import ChangeLog from '../changelog/connector-file-s3.md';

S3File

S3 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

  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 type

  • text
  • csv
  • parquet
  • orc
  • json
  • excel
  • xml
  • binary
  • markdown

Description

Read data from aws s3 file system.

Supported DataSource Info

Dependency

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 Zeta, It automatically integrated the hadoop jar when you download and install SeaTunnel Zeta. You can check the jar package under ${SEATUNNEL_HOME}/lib to confirm this.

To use this connector you need put hadoop-aws-3.1.4.jar and aws-java-sdk-bundle-1.12.692.jar in ${SEATUNNEL_HOME}/lib dir.

Data Type Mapping

Data type mapping is related to the type of file being read, We supported as the following file types:

text csv parquet orc json excel xml

JSON File Type

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:

Text Or CSV File Type

If you set the file_format_type to text,excel,csv,xml. Then it's required to set the schema field to tell connector how to parse data to the row.

If you set the schema field, you should also set the option field_delimiter, except the file_format_type is csv, xml, excel

you can set schema and delimiter as the following:

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

connector will generate data as the following:

Orc File Type

If you assign file type to parquet orc, schema option not required, connector can find the schema of upstream data automatically.

Parquet File Type

If you assign file type to parquet orc, schema option not required, connector can find the schema of upstream data automatically.

Options

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 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.

delimiter/field_delimiter [string]

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

row_delimiter [string]

Only need to be configured when file_format is text

Row delimiter, used to tell connector how to slice and dice rows

default \n

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

enable_file_split [boolean]

Turn on the file splitting function, the default is false. It can be selected when the file type is csv, text, json, parquet and non-compressed format.

• text/csv/json: split by file_split_size and align to the next row_delimiter to avoid breaking records.
• parquet: split by RowGroup (logical split), never breaks a RowGroup.

Recommendations

• Enable when reading a few large files and you want higher read parallelism.
• Disable when reading many small files, or when parallelism is low (splitting adds overhead).

Limitations

• Not supported for compressed files (compress_codec != none) or archive files (archive_compress_codec != none) — it will fall back to non-splitting and emit a warning log.
• For text/csv/json, actual split size may be larger than file_split_size because the split end is aligned to the next row_delimiter.
• For json, splitting is only supported for JSON Lines (one JSON object per line).
• When splitting is enabled, global record order is not guaranteed because splits can be processed in parallel. Set parallelism=1 if strict ordering is required.

file_split_size [long]

File split size, which can be filled in when the enable_file_split parameter is true. The unit is the number of bytes. The default value is the number of bytes of 128MB, which is 134217728.

Tuning

• Start with the default (128MB). Decrease it if parallelism is under-utilized; increase it if the number of splits is too large.

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.

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.

Example

1. In this example, We read data from s3 path s3a://seatunnel-test/seatunnel/text and the file type is orc in this path. We use org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider to authentication so access_key and secret_key is required. All columns in the file will be read and send to sink.

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

source {
  S3File {
    path = "/seatunnel/text"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider = "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider"
    access_key = "xxxxxxxxxxxxxxxxx"
    secret_key = "xxxxxxxxxxxxxxxxx"
    bucket = "s3a://seatunnel-test"
    file_format_type = "orc"
  }
}

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/transforms
}

sink {
  Console {}
}

2. Use InstanceProfileCredentialsProvider to authentication The file type in S3 is json, so need config schema option.

  S3File {
    path = "/seatunnel/json"
    bucket = "s3a://seatunnel-test"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
    file_format_type = "json"
    schema {
      fields {
        id = int 
        name = string
      }
    }
  }

3. Use InstanceProfileCredentialsProvider to authentication The file type in S3 is json and has five fields (id, name, age, sex, type), so need config schema option. In this job, we only need send id and name column to mysql.

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

source {
  S3File {
    path = "/seatunnel/json"
    bucket = "s3a://seatunnel-test"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
    file_format_type = "json"
    read_columns = ["id", "name"]
    schema {
      fields {
        id = int 
        name = string
        age = int
        sex = int
        type = string
      }
    }
  }
}

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/transforms
}

sink {
  Console {}
}

Filter File

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

source {
  S3File {
    path = "/seatunnel/json"
    bucket = "s3a://seatunnel-test"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
    file_format_type = "json"
    read_columns = ["id", "name"]
    // file example abcD2024.csv
    file_filter_pattern = "abc[DX]*.*"
  }
}

sink {
  Console {
  }
}

Changelog