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Version: 2.3.4


Sftp file source connector

Support Those Engines​

SeaTunnel Zeta

Key Features​


Read data from sftp file server.

Supported DataSource Info​

In order to use the SftpFile connector, the following dependencies are required. They can be downloaded via or from the Maven central repository.

DatasourceSupported VersionsDependency

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.

We made some trade-offs in order to support more file types, so we used the HDFS protocol for internal access to Sftp and this connector need some hadoop dependencies. It only supports hadoop version 2.9.X+.

Data Type Mapping​

The File does not have a specific type list, and we can indicate which SeaTunnel data type the corresponding data needs to be converted to by specifying the Schema in the config.

SeaTunnel Data type

Source Options​

NameTypeRequireddefault valueDescription
hostStringYes-The target sftp host is required
portIntYes-The target sftp port is required
userStringYes-The target sftp username is required
passwordStringYes-The target sftp password is required
pathStringYes-The source file path.
file_format_typeStringYes-Please check #file_format_type below
file_filter_patternStringNo-Filter pattern, which used for filtering files.
delimiter/field_delimiterStringNo\001delimiter parameter will deprecate after version 2.3.5, please use field_delimiter instead.
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_pathBooleanNotrueControl whether parse the partition keys and values from file path
For example if you read a file from path oss://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
date_formatStringNoyyyy-MM-ddDate 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_formatStringNoyyyy-MM-dd HH:mm:ssDatetime 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_formatStringNoHH:mm:ssTime 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_numberLongNo0Skip 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
read_columnslistno-The read column list of the data source, user can use it to implement field projection.
sheet_nameStringNo-Reader the sheet of the workbook,Only used when file_format is excel.
schemaConfigNo-Please check #schema below
compress_codecStringNoNoneThe compress codec of files and the details that supported as the following shown:
- txt: lzo None
- json: lzo None
- csv: lzo None
- orc: lzo snappy lz4 zlib None
- parquet: lzo snappy lz4 gzip brotli zstd None
Tips: excel type does Not support any compression format
common-optionsNo-Source plugin common parameters, please refer to Source Common Options for details.

file_format_type [string]​

File type, supported as the following file types: text csv parquet orc json excel 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:


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 field_delimiter too except CSV file type you should assign schema and delimiter as the following:

field_delimiter = "#"
schema {
fields {
name = string
age = int
gender = string

connector will generate data as the following: | name | age | gender | |---------------|-----|--------| | tyrantlucifer | 26 | male |

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.

schema [config]​

fields [Config]​

The schema of upstream data.

How to Create a Sftp Data Synchronization Jobs​

The following example demonstrates how to create a data synchronization job that reads data from sftp and prints it on the local client:

# Set the basic configuration of the task to be performed
env {
parallelism = 1
job.mode = "BATCH"

# Create a source to connect to sftp
source {
SftpFile {
host = "sftp"
port = 22
user = seatunnel
password = pass
path = "tmp/seatunnel/read/json"
file_format_type = "json"
result_table_name = "sftp"
schema = {
fields {
c_map = "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_bytes = bytes
c_date = date
c_decimal = "decimal(38, 18)"
c_timestamp = timestamp
c_row = {
C_MAP = "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_BYTES = bytes
C_DATE = date
C_DECIMAL = "decimal(38, 18)"
C_TIMESTAMP = timestamp

# Console printing of the read sftp data
sink {
Console {
parallelism = 1