S3 File Sink Connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key Features

• exactly-once
• cdc

By default, we use 2PC commit to ensure exactly-once

• file format type
  • text
  • csv
  • parquet
  • orc
  • json
  • excel
  • xml

Description

Output data to aws s3 file system.

Supported DataSource Info

Database 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 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. 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

If write to csv, text file type, All column will be string.

Orc File Type

Parquet File Type

Sink Options

path [string]

Store the path of the data file to support variable replacement. For example: path=/test/${database_name}/${schema_name}/${table_name}

hadoop_s3_properties [map]

If you need to add a other option, you could add it here and refer to this link

hadoop_s3_properties {
      "fs.s3a.buffer.dir" = "/data/st_test/s3a"
      "fs.s3a.fast.upload.buffer" = "disk"
   }

custom_filename [boolean]

Whether custom the filename

file_name_expression [string]

Only used when custom_filename is true

file_name_expression describes the file expression which will be created into the path. We can add the variable ${now} or ${uuid} in the file_name_expression, like test_${uuid}_${now}, ${now} represents the current time, and its format can be defined by specifying the option filename_time_format.

Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.

filename_time_format [string]

Only used when custom_filename is true

When the format in the file_name_expression parameter is xxxx-${now} , filename_time_format can specify the time format of the path, and the default value is yyyy.MM.dd . The commonly used time formats are listed as follows:

file_format_type [string]

We supported as the following file types:

text json csv orc parquet excel xml

Please note that, The final file name will end with the file_format_type’s suffix, the suffix of the text file is txt.

field_delimiter [string]

The separator between columns in a row of data. Only needed by text file format.

row_delimiter [string]

The separator between rows in a file. Only needed by text file format.

have_partition [boolean]

Whether you need processing partitions.

partition_by [array]

Only used when have_partition is true.

Partition data based on selected fields.

partition_dir_expression [string]

Only used when have_partition is true.

If the partition_by is specified, we will generate the corresponding partition directory based on the partition information, and the final file will be placed in the partition directory.

Default partition_dir_expression is ${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/. k0 is the first partition field and v0 is the value of the first partition field.

is_partition_field_write_in_file [boolean]

Only used when have_partition is true.

If is_partition_field_write_in_file is true, the partition field and the value of it will be write into data file.

For example, if you want to write a Hive Data File, Its value should be false.

sink_columns [array]

Which columns need be written to file, default value is all the columns get from Transform or Source. The order of the fields determines the order in which the file is actually written.

is_enable_transaction [boolean]

If is_enable_transaction is true, we will ensure that data will not be lost or duplicated when it is written to the target directory.

Please note that, If is_enable_transaction is true, we will auto add ${transactionId}_ in the head of the file.

Only support true now.

batch_size [int]

The maximum number of rows in a file. For SeaTunnel Engine, the number of lines in the file is determined by batch_size and checkpoint.interval jointly decide. If the value of checkpoint.interval is large enough, sink writer will write rows in a file until the rows in the file larger than batch_size. If checkpoint.interval is small, the sink writer will create a new file when a new checkpoint trigger.

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: lzo snappy lz4 zlib none
• parquet: lzo snappy lz4 gzip brotli zstd none

Tips: excel type does not support any compression format

common options

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

max_rows_in_memory [int]

When File Format is Excel,The maximum number of data items that can be cached in the memory.

sheet_name [string]

Writer the sheet of the workbook

xml_root_tag [string]

Specifies the tag name of the root element within the XML file.

xml_row_tag [string]

Specifies the tag name of the data rows within the XML file.

xml_use_attr_format [boolean]

Specifies Whether to process data using the tag attribute format.

schema_save_mode[Enum]

Before turning on the synchronous task, do different treatment of the target path.
Option introduction：
RECREATE_SCHEMA ：Will be created when the path does not exist. If the path already exists, delete the path and recreate it.
CREATE_SCHEMA_WHEN_NOT_EXIST ：Will Created when the path does not exist, use the path when the path is existed.
ERROR_WHEN_SCHEMA_NOT_EXIST ：Error will be reported when the path does not exist

data_save_mode[Enum]

Before opening the synchronous task, the data file in the target path is differently processed. Option introduction：
DROP_DATA： use the path but delete data files in the path. APPEND_DATA：use the path, and add new files in the path for write data.
ERROR_WHEN_DATA_EXISTS：When there are some data files in the path, an error will is reported.

encoding [string]

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

Example

Simple:

This example defines a SeaTunnel synchronization task that automatically generates data through FakeSource and sends it to S3File Sink. FakeSource generates a total of 16 rows of data (row.num=16), with each row having two fields, name (string type) and age (int type). The final target s3 dir will also create a file and all of the data in write in it. Before run this job, you need create s3 path: /seatunnel/text. And if you have not yet installed and deployed SeaTunnel, you need to follow the instructions in Install SeaTunnel to install and deploy SeaTunnel. And then follow the instructions in Quick Start With SeaTunnel Engine to run this job.

