import ChangeLog from ‘../changelog/connector-file-sftp.md’;

SftpFile

Sftp file source connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key Features

[x] batch
[ ] stream
[x] multimodal
Use binary file format to read and write files in any format, such as videos, pictures, etc. In short, any files can be synchronized to the target place.
[ ] exactly-once
[x] column projection
[x] parallelism
[ ] support user-defined split
[x] file format type
- [x] text
- [x] csv
- [x] json
- [x] excel
- [x] xml
- [x] binary
- [x] markdown

Description

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 install-plugin.sh or from the Maven central repository.

Datasource	Supported Versions	Dependency
SftpFile	universal	Download

:::tip

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
STRING
SHORT
INT
BIGINT
BOOLEAN
DOUBLE
DECIMAL
FLOAT
DATE
TIME
TIMESTAMP
BYTES
ARRAY
MAP

Source Options

Name	Type	Required	default value	Description
host	String	Yes	-	The target sftp host is required
port	Int	Yes	-	The target sftp port is required
user	String	Yes	-	The target sftp username is required
password	String	Yes	-	The target sftp password is required
path	String	Yes	-	The source file path.
file_format_type	String	Yes	-	Please check #file_format_type below
file_filter_pattern	String	No	-	Filter pattern, which used for filtering files.
filename_extension	string	no	-	Filter filename extension, which used for filtering files with specific extension. Example: `csv` `.txt` `json` `.xml`.
delimiter/field_delimiter	String	No	\001 for text and ‘,’ for csv	delimiter 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
row_delimiter	string	no	\n	Row delimiter, used to tell connector how to slice and dice rows when reading text files. Default `\n`
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 `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_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`
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`
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
read_columns	list	no	-	The read column list of the data source, user can use it to implement field projection.
sheet_name	String	No	-	Reader the sheet of the workbook,Only used when file_format is excel.
xml_row_tag	string	no	-	Specifies the tag name of the data rows within the XML file, only used when file_format is xml.
xml_use_attr_format	boolean	no	-	Specifies whether to process data using the tag attribute format, only used when file_format is xml.
csv_use_header_line	boolean	no	false	Whether to use the header line to parse the file, only used when the file_format is `csv` and the file contains the header line that match RFC 4180
schema	Config	No	-	Please check #schema below
compress_codec	String	No	None	The 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
archive_compress_codec	string	no	none
encoding	string	no	UTF-8
null_format	string	no	-	Only used when file_format_type is text. null_format to define which strings can be represented as null. e.g: `\N`
binary_chunk_size	int	no	1024	Only used when file_format_type is binary. The chunk size (in bytes) for reading binary files. Default is 1024 bytes. Larger values may improve performance for large files but use more memory.
binary_complete_file_mode	boolean	no	false	Only used when file_format_type is binary. Whether to read the complete file as a single chunk instead of splitting into chunks. When enabled, the entire file content will be read into memory at once. Default is false.
common-options		No	-	Source plugin common parameters, please refer to Source Common Options for details.
file_filter_modified_start	string	no	-	File modification time filter. The connector will filter some files base on the last modification start time (include start time). The default data format is `yyyy-MM-dd HH:mm:ss`.
file_filter_modified_end	string	no	-	File modification time filter. The connector will filter some files base on the last modification end time (not include end time). The default data format is `yyyy-MM-dd HH:mm:ss`.

file_filter_pattern [string]

Filter pattern, which used for filtering files.

The pattern follows standard regular expressions. For details, please refer to https://en.wikipedia.org/wiki/Regular_expression. There are some examples.

