i":206119,"t":"ClickHouse user username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#username-string","p":206104},{"i":206121,"t":"ClickHouse user password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#password-string","p":206104},{"i":206123,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#schema-config","p":206104},{"i":206125,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#common-options","p":206104},{"i":206127,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#examples","p":206104},{"i":206130,"t":"Add ClickHouse Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#220-beta-2022-09-26","p":206104},{"i":206132,"t":"[Improve] Clickhouse Source random use host when config multi-host (3108)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Clickhouse","h":"#230-beta-2022-10-20","p":206104},{"i":206134,"t":"Neo4j sink connector","s":"Neo4j","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"","p":206133},{"i":206136,"t":"Write data to Neo4j. neo4j-java-driver version 4.4.9","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#description","p":206133},{"i":206138,"t":"exactly-once schema projection","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#key-features","p":206133},{"i":206140,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#options","p":206133},{"i":206142,"t":"The URI of the Neo4j database. Refer to a case: neo4j://localhost:7687","s":"uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#uri-string","p":206133},{"i":206144,"t":"username of the Neo4j","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#username-string","p":206133},{"i":206146,"t":"password of the Neo4j. required if username is provided","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#password-string","p":206133},{"i":206148,"t":"base64 encoded bearer token of the Neo4j. for Auth.","s":"bearer_token [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#bearer_token-string","p":206133},{"i":206150,"t":"base64 encoded kerberos ticket of the Neo4j. for Auth.","s":"kerberos_ticket [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#kerberos_ticket-string","p":206133},{"i":206152,"t":"database name.","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#database-string","p":206133},{"i":206154,"t":"Query statement. contain parameter placeholders that are substituted with the corresponding values at runtime","s":"query [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#query-string","p":206133},{"i":206156,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#queryparamposition-object","p":206133},{"i":206158,"t":"maximum transaction retry time(seconds). transaction fail if exceeded","s":"max_transaction_retry_time [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#max_transaction_retry_time-long","p":206133},{"i":206160,"t":"The maximum amount of time to wait for a TCP connection to be established (seconds)","s":"max_connection_timeout [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#max_connection_timeout-long","p":206133},{"i":206162,"t":"Sink plugin common parameters, please refer to Sink Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#common-options","p":206133},{"i":206164,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#example","p":206133},{"i":206167,"t":"Add Neo4j Sink Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/sink/Neo4j","h":"#220-beta-2022-09-26","p":206133},{"i":206169,"t":"Common parameters of source connectors name type required default value result_table_name string no - parallelism int no -","s":"Common Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/common-options","h":"","p":206168},{"i":206171,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/common-options","h":"#result_table_name-string","p":206168},{"i":206173,"t":"When parallelism is not specified, the parallelism in env is used by default. When parallelism is specified, it will override the parallelism in env.","s":"parallelism [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/common-options","h":"#parallelism-int","p":206168},{"i":206175,"t":"source { FakeSourceStream { result_table_name = \"fake\" } } 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 .","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/common-options","h":"#example","p":206168},{"i":206177,"t":"Hdfs file source connector","s":"HdfsFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"","p":206176},{"i":206179,"t":"Read data from hdfs file system.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#description","p":206176},{"i":206181,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#key-features","p":206176},{"i":206183,"t":"name type required default value path string yes - type string yes - fs.defaultFS string yes - delimiter string no \\001 parse_partition_from_path boolean no true date_format string no yyyy-MM-dd datetime_format string no yyyy-MM-dd HH:mm:ss time_format string no HH:mm:ss schema config no - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#options","p":206176},{"i":206185,"t":"The source file path.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#path-string","p":206176},{"i":206187,"t":"Field delimiter, used to tell connector how to slice and dice fields when reading text files default \\001, the same as hive's default delimiter","s":"delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#delimiter-string","p":206176},{"i":206189,"t":"Control whether parse the partition keys and values from file path For example if you read a file from path hdfs://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26 Every record data from file will be added these two fields: name age tyrantlucifer 26 Tips: Do not define partition fields in schema option","s":"parse_partition_from_path [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#parse_partition_from_path-boolean","p":206176},{"i":206191,"t":"Date type format, used to tell connector how to convert string to date, supported as the following formats: yyyy-MM-dd yyyy.MM.dd yyyy/MM/dd default yyyy-MM-dd","s":"date_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#date_format-string","p":206176},{"i":206193,"t":"Datetime type format, used to tell connector how to convert string to datetime, supported as the following formats: yyyy-MM-dd HH:mm:ss yyyy.MM.dd HH:mm:ss yyyy/MM/dd HH:mm:ss yyyyMMddHHmmss default yyyy-MM-dd HH:mm:ss","s":"datetime_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#datetime_format-string","p":206176},{"i":206195,"t":"Time type format, used to tell connector how to convert string to time, supported as the following formats: HH:mm:ss HH:mm:ss.SSS default HH:mm:ss","s":"time_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#time_format-string","p":206176},{"i":206197,"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, you can choose to specify the schema information or not. For example, upstream data is the following: tyrantlucifer#26#male If you do not assign data schema connector will treat the upstream data as the following: content tyrantlucifer#26#male If you assign data schema, you should also assign the option delimiter too except CSV file type you should assign schema and delimiter as the following: delimiter = \"#\" schema { fields { name = string age = int gender = string } } connector will generate data as the following: name age gender tyrantlucifer 26 male","s":"type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#type-string","p":206176},{"i":206199,"t":"Hdfs cluster address.","s":"fs.defaultFS [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#fsdefaultfs-string","p":206176},{"i":206201,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#schema-config","p":206176},{"i":206203,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#common-options","p":206176},{"i":206205,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#example","p":206176},{"i":206208,"t":"Add HDFS File Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#220-beta-2022-09-26","p":206176},{"i":206210,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [Improve] Support extract partition from SeaTunnelRow fields (3085) [Improve] Support parse field from file path (2985)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/HdfsFile","h":"#230-beta-2022-10-20","p":206176},{"i":206212,"t":"Ftp file source connector","s":"FtpFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"","p":206211},{"i":206214,"t":"Read data from ftp file server.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#description","p":206211},{"i":206216,"t":"batch stream exactly-once schema projection parallelism support user-defined split file format text csv json","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#key-features","p":206211},{"i":206218,"t":"name type required default value host string yes - port int yes - user string yes - password string yes - path string yes - type string yes - delimiter string no \\001 parse_partition_from_path boolean no true date_format string no yyyy-MM-dd datetime_format string no yyyy-MM-dd HH:mm:ss time_format string no HH:mm:ss schema config no - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#options","p":206211},{"i":206220,"t":"The target ftp host is required","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#host-string","p":206211},{"i":206222,"t":"The target ftp port is required","s":"port [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#port-int","p":206211},{"i":206224,"t":"The target ftp username is required","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#username-string","p":206211},{"i":206226,"t":"The target ftp password is required","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#password-string","p":206211},{"i":206228,"t":"The source file path.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#path-string","p":206211},{"i":206230,"t":"Field delimiter, used to tell connector how to slice and dice fields when reading text files default \\001, the same as hive's default delimiter","s":"delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#delimiter-string","p":206211},{"i":206232,"t":"Control whether parse the partition keys and values from file path For example if you read a file from path ftp://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26 Every record data from file will be added these two fields: name age tyrantlucifer 26 Tips: Do not define partition fields in schema option","s":"parse_partition_from_path [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#parse_partition_from_path-boolean","p":206211},{"i":206234,"t":"Date type format, used to tell connector how to convert string to date, supported as the following formats: yyyy-MM-dd yyyy.MM.dd yyyy/MM/dd default yyyy-MM-dd","s":"date_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#date_format-string","p":206211},{"i":206236,"t":"Datetime type format, used to tell connector how to convert string to datetime, supported as the following formats: yyyy-MM-dd HH:mm:ss yyyy.MM.dd HH:mm:ss yyyy/MM/dd HH:mm:ss yyyyMMddHHmmss default yyyy-MM-dd HH:mm:ss","s":"datetime_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#datetime_format-string","p":206211},{"i":206238,"t":"Time type format, used to tell connector how to convert string to time, supported as the following formats: HH:mm:ss HH:mm:ss.SSS default HH:mm:ss","s":"time_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#time_format-string","p":206211},{"i":206240,"t":"The schema information of upstream data.","s":"schema [config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#schema-config","p":206211},{"i":206242,"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, you can choose to specify the schema information or not. For example, upstream data is the following: tyrantlucifer#26#male If you do not assign data schema connector will treat the upstream data as the following: content tyrantlucifer#26#male If you assign data schema, you should also assign the option delimiter too except CSV file type you should assign schema and delimiter as the following: delimiter = \"#\" schema { fields { name = string age = int gender = string } } connector will generate data as the following: name age gender tyrantlucifer 26 male","s":"type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#type-string","p":206211},{"i":206244,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#common-options","p":206211},{"i":206246,"t":"FtpFile { path = \"/tmp/seatunnel/sink/text\" host = \"192.168.31.48\" port = 21 user = tyrantlucifer password = tianchao type = \"text\" schema = { name = string age = int } delimiter = \"#\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#example","p":206211},{"i":206249,"t":"Add Ftp Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#220-beta-2022-09-26","p":206211},{"i":206251,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [Improve] Support extract partition from SeaTunnelRow fields (3085) [Improve] Support parse field from file path (2985)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FtpFile","h":"#230-beta-2022-10-20","p":206211},{"i":206253,"t":"Hive source connector","s":"Hive","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"","p":206252},{"i":206255,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#description","p":206252},{"i":206257,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#key-features","p":206252},{"i":206259,"t":"name type required default value table_name string yes - metastore_uri string yes - schema config No - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#options","p":206252},{"i":206261,"t":"Target Hive table name eg: db1.table1","s":"table_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#table_name-string","p":206252},{"i":206263,"t":"Hive metastore uri","s":"metastore_uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#metastore_uri-string","p":206252},{"i":206265,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#schema-config","p":206252},{"i":206267,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#common-options","p":206252},{"i":206269,"t":"Hive { table_name = \"default.seatunnel_orc\" metastore_uri = \"thrift://namenode001:9083\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#example","p":206252},{"i":206272,"t":"Add Hive Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hive","h":"#220-beta-2022-09-26","p":206252},{"i":206274,"t":"FakeSource connector","s":"FakeSource","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"","p":206273},{"i":206276,"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 some test cases such as type conversion or connector new feature testing","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#description","p":206273},{"i":206278,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#key-features","p":206273},{"i":206280,"t":"name type required default value schema config yes - row.num int no 5 split.num int no 1 split.read-interval long no 1 map.size int no 5 array.size int no 5 bytes.length int no 5 string.length int no 5 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#options","p":206273},{"i":206282,"t":"fields [Config]​ The schema of fake data that you want to generate","s":"schema [config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#schema-config","p":206273},{"i":206284,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#common-options","p":206273},{"i":206286,"t":"schema = { fields { c_map = \"map<string, array<int>>\" c_array = \"array<int>\" 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_timestamp = timestamp c_row = { c_map = \"map<string, map<string, string>>\" c_array = \"array<int>\" 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_timestamp = timestamp } } }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#examples","p":206273},{"i":206288,"t":"The total number of data generated per degree of parallelism","s":"row.num","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#rownum","p":206273},{"i":206290,"t":"the number of splits generated by the enumerator for each degree of parallelism","s":"split.num","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#splitnum","p":206273},{"i":206292,"t":"The interval(mills) between two split reads in a reader","s":"split.read-interval","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#splitread-interval","p":206273},{"i":206294,"t":"The size of map type that connector generated","s":"map.size","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#mapsize","p":206273},{"i":206296,"t":"The size of array type that connector generated","s":"array.size","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#arraysize","p":206273},{"i":206298,"t":"The length of bytes type that connector generated","s":"bytes.length","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#byteslength","p":206273},{"i":206300,"t":"The length of string type that connector generated","s":"string.length","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#stringlength","p":206273},{"i":206302,"t":"FakeSource { row.num = 10 map.size = 10 array.size = 10 bytes.length = 10 string.length = 10 schema = { fields { c_map = \"map<string, array<int>>\" c_array = \"array<int>\" 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_timestamp = timestamp c_row = { c_map = \"map<string, map<string, string>>\" c_array = \"array<int>\" 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_timestamp = timestamp } } } }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#example","p":206273},{"i":206305,"t":"Add FakeSource Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#220-beta-2022-09-26","p":206273},{"i":206307,"t":"[Improve] Supports direct definition of data values(row) (2839) [Improve] Improve fake source connector: (2944) Support user-defined map size Support user-defined array size Support user-defined string length Support user-defined bytes length [Improve] Support multiple splits for fake source connector (2974) [Improve] Supports setting the number of splits per parallelism and the reading interval between two splits (3098)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/FakeSource","h":"#230-beta-2022-10-20","p":206273},{"i":206309,"t":"Hudi source connector","s":"Hudi","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"","p":206308},{"i":206311,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#description","p":206308},{"i":206313,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#key-features","p":206308},{"i":206315,"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 - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#options","p":206308},{"i":206317,"t":"table.path The hdfs root path of hudi table,such as 'hdfs://nameserivce/data/hudi/hudi_table/'.","s":"table.path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#tablepath-string","p":206308},{"i":206319,"t":"table.type The type of hudi table. Now we only support 'cow', 'mor' is not support yet.","s":"table.type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#tabletype-string","p":206308},{"i":206321,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#conffiles-string","p":206308},{"i":206323,"t":"use.kerberos Whether to enable Kerberos, default is false.","s":"use.kerberos [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#usekerberos-boolean","p":206308},{"i":206325,"t":"kerberos.principal When use kerberos, we should set kerberos princal such as 'test_user@xxx'.","s":"kerberos.principal [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#kerberosprincipal-string","p":206308},{"i":206327,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#kerberosprincipalfile-string","p":206308},{"i":206329,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#common-options","p":206308},{"i":206331,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#examples","p":206308},{"i":206334,"t":"Add Hudi Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Hudi","h":"#220-beta-2022-09-26","p":206308},{"i":206336,"t":"IoTDB source connector","s":"IoTDB","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"","p":206335},{"i":206338,"t":"Read external data source data through IoTDB.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#description","p":206335},{"i":206340,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#key-features","p":206335},{"i":206342,"t":"name type required default value host string no - port int no - node_urls string no - username string yes - password string yes - sql string yes - fields config yes - fetch_size int 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 - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#options","p":206335},{"i":206344,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#single-node-you-need-to-set-host-and-port-to-connect-to-the-remote-data-source","p":206335},{"i":206346,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#multi-node-you-need-to-set-node_urls-to-connect-to-the-remote-data-source","p":206335},{"i":206348,"t":"sql [string] execute sql statement e.g. select name,age from test","s":"other parameters","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#other-parameters","p":206335},{"i":206350,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#fields-string","p":206335},{"i":206353,"t":"the fetch_size of the IoTDB when you select","s":"fetch_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#fetch_size-int","p":206335},{"i":206355,"t":"the username of the IoTDB when you select","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#username-string","p":206335},{"i":206357,"t":"the password of the IoTDB when you select","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#password-string","p":206335},{"i":206359,"t":"the lower_bound of the IoTDB when you select","s":"lower_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#lower_bound-long","p":206335},{"i":206361,"t":"the upper_bound of the IoTDB when you select","s":"upper_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#upper_bound-long","p":206335},{"i":206363,"t":"the num_partitions of the IoTDB when you select","s":"num_partitions [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#num_partitions-int","p":206335},{"i":206365,"t":"the thrift_default_buffer_size of the IoTDB when you select","s":"thrift_default_buffer_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#thrift_default_buffer_size-int","p":206335},{"i":206367,"t":"enable_cache_leader of the IoTDB when you select","s":"enable_cache_leader [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#enable_cache_leader-boolean","p":206335},{"i":206369,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#version-string","p":206335},{"i":206371,"t":"we can split the partitions of the IoTDB and we used time column split num_partitions [int]​ split num","s":"split partitions","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#split-partitions","p":206335},{"i":206373,"t":"upper bound of the time column","s":"upper_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#upper_bound-long-1","p":206335},{"i":206375,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#lower_bound-long-1","p":206335},{"i":206377,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#common-options","p":206335},{"i":206380,"t":"Common options: source { IoTDB { node_urls = \"localhost:6667\" username = \"root\" password = \"root\" } } When you assign sql、fields、partition, for example: sink { IoTDB { ... sql = \"SELECT temperature, moisture FROM root.test_group.