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

SftpFile

Sftp file source connector

Support Those Engines

Spark

Flink

SeaTunnel Zeta

Key Features

• batch

• stream

• 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

• column projection

• parallelism

• support user-defined split

• file format type

  • text
  • csv
  • json
  • excel
  • xml
  • binary
  • 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.

:::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.

Source Options

  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. | | sync_mode | string | no | full | File sync mode. Supported values: full, update. When update, the source compares files between source/target and only reads new/changed files (currently only supports file_format_type=binary). | | target_path | string | no | - | Only used when sync_mode=update. Target base path used for comparison (it should usually be the same as sink path). | | target_hadoop_conf | map | no | - | Only used when sync_mode=update. Extra Hadoop configuration for target filesystem. You can set fs.defaultFS in this map to override target defaultFS. | | update_strategy | string | no | distcp | Only used when sync_mode=update. Supported values: distcp (default), strict. | | compare_mode | string | no | len_mtime | Only used when sync_mode=update. Supported values: len_mtime (default), checksum (only valid when update_strategy=strict). | | 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. | | quote_char | string | no | " | A single character that encloses CSV fields, allowing fields with commas, line breaks, or quotes to be read correctly. | | escape_char | string | no | - | A single character that allows the quote or other special characters to appear inside a CSV field without ending the field. | | metalake_type | string | no | gravitino | The type of metalake service, currently supports gravitino. |

file_filter_pattern [string]

Filter pattern, which used for filtering files. If you only want to filter based on file names, simply write the regular file names; If you want to filter based on the file directory at the same time, the expression needs to start with path.

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

If the path is /data/seatunnel, and the file structure example is:

/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:

.*.txt

The result of this example matching is:

/data/seatunnel/20241001/report.txt

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

abc.*

The result of this example matching is:

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

Example 3: Match all files starting with abc in folder 20241007，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:

tyrantlucifer#26#male

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

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

connector will generate data as the following:

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:

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.

sync_mode [string]

File sync mode. Supported values: full (default), update. When update, the source compares files between source/target and only reads new/changed files (currently only supports file_format_type=binary).

Performance considerations

• Update mode triggers an extra getFileStatus call on the target for each source file.
• For remote file systems (FTP/SFTP), this adds per-file network overhead. It is not recommended for massive small-file scenarios.

Requirements / limitations

• target_path should typically align with sink path (same filesystem and same relative path layout).
• When update_strategy=distcp, correctness depends on source/target clock synchronization.
• When compare_mode=checksum, filesystem checksum support is required. If checksum is unavailable, SeaTunnel falls back to content comparison (more expensive) and logs a warning.

Example:

sync_mode = "update"
file_format_type = "binary"
target_path = "/path/to/your/sink/path"
update_strategy = "distcp"
compare_mode = "len_mtime"

target_path [string]

Only used when sync_mode=update. Target base path used for comparison (it should usually be the same as sink path).

target_hadoop_conf [map]

Only used when sync_mode=update. Extra Hadoop configuration for target filesystem. You can set fs.defaultFS in this map to override target defaultFS.

update_strategy [string]

Only used when sync_mode=update. Supported values: distcp (default), strict.

compare_mode [string]

Only used when sync_mode=update. Supported values: len_mtime (default), checksum (only valid when update_strategy=strict).

quote_char [string]

A single character that encloses CSV fields, allowing fields with commas, line breaks, or quotes to be read correctly.

escape_char [string]

A single character that allows the quote or other special characters to appear inside a CSV field without ending the field.

schema [config]

fields [Config]

The schema of upstream data. For more details, please refer to Schema Feature.

schema_url [string]

Get the http url of metadata information through restApi, such as: http://localhost:8090/api/metalakes/laowang_test/catalogs/221-pgsql/schemas/ykw/tables/all_type

When using Gravitino as the metadata source, the column types from Gravitino will be automatically converted to SeaTunnel data types. For detailed type mapping information, please refer to Gravitino Type Mapping.

metalake_type [string]

The type of metalake service, currently only supports gravitino. When using schema_url to obtain metadata from Gravitino, you can specify this parameter (default is gravitino).

For more information about Metalake, please refer to Metalake.

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 {
  }
}

Incremental Sync (sync_mode=update, binary)

sync_mode=update compares files between source and target_path, then only reads new/changed files. In most cases, target_path should be aligned with sink path (same filesystem and same relative paths).

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

source {
  SftpFile {
    host = "sftp"
    port = 22
    user = seatunnel
    password = pass

    path = "tmp/seatunnel/update/src"
    file_format_type = "binary"

    sync_mode = "update"
    target_path = "tmp/seatunnel/update/dst"
    update_strategy = "distcp"
    compare_mode = "len_mtime"
  }
}

sink {
  SftpFile {
    host = "sftp"
    port = 22
    user = seatunnel
    password = pass

    path = "tmp/seatunnel/update/dst"
    tmp_path = "tmp/seatunnel/update/tmp"
    file_format_type = "binary"
  }
}

Changelog