File Structure Example:

/data/seatunnel/20241001/report.txt
/data/seatunnel/20241007/abch202410.csv
/data/seatunnel/20241002/abcg202410.csv
/data/seatunnel/20241005/old_data.csv
/data/seatunnel/20241012/logo.png

Matching Rules Example:

Example 1: Match all .txt files，Regular Expression:

/data/seatunnel/20241001/.*\.txt

The result of this example matching is:

/data/seatunnel/20241001/report.txt

Example 2: Match all file starting with abc，Regular Expression:

/data/seatunnel/20241002/abc.*

The result of this example matching is:

/data/seatunnel/20241007/abch202410.csv
/data/seatunnel/20241002/abcg202410.csv

Example 3: Match all file starting with abc，And the fourth character is either h or g, the Regular Expression:

/data/seatunnel/20241007/abc[h,g].*

The result of this example matching is:

/data/seatunnel/20241007/abch202410.csv

Example 4: Match third level folders starting with 202410 and files ending with .csv, the Regular Expression:

/data/seatunnel/202410\d*/.*\.csv

The result of this example matching is:

/data/seatunnel/20241007/abch202410.csv
/data/seatunnel/20241002/abcg202410.csv
/data/seatunnel/20241005/old_data.csv

file_format_type [string]

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

If you assign file type to binary, SeaTunnel can synchronize files in any format, such as compressed packages, pictures, etc. In short, any files can be synchronized to the target place. Under this requirement, you need to ensure that the source and sink use binary format for file synchronization at the same time.

If you assign file type to markdown, SeaTunnel can parse markdown files and extract structured data. The markdown parser extracts various elements including headings, paragraphs, lists, code blocks, tables, and more. Each element is converted to a row with the following schema:

element_id: Unique identifier for the element
element_type: Type of the element (Heading, Paragraph, ListItem, etc.)
heading_level: Level of heading (1-6, null for non-heading elements)
text: Text content of the element
page_number: Page number (default: 1)
position_index: Position index within the document
parent_id: ID of the parent element
child_ids: Comma-separated list of child element IDs

Note: Markdown format only supports reading, not writing.

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.

archive_compress_codec [string]

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

archive_compress_codec	file_format	archive_compress_suffix
ZIP	txt,json,excel,xml	.zip
TAR	txt,json,excel,xml	.tar
TAR_GZ	txt,json,excel,xml	.tar.gz
GZ	txt,json,excel,xml	.gz
NONE	all	.*

Note: gz compressed excel file needs to compress the original file or specify the file suffix, such as e2e.xls ->e2e_test.xls.gz

encoding [string]

Only used when file_format_type is json,text,csv,xml. The encoding of the file to read. This param will be parsed by Charset.forName(encoding).

binary_chunk_size [int]

Only used when file_format_type is binary.

The chunk size (in bytes) for reading binary files. Default is 1024 bytes. Larger values may improve performance for large files but use more memory.

binary_complete_file_mode [boolean]

Only used when file_format_type is binary.

Whether to read the complete file as a single chunk instead of splitting into chunks. When enabled, the entire file content will be read into memory at once. Default is false.

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"
    plugin_output = "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
  }
}

Multiple Table


SftpFile {
  tables_configs = [
    {
      schema {
        table = "student"
        fields {
          name = string
          age = int
        }
      }
      path = "/tmp/seatunnel/sink/text"
      host = "192.168.31.48"
      port = 21
      user = tyrantlucifer
      password = tianchao
      file_format_type = "parquet"
    },
    {
      schema {
        table = "teacher"
        fields {
          name = string
          age = int
        }
      }
      path = "/tmp/seatunnel/sink/text"
      host = "192.168.31.48"
      port = 21
      user = tyrantlucifer
      password = tianchao
      file_format_type = "parquet"
    }
  ]
}

Filter File

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  SftpFile {
    host = "sftp"
    port = 22
    user = seatunnel
    password = pass
    path = "tmp/seatunnel/read/json"
    file_format_type = "json"
    plugin_output = "sftp"
    // file example abcD2024.csv
    file_filter_pattern = "abc[DX]*.*"
  }
}

sink {
  Console {
  }
}

SftpFile

Support Those Engines

Key Features

Description

Supported DataSource Info

Data Type Mapping

Source Options

file_filter_pattern [string]

file_format_type [string]

compress_codec [string]

archive_compress_codec [string]

encoding [string]

binary_chunk_size [int]

binary_complete_file_mode [boolean]

schema [config]

fields [Config]

How to Create a Sftp Data Synchronization Jobs

Multiple Table

Filter File

Changelog