* WHERE time < 4102329600000 align by device\" lower_bound = 1 upper_bound = 4102329600000 num_partitions = 10 fields { ts = bigint device_name = string temperature = float moisture = bigint } } } Upstream IoTDB data format is the following: IoTDB> SELECT temperature, moisture FROM root.test_group.* WHERE time < 4102329600000 align by device; +------------------------+------------------------+--------------+-----------+ | Time| Device| temperature| moisture| +------------------------+------------------------+--------------+-----------+ |2022-09-25T00:00:00.001Z|root.test_group.device_a| 36.1| 100| |2022-09-25T00:00:00.001Z|root.test_group.device_b| 36.2| 101| |2022-09-25T00:00:00.001Z|root.test_group.device_c| 36.3| 102| +------------------------+------------------------+--------------+-----------+ Loaded to SeaTunnelRow data format is the following: ts device_name temperature moisture 1664035200001 root.test_group.device_a 36.1 100 1664035200001 root.test_group.device_b 36.2 101 1664035200001 root.test_group.device_c 36.3 102","s":"Case1","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#case1","p":206335},{"i":206383,"t":"Add IoTDB Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#220-beta-2022-09-26","p":206335},{"i":206385,"t":"[Improve] Improve IoTDB Source Connector (2917) Support extract timestamp、device、measurement from SeaTunnelRow Support TINYINT、SMALLINT Support flush cache to database before prepareCommit","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/IoTDB","h":"#230-beta-2022-10-20","p":206335},{"i":206387,"t":"JDBC source connector","s":"JDBC","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"","p":206386},{"i":206389,"t":"Read external data source data through JDBC.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#description","p":206386},{"i":206391,"t":"batch stream exactly-once schema projection supports query SQL and can achieve projection effect. parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#key-features","p":206386},{"i":206393,"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 - partition_num Int No job parallelism common-options No -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#options","p":206386},{"i":206395,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#driver-string","p":206386},{"i":206397,"t":"userName","s":"user [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#user-string","p":206386},{"i":206399,"t":"password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#password-string","p":206386},{"i":206401,"t":"The URL of the JDBC connection. Refer to a case: jdbc:postgresql://localhost/test","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#url-string","p":206386},{"i":206403,"t":"Query statement","s":"query [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#query-string","p":206386},{"i":206405,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#connection_check_timeout_sec-int","p":206386},{"i":206407,"t":"The column name for parallelism's partition, only support numeric type.","s":"partition_column [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#partition_column-string","p":206386},{"i":206409,"t":"The partition_column max value for scan, if not set SeaTunnel will query database get max value.","s":"partition_upper_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#partition_upper_bound-long","p":206386},{"i":206411,"t":"The partition_column min value for scan, if not set SeaTunnel will query database get min value.","s":"partition_lower_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#partition_lower_bound-long","p":206386},{"i":206413,"t":"The number of partition count, only support positive integer. default value is job parallelism","s":"partition_num [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#partition_num-int","p":206386},{"i":206415,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#common-options","p":206386},{"i":206417,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#tips","p":206386},{"i":206419,"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 phoenix org.apache.phoenix.queryserver.client.Driver jdbc:phoenix:thin:url=http://localhost:8765;serialization=PROTOBUF https://mvnrepository.com/artifact/com.aliyun.phoenix/ali-phoenix-shaded-thin-client sqlserver com.microsoft.sqlserver.jdbc.SQLServerDriver jdbc:microsoft:sqlserver://localhost:1433 https://mvnrepository.com/artifact/com.microsoft.sqlserver/mssql-jdbc oracle oracle.jdbc.OracleDriver jdbc:oracle:thin:@localhost:1521/xepdb1 https://mvnrepository.com/artifact/com.oracle.database.jdbc/ojdbc8 gbase8a com.gbase.jdbc.Driver jdbc:gbase://e2e_gbase8aDb:5258/test https://www.gbase8.cn/wp-content/uploads/2020/10/gbase-connector-java-8.3.81.53-build55.5.7-bin_min_mix.jar starrocks com.mysql.cj.jdbc.Driver jdbc:mysql://localhost:3306/test https://mvnrepository.com/artifact/mysql/mysql-connector-java","s":"appendix","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#appendix","p":206386},{"i":206421,"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\" partition_num = 10 }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#example","p":206386},{"i":206424,"t":"Add ClickHouse Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#220-beta-2022-09-26","p":206386},{"i":206426,"t":"[Feature] Support Phoenix JDBC Source (2499) [Feature] Support SQL Server JDBC Source (2646) [Feature] Support Oracle JDBC Source (2550) [Feature] Support StarRocks JDBC Source (3060) [Feature] Support GBase8a JDBC Source (3026)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Jdbc","h":"#230-beta-2022-10-20","p":206386},{"i":206428,"t":"Apache Iceberg source connector","s":"Apache Iceberg","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"","p":206427},{"i":206430,"t":"Source connector for Apache Iceberg. It can support batch and stream mode.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#description","p":206427},{"i":206432,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#key-features","p":206427},{"i":206434,"t":"name type required default value catalog_name string yes - catalog_type string yes - uri string no - warehouse string yes - namespace string yes - table string yes - case_sensitive boolean no false fields config no - start_snapshot_timestamp long no - start_snapshot_id long no - end_snapshot_id long no - use_snapshot_id long no - use_snapshot_timestamp long no - stream_scan_strategy enum no FROM_LATEST_SNAPSHOT common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#options","p":206427},{"i":206436,"t":"User-specified catalog name.","s":"catalog_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#catalog_name-string","p":206427},{"i":206438,"t":"The optional values are: hive: The hive metastore catalog. hadoop: The hadoop catalog.","s":"catalog_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#catalog_type-string","p":206427},{"i":206440,"t":"The Hive metastore’s thrift URI.","s":"uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#uri-string","p":206427},{"i":206442,"t":"The location to store metadata files and data files.","s":"warehouse [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#warehouse-string","p":206427},{"i":206444,"t":"The iceberg database name in the backend catalog.","s":"namespace [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#namespace-string","p":206427},{"i":206446,"t":"The iceberg table name in the backend catalog.","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#table-string","p":206427},{"i":206448,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#case_sensitive-boolean","p":206427},{"i":206450,"t":"Use projection to select data columns and columns order.","s":"fields [config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#fields-config","p":206427},{"i":206452,"t":"Instructs this scan to look for changes starting from a particular snapshot (exclusive).","s":"start_snapshot_id [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#start_snapshot_id-long","p":206427},{"i":206454,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#start_snapshot_timestamp-long","p":206427},{"i":206456,"t":"Instructs this scan to look for changes up to a particular snapshot (inclusive).","s":"end_snapshot_id [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#end_snapshot_id-long","p":206427},{"i":206458,"t":"Instructs this scan to look for use the given snapshot ID.","s":"use_snapshot_id [long]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#use_snapshot_id-long","p":206427},{"i":206460,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#use_snapshot_timestamp-long","p":206427},{"i":206462,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#stream_scan_strategy-enum","p":206427},{"i":206464,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#common-options","p":206427},{"i":206466,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#example","p":206427},{"i":206469,"t":"Add Iceberg Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Iceberg","h":"#220-beta-2022-09-26","p":206427},{"i":206471,"t":"Local file source connector","s":"LocalFile","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"","p":206470},{"i":206473,"t":"Read data from local file system.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#description","p":206470},{"i":206475,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#key-features","p":206470},{"i":206477,"t":"name type required default value path string yes - type string yes - delimiter string no \\001 parse_partition_from_path boolean no true date_format string no yyyy-MM-dd datetime_format string no yyyy-MM-dd HH:mm:ss time_format string no HH:mm:ss schema config no - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#options","p":206470},{"i":206479,"t":"The source file path.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#path-string","p":206470},{"i":206481,"t":"Field delimiter, used to tell connector how to slice and dice fields when reading text files default \\001, the same as hive's default delimiter","s":"delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#delimiter-string","p":206470},{"i":206483,"t":"Control whether parse the partition keys and values from file path For example if you read a file from path file://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26 Every record data from file will be added these two fields: name age tyrantlucifer 26 Tips: Do not define partition fields in schema option","s":"parse_partition_from_path [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#parse_partition_from_path-boolean","p":206470},{"i":206485,"t":"Date type format, used to tell connector how to convert string to date, supported as the following formats: yyyy-MM-dd yyyy.MM.dd yyyy/MM/dd default yyyy-MM-dd","s":"date_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#date_format-string","p":206470},{"i":206487,"t":"Datetime type format, used to tell connector how to convert string to datetime, supported as the following formats: yyyy-MM-dd HH:mm:ss yyyy.MM.dd HH:mm:ss yyyy/MM/dd HH:mm:ss yyyyMMddHHmmss default yyyy-MM-dd HH:mm:ss","s":"datetime_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#datetime_format-string","p":206470},{"i":206489,"t":"Time type format, used to tell connector how to convert string to time, supported as the following formats: HH:mm:ss HH:mm:ss.SSS default HH:mm:ss","s":"time_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#time_format-string","p":206470},{"i":206491,"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, you can choose to specify the schema information or not. For example, upstream data is the following: tyrantlucifer#26#male If you do not assign data schema connector will treat the upstream data as the following: content tyrantlucifer#26#male If you assign data schema, you should also assign the option delimiter too except CSV file type you should assign schema and delimiter as the following: delimiter = \"#\" schema { fields { name = string age = int gender = string } } connector will generate data as the following: name age gender tyrantlucifer 26 male","s":"type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#type-string","p":206470},{"i":206493,"t":"fields [Config]​ The schema information of upstream data.","s":"schema [config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#schema-config","p":206470},{"i":206495,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#common-options","p":206470},{"i":206497,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#example","p":206470},{"i":206500,"t":"Add Local File Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#220-beta-2022-09-26","p":206470},{"i":206502,"t":"[BugFix] Fix the bug of incorrect path in windows environment (2980) [Improve] Support extract partition from SeaTunnelRow fields (3085) [Improve] Support parse field from file path (2985)","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/LocalFile","h":"#230-beta-2022-10-20","p":206470},{"i":206504,"t":"MongoDb source connector","s":"MongoDb","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"","p":206503},{"i":206506,"t":"Read data from MongoDB.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#description","p":206503},{"i":206508,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#key-features","p":206503},{"i":206510,"t":"name type required default value uri string yes - database string yes - collection string yes - schema object yes - common-options yes -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#options","p":206503},{"i":206512,"t":"MongoDB uri","s":"uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#uri-string","p":206503},{"i":206514,"t":"MongoDB database","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#database-string","p":206503},{"i":206516,"t":"MongoDB collection","s":"collection [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#collection-string","p":206503},{"i":206518,"t":"fields [Config]​ 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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#schema-object","p":206503},{"i":206520,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#common-options","p":206503},{"i":206522,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#example","p":206503},{"i":206525,"t":"Add MongoDB Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/MongoDB","h":"#220-beta-2022-09-26","p":206503},{"i":206527,"t":"Kafka source connector","s":"Kafka","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"","p":206526},{"i":206529,"t":"Source connector for Apache Kafka.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#description","p":206526},{"i":206531,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#key-features","p":206526},{"i":206533,"t":"name type required default value topic String yes - bootstrap.servers String yes - pattern Boolean no false consumer.group String no SeaTunnel-Consumer-Group commit_on_checkpoint Boolean no true kafka.* String no - common-options no - schema no - format String no json","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#options","p":206526},{"i":206535,"t":"Kafka topic name. If there are multiple topics, use , to split, for example: \"tpc1,tpc2\".","s":"topic [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#topic-string","p":206526},{"i":206537,"t":"Kafka cluster address, separated by \",\".","s":"bootstrap.servers [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#bootstrapservers-string","p":206526},{"i":206539,"t":"If pattern is set to true,the regular expression for a pattern of topic names to read from. All topics in clients with names that match the specified regular expression will be subscribed by the consumer.","s":"pattern [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#pattern-boolean","p":206526},{"i":206541,"t":"Kafka consumer group id, used to distinguish different consumer groups.","s":"consumer.group [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#consumergroup-string","p":206526},{"i":206543,"t":"If true the consumer's offset will be periodically committed in the background.","s":"commit_on_checkpoint [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#commit_on_checkpoint-boolean","p":206526},{"i":206545,"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 kafka. to the original parameter name. For example, the way to specify auto.offset.reset is: kafka.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":"kafka.* [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#kafka-string","p":206526},{"i":206547,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common-options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#common-options","p":206526},{"i":206549,"t":"The structure of the data, including field names and field types.","s":"schema","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#schema","p":206526},{"i":206551,"t":"Data format. The default format is json. Optional text format. The default field separator is \", \". If you customize the delimiter, add the \"field_delimiter\" option.","s":"format","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#format","p":206526},{"i":206554,"t":"source { Kafka { result_table_name = \"kafka_name\" schema = { fields { name = \"string\" age = \"int\" } } format = text field_delimiter = \"#“ topic = \"topic_1,topic_2,topic_3\" bootstrap.server = \"localhost:9092\" kafka.max.poll.records = 500 kafka.client.id = client_1 } }","s":"Simple","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#simple","p":206526},{"i":206556,"t":"source { Kafka { topic = \".*seatunnel*.\" pattern = \"true\" bootstrap.servers = \"localhost:9092\" consumer.group = \"seatunnel_group\" } }","s":"Regex Topic","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#regex-topic","p":206526},{"i":206559,"t":"Add Kafka Source Connector","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/kafka","h":"#230-beta-2022-10-20","p":206526},{"i":206561,"t":"Kudu source connector","s":"Kudu","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"","p":206560},{"i":206563,"t":"Used to read data from Kudu. The tested kudu version is 1.11.1.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#description","p":206560},{"i":206565,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#key-features","p":206560},{"i":206567,"t":"name type required default value kudu_master string yes - kudu_table string yes - columnsList string yes - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#options","p":206560},{"i":206569,"t":"kudu_master The address of kudu master,such as '192.168.88.110:7051'.","s":"kudu_master [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#kudu_master-string","p":206560},{"i":206571,"t":"kudu_table The name of kudu table..","s":"kudu_table [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#kudu_table-string","p":206560},{"i":206573,"t":"columnsList Specifies the column names of the table.","s":"columnsList [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#columnslist-string","p":206560},{"i":206575,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#common-options","p":206560},{"i":206577,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#examples","p":206560},{"i":206580,"t":"Add Kudu Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Kudu","h":"#220-beta-2022-09-26","p":206560},{"i":206582,"t":"Socket source connector","s":"Socket","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"","p":206581},{"i":206584,"t":"Used to read data from Socket.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#description","p":206581},{"i":206586,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#key-features","p":206581},{"i":206588,"t":"name type required default value host String No localhost port Integer No 9999 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#options","p":206581},{"i":206590,"t":"socket server host","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#host-string","p":206581},{"i":206592,"t":"socket server port","s":"port [integer]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#port-integer","p":206581},{"i":206594,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#common-options","p":206581},{"i":206596,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#example","p":206581},{"i":206599,"t":"Add Socket Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Socket","h":"#220-beta-2022-09-26","p":206581},{"i":206601,"t":"S3 file source connector","s":"S3File","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"","p":206600},{"i":206603,"t":"Read data from aws s3 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 S3 and this connector need some hadoop dependencies. It's only support hadoop version 2.6.5+.