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Version: 2.3.1


Hdfs file source connector


Read data from hdfs file system.


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.

Key features

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


nametyperequireddefault value
datetime_formatstringnoyyyy-MM-dd HH:mm:ss

path [string]

The source file path.

delimiter [string]

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]

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:


Tips: Do not define partition fields in schema option

date_format [string]

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]

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]

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

skip_header_row_number [long]

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

file_format_type [string]

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:

200get successtrue

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:


If you do not assign data schema connector will treat the upstream data as the following:


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:


fs.defaultFS [string]

Hdfs cluster address.

hdfs_site_path [string]

The path of hdfs-site.xml, used to load ha configuration of namenodes

kerberos_principal [string]

The principal of kerberos

kerberos_keytab_path [string]

The keytab path of kerberos

schema [Config]

fields [Config]

the schema fields of upstream data

read_columns [list]

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

Tips: If the user wants to use this feature when reading text json csv files, the schema option must be configured

common options

Source plugin common parameters, please refer to Source Common Options for details.


HdfsFile {
path = "/apps/hive/demo/student"
file_format_type = "parquet"
fs.defaultFS = "hdfs://namenode001"

HdfsFile {
schema {
fields {
name = string
age = int
path = "/apps/hive/demo/student"
type = "json"
fs.defaultFS = "hdfs://namenode001"


2.2.0-beta 2022-09-26

  • Add HDFS File Source Connector

2.3.0-beta 2022-10-20

  • [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)

next version

  • [Improve] Support skip header for csv and txt files (3900)
  • [Improve] Support kerberos authentication (3840)