# Defining the runtime environment
env {
  parallelism = 1
  job.mode = "BATCH"
}
source {
  # This is a example source plugin **only for test and demonstrate the feature source plugin**
  FakeSource {
    parallelism = 1
    result_table_name = "fake"
    row.num = 16
    schema = {
      fields {
        c_map = "map<string, array<int>>"
        c_array = "array<int>"
        name = string
        c_boolean = boolean
        age = tinyint
        c_smallint = smallint
        c_int = int
        c_bigint = bigint
        c_float = float
        c_double = double
        c_decimal = "decimal(16, 1)"
        c_null = "null"
        c_bytes = bytes
        c_date = date
        c_timestamp = timestamp
      }
    }
  }
  # If you would like to get more information about how to configure seatunnel and see full list of source plugins,
  # please go to https://seatunnel.apache.org/docs/category/source-v2
}
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/category/transform-v2
}
sink {
    S3File {
      bucket = "s3a://seatunnel-test"
      tmp_path = "/tmp/seatunnel"
      path="/seatunnel/text"
      fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
      fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
      file_format_type = "text"
      field_delimiter = "\t"
      row_delimiter = "\n"
      have_partition = true
      partition_by = ["age"]
      partition_dir_expression = "${k0}=${v0}"
      is_partition_field_write_in_file = true
      custom_filename = true
      file_name_expression = "${transactionId}_${now}"
      filename_time_format = "yyyy.MM.dd"
      sink_columns = ["name","age"]
      is_enable_transaction=true
      hadoop_s3_properties {
        "fs.s3a.buffer.dir" = "/data/st_test/s3a"
        "fs.s3a.fast.upload.buffer" = "disk"
      }
  }
  # If you would like to get more information about how to configure seatunnel and see full list of sink plugins,
  # please go to https://seatunnel.apache.org/docs/category/sink-v2
}

For text file format with have_partition and custom_filename and sink_columns and com.amazonaws.auth.InstanceProfileCredentialsProvider

  S3File {
    bucket = "s3a://seatunnel-test"
    tmp_path = "/tmp/seatunnel"
    path="/seatunnel/text"
    fs.s3a.endpoint="s3.cn-north-1.amazonaws.com.cn"
    fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
    file_format_type = "text"
    field_delimiter = "\t"
    row_delimiter = "\n"
    have_partition = true
    partition_by = ["age"]
    partition_dir_expression = "${k0}=${v0}"
    is_partition_field_write_in_file = true
    custom_filename = true
    file_name_expression = "${transactionId}_${now}"
    filename_time_format = "yyyy.MM.dd"
    sink_columns = ["name","age"]
    is_enable_transaction=true
    hadoop_s3_properties {
      "fs.s3a.buffer.dir" = "/data/st_test/s3a"
      "fs.s3a.fast.upload.buffer" = "disk"
    }
  }

For parquet file format simple config with org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider

  S3File {
    bucket = "s3a://seatunnel-test"
    tmp_path = "/tmp/seatunnel"
    path="/seatunnel/parquet"
    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"
    file_format_type = "parquet"
    hadoop_s3_properties {
      "fs.s3a.buffer.dir" = "/data/st_test/s3a"
      "fs.s3a.fast.upload.buffer" = "disk"
    }
  }

For orc file format simple config with org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider

  S3File {
    bucket = "s3a://seatunnel-test"
    tmp_path = "/tmp/seatunnel"
    path="/seatunnel/orc"
    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"
    file_format_type = "orc"
    schema_save_mode = "CREATE_SCHEMA_WHEN_NOT_EXIST"
    data_save_mode="APPEND_DATA"
  }

Multi-table writing and saveMode

env {
"job.name"="SeaTunnel_job"
"job.mode"=STREAMING
}
source {
MySQL-CDC {
    "connect.max-retries"=3
    "connection.pool.size"=6
    "startup.mode"=INITIAL
    "exactly_once"="true"
    "stop.mode"=NEVER
    parallelism=1
    "result_table_name"=Table11519548644512
    "dag-parsing.mode"=MULTIPLEX
    catalog {
        factory=Mysql
    }
    database-names=[
        "wls_t1"
    ]
    table-names=[
        "wls_t1.mysqlcdc_to_s3_t3",
        "wls_t1.mysqlcdc_to_s3_t4",
        "wls_t1.mysqlcdc_to_s3_t5",
        "wls_t1.mysqlcdc_to_s3_t1",
        "wls_t1.mysqlcdc_to_s3_t2"
    ]
    password="xxxxxx"
    username="xxxxxxxxxxxxx"
    base-url="jdbc:mysql://localhost:3306/qa_source"
    server-time-zone=UTC
}
}
transform {
}
sink {
S3File {
    bucket = "s3a://seatunnel-test"
    tmp_path = "/tmp/seatunnel"
    path="/test/${table_name}"
    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"
    file_format_type = "orc"
    schema_save_mode = "CREATE_SCHEMA_WHEN_NOT_EXIST"
    data_save_mode="APPEND_DATA"
  }
}

Changelog

2.3.0-beta 2022-10-20

• Add S3File Sink Connector

2.3.0 2022-12-30

• [BugFix] Fixed the following bugs that failed to write data to files (3258)
  • When field from upstream is null it will throw NullPointerException
  • Sink columns mapping failed
  • When restore writer from states getting transaction directly failed
• [Feature] Support S3A protocol (3632)
  • Allow user to add additional hadoop-s3 parameters
  • Allow the use of the s3a protocol
  • Decouple hadoop-aws dependencies
• [Improve] Support setting batch size for every file (3625)
• [Feature]Set S3 AK to optional (3688)

Next version

• ​ [Improve] Support file compress (3899)