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#description","p":206600},{"i":206605,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#key-features","p":206600},{"i":206607,"t":"name type required default value path string yes - type string yes - bucket string yes - access_key string yes - access_secret string yes - delimiter string no \\001 parse_partition_from_path boolean no true date_format string no yyyy-MM-dd datetime_format string no yyyy-MM-dd HH:mm:ss time_format string no HH:mm:ss schema config no - common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#options","p":206600},{"i":206609,"t":"The source file path.","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#path-string","p":206600},{"i":206611,"t":"Field delimiter, used to tell connector how to slice and dice fields when reading text files default \\001, the same as hive's default delimiter","s":"delimiter [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#delimiter-string","p":206600},{"i":206613,"t":"Control whether parse the partition keys and values from file path For example if you read a file from path s3n://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26 Every record data from file will be added these two fields: name age tyrantlucifer 26 Tips: Do not define partition fields in schema option","s":"parse_partition_from_path [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#parse_partition_from_path-boolean","p":206600},{"i":206615,"t":"Date type format, used to tell connector how to convert string to date, supported as the following formats: yyyy-MM-dd yyyy.MM.dd yyyy/MM/dd default yyyy-MM-dd","s":"date_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#date_format-string","p":206600},{"i":206617,"t":"Datetime type format, used to tell connector how to convert string to datetime, supported as the following formats: yyyy-MM-dd HH:mm:ss yyyy.MM.dd HH:mm:ss yyyy/MM/dd HH:mm:ss yyyyMMddHHmmss default yyyy-MM-dd HH:mm:ss","s":"datetime_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#datetime_format-string","p":206600},{"i":206619,"t":"Time type format, used to tell connector how to convert string to time, supported as the following formats: HH:mm:ss HH:mm:ss.SSS default HH:mm:ss","s":"time_format [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#time_format-string","p":206600},{"i":206621,"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, you can choose to specify the schema information or not. For example, upstream data is the following: tyrantlucifer#26#male If you do not assign data schema connector will treat the upstream data as the following: content tyrantlucifer#26#male If you assign data schema, you should also assign the option delimiter too except CSV file type you should assign schema and delimiter as the following: delimiter = \"#\" schema { fields { name = string age = int gender = string } } connector will generate data as the following: name age gender tyrantlucifer 26 male","s":"type [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#type-string","p":206600},{"i":206623,"t":"The bucket address of s3 file system, for example: s3n://seatunnel-test Tips: SeaTunnel S3 file connector only support s3n protocol, not support s3 and s3a","s":"bucket [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#bucket-string","p":206600},{"i":206625,"t":"The access key of s3 file system.","s":"access_key [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#access_key-string","p":206600},{"i":206627,"t":"The access secret of s3 file system.","s":"access_secret [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#access_secret-string","p":206600},{"i":206629,"t":"fields [Config]​ The schema of upstream data.","s":"schema [config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#schema-config","p":206600},{"i":206631,"t":"Source plugin common parameters, please refer to Source Common Options for details.","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#common-options","p":206600},{"i":206633,"t":"S3File { path = \"/seatunnel/text\" access_key = \"xxxxxxxxxxxxxxxxx\" secret_key = \"xxxxxxxxxxxxxxxxx\" bucket = \"s3n://seatunnel-test\" type = \"text\" } S3File { path = \"/seatunnel/json\" bucket = \"s3n://seatunnel-test\" access_key = \"xxxxxxxxxxxxxxxxx\" access_secret = \"xxxxxxxxxxxxxxxxxxxxxx\" type = \"json\" schema { fields { id = int name = string } } }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#example","p":206600},{"i":206636,"t":"Add S3File Source Connector","s":"2.3.0-beta 2022-10-20","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/S3File","h":"#230-beta-2022-10-20","p":206600},{"i":206638,"t":"JDBC connector based flink sql","s":"Flink SQL JDBC Connector","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Jdbc","h":"","p":206637},{"i":206640,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Jdbc","h":"#description","p":206637},{"i":206643,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Jdbc","h":"#1-download-driver","p":206637},{"i":206645,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Jdbc","h":"#2-prepare-data","p":206637},{"i":206647,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Jdbc","h":"#3-seatunnel-config","p":206637},{"i":206649,"t":"./bin/start-seatunnel-sql.sh --config <path/to/your/config>","s":"4. run job","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Jdbc","h":"#4-run-job","p":206637},{"i":206651,"t":"Kafka connector based by flink sql","s":"Flink SQL Kafka Connector","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"","p":206650},{"i":206653,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#description","p":206650},{"i":206655,"t":"Let us have a brief example to show how to use the connector from end to end.","s":"Usage","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#usage","p":206650},{"i":206657,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#1-kafka-prepare","p":206650},{"i":206659,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#2-prepare-seatunnel-configuration","p":206650},{"i":206661,"t":"$ ${FLINK_HOME}/bin/start-cluster.sh","s":"3. start flink local cluster","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#3-start-flink-local-cluster","p":206650},{"i":206663,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#4-start-flink-sql-job","p":206650},{"i":206665,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/Kafka","h":"#5-verify-result","p":206650},{"i":206667,"t":"Tutorial of flink sql module","s":"How to use flink sql module","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/usage","h":"","p":206666},{"i":206670,"t":"bin/start-seatunnel-sql.sh","s":"1. Command Entrypoint","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/usage","h":"#1-command-entrypoint","p":206666},{"i":206672,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/usage","h":"#2-seatunnel-config","p":206666},{"i":206674,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/usage","h":"#3-run-job","p":206666},{"i":206676,"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, we need to create a 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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/usage","h":"#example","p":206666},{"i":206678,"t":"Assert sink connector","s":"Assert","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"","p":206677},{"i":206680,"t":"A flink sink plugin which can assert illegal data by user defined rules 提示 Engine Supported and plugin name Spark：Assert Flink: Assert","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#description","p":206677},{"i":206682,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#options","p":206677},{"i":206684,"t":"Rule definition of user's available data. Each rule represents one field validation.","s":"rules [ConfigList]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#rules-configlist","p":206677},{"i":206686,"t":"field name（string）","s":"field_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#field_name-string","p":206677},{"i":206688,"t":"field type (string), e.g. string,boolean,byte,short,int,long,float,double,char,void,BigInteger,BigDecimal,Instant","s":"field_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#field_type-string","p":206677},{"i":206690,"t":"A list value rule define the data value validation","s":"field_value [ConfigList]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#field_value-configlist","p":206677},{"i":206692,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#rule_type-string","p":206677},{"i":206694,"t":"the value related to rule type","s":"rule_value [double]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#rule_value-double","p":206677},{"i":206696,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Assert","h":"#example","p":206677},{"i":206698,"t":"Clickhouse sink connector","s":"Clickhouse","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"","p":206697},{"i":206700,"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 提示 Engine Supported and plugin name Spark: Clickhouse Flink: Clickhouse","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#description","p":206697},{"i":206702,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#options","p":206697},{"i":206704,"t":"The number of rows written through Clickhouse-jdbc each time, the default is 20000 .","s":"bulk_size [number]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#bulk_size-number","p":206697},{"i":206706,"t":"database name","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#database-string","p":206697},{"i":206708,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#fields-array","p":206697},{"i":206710,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#host-string","p":206697},{"i":206712,"t":"ClickHouse user password . This field is only required when the permission is enabled in ClickHouse .","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#password-string","p":206697},{"i":206714,"t":"The number of retries, the default is 1","s":"retry [number]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#retry-number","p":206697},{"i":206716,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#retry_codes-array","p":206697},{"i":206718,"t":"table name","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#table-string","p":206697},{"i":206720,"t":"ClickHouse user username, this field is only required when permission is enabled in ClickHouse","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#username-string","p":206697},{"i":206722,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#clickhouse-string","p":206697},{"i":206724,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#split_mode-boolean","p":206697},{"i":206726,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#sharding_key-string","p":206697},{"i":206728,"t":"Sink plugin common parameters, please refer to common options for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#common-options-string","p":206697},{"i":206730,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#clickhouse-type-comparison-table","p":206697},{"i":206732,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Clickhouse","h":"#examples","p":206697},{"i":206734,"t":"Http source connector","s":"Http","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"","p":206733},{"i":206736,"t":"Used to read data from Http.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#description","p":206733},{"i":206738,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#key-features","p":206733},{"i":206740,"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 common-options No -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#options","p":206733},{"i":206742,"t":"http request url","s":"url [String]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#url-string","p":206733},{"i":206744,"t":"http request method, only supports GET, POST method.","s":"method [String]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#method-string","p":206733},{"i":206746,"t":"http headers","s":"headers [Map]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#headers-map","p":206733},{"i":206748,"t":"http params","s":"params [Map]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#params-map","p":206733},{"i":206750,"t":"http body","s":"body [String]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#body-string","p":206733},{"i":206752,"t":"request http api interval(millis) in stream mode","s":"poll_interval_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#poll_interval_ms-int","p":206733},{"i":206754,"t":"The max retry times if request http return to IOException","s":"retry [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#retry-int","p":206733},{"i":206756,"t":"The retry-backoff times(millis) multiplier if request http failed","s":"retry_backoff_multiplier_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#retry_backoff_multiplier_ms-int","p":206733},{"i":206758,"t":"The maximum retry-backoff times(millis) if request http failed","s":"retry_backoff_max_ms [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#retry_backoff_max_ms-int","p":206733},{"i":206760,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#format-string","p":206733},{"i":206762,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#schema-config","p":206733},{"i":206764,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#common-options","p":206733},{"i":206766,"t":"simple: Http { url = \"https://tyrantlucifer.com/api/getDemoData\" schema { fields { code = int message = string data = string ok = boolean } } }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#example","p":206733},{"i":206769,"t":"Add Http Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Http","h":"#220-beta-2022-09-26","p":206733},{"i":206771,"t":"Clickhouse file sink connector","s":"ClickhouseFile","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"","p":206770},{"i":206773,"t":"Generate the clickhouse data file with the clickhouse-local program, and then send it to the clickhouse server, also call bulk load. 提示 Engine Supported and plugin name Spark: ClickhouseFile Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#description","p":206770},{"i":206775,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#options","p":206770},{"i":206777,"t":"database name","s":"database [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#database-string","p":206770},{"i":206779,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#fields-array","p":206770},{"i":206781,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#host-string","p":206770},{"i":206783,"t":"ClickHouse user password . This field is only required when the permission is enabled in ClickHouse .","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#password-string","p":206770},{"i":206785,"t":"table name","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#table-string","p":206770},{"i":206787,"t":"ClickHouse user username, this field is only required when permission is enabled in ClickHouse","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#username-string","p":206770},{"i":206789,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#sharding_key-string","p":206770},{"i":206791,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#clickhouse_local_path-string","p":206770},{"i":206793,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#tmp_batch_cache_line-int","p":206770},{"i":206795,"t":"Specifies the method used to transfer files, the default is scp, optional scp and rsync","s":"copy_method [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#copy_method-string","p":206770},{"i":206797,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#node_free_password-boolean","p":206770},{"i":206799,"t":"Used to save the addresses and corresponding passwords of all clickhouse servers","s":"node_pass [list]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#node_pass-list","p":206770},{"i":206801,"t":"The address corresponding to the clickhouse server","s":"node_pass.node_address [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#node_passnode_address-string","p":206770},{"i":206803,"t":"The password corresponding to the clickhouse server, only support root user yet.","s":"node_pass.node_password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#node_passnode_password-string","p":206770},{"i":206805,"t":"Sink plugin common parameters, please refer to common options for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#common-options-string","p":206770},{"i":206807,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#clickhouse-type-comparison-table","p":206770},{"i":206809,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/ClickhouseFile","h":"#examples","p":206770},{"i":206811,"t":"Common parameters of sink connectors name type required default value source_table_name string no -","s":"Common Options","u":"/zh-CN/docs/2.3.0-beta/connector/sink/common-options","h":"","p":206810},{"i":206813,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/common-options","h":"#source_table_name-string","p":206810},{"i":206815,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/common-options","h":"#examples","p":206810},{"i":206817,"t":"Console sink connector","s":"Console","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"","p":206816},{"i":206819,"t":"Output data to standard terminal or Flink taskManager, which is often used for debugging and easy to observe the data. 提示 Engine Supported and plugin name Spark: Console Flink: Console","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#description","p":206816},{"i":206821,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#options","p":206816},{"i":206823,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#limit-number","p":206816},{"i":206825,"t":"The format of serialization when outputting. Available serializers include: json , plain","s":"serializer [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#serializer-string","p":206816},{"i":206827,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#common-options-string","p":206816},{"i":206829,"t":"name type required default value limit int no INT_MAX common-options string no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#options-1","p":206816},{"i":206831,"t":"limit console result lines","s":"limit [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#limit-int","p":206816},{"i":206833,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#common-options-string-1","p":206816},{"i":206835,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#examples","p":206816},{"i":206837,"t":"Flink's console output is in flink's WebUI","s":"Note","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Console","h":"#note","p":206816},{"i":206839,"t":"Redis source connector","s":"Redis","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"","p":206838},{"i":206841,"t":"Used to read data from Redis.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#description","p":206838},{"i":206843,"t":"batch stream exactly-once schema projection parallelism support user-defined split","s":"Key features","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#key-features","p":206838},{"i":206845,"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 common-options no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#options","p":206838},{"i":206847,"t":"redis host","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#host-string","p":206838},{"i":206849,"t":"redis port","s":"port [int]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#port-int","p":206838},{"i":206851,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#keys-string","p":206838},{"i":206853,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#data_type-string","p":206838},{"i":206855,"t":"redis authentication password, you need it when you connect to an encrypted cluster","s":"auth [String]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#auth-string","p":206838},{"i":206857,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#format-string","p":206838},{"i":206859,"t":"fields [Config]​ the schema fields of upstream data","s":"schema [Config]","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#schema-config","p":206838},{"i":206861,"t":"Source plugin common parameters, please refer to Source Common Options for details","s":"common options","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#common-options","p":206838},{"i":206863,"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":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#example","p":206838},{"i":206866,"t":"Add Redis Source Connector","s":"2.2.0-beta 2022-09-26","u":"/zh-CN/docs/2.3.0-beta/connector-v2/source/Redis","h":"#220-beta-2022-09-26","p":206838},{"i":206868,"t":"ElasticSearch connector based flink sql","s":"Flink SQL ElasticSearch Connector","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"","p":206867},{"i":206870,"t":"With elasticsearch connector, you can use the Flink SQL to write data into ElasticSearch.