HdfsFile

Hdfs File Source Connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key Features

• batch
• stream
• exactly-once

Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot.

• column projection
• parallelism
• support user-defined split
• file format file
  • text
  • csv
  • parquet
  • orc
  • json
  • excel

Description

Read data from hdfs file system.

Supported DataSource Info

Datasource	Supported Versions
HdfsFile	hadoop 2.x and 3.x

Source Options

Name	Type	Required	Default	Description
path	string	yes	-	The source file path.
file_format_type	string	yes	-	We supported as the following file types:text json csv orc parquet excel.Please note that, The final file name will end with the file_format's suffix, the suffix of the text file is txt.
fs.defaultFS	string	yes	-	The hadoop cluster address that start with hdfs://, for example: hdfs://hadoopcluster
read_columns	list	yes	-	The read column list of the data source, user can use it to implement field projection.The file type supported column projection as the following shown:[text,json,csv,orc,parquet,excel].Tips: If the user wants to use this feature when reading text json csv files, the schema option must be configured.
hdfs_site_path	string	no	-	The path of hdfs-site.xml, used to load ha configuration of namenodes
delimiter/field_delimiter	string	no	\001	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
parse_partition_from_path	boolean	no	true	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:tyrantlucifer,age:26].Tips:Do not define partition fields in schema option.
date_format	string	no	yyyy-MM-dd	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.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
datetime_format	string	no	yyyy-MM-dd HH:mm:ss	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
time_format	string	no	HH:mm:ss	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
remote_user	string	no	-	The login user used to connect to hadoop login name. It is intended to be used for remote users in RPC, it won't have any credentials.
krb5_path	string	no	/etc/krb5.conf	The krb5 path of kerberos
kerberos_principal	string	no	-	The principal of kerberos
kerberos_keytab_path	string	no	-	The keytab path of kerberos
skip_header_row_number	long	no	0	Skip the first few lines, but only for the txt and csv.For example, set like following:skip_header_row_number = 2.then Seatunnel will skip the first 2 lines from source files
schema	config	no	-	the schema fields of upstream data
sheet_name	string	no	-	Reader the sheet of the workbook,Only used when file_format is excel.
compress_codec	string	no	none	The compress codec of files
common-options		no	-	Source plugin common parameters, please refer to Source Common Options for details.

delimiter/field_delimiter [string]

delimiter parameter will deprecate after version 2.3.5, please use field_delimiter instead.

compress_codec [string]

The compress codec of files and the details that supported as the following shown:

• txt: lzo none
• json: lzo none
• csv: lzo none
• orc/parquet:
  automatically recognizes the compression type, no additional settings required.

Tips

If you use spark/flink, In order to use this connector, You must ensure your spark/flink cluster already integrated hadoop. The tested hadoop version is 2.x. If you use SeaTunnel Engine, It automatically integrated the hadoop jar when you download and install SeaTunnel Engine. You can check the jar package under ${SEATUNNEL_HOME}/lib to confirm this.

Task Example

Simple:

This example defines a SeaTunnel synchronization task that read data from Hdfs and sends it to Hdfs.

# Defining the runtime environment
env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  HdfsFile {
  schema {
    fields {
      name = string
      age = int
    }
  }
  path = "/apps/hive/demo/student"
  type = "json"
  fs.defaultFS = "hdfs://namenode001"
  }
  # If you would like to get more information about how to configure seatunnel and see full list of source plugins,
  # please go to https://seatunnel.apache.org/docs/category/source-v2
}

transform {
  # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/category/transform-v2
}

sink {
    HdfsFile {
      fs.defaultFS = "hdfs://hadoopcluster"
      path = "/tmp/hive/warehouse/test2"
      file_format = "orc"
    }
  # If you would like to get more information about how to configure seatunnel and see full list of sink plugins,
  # please go to https://seatunnel.apache.org/docs/category/sink-v2
}