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"#description","p":206867},{"i":206872,"t":"Let us have a brief example to show how to use the connector.","s":"Usage","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"#usage","p":206867},{"i":206874,"t":"Please refer to the Elastic Doc to prepare elastic environment.","s":"1. Elastic prepare","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"#1-elastic-prepare","p":206867},{"i":206876,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"#2-prepare-seatunnel-configuration","p":206867},{"i":206878,"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":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"#3-start-flink-sql-job","p":206867},{"i":206880,"t":"Verify result from elasticsearch.","s":"4. verify result","u":"/zh-CN/docs/2.3.0-beta/connector/flink-sql/ElasticSearch","h":"#4-verify-result","p":206867},{"i":206882,"t":"Doris sink connector","s":"Doris","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Doris","h":"","p":206881},{"i":206884,"t":"Write Data to a Doris Table. 提示 Engine Supported and plugin name Spark: Doris Flink: DorisSink","s":"Description:","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Doris","h":"#description","p":206881},{"i":206886,"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]https://doris.apache.org/docs/data-operate/import/import-way/stream-load-manual) 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":"/zh-CN/docs/2.3.0-beta/connector/sink/Doris","h":"#options","p":206881},{"i":206888,"t":"The parallelism of an individual operator, for DorisSink","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Doris","h":"#parallelism-int","p":206881},{"i":206890,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Doris","h":"#examples","p":206881},{"i":206892,"t":"Druid sink connector","s":"Druid","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"","p":206891},{"i":206894,"t":"Write data to Apache Druid. 提示 Engine Supported and plugin name Spark Flink: Druid","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#description","p":206891},{"i":206896,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#options","p":206891},{"i":206898,"t":"The URL of Coordinator service in Apache Druid.","s":"coordinator_url [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#coordinator_url-string","p":206891},{"i":206900,"t":"The DataSource name in Apache Druid.","s":"datasource [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#datasource-string","p":206891},{"i":206902,"t":"The timestamp column name in Apache Druid, the default value is timestamp.","s":"timestamp_column [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#timestamp_column-string","p":206891},{"i":206904,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#timestamp_format-string","p":206891},{"i":206906,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#timestamp_missing_value-string","p":206891},{"i":206908,"t":"The parallelism of an individual operator, for DruidSink","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#parallelism-int","p":206891},{"i":206911,"t":"DruidSink { coordinator_url = \"http://localhost:8081/\" datasource = \"wikipedia\" }","s":"Simple","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#simple","p":206891},{"i":206913,"t":"DruidSink { coordinator_url = \"http://localhost:8081/\" datasource = \"wikipedia\" timestamp_column = \"timestamp\" timestamp_format = \"auto\" }","s":"Specified timestamp column and format","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#specified-timestamp-column-and-format","p":206891},{"i":206915,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Druid","h":"#specified-timestamp-column-format-and-missing-value","p":206891},{"i":206917,"t":"Elasticsearch sink connector","s":"Elasticsearch","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"","p":206916},{"i":206919,"t":"Output data to Elasticsearch. 提示 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":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#description","p":206916},{"i":206921,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#options","p":206916},{"i":206923,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#hosts-array","p":206916},{"i":206925,"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-${now} , index_time_format can specify the time format of the index name, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows: Symbol Description y Year M Month d Day of month H Hour in day (0-23) m Minute in hour s Second in minute See Java SimpleDateFormat for detailed time format syntax.","s":"index_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#index_type-string","p":206916},{"i":206927,"t":"Elasticsearch index name. If you need to generate an index based on time, you can specify a time variable, such as seatunnel-${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":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#index-string","p":206916},{"i":206929,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#es-string","p":206916},{"i":206931,"t":"The parallelism of an individual operator, data source, or data sink","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#parallelism-int","p":206916},{"i":206933,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#common-options-string","p":206916},{"i":206935,"t":"elasticsearch { hosts = [\"localhost:9200\"] index = \"seatunnel\" }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Elasticsearch","h":"#examples","p":206916},{"i":206937,"t":"Email sink connector","s":"Email","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"","p":206936},{"i":206939,"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. 提示 Engine Supported and plugin name Spark: Email Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#description","p":206936},{"i":206941,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#options","p":206936},{"i":206943,"t":"Email Subject","s":"subject [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#subject-string","p":206936},{"i":206945,"t":"Email sender","s":"from [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#from-string","p":206936},{"i":206947,"t":"Email recipients, multiple recipients separated by ,","s":"to [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#to-string","p":206936},{"i":206949,"t":"Email content, text format","s":"bodyText [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#bodytext-string","p":206936},{"i":206951,"t":"Email content, hypertext content","s":"bodyHtml [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#bodyhtml-string","p":206936},{"i":206953,"t":"Email CC, multiple CCs separated by ,","s":"cc [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#cc-string","p":206936},{"i":206955,"t":"Email Bcc, multiple Bccs separated by ,","s":"bcc [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#bcc-string","p":206936},{"i":206957,"t":"Email server address, for example: stmp.exmail.qq.com","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#host-string","p":206936},{"i":206959,"t":"Email server port For example: 25","s":"port [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#port-string","p":206936},{"i":206961,"t":"The password of the email sender, the user name is the sender specified by from","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#password-string","p":206936},{"i":206963,"t":"The number of rows to include, the default is 100000","s":"limit [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#limit-string","p":206936},{"i":206965,"t":"The security properties for encrypted link to smtp server, the default is false","s":"use_ssl [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#use_ssl-boolean","p":206936},{"i":206967,"t":"The security properties for encrypted link to smtp server, the default is false","s":"use_tls [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#use_tls-boolean","p":206936},{"i":206969,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Email","h":"#examples","p":206936},{"i":206971,"t":"File sink connector","s":"File","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"","p":206970},{"i":206973,"t":"Output data to local or hdfs file. 提示 Engine Supported and plugin name Spark: File Flink: File","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#description","p":206970},{"i":206975,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#options","p":206970},{"i":206977,"t":"Custom parameters","s":"options [object]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#options-object","p":206970},{"i":206979,"t":"Partition data based on selected fields","s":"partition_by [array]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#partition_by-array","p":206970},{"i":206981,"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, ${now} represents the current time, and its format can be defined by specifying the option path_time_format","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#path-string","p":206970},{"i":206983,"t":"When the format in the path parameter is xxxx-${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":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#path_time_format-string","p":206970},{"i":206985,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#save_mode-string","p":206970},{"i":206987,"t":"Serialization method, currently supports csv , json , parquet , orc and text","s":"serializer [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#serializer-string","p":206970},{"i":206989,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#common-options-string","p":206970},{"i":206991,"t":"Currently, csv , json , and text are supported. The streaming mode currently only supports text","s":"format [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#format-string","p":206970},{"i":206993,"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, ${now} represents the current time, and its format can be defined by specifying the option path_time_format","s":"path [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#path-string-1","p":206970},{"i":206995,"t":"When the format in the path parameter is xxxx-${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":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#path_time_format-string-1","p":206970},{"i":206997,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#write_mode-string","p":206970},{"i":206999,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#common-options-string-1","p":206970},{"i":207001,"t":"The parallelism of an individual operator, for FileSink","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#parallelism-int","p":206970},{"i":207003,"t":"The new file part rollover interval, unit min.","s":"rollover_interval [long]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#rollover_interval-long","p":206970},{"i":207005,"t":"The max size of each file part, unit MB.","s":"max_part_size [long]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#max_part_size-long","p":206970},{"i":207007,"t":"The prefix of each file part.","s":"prefix [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#prefix-string","p":206970},{"i":207009,"t":"The suffix of each file part.","s":"suffix [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#suffix-string","p":206970},{"i":207011,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/File","h":"#example","p":206970},{"i":207013,"t":"Iceberg sink connector","s":"Iceberg","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"","p":207012},{"i":207015,"t":"Write data to Iceberg. 提示 Engine Supported and plugin name Spark: Iceberg Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#description","p":207012},{"i":207017,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#options","p":207012},{"i":207019,"t":"Iceberg table location.","s":"path","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#path","p":207012},{"i":207021,"t":"append or overwrite. Only these two modes are supported by iceberg. The default value is append.","s":"saveMode","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#savemode","p":207012},{"i":207023,"t":"Overrides this table’s write.target-file-size-bytes","s":"target-file-size-bytes","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#target-file-size-bytes","p":207012},{"i":207025,"t":"Sets the nullable check on fields","s":"check-nullability","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#check-nullability","p":207012},{"i":207027,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#snapshot-propertycustom-key","p":207012},{"i":207029,"t":"Overrides this table’s write.spark.fanout.enabled","s":"fanout-enabled","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#fanout-enabled","p":207012},{"i":207031,"t":"Checks if input schema and table schema are same","s":"check-ordering","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#check-ordering","p":207012},{"i":207033,"t":"iceberg { path = \"hdfs://localhost:9000/iceberg/warehouse/db/table\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Iceberg","h":"#example","p":207012},{"i":207035,"t":"Hive sink connector","s":"Hive","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hive","h":"","p":207034},{"i":207037,"t":"Write Rows to Apache Hive. 提示 Engine Supported and plugin name Spark: Hive Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hive","h":"#description","p":207034},{"i":207039,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hive","h":"#options","p":207034},{"i":207041,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hive","h":"#example","p":207034},{"i":207043,"t":"Hbase sink connector","s":"Hbase","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"","p":207042},{"i":207045,"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. 提示 Engine Supported and plugin name Spark: Hbase Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#description","p":207042},{"i":207047,"t":"name type required default value hbase.zookeeper.quorum string yes catalog string yes staging_dir string yes save_mode string no append nullable bool no false hbase.* string no","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#options","p":207042},{"i":207049,"t":"The address of the zookeeper cluster, the format is: host01:2181,host02:2181,host03:2181","s":"hbase.zookeeper.quorum [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#hbasezookeeperquorum-string","p":207042},{"i":207051,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#catalog-string","p":207042},{"i":207053,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#staging_dir-string","p":207042},{"i":207055,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#save_mode-string","p":207042},{"i":207057,"t":"Whether the null value is written to hbase","s":"nullable [bool]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#nullable-bool","p":207042},{"i":207059,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#hbase-string","p":207042},{"i":207061,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#common-options-string","p":207042},{"i":207063,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hbase","h":"#examples","p":207042},{"i":207065,"t":"Hudi sink connector","s":"Hudi","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hudi","h":"","p":207064},{"i":207067,"t":"Write Rows to a Hudi. 提示 Engine Supported and plugin name Spark: Hudi Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hudi","h":"#description","p":207064},{"i":207069,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hudi","h":"#options","p":207064},{"i":207071,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Hudi","h":"#hoodiebasepath-string","p":207064},{"i":207073,"t":"Table name that will be used for registering with Hive. Needs to be same across runs.","s":"hoodie.table.name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hudi","h":"#hoodietablename-string","p":207064},{"i":207075,"t":"hudi { hoodie.base.path = \"hdfs://\" hoodie.table.name = \"seatunnel_hudi\" }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Hudi","h":"#examples","p":207064},{"i":207077,"t":"InfluxDB sink connector","s":"InfluxDB","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"","p":207076},{"i":207079,"t":"Write data to InfluxDB. 提示 Engine Supported and plugin name Spark Flink: InfluxDB","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#description","p":207076},{"i":207081,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#options","p":207076},{"i":207083,"t":"The URL of InfluxDB Server.","s":"server_url [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#server_url-string","p":207076},{"i":207085,"t":"The username of InfluxDB Server.","s":"username [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#username-string","p":207076},{"i":207087,"t":"The password of InfluxDB Server.","s":"password [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#password-string","p":207076},{"i":207089,"t":"The database name in InfluxDB.","s":"database [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#database-string","p":207076},{"i":207091,"t":"The Measurement name in InfluxDB.","s":"measurement [String]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#measurement-string","p":207076},{"i":207093,"t":"The list of Tag in InfluxDB.","s":"tags [List<String>]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#tags-liststring","p":207076},{"i":207095,"t":"The list of Field in InfluxDB.","s":"fields [List<String>]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#fields-liststring","p":207076},{"i":207097,"t":"The parallelism of an individual operator, for InfluxDbSink","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#parallelism-int","p":207076},{"i":207100,"t":"InfluxDbSink { server_url = \"http://127.0.0.1:8086/\" database = \"influxdb\" measurement = \"m\" tags = [\"country\", \"city\"] fields = [\"count\"] }","s":"Simple","u":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#simple","p":207076},{"i":207102,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/InfluxDB","h":"#auth","p":207076},{"i":207104,"t":"JDBC sink connector","s":"Jdbc","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"","p":207103},{"i":207106,"t":"Write data through jdbc 提示 Engine Supported and plugin name Spark: Jdbc Flink: Jdbc","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#description","p":207103},{"i":207108,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#options","p":207103},{"i":207110,"t":"The URL of the JDBC connection. Refer to a case: jdbc:mysql://localhost/dbName","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#url-string","p":207103},{"i":207112,"t":"username password [string]​ user password","s":"user [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#user-string","p":207103},{"i":207114,"t":"Sink table name, if the table does not exist, it will be created.","s":"dbTable [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#dbtable-string","p":207103},{"i":207116,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#savemode-string","p":207103},{"i":207118,"t":"Configure when saveMode is specified as update , whether to enable ssl, the default value is false","s":"useSsl [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#usessl-string","p":207103},{"i":207120,"t":"The transaction isolation level, which applies to current connection. The default value is READ_UNCOMMITTED","s":"isolationLevel [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#isolationlevel-string","p":207103},{"i":207122,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#customupdatestmt-string","p":207103},{"i":207124,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#duplicateincs-string","p":207103},{"i":207126,"t":"Configure when saveMode is specified as update , whether to show sql","s":"showSql","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#showsql","p":207103},{"i":207128,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#driver-string","p":207103},{"i":207130,"t":"The URL of the JDBC connection. Such as: jdbc:mysql://localhost:3306/test","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#url-string-1","p":207103},{"i":207132,"t":"username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#username-string","p":207103},{"i":207134,"t":"password","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#password-string-1","p":207103},{"i":207136,"t":"Insert statement","s":"query [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#query-string","p":207103},{"i":207138,"t":"Number of writes per batch","s":"batch_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#batch_size-int","p":207103},{"i":207140,"t":"The parallelism of an individual operator, for JdbcSink.","s":"parallelism [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#parallelism-int","p":207103},{"i":207142,"t":"This sql can be executed before output.","s":"pre_sql [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#pre_sql-string","p":207103},{"i":207144,"t":"This sql can be executed after output, and just supports for batch job.","s":"post_sql [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#post_sql-string","p":207103},{"i":207146,"t":"Whether to ignore post_sql exceptions.","s":"ignore_post_sql_exceptions [boolean]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#ignore_post_sql_exceptions-boolean","p":207103},{"i":207148,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#common-options-string","p":207103},{"i":207150,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Jdbc","h":"#examples","p":207103},{"i":207152,"t":"Kudu sink connector","s":"Kudu","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"","p":207151},{"i":207154,"t":"Write data to Kudu. 提示 Engine Supported and plugin name Spark: Kudu Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"#description","p":207151},{"i":207156,"t":"name type required default value kudu_master string yes - kudu_table string yes - mode string no insert","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"#options","p":207151},{"i":207158,"t":"Kudu master, multiple masters are separated by commas","s":"kudu_master [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"#kudu_master-string","p":207151},{"i":207160,"t":"The name of the table to be written in kudu, the table must already exist","s":"kudu_table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"#kudu_table-string","p":207151},{"i":207162,"t":"Write the mode adopted in kudu, support insert|update|upsert|insertIgnore, the default is insert.","s":"mode [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"#mode-string","p":207151},{"i":207164,"t":"kudu { kudu_master=\"hadoop01:7051,hadoop02:7051,hadoop03:7051\" kudu_table=\"my_kudu_table\" mode=\"upsert\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kudu","h":"#example","p":207151},{"i":207166,"t":"MongoDB sink connector","s":"MongoDB","u":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"","p":207165},{"i":207168,"t":"Write data to MongoDB 提示 Engine Supported and plugin name Spark: MongoDB Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#description","p":207165},{"i":207170,"t":"name type required default value writeconfig.uri string yes - writeconfig.database string yes - writeconfig.collection string yes - writeconfig.* string no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#options","p":207165},{"i":207172,"t":"uri to write to mongoDB","s":"writeconfig.uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#writeconfiguri-string","p":207165},{"i":207174,"t":"database to write to mongoDB","s":"writeconfig.database [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#writeconfigdatabase-string","p":207165},{"i":207176,"t":"collection to write to mongoDB","s":"writeconfig.collection [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#writeconfigcollection-string","p":207165},{"i":207178,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#writeconfig-string","p":207165},{"i":207180,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/MongoDB","h":"#examples","p":207165},{"i":207182,"t":"Phoenix sink connector","s":"Phoenix","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"","p":207181},{"i":207184,"t":"Export data to Phoenix , compatible with Kerberos authentication 提示 Engine Supported and plugin name Spark: Phoenix Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#description","p":207181},{"i":207186,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#options","p":207181},{"i":207188,"t":"Connection string, configuration example: host1:2181,host2:2181,host3:2181 [/znode]","s":"zk-connect [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#zk-connect-string","p":207181},{"i":207190,"t":"Target table name tenantId [string]​ Tenant ID, optional configuration item","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#table-string","p":207181},{"i":207192,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#skipnormalizingidentifier-boolean","p":207181},{"i":207194,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#common-options-string","p":207181},{"i":207196,"t":"Phoenix { zk-connect = \"host1:2181,host2:2181,host3:2181\" table = \"tableName\" }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Phoenix","h":"#examples","p":207181},{"i":207198,"t":"Redis sink connector","s":"Redis","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"","p":207197},{"i":207200,"t":"Write Rows to a Redis. 提示 Engine Supported and plugin name Spark: Redis Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#description","p":207197},{"i":207202,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#options","p":207197},{"i":207204,"t":"Redis server address, default \"localhost\"","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#host-string","p":207197},{"i":207206,"t":"Redis service port, default 6379","s":"port [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#port-int","p":207197},{"i":207208,"t":"Redis authentication password","s":"auth [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#auth-string","p":207197},{"i":207210,"t":"Redis database index ID. It is connected to db 0 by default","s":"db_num [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#db_num-int","p":207197},{"i":207212,"t":"Redis timeout","s":"redis_timeout [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#redis_timeout-int","p":207197},{"i":207214,"t":"Redis data type eg: KV HASH LIST SET ZSET","s":"data_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#data_type-string","p":207197},{"i":207216,"t":"if redis data type is HASH must config hash name","s":"hash_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#hash_name-string","p":207197},{"i":207218,"t":"if redis data type is LIST must config list name","s":"list_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#list_name-string","p":207197},{"i":207220,"t":"if redis data type is ZSET must config zset name","s":"zset_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#zset_name-string","p":207197},{"i":207222,"t":"if redis data type is SET must config set name","s":"set_name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#set_name-string","p":207197},{"i":207224,"t":"redis data expiration ttl, 0 means no expiration.","s":"ttl [int]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#ttl-int","p":207197},{"i":207226,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#is_self_achieved-boolean","p":207197},{"i":207228,"t":"redis { host = \"localhost\" port = 6379 auth = \"myPassword\" db_num = 1 data_type = \"HASH\" hash_name = \"test\" is_self_achieved = false }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Redis","h":"#examples","p":207197},{"i":207230,"t":"Kafka sink connector","s":"Kafka","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"","p":207229},{"i":207232,"t":"Write Rows to a Kafka topic. 提示 Engine Supported and plugin name Spark: Kafka Flink: Kafka","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#description","p":207229},{"i":207234,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#options","p":207229},{"i":207236,"t":"Kafka Brokers List","s":"producer.bootstrap.servers [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#producerbootstrapservers-string","p":207229},{"i":207238,"t":"Kafka Topic","s":"topic [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#topic-string","p":207229},{"i":207240,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#producer-string","p":207229},{"i":207242,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#semantic-string","p":207229},{"i":207244,"t":"Sink plugin common parameters, please refer to Sink Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#common-options-string","p":207229},{"i":207246,"t":"kafka { topics = \"seatunnel\" producer.bootstrap.servers = \"localhost:9092\" }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Kafka","h":"#examples","p":207229},{"i":207248,"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":"/zh-CN/docs/2.3.0-beta/connector/source/common-options","h":"","p":207247},{"i":207250,"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":"/zh-CN/docs/2.3.0-beta/connector/source/common-options","h":"#result_table_name-string","p":207247},{"i":207252,"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":"/zh-CN/docs/2.3.0-beta/connector/source/common-options","h":"#table_name-string","p":207247},{"i":207254,"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":"/zh-CN/docs/2.3.0-beta/connector/source/common-options","h":"#result_table_name-string-1","p":207247},{"i":207256,"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":"/zh-CN/docs/2.3.0-beta/connector/source/common-options","h":"#field_name-string","p":207247},{"i":207258,"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":"/zh-CN/docs/2.3.0-beta/connector/source/common-options","h":"#example","p":207247},{"i":207260,"t":"TiDB sink connector","s":"TiDB","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Tidb","h":"","p":207259},{"i":207262,"t":"Write data to TiDB. 提示 Engine Supported and plugin name Spark: TiDB Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/sink/Tidb","h":"#description","p":207259},{"i":207264,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Tidb","h":"#env-options","p":207259},{"i":207266,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Tidb","h":"#options","p":207259},{"i":207268,"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":"/zh-CN/docs/2.3.0-beta/connector/sink/Tidb","h":"#examples","p":207259},{"i":207270,"t":"Druid source connector","s":"Druid","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"","p":207269},{"i":207272,"t":"Read data from Apache Druid. 提示 Engine Supported and plugin name Spark Flink: Druid","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#description","p":207269},{"i":207274,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#options","p":207269},{"i":207276,"t":"The URL of JDBC of Apache Druid.","s":"jdbc_url [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#jdbc_url-string","p":207269},{"i":207278,"t":"The DataSource name in Apache Druid.","s":"datasource [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#datasource-string","p":207269},{"i":207280,"t":"The start date of DataSource, for example, '2016-06-27', '2016-06-27 00:00:00', etc.","s":"start_date [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#start_date-string","p":207269},{"i":207282,"t":"The end date of DataSource, for example, '2016-06-28', '2016-06-28 00:00:00', etc.","s":"end_date [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#end_date-string","p":207269},{"i":207284,"t":"These columns that you want to query of DataSource.","s":"columns [List<String>]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#columns-liststring","p":207269},{"i":207286,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#common-options-string","p":207269},{"i":207288,"t":"The parallelism of an individual operator, for DruidSource","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#parallelism-int","p":207269},{"i":207290,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Druid","h":"#example","p":207269},{"i":207292,"t":"Elasticsearch source connector","s":"Elasticsearch","u":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"","p":207291},{"i":207294,"t":"Read data from Elasticsearch 提示 Engine Supported and plugin name Spark: Elasticsearch Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#description","p":207291},{"i":207296,"t":"name type required default value hosts array yes - index string yes es.* string no common-options string yes -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#options","p":207291},{"i":207298,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#hosts-array","p":207291},{"i":207300,"t":"ElasticSearch index name, support * fuzzy matching","s":"index [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#index-string","p":207291},{"i":207302,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#es-string","p":207291},{"i":207304,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#common-options-string","p":207291},{"i":207306,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Elasticsearch","h":"#examples","p":207291},{"i":207308,"t":"Fake source connector","s":"Fake","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"","p":207307},{"i":207310,"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. 备注 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":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#description","p":207307},{"i":207312,"t":"Spark Flink 备注 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":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#options","p":207307},{"i":207314,"t":"List of test data strings","s":"content [array]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#content-array","p":207307},{"i":207316,"t":"Number of test cases generated per second","s":"rate [number]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#rate-number","p":207307},{"i":207318,"t":"The parallelism of an individual operator, for Fake Source Stream","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#parallelism-int","p":207307},{"i":207320,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#common-options-string","p":207307},{"i":207322,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#mock_data_schema-option--listcolumn_config","p":207307},{"i":207324,"t":"Config mock data size.","s":"mock_data_size Option [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#mock_data_size-option-int","p":207307},{"i":207326,"t":"Config the data can mock with interval, The unit is SECOND.","s":"mock_data_interval Option [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#mock_data_interval-option-int","p":207307},{"i":207328,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#examples","p":207307},{"i":207329,"t":"Fake { result_table_name = \"my_dataset\" }","s":"Fake","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#fake-1","p":207307},{"i":207331,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#fakestream","p":207307},{"i":207333,"t":"source { FakeSourceStream { result_table_name = \"fake\" field_name = \"name,age\" } }","s":"FakeSourceStream","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#fakesourcestream","p":207307},{"i":207335,"t":"source { FakeSource { result_table_name = \"fake\" field_name = \"name,age\" mock_data_size = 100 // will generate 100 rows mock data. } }","s":"FakeSource","u":"/zh-CN/docs/2.3.0-beta/connector/source/Fake","h":"#fakesource","p":207307},{"i":207337,"t":"File source connector","s":"File","u":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"","p":207336},{"i":207339,"t":"Read data from local or hdfs file. 提示 Engine Supported and plugin name Spark: File Flink: File","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#description","p":207336},{"i":207341,"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":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#options","p":207336},{"i":207343,"t":"The format for reading files from the file system, currently supports csv , json , parquet , orc and text .","s":"format.type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#formattype-string","p":207336},{"i":207345,"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":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#schema-string","p":207336},{"i":207347,"t":"The parallelism of an individual operator, for FileSource","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#parallelism-int","p":207336},{"i":207349,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#common-options-string","p":207336},{"i":207351,"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":"/zh-CN/docs/2.3.0-beta/connector/source/File","h":"#examples","p":207336},{"i":207353,"t":"Hbase source connector","s":"HBase","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"","p":207352},{"i":207355,"t":"Get data from HBase 提示 Engine Supported and plugin name Spark: HBase Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"#description","p":207352},{"i":207357,"t":"name type required default value hbase.zookeeper.quorum string yes catalog string yes common-options string yes -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"#options","p":207352},{"i":207359,"t":"The address of the zookeeper cluster, the format is: host01:2181,host02:2181,host03:2181","s":"hbase.zookeeper.quorum [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"#hbasezookeeperquorum-string","p":207352},{"i":207361,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"#catalog-string","p":207352},{"i":207363,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"#common-options-string","p":207352},{"i":207365,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hbase","h":"#example","p":207352},{"i":207367,"t":"Feishu sheet source connector","s":"Feishu Sheet","u":"/zh-CN/docs/2.3.0-beta/connector/source/FeishuSheet","h":"","p":207366},{"i":207369,"t":"Get data from Feishu sheet 提示 Engine Supported and plugin name Spark: FeishuSheet Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/FeishuSheet","h":"#description","p":207366},{"i":207371,"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":"/zh-CN/docs/2.3.0-beta/connector/source/FeishuSheet","h":"#options","p":207366},{"i":207373,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/FeishuSheet","h":"#common-options-string","p":207366},{"i":207375,"t":"FeishuSheet { result_table_name = \"my_dataset\" appId = \"cli_a2cbxxxxxx\" appSecret = \"IvhtW7xxxxxxxxxxxxxxx\" sheetToken = \"shtcn6K3DIixxxxxxxxxxxx\" # range = \"A1:D4\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/FeishuSheet","h":"#example","p":207366},{"i":207377,"t":"Hudi source connector","s":"Hudi","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"","p":207376},{"i":207379,"t":"Read data from Hudi. 提示 Engine Supported and plugin name Spark: Hudi Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#description","p":207376},{"i":207381,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#options","p":207376},{"i":207383,"t":"Comma separated list of file paths to read within a Hudi table.","s":"hoodie.datasource.read.paths","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadpaths","p":207376},{"i":207385,"t":"Enables use of the spark file index implementation for Hudi, that speeds up listing of large tables.","s":"hoodie.file.index.enable","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiefileindexenable","p":207376},{"i":207387,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadendinstanttime","p":207376},{"i":207389,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcewriteprecombinefield","p":207376},{"i":207391,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadincrfilters","p":207376},{"i":207393,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcemergetype","p":207376},{"i":207395,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadbegininstanttime","p":207376},{"i":207397,"t":"enable data skipping to boost query after doing z-order optimize for current table","s":"hoodie.enable.data.skipping","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodieenabledataskipping","p":207376},{"i":207399,"t":"The query instant for time travel. Without specified this option, we query the latest snapshot.","s":"as.of.instant","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#asofinstant","p":207376},{"i":207401,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcequerytype","p":207376},{"i":207403,"t":"Uses end instant schema when incrementally fetched data to. Default: users latest instant schema.","s":"hoodie.datasource.read.schema.use.end.instanttime","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#hoodiedatasourcereadschemauseendinstanttime","p":207376},{"i":207405,"t":"hudi { hoodie.datasource.read.paths = \"hdfs://\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hudi","h":"#example","p":207376},{"i":207407,"t":"Http source connector","s":"Http","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"","p":207406},{"i":207409,"t":"Get data from http or https interface 提示 Engine Supported and plugin name Spark: Http Flink: Http","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#description","p":207406},{"i":207411,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#options","p":207406},{"i":207413,"t":"HTTP request path, starting with http:// or https://.","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#url-string","p":207406},{"i":207415,"t":"HTTP request method, GET or POST, default GET.","s":"method[string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#methodstring","p":207406},{"i":207417,"t":"HTTP request header, json format.","s":"header[string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#headerstring","p":207406},{"i":207419,"t":"HTTP request parameters, json format. Use string with escapes to save json","s":"request_params[string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#request_paramsstring","p":207406},{"i":207421,"t":"HTTP multiple requests, the storage path of parameters used for synchronization (hdfs).","s":"sync_path[string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#sync_pathstring","p":207406},{"i":207423,"t":"Source plugin common parameters, please refer to Source Plugin for details.","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#common-options-string","p":207406},{"i":207425,"t":"Http { url = \"http://date.jsontest.com/\" result_table_name= \"response_body\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#example","p":207406},{"i":207427,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Http","h":"#notes","p":207406},{"i":207429,"t":"Hive source connector","s":"Hive","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"","p":207428},{"i":207431,"t":"Get data from hive 提示 Engine Supported and plugin name Spark: Hive Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"#description","p":207428},{"i":207433,"t":"name type required default value pre_sql string yes - common-options string yes -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"#options","p":207428},{"i":207435,"t":"For preprocessed sql , if preprocessing is not required, you can use select * from hive_db.hive_table .","s":"pre_sql [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"#pre_sql-string","p":207428},{"i":207437,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"#common-options-string","p":207428},{"i":207439,"t":"env { ... spark.sql.catalogImplementation = \"hive\" ... } source { hive { pre_sql = \"select * from mydb.mytb\" result_table_name = \"myTable\" } } ...","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"#example","p":207428},{"i":207441,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Hive","h":"#notes","p":207428},{"i":207443,"t":"Iceberg source connector","s":"Iceberg","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"","p":207442},{"i":207445,"t":"Read data from Iceberg. 提示 Engine Supported and plugin name Spark: Iceberg Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#description","p":207442},{"i":207447,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#options","p":207442},{"i":207449,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common-options","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#common-options","p":207442},{"i":207451,"t":"Iceberg table location.","s":"path","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#path","p":207442},{"i":207453,"t":"SQL statements queried from iceberg table. Note that the table name is result_table_name configuration","s":"pre_sql","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#pre_sql","p":207442},{"i":207455,"t":"Snapshot ID of the table snapshot to read","s":"snapshot-id","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#snapshot-id","p":207442},{"i":207457,"t":"A timestamp in milliseconds; the snapshot used will be the snapshot current at this time.","s":"as-of-timestamp","u":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#as-of-timestamp","p":207442},{"i":207459,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Iceberg","h":"#example","p":207442},{"i":207461,"t":"InfluxDB source connector","s":"InfluxDB","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"","p":207460},{"i":207463,"t":"Read data from InfluxDB. 提示 Engine Supported and plugin name Spark Flink: InfluxDB","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#description","p":207460},{"i":207465,"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":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#options","p":207460},{"i":207467,"t":"The URL of InfluxDB Server.","s":"server_url [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#server_url-string","p":207460},{"i":207469,"t":"The username of InfluxDB Server.","s":"username [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#username-string","p":207460},{"i":207471,"t":"The password of InfluxDB Server.","s":"password [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#password-string","p":207460},{"i":207473,"t":"The database name in InfluxDB.","s":"database [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#database-string","p":207460},{"i":207475,"t":"The Measurement name in InfluxDB.","s":"measurement [String]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#measurement-string","p":207460},{"i":207477,"t":"The list of Field in InfluxDB.","s":"fields [List<String>]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#fields-liststring","p":207460},{"i":207479,"t":"The list of Field Types in InfluxDB.","s":"field_types [List<String>]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#field_types-liststring","p":207460},{"i":207481,"t":"The parallelism of an individual operator, for InfluxDbSource.","s":"parallelism [Int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#parallelism-int","p":207460},{"i":207484,"t":"InfluxDbSource { server_url = \"http://127.0.0.1:8086/\" database = \"influxdb\" measurement = \"m\" fields = [\"time\", \"temperature\"] field_types = [\"STRING\", \"DOUBLE\"] }","s":"Simple","u":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#simple","p":207460},{"i":207486,"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":"/zh-CN/docs/2.3.0-beta/connector/source/InfluxDB","h":"#auth","p":207460},{"i":207488,"t":"Kafka source connector","s":"Kafka","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"","p":207487},{"i":207490,"t":"To consume data from Kafka , supported Kafka version >= 0.10.0 . 提示 Engine Supported and plugin name Spark: KafkaStream Flink: Kafka","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#description","p":207487},{"i":207492,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#options","p":207487},{"i":207494,"t":"Kafka topic name. If there are multiple topics, use , to split, for example: \"tpc1,tpc2\"","s":"topics [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#topics-string","p":207487},{"i":207496,"t":"Kafka consumer group id, used to distinguish different consumer groups","s":"consumer.group.id [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#consumergroupid-string","p":207487},{"i":207498,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#consumerbootstrapservers-string","p":207487},{"i":207500,"t":"Currently supports three formats json csv avro","s":"format.type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#formattype-string","p":207487},{"i":207502,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#format-string","p":207487},{"i":207504,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#schema-string","p":207487},{"i":207506,"t":"Extract timestamp using current configuration field for flink event time watermark","s":"rowtime.field [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#rowtimefield-string","p":207487},{"i":207508,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#watermark-long","p":207487},{"i":207510,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#offsetreset-string","p":207487},{"i":207512,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#consumer-string","p":207487},{"i":207514,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#common-options-string","p":207487},{"i":207516,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Kafka","h":"#examples","p":207487},{"i":207518,"t":"Kudu source connector","s":"Kudu","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"","p":207517},{"i":207520,"t":"Read data from Kudu. 提示 Engine Supported and plugin name Spark: Kudu Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"#description","p":207517},{"i":207522,"t":"name type required default value kudu_master string yes - kudu_table string yes -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"#options","p":207517},{"i":207524,"t":"Kudu Master","s":"kudu_master [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"#kudu_master-string","p":207517},{"i":207526,"t":"Kudu Table","s":"kudu_table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"#kudu_table-string","p":207517},{"i":207528,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"#common-options-string","p":207517},{"i":207530,"t":"kudu { kudu_master = \"master:7051\" kudu_table = \"impala::test_db.test_table\" result_table_name = \"kudu_result_table\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/Kudu","h":"#example","p":207517},{"i":207532,"t":"MongoDb source connector","s":"MongoDb","u":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"","p":207531},{"i":207534,"t":"Read data from MongoDB. 提示 Engine Supported and plugin name Spark: MongoDb Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#description","p":207531},{"i":207536,"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":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#options","p":207531},{"i":207538,"t":"MongoDB uri","s":"readconfig.uri [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#readconfiguri-string","p":207531},{"i":207540,"t":"MongoDB database","s":"readconfig.database [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#readconfigdatabase-string","p":207531},{"i":207542,"t":"MongoDB collection","s":"readconfig.collection [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#readconfigcollection-string","p":207531},{"i":207544,"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":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#readconfig-string","p":207531},{"i":207546,"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":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#schema-string","p":207531},{"i":207548,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#common-options-string","p":207531},{"i":207550,"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":"/zh-CN/docs/2.3.0-beta/connector/source/MongoDB","h":"#example","p":207531},{"i":207552,"t":"JDBC source connector","s":"Jdbc","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"","p":207551},{"i":207554,"t":"Read external data source data through JDBC 提示 Engine Supported and plugin name Spark: Jdbc Flink: Jdbc","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#description","p":207551},{"i":207556,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#options","p":207551},{"i":207558,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#driver-string","p":207551},{"i":207560,"t":"password​","s":"password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#password-string","p":207551},{"i":207562,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#url-string","p":207551},{"i":207564,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#jdbc-string","p":207551},{"i":207566,"t":"username","s":"user [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#user-string","p":207551},{"i":207568,"t":"table name","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#table-string","p":207551},{"i":207570,"t":"username","s":"username [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#username-string","p":207551},{"i":207572,"t":"Query statement","s":"query [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#query-string","p":207551},{"i":207574,"t":"fetch size","s":"fetch_size [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#fetch_size-int","p":207551},{"i":207576,"t":"The parallelism of an individual operator, for JdbcSource.","s":"parallelism [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#parallelism-int","p":207551},{"i":207578,"t":"The column name for parallelism's partition, only support numeric type.","s":"partition_column [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#partition_column-string","p":207551},{"i":207580,"t":"The partition_column max value for scan, if not set SeaTunnel will query database get max value.","s":"partition_upper_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#partition_upper_bound-long","p":207551},{"i":207582,"t":"The partition_column min value for scan, if not set SeaTunnel will query database get min value.","s":"partition_lower_bound [long]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#partition_lower_bound-long","p":207551},{"i":207584,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#common-options-string","p":207551},{"i":207586,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Jdbc","h":"#example","p":207551},{"i":207588,"t":"Phoenix source connector","s":"Phoenix","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"","p":207587},{"i":207590,"t":"Read external data source data through Phoenix , compatible with Kerberos authentication 提示 Engine Supported and plugin name Spark: Phoenix Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#description","p":207587},{"i":207592,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#options","p":207587},{"i":207594,"t":"Connection string, configuration example: host1:2181,host2:2181,host3:2181 [/znode]","s":"zk-connect [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#zk-connect-string","p":207587},{"i":207596,"t":"Source data table name","s":"table [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#table-string","p":207587},{"i":207598,"t":"Read column name configuration. Read all columns set to [] , optional configuration item, default is [] .","s":"columns [string-list]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#columns-string-list","p":207587},{"i":207600,"t":"Tenant ID, optional configuration item","s":"tenant-id [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#tenant-id-string","p":207587},{"i":207602,"t":"Conditional filter string configuration, optional configuration items","s":"predicate [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#predicate-string","p":207587},{"i":207604,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#common-options-string","p":207587},{"i":207606,"t":"Phoenix { zk-connect = \"host1:2181,host2:2181,host3:2181\" table = \"table22\" result_table_name = \"tmp1\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/Phoenix","h":"#example","p":207587},{"i":207608,"t":"Neo4j source connector","s":"Neo4j","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"","p":207607},{"i":207610,"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 提示 Engine Supported and plugin name Spark: Neo4j Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#description","p":207607},{"i":207612,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#options","p":207607},{"i":207614,"t":"result table name","s":"result.table.name [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#resulttablename-string","p":207607},{"i":207616,"t":"authentication type","s":"authentication.type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#authenticationtype-string","p":207607},{"i":207618,"t":"username","s":"authentication.basic.username [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#authenticationbasicusername-string","p":207607},{"i":207620,"t":"password","s":"authentication.basic.password [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#authenticationbasicpassword-string","p":207607},{"i":207622,"t":"url","s":"url [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#url-string","p":207607},{"i":207624,"t":"Cypher query to read the data.You must specify one option from [query, labels OR relationship]","s":"query [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#query-string","p":207607},{"i":207626,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#labels-string","p":207607},{"i":207628,"t":"Name of a relationship. You must specify one option from [query, labels OR relationship]","s":"relationship [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#relationship-string","p":207607},{"i":207630,"t":"Number of records to be used to create the Schema (only if APOC are not installed)","s":"schema.flatten.limit [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#schemaflattenlimit-string","p":207607},{"i":207632,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#schemastrategy-string","p":207607},{"i":207634,"t":"Enable or disable the Push Down Filters support","s":"pushdown.filters.enabled [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#pushdownfiltersenabled-string","p":207607},{"i":207636,"t":"Enable or disable the Push Down Column support","s":"pushdown.columns.enabled [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#pushdowncolumnsenabled-string","p":207607},{"i":207638,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#partitions-string","p":207607},{"i":207640,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#querycount-string","p":207607},{"i":207642,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#relationshipnodesmap-string","p":207607},{"i":207644,"t":"List of source node Labels separated by :","s":"relationship.source.labels [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#relationshipsourcelabels-string","p":207607},{"i":207646,"t":"List of target node Labels separated by :","s":"relationship.target.labels [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#relationshiptargetlabels-string","p":207607},{"i":207648,"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":"/zh-CN/docs/2.3.0-beta/connector/source/neo4j","h":"#example","p":207607},{"i":207650,"t":"Redis source connector","s":"Redis","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"","p":207649},{"i":207652,"t":"Read data from Redis. 提示 Engine Supported and plugin name Spark: Redis Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#description","p":207649},{"i":207654,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#options","p":207649},{"i":207656,"t":"Redis server address, default \"localhost\"","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#host-string","p":207649},{"i":207658,"t":"Redis service port, default 6379","s":"port [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#port-int","p":207649},{"i":207660,"t":"Redis authentication password","s":"auth [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#auth-string","p":207649},{"i":207662,"t":"Redis database index ID. It is connected to db 0 by default","s":"db_num [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#db_num-int","p":207649},{"i":207664,"t":"Redis Key, support fuzzy matching","s":"keys_or_key_pattern [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#keys_or_key_pattern-string","p":207649},{"i":207666,"t":"Number of Redis shards. The default is 3","s":"partition_num [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#partition_num-int","p":207649},{"i":207668,"t":"Redis data type eg: KV HASH LIST SET ZSET","s":"data_type [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#data_type-string","p":207649},{"i":207670,"t":"Redis timeout","s":"timeout [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#timeout-int","p":207649},{"i":207672,"t":"Source Plugin common parameters, refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#common-options-string","p":207649},{"i":207674,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#is_self_achieved-boolean","p":207649},{"i":207676,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Redis","h":"#example","p":207649},{"i":207678,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"","p":207677},{"i":207680,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"#create-plugin-module","p":207677},{"i":207682,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"#add-plugin-implementation","p":207677},{"i":207684,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"#add-plugin-to-the-distribution","p":207677},{"i":207686,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"#add-information-to-plugin-mappingproperties-file","p":207677},{"i":207688,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"","p":207677},{"i":207690,"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":"/zh-CN/docs/2.3.0-beta/contribution/contribute-plugin","h":"","p":207677},{"i":207692,"t":"Socket source connector","s":"Socket","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"","p":207691},{"i":207694,"t":"SocketStream is mainly used to receive Socket data and is used to quickly verify Spark streaming computing. 提示 Engine Supported and plugin name Spark: SocketStream Flink: SocketStream","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#description","p":207691},{"i":207696,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#options","p":207691},{"i":207698,"t":"socket server hostname","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#host-string","p":207691},{"i":207700,"t":"socket server port","s":"port [number]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#port-number","p":207691},{"i":207702,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#common-options-string","p":207691},{"i":207704,"t":"socket server hostname","s":"host [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#host-string-1","p":207691},{"i":207706,"t":"socket server port","s":"port [int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#port-int","p":207691},{"i":207708,"t":"Source plugin common parameters, please refer to Source Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#common-options-string-1","p":207691},{"i":207710,"t":"Spark Flink source { SocketStream { port = 9999 } } source { SocketStream{ result_table_name = \"socket\" field_name = \"info\" } }","s":"Examples","u":"/zh-CN/docs/2.3.0-beta/connector/source/Socket","h":"#examples","p":207691},{"i":207713,"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":"/zh-CN/docs/2.3.0-beta/contribution/new-license","h":"#asf-3rd-party-license-policy","p":207711},{"i":207715,"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":"/zh-CN/docs/2.3.0-beta/contribution/new-license","h":"#how-to-legally-use-3rd-party-open-source-software-in-the-seatunnel","p":207711},{"i":207717,"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 check dependency license fail --- /dev/fd/63 2020-12-03 03:08:57.191579482 +0000 +++ /dev/fd/62 2020-12-03 03:08:57.191579482 +0000 @@ -1,0 +2 @@ +HikariCP-java6-2.3.13.jar @@ -16,0 +18 @@ +c3p0-0.9.5.2.jar @@ -149,0 +152 @@ +mchange-commons-java-0.2.11.jar - commons-lang-2.1.3.jar Error: Process completed with exit code 1. Generally speaking, the work of adding a jar is often not so easy to end, because it often depends on various other jars, and we also need to add corresponding licenses for these jars. In this case, we will get the error message of check dependency license fail in check. As above, we are missing the license declaration of HikariCP-java6-2.3.13, c3p0, etc. (+ means new, - means need to delete ), follow the steps to add jar to add","s":"SeaTunnel-License Check Rules","u":"/zh-CN/docs/2.3.0-beta/contribution/new-license","h":"#seatunnel-license-check-rules","p":207711},{"i":207719,"t":"COMMUNITY-LED DEVELOPMENT \"THE APACHE WAY\" ASF 3RD PARTY LICENSE POLICY","s":"References","u":"/zh-CN/docs/2.3.0-beta/contribution/new-license","h":"#references","p":207711},{"i":207721,"t":"Webhook source connector","s":"WebhookStream","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"","p":207720},{"i":207723,"t":"Provide http interface to push data，only supports post requests. 提示 Engine Supported and plugin name Spark: WebhookStream Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"#description","p":207720},{"i":207725,"t":"name type required default value port int no 9999 path string no /","s":"Options","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"#options","p":207720},{"i":207727,"t":"Port for push requests, default 9999.","s":"port[int]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"#portint","p":207720},{"i":207729,"t":"Push request path, default \"/\".","s":"path[string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"#pathstring","p":207720},{"i":207731,"t":"Source plugin common parameters, please refer to Source Plugin for details.","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"#common-options-string","p":207720},{"i":207733,"t":"WebhookStream { result_table_name = \"request_body\" }","s":"Example","u":"/zh-CN/docs/2.3.0-beta/connector/source/Webhook","h":"#example","p":207720},{"i":207735,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"","p":207734},{"i":207737,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#prepare","p":207734},{"i":207740,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#clone-the-source-code","p":207734},{"i":207742,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#install-subproject-locally","p":207734},{"i":207744,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#building-seatunnel-from-source","p":207734},{"i":207746,"t":"If you want to build submodules separately,you can use the follow command to compile and package. # This is an example of building the redis connector separately mvn clean package -pl seatunnel-connectors-v2/connector-redis -am -DskipTests -T 1C","s":"Building sub module","u":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#building-sub-module","p":207734},{"i":207748,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#install-jetbrains-idea-scala-plugin","p":207734},{"i":207750,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#install-jetbrains-idea-lombok-plugin","p":207734},{"i":207752,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#install-jetbrains-idea-checkstyle-idea-plugin","p":207734},{"i":207754,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#run-simple-example","p":207734},{"i":207756,"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":"/zh-CN/docs/2.3.0-beta/contribution/setup","h":"#whats-more","p":207734},{"i":207758,"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":"/zh-CN/docs/2.3.0-beta/intro/about","h":"","p":207757},{"i":207760,"t":"Mass data synchronization Mass data integration ETL with massive data Mass data aggregation Multi-source data processing","s":"Use Scenarios","u":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#use-scenarios","p":207757},{"i":207762,"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":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#features","p":207757},{"i":207764,"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":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#workflow","p":207757},{"i":207766,"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":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#connector","p":207757},{"i":207768,"t":"SeaTunnel have lots of users which you can find more information in users","s":"Who Use SeaTunnel","u":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#who-use-seatunnel","p":207757},{"i":207770,"t":"SeaTunnel enriches the CNCF CLOUD NATIVE Landscape.","s":"Landscapes","u":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#landscapes","p":207757},{"i":207772,"t":"You can see Quick Start for the next step.","s":"What's More","u":"/zh-CN/docs/2.3.0-beta/intro/about","h":"#whats-more","p":207757},{"i":207774,"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":"/zh-CN/docs/2.3.0-beta/deployment","h":"","p":207773},{"i":207777,"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":"/zh-CN/docs/2.3.0-beta/deployment","h":"#local-modespark-only","p":207773},{"i":207779,"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":"/zh-CN/docs/2.3.0-beta/deployment","h":"#standalone-cluster","p":207773},{"i":207781,"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":"/zh-CN/docs/2.3.0-beta/deployment","h":"#yarn-cluster","p":207773},{"i":207783,"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":"/zh-CN/docs/2.3.0-beta/deployment","h":"#mesos-clusterspark-only","p":207773},{"i":207785,"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":"/zh-CN/docs/2.3.0-beta/deployment","h":"#run-your-engine-in-scaling","p":207773},{"i":207787,"t":"SeaTunnel was formerly named WaterDrop","s":"History of Seatunnel","u":"/zh-CN/docs/2.3.0-beta/intro/history","h":"","p":207786},{"i":207789,"t":"This project renamed to SeaTunnel since Oct 12th, 2021.","s":"Rename to SeaTunnel","u":"/zh-CN/docs/2.3.0-beta/intro/history","h":"#rename-to-seatunnel","p":207786},{"i":207791,"t":"SeaTunnel joined the Apache Software Foundation’s Incubator program in Dec 9th, 2021.","s":"Enter Apache Software Foundation’s Incubator","u":"/zh-CN/docs/2.3.0-beta/intro/history","h":"#enter-apache-software-foundations-incubator","p":207786},{"i":207793,"t":"TiDB source connector","s":"Tidb","u":"/zh-CN/docs/2.3.0-beta/connector/source/Tidb","h":"","p":207792},{"i":207795,"t":"Read data from Tidb. 提示 Engine Supported and plugin name Spark: Tidb Flink","s":"Description","u":"/zh-CN/docs/2.3.0-beta/connector/source/Tidb","h":"#description","p":207792},{"i":207797,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Tidb","h":"#env-options","p":207792},{"i":207799,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Tidb","h":"#options","p":207792},{"i":207801,"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":"/zh-CN/docs/2.3.0-beta/connector/source/Tidb","h":"#example","p":207792},{"i":207804,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#why-i-should-install-computing-engine-like-spark-or-flink","p":207802},{"i":207806,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#i-have-a-question-but-i-can-not-solve-it-by-myself","p":207802},{"i":207808,"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 = '\"${date}\"'\" } } ... Taking Spark Local mode as an example, the startup command is as follows: ./bin/start-seatunnel-spark.sh \\ -c ./config/your_app.conf \\ -e client \\ -m local[2] \\ -i city=shanghai \\ -i date=20190319 You can use the parameter -i or --variable followed with key=value to specify the value of the variable, where the key needs to be same as the variable name in the configuration.","s":"how to declare variable","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-declare-variable","p":207802},{"i":207810,"t":"When a configured text is very long and you want to wrap it, you can use three double quotes to indicate it: var = \"\"\" whatever you want \"\"\"","s":"How to write a configuration item into multi-line text in the configuration file?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-write-a-configuration-item-into-multi-line-text-in-the-configuration-file","p":207802},{"i":207812,"t":"It is a little troublesome to do variable substitution in multi-line text, because the variable cannot be included in three double quotation marks: var = \"\"\" your string 1 \"\"\"${you_var}\"\"\" your string 2\"\"\" refer to: lightbend/config#456","s":"How to implement variable substitution for multi-line text?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-implement-variable-substitution-for-multi-line-text","p":207802},{"i":207814,"t":"Of course, please see the screenshot below:","s":"Is SeaTunnel supportted in Azkaban, Oozie, DolphinScheduler?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#is-seatunnel-supportted-in-azkaban-oozie-dolphinscheduler","p":207802},{"i":207816,"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":"/zh-CN/docs/2.3.0-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":207802},{"i":207818,"t":"There is hbase input plugin, download it from here: https://github.com/garyelephant/waterdrop-input-hbase","s":"Are there any HBase plugins?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#are-there-any-hbase-plugins","p":207802},{"i":207820,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-use-seatunnel-to-write-data-to-hive","p":207802},{"i":207822,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-does-seatunnel-write-multiple-instances-of-clickhouse-to-achieve-load-balancing","p":207802},{"i":207824,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-solve-oom-when-seatunnel-consumes-kafka","p":207802},{"i":207826,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-solve-the-error-exception-in-thread-main-javalangnosuchfielderror-instance","p":207802},{"i":207828,"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":"/zh-CN/docs/2.3.0-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":207802},{"i":207830,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-specify-a-different-jdk-version-for-seatunnel-on-yarn","p":207802},{"i":207832,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#what-should-i-do-if-oom-always-appears-when-running-seatunnel-in-spark-local-mode","p":207802},{"i":207834,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#where-can-the-self-written-plugins-or-third-party-jdbcjar-be-placed-to-be-loaded-by-seatunnel","p":207802},{"i":207836,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-configure-logging-related-parameters-in-seatunnel-v1spark","p":207802},{"i":207838,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#error-when-writing-to-clickhouse-classcastexception","p":207802},{"i":207840,"t":"Please refer to: #590","s":"How does SeaTunnel access kerberos-authenticated HDFS, YARN, Hive and other resources?","u":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-does-seatunnel-access-kerberos-authenticated-hdfs-yarn-hive-and-other-resources","p":207802},{"i":207842,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-troubleshoot-noclassdeffounderror-classnotfoundexception-and-other-issues","p":207802},{"i":207844,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#how-to-use-seatunnel-to-synchronize-data-across-hdfs-clusters","p":207802},{"i":207846,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#i-want-to-learn-the-source-code-of-seatunnel-where-should-i-start","p":207802},{"i":207848,"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":"/zh-CN/docs/2.3.0-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":207802},{"i":207850,"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":"/zh-CN/docs/2.3.0-beta/faq","h":"#import-project-compiler-has-exception-class-not-found-orgapacheseatunnelshadecomtypesafeconfigconfig","p":207802},{"i":207852,"t":"WIP","s":"Set Up with Docker","u":"/zh-CN/docs/2.3.0-beta/start-v2/docker","h":"","p":207851},{"i":207854,"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":"/zh-CN/docs/2.3.0-beta/intro/why","h":"","p":207853},{"i":207856,"t":"WIP","s":"Set Up with Docker","u":"/zh-CN/docs/2.3.0-beta/start/docker","h":"","p":207855},{"i":207858,"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":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"","p":207857},{"i":207860,"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":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#step-1-prepare-the-environment","p":207857},{"i":207862,"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=\"2.3.0-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":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#step-2-download-seatunnel","p":207857},{"i":207864,"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 2.3.0-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. 提示 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":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#step-3-install-connectors-plugin","p":207857},{"i":207867,"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.","s":"Spark or Flink","u":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#spark-or-flink","p":207857},{"i":207869,"t":"SeaTunnel Engine is the default engine for SeaTunnel, You do not need to do other additional configuration operations.","s":"SeaTunnel Engine","u":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#seatunnel-engine","p":207857},{"i":207871,"t":"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 { execution.parallelism = 1 job.mode = \"BATCH\" } source { FakeSource { result_table_name = \"fake\" row.num = 16 schema = { fields { name = \"string\" age = \"int\" } } } } transform { } sink { Console {} } More information about config please check config concept","s":"Add Job Config File to define a job","u":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#add-job-config-file-to-define-a-job","p":207857},{"i":207873,"t":"You could start the application by the following commands Spark Flink SeaTunnel Engine 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-${version}\" ./bin/start-seatunnel-flink-connector-v2.sh \\ --config ./config/seatunnel.streaming.conf.template cd \"apache-seatunnel-incubating-${version}\" ./bin/seatunnel.sh \\ --config ./config/seatunnel.streaming.conf.template -e local 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":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#step-5-run-seatunnel-application","p":207857},{"i":207875,"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":"/zh-CN/docs/2.3.0-beta/start-v2/local","h":"#whats-more","p":207857},{"i":207877,"t":"This section provides a quick guide to using SeaTunnel with Kubernetes.","s":"Set Up with Kubernetes","u":"/zh-CN/docs/2.3.0-beta/start-v2/kubernetes","h":"","p":207876},{"i":207879,"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":"/zh-CN/docs/2.3.0-beta/start-v2/kubernetes","h":"#prerequisites","p":207876},{"i":207882,"t":"To run the image with SeaTunnel, first create a Dockerfile: Flink FROM flink:1.13 ENV SEATUNNEL_VERSION=\"2.3.0-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:2.3.0-beta-flink-1.13 -f Dockerfile . Image seatunnel:2.3.0-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:2.3.0-beta-flink-1.13","s":"SeaTunnel docker image","u":"/zh-CN/docs/2.3.0-beta/start-v2/kubernetes","h":"#seatunnel-docker-image","p":207876},{"i":207884,"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":"/zh-CN/docs/2.3.0-beta/start-v2/kubernetes","h":"#deploying-the-operator","p":207876},{"i":207886,"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:2.3.0-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":"/zh-CN/docs/2.3.0-beta/start-v2/kubernetes","h":"#run-seatunnel-application","p":207876},{"i":207888,"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":"/zh-CN/docs/2.3.0-beta/start-v2/kubernetes","h":"#whats-more","p":207876},{"i":207890,"t":"Common parameters of transform plugins 提示 This transform both supported by engine Spark and Flink.","s":"Common Options","u":"/zh-CN/docs/2.3.0-beta/transform/common-options","h":"","p":207889},{"i":207892,"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":"/zh-CN/docs/2.3.0-beta/transform/common-options","h":"#transform-plugin-common-parameters","p":207889},{"i":207894,"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":"/zh-CN/docs/2.3.0-beta/transform/common-options","h":"#field_name-string","p":207889},{"i":207896,"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":"/zh-CN/docs/2.3.0-beta/transform/common-options","h":"#source_table_name-string","p":207889},{"i":207898,"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":"/zh-CN/docs/2.3.0-beta/transform/common-options","h":"#result_table_name-string","p":207889},{"i":207900,"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":"/zh-CN/docs/2.3.0-beta/transform/common-options","h":"#examples","p":207889},{"i":207902,"t":"This section provides a quick guide to using SeaTunnel with Kubernetes.","s":"Set Up with Kubernetes","u":"/zh-CN/docs/2.3.0-beta/start/kubernetes","h":"","p":207901},{"i":207904,"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":"/zh-CN/docs/2.3.0-beta/start/kubernetes","h":"#prerequisites","p":207901},{"i":207907,"t":"To run the image with SeaTunnel, first create a Dockerfile: Flink FROM flink:1.13 ENV SEATUNNEL_VERSION=\"2.1.2\" 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/spark Then run the following commands to build the image: docker build -t seatunnel:2.1.2-flink-1.13 -f Dockerfile . Image seatunnel:2.1.2-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:2.1.2-flink-1.13","s":"SeaTunnel docker image","u":"/zh-CN/docs/2.3.0-beta/start/kubernetes","h":"#seatunnel-docker-image","p":207901},{"i":207909,"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":"/zh-CN/docs/2.3.0-beta/start/kubernetes","h":"#deploying-the-operator","p":207901},{"i":207911,"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:2.1.2-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":"/zh-CN/docs/2.3.0-beta/start/kubernetes","h":"#run-seatunnel-application","p":207901},{"i":207913,"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":"/zh-CN/docs/2.3.0-beta/start/kubernetes","h":"#whats-more","p":207901},{"i":207915,"t":"NULL rate transform plugin","s":"NullRate","u":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"","p":207914},{"i":207917,"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 提示 This transform ONLY supported by Spark.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#description","p":207914},{"i":207919,"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":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#options","p":207914},{"i":207921,"t":"Which fields do you want to monitor .","s":"field [string_list]","u":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#field-string_list","p":207914},{"i":207923,"t":"It is consistent with the number of fields. Double type indicates the set null rate value .","s":"rates [double_list]","u":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#rates-double_list","p":207914},{"i":207925,"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":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#throw_exception_enable-boolean","p":207914},{"i":207927,"t":"Whether the current verification value is output to the table. It is not output by defaul .","s":"save_to_table_name [string]","u":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#save_to_table_name-string","p":207914},{"i":207929,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#common-options-string","p":207914},{"i":207931,"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":"/zh-CN/docs/2.3.0-beta/transform/nullRate","h":"#examples","p":207914},{"i":207934,"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":"Prepare","u":"/zh-CN/docs/2.3.0-beta/start/local","h":"#prepare","p":207932},{"i":207936,"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=\"2.1.0\" 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":"Installation","u":"/zh-CN/docs/2.3.0-beta/start/local","h":"#installation","p":207932},{"i":207938,"t":"Since 2.3.0-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 connectors directory). sh bin/install_plugin.sh If you need to specify the version of the connector, take 2.3.0-beta as an example, we need to execute sh bin/install_plugin.sh 2.3.0-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-assert plugin, then you can modify plugin.properties as --flink-connectors-- seatunnel-connector-flink-assert --end--","s":"Install connectors plugin","u":"/zh-CN/docs/2.3.0-beta/start/local","h":"#install-connectors-plugin","p":207932},{"i":207940,"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. Run Application with Build-in Configure: We already provide an out-of-box configuration in the directory config which you could find when you extract the tarball. 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":"Run SeaTunnel Application","u":"/zh-CN/docs/2.3.0-beta/start/local","h":"#run-seatunnel-application","p":207932},{"i":207942,"t":"Our local quick start is using one of the build-in example in directory config, and we provide 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":"/zh-CN/docs/2.3.0-beta/start/local","h":"#explore-more-build-in-examples","p":207932},{"i":207944,"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":"/zh-CN/docs/2.3.0-beta/start/local","h":"#whats-more","p":207932},{"i":207946,"t":"Json transform plugin","s":"Json","u":"/zh-CN/docs/2.3.0-beta/transform/json","h":"","p":207945},{"i":207948,"t":"Json analysis of the specified fields of the original data set 提示 This transform ONLY supported by Spark.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#description","p":207945},{"i":207950,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#options","p":207945},{"i":207952,"t":"Source field, if not configured, the default is raw_message","s":"source_field [string]","u":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#source_field-string","p":207945},{"i":207954,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#target_field-string","p":207945},{"i":207956,"t":"Style directory, if not configured, the default is $seatunnelRoot/plugins/json/files/schemas/","s":"schema_dir [string]","u":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#schema_dir-string","p":207945},{"i":207958,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#schema_file-string","p":207945},{"i":207960,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#common-options-string","p":207945},{"i":207962,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#schema-use-cases","p":207945},{"i":207965,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#do-not-use-target_field","p":207945},{"i":207967,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#use-target_field","p":207945},{"i":207969,"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":"/zh-CN/docs/2.3.0-beta/transform/json","h":"#use-schema_file","p":207945},{"i":207971,"t":"NULL default value transform plugin","s":"Nulltf","u":"/zh-CN/docs/2.3.0-beta/transform/nulltf","h":"","p":207970},{"i":207973,"t":"set default value for null field 提示 This transform only supported by engine Spark.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/nulltf","h":"#description","p":207970},{"i":207975,"t":"name type required default value fields array no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/transform/nulltf","h":"#options","p":207970},{"i":207977,"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":"/zh-CN/docs/2.3.0-beta/transform/nulltf","h":"#fields-list","p":207970},{"i":207979,"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":"/zh-CN/docs/2.3.0-beta/transform/nulltf","h":"#examples","p":207970},{"i":207981,"t":"Split transform plugin","s":"Split","u":"/zh-CN/docs/2.3.0-beta/transform/split","h":"","p":207980},{"i":207983,"t":"A string cutting function is defined, which is used to split the specified field in the Sql plugin. 提示 This transform both supported by engine Spark and Flink.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#description","p":207980},{"i":207985,"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":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#options","p":207980},{"i":207987,"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":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#separator-string","p":207980},{"i":207989,"t":"The source field of the string before being split, if not configured, the default is raw_message","s":"source_field [string]","u":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#source_field-string","p":207980},{"i":207991,"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":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#target_field-string","p":207980},{"i":207993,"t":"The specified delimiter, the default is ,","s":"separator [string]","u":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#separator-string-1","p":207980},{"i":207995,"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":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#fields-list","p":207980},{"i":207997,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#common-options-string","p":207980},{"i":207999,"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":"/zh-CN/docs/2.3.0-beta/transform/split","h":"#examples","p":207980},{"i":208001,"t":"Replace transform plugin","s":"Replace","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"","p":208000},{"i":208003,"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. 提示 This transform ONLY supported by Spark.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#description","p":208000},{"i":208005,"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":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#options","p":208000},{"i":208007,"t":"Source field, if not configured, the default is raw_message","s":"source_field [string]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#source_field-string","p":208000},{"i":208009,"t":"The name of the field to replaced.","s":"field [string]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#field-string","p":208000},{"i":208011,"t":"The string to match.","s":"pattern [string]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#pattern-string","p":208000},{"i":208013,"t":"The replacement pattern (is_regex is true) or string literal (is_regex is false).","s":"replacement [string]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#replacement-string","p":208000},{"i":208015,"t":"Whether or not to interpret the pattern as a regex (true) or string literal (false).","s":"is_regex [boolean]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#is_regex-boolean","p":208000},{"i":208017,"t":"Whether or not to skip any matches beyond the first match.","s":"replace_first [boolean]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#replace_first-boolean","p":208000},{"i":208019,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#common-options-string","p":208000},{"i":208021,"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":"/zh-CN/docs/2.3.0-beta/transform/replace","h":"#examples","p":208000},{"i":208023,"t":"About SeaTunnel Web 版本：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. 编辑此页 下一页 Deployment of Apache SeaTunnel Web","s":"About SeaTunnel Web","u":"/zh-CN/seatunnel_web/1.0.0/about","h":"","p":208022},{"i":208025,"t":"DataSource 版本：1.0.0 Supported DataSources List all datasource supported Apache SeaTunnel Web for now. 📄️ JDBC-MySQL DataSource 上一页 Define And Run SeaTunnel Job 下一页 JDBC-MySQL DataSource","s":"Supported DataSources","u":"/zh-CN/seatunnel_web/1.0.0/category/datasource","h":"","p":208024},{"i":208027,"t":"SinkNodes 版本：1.0.0 Supported SinkNodes List all sink nodes supported Apache SeaTunnel Web for now. 📄️ JDBC-MySQL Sink Node 上一页 JDBC-MySQL Source Node 下一页 JDBC-MySQL Sink Node","s":"Supported SinkNodes","u":"/zh-CN/seatunnel_web/1.0.0/category/sink_node","h":"","p":208026},{"i":208029,"t":"SourceNodes 版本：1.0.0 Supported SourceNodes List all source nodes supported Apache SeaTunnel Web for now. 📄️ JDBC-MySQL Source Node 上一页 JDBC-MySQL DataSource 下一页 JDBC-MySQL Source Node","s":"Supported SourceNodes","u":"/zh-CN/seatunnel_web/1.0.0/category/source_node","h":"","p":208028},{"i":208031,"t":"DataSource JDBC-MySQL DataSource 版本：1.0.0 JDBC-MySQL DataSource 编辑此页 上一页 DataSource 下一页 SourceNodes","s":"JDBC-MySQL DataSource","u":"/zh-CN/seatunnel_web/1.0.0/datasources/jdbc-mysql","h":"","p":208030},{"i":208033,"t":"Deployment of Apache SeaTunnel Web 版本：1.0.0 本页总览 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) 编辑此页 上一页 About SeaTunnel Web 下一页 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":"/zh-CN/seatunnel_web/1.0.0/deploy","h":"","p":208032},{"i":208035,"t":"Sql transform plugin","s":"Sql","u":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"","p":208034},{"i":208037,"t":"Use SQL to process data and support engine's UDF function. 提示 This transform both supported by engine Spark and Flink.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#description","p":208034},{"i":208039,"t":"name type required default value sql string yes - common-options string no -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#options","p":208034},{"i":208041,"t":"SQL statement, the table name used in SQL configured in the Source or Transform plugin","s":"sql [string]","u":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#sql-string","p":208034},{"i":208043,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#common-options-string","p":208034},{"i":208046,"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":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#simple-select","p":208034},{"i":208048,"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":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#use-udf","p":208034},{"i":208050,"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":"/zh-CN/docs/2.3.0-beta/transform/sql","h":"#use-udaf","p":208034},{"i":208052,"t":"Define And Run SeaTunnel Job 版本：1.0.0 Define And Run SeaTunnel Job 编辑此页 上一页 Deployment of Apache SeaTunnel Web 下一页 DataSource","s":"Define And Run SeaTunnel Job","u":"/zh-CN/seatunnel_web/1.0.0/how_to_use","h":"","p":208051},{"i":208054,"t":"SinkNodes JDBC-MySQL Sink Node 版本：1.0.0 JDBC-MySQL Sink Node 编辑此页 上一页 SinkNodes","s":"JDBC-MySQL Sink Node","u":"/zh-CN/seatunnel_web/1.0.0/sinknodes/jdbc-mysql","h":"","p":208053},{"i":208056,"t":"SourceNodes JDBC-MySQL Source Node 版本：1.0.0 JDBC-MySQL Source Node 编辑此页 上一页 SourceNodes 下一页 SinkNodes","s":"JDBC-MySQL Source Node","u":"/zh-CN/seatunnel_web/1.0.0/sourcenodes/jdbc-mysql","h":"","p":208055},{"i":208058,"t":"此为 Apache SeaTunnel Next 版尚未发行的文档。 最新的文档请参阅 最新版本 (1.0.0)。 版本：Next 本页总览 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​ 编辑此页 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":"/zh-CN/seatunnel_web/next/datasources/jdbc-mysql","h":"","p":208057},{"i":208060,"t":"此为 Apache SeaTunnel Next 版尚未发行的文档。 最新的文档请参阅 最新版本 (1.0.0)。 About SeaTunnel Web 版本：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 编辑此页","s":"About SeaTunnel Web","u":"/zh-CN/seatunnel_web/next/about","h":"","p":208059},{"i":208062,"t":"此为 Apache SeaTunnel Next 版尚未发行的文档。 最新的文档请参阅 最新版本 (1.0.0)。 版本：Next Define And Run SeaTunnel Job 编辑此页","s":"Define And Run SeaTunnel Job","u":"/zh-CN/seatunnel_web/next/how_to_use","h":"","p":208061},{"i":208064,"t":"此为 Apache SeaTunnel Next 版尚未发行的文档。 最新的文档请参阅 最新版本 (1.0.0)。 版本：Next 本页总览 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. 编辑此页 1 Preparing the Apache SeaTunnel environment 2 Run SeaTunnel Web In Server","s":"Deploy","u":"/zh-CN/seatunnel_web/next/deploy","h":"","p":208063},{"i":208066,"t":"此为 Apache SeaTunnel Next 版尚未发行的文档。 最新的文档请参阅 最新版本 (1.0.0)。 版本：Next JDBC-MySQL Source Node 编辑此页","s":"JDBC-MySQL Source Node","u":"/zh-CN/seatunnel_web/next/sourcenodes/jdbc-mysql","h":"","p":208065},{"i":208068,"t":"此为 Apache SeaTunnel Next 版尚未发行的文档。 最新的文档请参阅 最新版本 (1.0.0)。 版本：Next JDBC-MySQL Sink Node 编辑此页","s":"JDBC-MySQL Sink Node","u":"/zh-CN/seatunnel_web/next/sinknodes/jdbc-mysql","h":"","p":208067},{"i":208070,"t":"贡献指南 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/ 编辑此页 上一页 订阅邮件列表 下一页 成为 Apache SeaTunnel Committer","s":"Code Review Guidance","u":"/zh-CN/community/contribution_guide/code-review","h":"","p":208069},{"i":208072,"t":"贡献指南 订阅邮件列表 订阅邮件列表 强烈推荐订阅开发邮件列表，与社区保持最新信息同步，这一点非常重要。 在使用 Seatunnel 的过程中，如果您有任何问题或者想法、建议，都可以通过 Apache 邮件列表参与到 Seatunnel 的社区建设中来。 发送订阅邮件也非常简单，步骤如下: 用自己的邮箱向 dev-subscribe@seatunnel.apache.org 发送一封邮件，主题和内容任意。 接收确认邮件并回复。 完成步骤1后，您将收到一封来自 dev-help@seatunnel.apache.org 的确认邮件（如未收到，请确认邮件是否被自动归入垃圾邮件、推广邮件、订阅邮件等文件夹）。然后直接回复该邮件，或点击邮件里的链接快捷回复即可，主题和内容任意。 接收欢迎邮件。 完成以上步骤后，您会收到一封主题为 WELCOME to dev@seatunnel.apache.org 的欢迎邮件，至此您已成功订阅 Apache Seatunnel 的邮件列表。 取消订阅邮件列表 如果您不再需要了解 Seatunnel 的动态，可以取消订阅邮件列表。 取消订阅邮件列表步骤如下: 用已经订阅的邮箱向 dev-unsubscribe@seatunnel.apache.org 发送一封邮件，主题和内容任意。 接收确认邮件并回复。 完成步骤1后，您将收到一封来自 dev-help@seatunnel.apache.org 的确认邮件（如未收到，请确认邮件是否被自动归入垃圾邮件、推广邮件、订阅邮件等文件夹）。然后直接回复该邮件，或点击邮件里的链接快捷回复即可，主题和内容任意。 接收告别邮件。 完成以上步骤后，您会收到一封主题为 GOODBYE from dev@seatunnel.apache.org 的告别邮件，至此您已成功取消订阅 Apache Seatunnel 的邮件列表，以后将不会再接收来自 dev@seatunnel.apache.org 的邮件通知。 编辑此页 上一页 参与贡献 下一页 Code Review Guidance","s":"订阅邮件列表","u":"/zh-CN/community/contribution_guide/subscribe","h":"","p":208071},{"i":208074,"t":"贡献指南 成为 Apache SeaTunnel Committer 本页总览 成为 Apache SeaTunnel Committer SeaTunnel Project Management Committee (PMC) 负责评估候选人的贡献。 与许多 Apache 项目一样，SeaTunnel 欢迎所有贡献，包括代码贡献、博客文章、新用户指南、公开演讲以及以各种方式增强项目。 成为 Committer​ 要开始为 SeaTunnel 做贡献，请了解如何贡献——任何人都可以向项目提交补丁、文档和示例。 PMC 会根据积极贡献者对 SeaTunnel 的贡献定期增加新的 Committer。新 Committer 的资格包括： 持续为 SeaTunnel 做贡献：Committer 应该有对 SeaTunnel 做主要贡献的历史。理想的 Committer 应该在整个项目中都有广泛的贡献，并且至少贡献了一个主要组件，并在其中扮演了“所有者”的角色。所有者角色意味着现有贡献者认为他们应该让这个人来运行这个组件的补丁。 贡献质量：Committer 比任何其他社区成员都更应该提交简单、经过良好测试和设计良好的补丁。此外，他们应该表现出足够的专业知识来审查补丁，包括确保它们符合 SeaTunnel 的工程实践（可测试性、文档、API 稳定性、代码风格等）。Committer 团队集体负责 SeaTunnel 的软件质量和可维护性。请注意，对 SeaTunnel 关键部分（如核心和 SQL 模块）的贡献在评估质量时将有更高的标准。这些领域的贡献者将面临对其更改的更多审查。 社区参与：Committer 在所有社区互动中都应该有建设性和友好的态度。他们还应该活跃在开发和用户列表中，并帮助指导新的贡献者和用户。在设计讨论中，即使面对分歧，Committer 也应该保持专业和外交的态度。 Committer​ 提名新 Committer​ 在 SeaTunnel 中，新 Committer 提名只能由现有的 PMC 成员正式启动。如果新 Committer 觉得他/她有资格，他/她应该联系任何现有的 PMC 成员并进行讨论。如果在 PMC 的一些成员之间达成一致，该过程将启动。 建议采取以下步骤（仅由现有 PMC 成员发起）： 发送标题为 [DISCUSS] Promote xxx as new committer 的电子邮件到 private@seatunnel.apache.org。列出候选人的重要贡献，以便你可以为你的提案收集其他 PMC 成员的支持。 保持讨论开放超过 3 天但不超过 1 周，除非有任何明确的反对或担忧。 如果 PMC 普遍同意该提案，请发送标题为 [VOTE] Promote xxx as new committer 的电子邮件到 private@seatunnel.apache.org。 保持投票过程开放超过 3 天，但不超过 1 周。如果有三个 +1 票且 +1 票 > -1 票，则视为 Consensus Approval。 发送标题为 [RESULT][VOTE] Promote xxx as new committer 的电子邮件到 private@seatunnel.apache.org，并列出投票详情，包括选民是谁。 邀请新 Committer​ 发起晋升的 PMC 成员负责向新 Committer 发送邀请，并指导他/她设置 ASF 环境。 PMC 成员应使用以下模板向新 Committer 发送电子邮件： 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. 邀请接受流程​ 新 Committer 应回复 private@seatunnel.apache.org（选择“回复所有人”），并表达他/她接受邀请的意愿。然后，此邀请将被视为项目 PMC 已接受。当然，新 Committer 也可以选择拒绝邀请。 一旦邀请被接受，新 Committer 必须采取以下步骤： 订阅 dev@seatunnel.apache.org。通常这已经完成。 选择一个未在 apache committers list page 上的 Apache ID。 下载 ICLA（如果新 Committer 作为日常工作为项目做出贡献，则需要 CCLA）。 用正确的信息填写 icla.pdf（或 ccla.pdf）后，打印并手写签​​名，扫描为 PDF，并作为附件发送到 secretary@apache.org。（如果首选电子签名，请按照 此页面 上的步骤操作） PMC 将等待 Apache 秘书确认已提交的 ICLA（或 CCLA）。新 Committer 和 PMC 将收到以下电子邮件： 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. 如果帐户尚未被请求，PMC 成员应联系项目 V.P.。V.P.可以通过 Apache Account Submission Helper Form 请求。 几天后，新 Committer 将收到确认创建帐户的电子邮件，标题为 Welcome to the Apache Software Foundation (ASF)!。恭喜！新 Committer 现在拥有正式的 Apache ID。 PMC 成员应通过 roster 将新 Committer 添加到官方 Committer 列表中。 设置 Apache ID 和开发环境​ 前往 Apache Account Utility Platform，创建你的密码，设置你的个人邮箱（Forwarding email address）和 GitHub 帐户（Your GitHub Username）。之后不久（2 小时内），你将通过电子邮件收到组织邀请。 如果你想使用 xxx@apache.org 电子邮件服务，请参阅 此处。建议使用 Gmail，因为这种转发模式在大多数邮箱服务设置中不容易找到。 按照 authorized GitHub 2FA wiki 启用 Github 上的双因素认证 (2FA)。当你将 2FA 设置为“关”时，你将被相应的 Apache committer 写权限组除名，直到你再次设置它。（注意：像对待密码一样对待你的恢复代码！） 使用 GitBox Account Linking Utility 获取 SeaTunnel 项目的写权限。 按照此 doc 更新网站。 如果你想在 Apache GitHub 组织中公开显示，你需要转到 Apache GitHub org people page，搜索你自己，并将 Organization visibility 选择为 Public。 Committer 权利、义务和责任​ SeaTunnel 项目不要求你在成为 Committer 后继续做出贡献，但我们真诚地希望你继续在我们的社区中发挥作用！ 作为 Committer，你可以： 审查并将拉取请求合并到 Apache 存储库中的 master 分支。拉取请求通常包含多个提交。这些提交必须压缩并合并为带有解释性评论的单个提交。建议新 Committer 请求高级 Committer 重新检查拉取请求。 创建并将代码推送到 Apache 存储库中的新分支。 按照 发布流程 准备新版本。记得与 Committer 团队确认现在是创建发布的合适时机。 PMC 希望新 Committer 参与发布过程以及发布投票，即使他们的投票将被视为 +1 no binding。熟悉发布过程是晋升为 PMC 成员角色的关键。 项目管理委员会 (PMC)​ 项目管理委员会 (PMC) 成员在代码贡献方面没有任何特殊权利。 他们只是监督项目并确保其符合 Apache 要求。其职能包括： 发布的绑定投票和许可证检查； 新 Committer 和 PMC 成员认可； 品牌问题的识别和品牌保护；以及 回答 ASF 董事会提出的问题，并采取必要行动。 PMC 的 V.P. 和主席是秘书，负责初始化董事会报告。 在大多数情况下，新 PMC 成员是从 Committer 团队提名的。但也有可能直接成为 PMC 成员，只要 PMC 同意提名并确信候选人已准备好。例如，这可以通过他/她已经是 Apache 成员、Apache 官员或其他项目的 PMC 成员这一事实来证明。 新 PMC 投票过程也应遵循 [DISCUSS]、[VOTE] 和 [RESULT][VOTE] 程序，使用私人邮件列表，就像 新 Committer 投票过程 一样。 一旦邀请被接受，PMC 成员应通过 roster 将新成员添加到官方 PMC 列表中。 编辑此页 上一页 Code Review Guidance 下一页 Become Apache Release Manager 成为 Committer Committer 提名新 Committer 邀请新 Committer 邀请接受流程 设置 Apache ID 和开发环境 Committer 权利、义务和责任 项目管理委员会 (PMC)","s":"成为 Apache SeaTunnel Committer","u":"/zh-CN/community/contribution_guide/committer","h":"","p":208073},{"i":208076,"t":"UDF transform plugin","s":"udf","u":"/zh-CN/docs/2.3.0-beta/transform/udf","h":"","p":208075},{"i":208078,"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' 提示 This transform ONLY supported by Flink.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/udf","h":"#description","p":208075},{"i":208080,"t":"name type required default value function string yes -","s":"Options","u":"/zh-CN/docs/2.3.0-beta/transform/udf","h":"#options","p":208075},{"i":208082,"t":"A config prefix, use like function.test=\"xxx.Test\".","s":"function [string]","u":"/zh-CN/docs/2.3.0-beta/transform/udf","h":"#function-string","p":208075},{"i":208084,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/udf","h":"#common-options-string","p":208075},{"i":208086,"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":"/zh-CN/docs/2.3.0-beta/transform/udf","h":"#examples","p":208075},{"i":208088,"t":"提交指南 License 须知 本页总览 License 须知 如您所知，Seatunnel 现属于 ASF ( Apache 基金会)下的开源项目，这意味着当您想要成为 Seatunnel 的贡献者的时候，就必须按照 Apache 的规则来，而 Apache 对于 License 有着极其严苛的规则，为了避免贡献者在 License 上浪费过多的时间， 本文将为您讲解 ASF—License 以及参与 Seatunnel 如何过早的规避掉 License 风险。 注：本文仅适用于 Apache 项目。 Apache 项目可接受的 License​ 当您想要为 Seatunnel（亦或其他 Apache 项目）增添一个新的功能，这个功能涉及到其他开源软件的引用，那么您必须注意，目前 Apache 项目支持遵从以下协议的开源软件（如果有遗漏，欢迎补充）： ASF第三方许可证策 如果您所使用的第三方软件并不在以上协议之中，那么很抱歉，您的代码将无法通过审核，建议您找寻其他替代方案。 另外，当您需要使用新的软件的时候，请将您这样做的原因、最终产出结果发邮件至 dev@seatunnel.apache.org 讨论，当得到至少3票 PPMC 认同的时候，您方可以引入。 如何在 Seatunnel 合法的使用第三方开源软件​ 当我们想要引入一个新的第三方软件(包含但不限于第三方的jar、文本、css、js、图片、图标、音视频等及在第三方基础上做的修改)至我们的项目中的时候，除了他们所遵从的协议是 Apache 允许的，另外一点很重要，就是合法的使用。您可以参考以下文章 COMMUNITY-LED DEVELOPMENT \"THE APACHE WAY\" 以 Apache 为例，当我们使用了 ZooKeeper，那么 ZooKeeper 的 NOTICE 文件（每个开源项目都会有 NOTICE 文件，一般位于根目录）则必须在我们的项目中体现，用Apache的话来讲，就是 \"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. 关于具体的各个开源协议使用协议，在此不做过多篇幅一一介绍，有兴趣可以自行查询了解。 Seatunnel-License 检测规则​ TODO 编辑此页 上一页 提交代码 Apache 项目可接受的 License 如何在 Seatunnel 合法的使用第三方开源软件 Seatunnel-License 检测规则","s":"License 须知","u":"/zh-CN/community/submit_guide/license","h":"","p":208087},{"i":208090,"t":"提交指南 文档须知 本页总览 文档须知 良好的使用文档对任何类型的软件都是至关重要的。欢迎任何可以改进 Seatunnel 文档的贡献。 获取文档项目​ Seatunnel 项目的文档维护在独立的 [git 仓库] (https://github.com/apache/seatunnel-website) 中。 首先你需要先将文档项目 fork 到自己的 github 仓库中，然后将 fork 的文档克隆到本地计算机中。 git clone https://github.com/<your-github-user-name>/seatunnel-website 预览并生成静态文件​ 本网站是使用node编译的，使用的是Docusaurus框架组件 下载并安装 nodejs(version>12.5.0) 克隆代码到本地 git clone git@github.com:apache/seatunnel-website.git 运行 npm install 来安装所需的依赖库。 在根目录运行npm run start，可以访问http://localhost:3000查看站点英文模式预览 在根目录运行npm run start-zh，可以访问http://localhost:3000查看站点的中文模式预览 要生成静态网站资源文件，运行 npm run build。构建的静态资源在build目录中。 目录结构​ |-- community //社区 |-- docs //文档 存方下一个即将发布的版本 |-- download //下载 |-- faq //Q&A |-- i18n | -- zh-CN //国际化中文 | |-- 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 // 架构/时序/流程图等的原始工程文件 |-- src | |-- components | |-- css | |-- js | |-- pages | | |-- home | | |-- index.jsx | | |-- team | | |-- user | |-- styles |-- static //图片静态资源 | |-- doc //文档的图片 | |-- user //用户的图片 | |-- home //首页的图片 | |-- img //公用图片 |-- docusaurus.config.js 规范​ 目录命名规范​ 全部采用小写方式， 以中下划线分隔，有复数结构时，要采用复数命名法， 缩写不用复数 正例： scripts / styles / components / images / utils / layouts / demo_styles / demo-scripts / img / doc 反例： script / style / demoStyles / imgs / docs vue以及静态资源文件命名规范​ 全部采用小写方式， 以中划线分隔 正例： render-dom.js / signup.css / index.html / company-logo.png 反例： renderDom.js / UserManagement.html 资源路径​ 图片资源统一放在static/{模块名}下 css等样式文件放在src/css目录下 页面内容修改​ 除了首页、团队、用户、Docs>All Version 模块页面外，其余页面都能通过底部的'Edit this page'按钮 直接跳转至对应的github的资源修改页 首页页面修改​ 访问页面 https://seatunnel.apache.org/ 位于 src/pages/home ├─home │ languages.json 首页中英文的配置 │ index.less 首页样式 团队页面修改​ 访问页面 https://seatunnel.apache.org/zh-CN/team 位于 src/pages/team ├─team │ languages.json │ index.js │ index.less 用户 列表页面修改​ 访问页面 https://seatunnel.apache.org/zh-CN/user/ 位于 `src/pages/user` └─versions data.json images.json index.js index.less languages.json version 列表页面修改​ 访问页面 https://seatunnel.apache.org/zh-CN/versions/ 位于 `src/pages/versions` └─versions languages.json index.jsorchestrator/overview.md index.less 编辑此页 上一页 Become Apache Release Manager 下一页 提交代码 获取文档项目 预览并生成静态文件 目录结构 规范 目录命名规范 vue以及静态资源文件命名规范 资源路径 页面内容修改 首页页面修改 团队页面修改 用户 列表页面修改 version 列表页面修改","s":"文档须知","u":"/zh-CN/community/submit_guide/document","h":"","p":208089},{"i":208092,"t":"贡献指南 参与贡献 本页总览 参与贡献 首先非常感谢大家选择和使用 Seatunnel，非常欢迎大家加入 Seatunnel 大家庭，融入开源世界！ 我们鼓励任何形式的参与社区，最终成为 Committer 或 PPMC，如： 将遇到的问题通过 github 上 issue 的形式反馈出来 回答别人遇到的 issue 问题 帮助完善文档 帮助项目增加测试用例 为代码添加注释 提交修复 Bug 或者 Feature 的 PR 发表应用案例实践、调度流程分析或者与调度相关的技术文章 帮助推广 Seatunnel，参与技术大会或者 meetup 的分享等 欢迎加入贡献的队伍，加入开源从提交第一个 PR 开始 比如添加代码注释或找到带有 ”easy to fix” 标记或一些非常简单的 issue(拼写错误等) 等等，先通过第一个简单的 PR 熟悉提交流程 注：贡献不仅仅限于 PR 哈，对促进项目发展的都是贡献 相信参与 Seatunnel，一定会让您从开源中受益！ 1. 参与文档贡献​ 参考参与贡献-文档需知 2. 参与代码贡献​ TODO 3. 如何领取 Issue，提交 Pull Request​ 如果你想实现某个 Feature 或者修复某个 Bug。请参考以下内容： 所有的 Bug 与新 Feature 建议使用 Issues Page 进行管理。 如果想要开发实现某个 Feature 功能，请先回复该功能所关联的 Issue，表明你当前正在这个 Issue 上工作。 并在回复的时候为自己设置一个 deadline，并添加的回复内容中。 最好在核心贡献者找到一个导师（指导者），导师会在设计与功能实现上给予即时的反馈。 你应该新建一个分支来开始你的工作，分支的名字参考参与贡献 Pull Request 需知。比如，你想完成 feature 功能并提交了 Issue 111，那么你的 branch 名字应为 feature-111。 功能名称可与导师讨论后确定。 完成后，发送一个 Pull Request 到 Apache Seatunnel，提交过程具体请参考下面《提交代码流程》。 如果是想提交 Pull Request 完成某一个 Feature 或者修复某个 Bug，这里都建议大家从小处做起，完成一个小功能就提交一次，每次别改动太多文件，改动文件太多也会给 Reviewer 造成很大的心理压力，建议通过多次 Pull Request 的方式完成。 编辑此页 下一页 订阅邮件列表 1. 参与文档贡献 2. 参与代码贡献 3. 如何领取 Issue，提交 Pull Request","s":"参与贡献","u":"/zh-CN/community/contribution_guide/contribute","h":"","p":208091},{"i":208094,"t":"提交指南 提交代码 提交代码 首先从远端仓库 https://github.com/apache/seatunnel.git fork一份代码到自己的仓库中 远端仓库中目前有三个分支： dev 日常开发分支 日常dev开发分支，新提交的代码都可以pull request到这个分支上。 2.3.9-release 发布版本分支 发布版本分支，后续会有3.0...等版本分支。 以您fork的仓库命名为seatunnel为例 把自己仓库clone到本地 git clone git@github.com:<YOUR_GITHUB_ID>/seatunnel.git 添加远端仓库地址，命名为upstream git remote add upstream git@github.com:apache/seatunnel.git 查看仓库： git remote -v 此时会有两个仓库：origin(自己的仓库)和upstream（远端仓库） 获取/更新远端仓库代码（已经是最新代码，就跳过） git fetch upstream 同步远端仓库代码到本地仓库 git checkout origin/dev git merge --no-ff upstream/dev 创建一个新的分支来修改代码 git checkout -b feature-xxx 在本地修改代码以后，提交到自己仓库： git commit -m 'commit content' git push 将修改提交到远端仓库 在 GitHub 的 PullRequest 页面，点击 \"New pull request\". 选择修改完的本地分支和要合并的目的分支，点击 \"Create pull request\". 接着社区 Committer 们会做 CodeReview，然后他会与您讨论一些细节（包括设计，实现，性能等）。当团队中所有人员对本次修改满意后，会将提交合并到dev分支 最后，恭喜您已经成为了 Apache SeaTunnel 的官方贡献者！ 编辑此页 上一页 文档须知 下一页 License 须知","s":"提交代码","u":"/zh-CN/community/submit_guide/submit-code","h":"","p":208093},{"i":208096,"t":"贡献指南 Become Apache Release Manager 本页总览 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 编辑此页 上一页 成为 Apache SeaTunnel Committer 下一页 文档须知 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":"/zh-CN/community/contribution_guide/release","h":"","p":208095},{"i":208098,"t":"UUID transform plugin","s":"UUID","u":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"","p":208097},{"i":208100,"t":"Generate a universally unique identifier on a specified field. 提示 This transform ONLY supported by Spark.","s":"Description","u":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"#description","p":208097},{"i":208102,"t":"name type required default value fields string yes - prefix string no - secure boolean no false","s":"Options","u":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"#options","p":208097},{"i":208104,"t":"The name of the field to generate.","s":"field [string]","u":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"#field-string","p":208097},{"i":208106,"t":"The prefix string constant to prepend to each generated UUID.","s":"prefix [string]","u":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"#prefix-string","p":208097},{"i":208108,"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":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"#secure-boolean","p":208097},{"i":208110,"t":"Transform plugin common parameters, please refer to Transform Plugin for details","s":"common options [string]","u":"/zh-CN/docs/2.3.0-beta/transform/uuid","h":"#common-options-string","p":208097},{"i":208112,"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\" 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