Oss file sink connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Usage Dependency

For Spark/Flink Engine

1. You must ensure your spark/flink cluster already integrated hadoop. The tested hadoop version is 2.x.
2. You must ensure hadoop-aliyun-xx.jar, aliyun-sdk-oss-xx.jar and jdom-xx.jar in ${SEATUNNEL_HOME}/plugins/ dir and the version of hadoop-aliyun jar need equals your hadoop version which used in spark/flink and aliyun-sdk-oss-xx.jar and jdom-xx.jar version needs to be the version corresponding to the hadoop-aliyun version. Eg: hadoop-aliyun-3.1.4.jar dependency aliyun-sdk-oss-3.4.1.jar and jdom-1.1.jar.

For SeaTunnel Zeta Engine

1. You must ensure seatunnel-hadoop3-3.1.4-uber.jar, aliyun-sdk-oss-3.4.1.jar, hadoop-aliyun-3.1.4.jar and jdom-1.1.jar in ${SEATUNNEL_HOME}/lib/ dir.

Key features

• exactly-once

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

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

Data Type Mapping

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

Orc File Type

Parquet File Type

Options

path [string]

The target dir path is required.

bucket [string]

The bucket address of oss file system, for example: oss://tyrantlucifer-image-bed

access_key [string]

The access key of oss file system.

access_secret [string]

The access secret of oss file system.

endpoint [string]

The endpoint of oss file system.

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.

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).

How to Create an Oss Data Synchronization Jobs

The following example demonstrates how to create a data synchronization job that reads data from Fake Source and writes it to the Oss:

For text file format with have_partition and custom_filename and sink_columns

# Set the basic configuration of the task to be performed
env {
  parallelism = 1
  job.mode = "BATCH"
}
# Create a source to product data
source {
  FakeSource {
    schema = {
      fields {
        name = string
        age = int
      }
    }
  }
}
# write data to Oss
sink {
  OssFile {
    path="/seatunnel/sink"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxx"
    access_secret = "xxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    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
  }
}

For parquet file format with have_partition and sink_columns

# Set the basic configuration of the task to be performed
env {
  parallelism = 1
  job.mode = "BATCH"
}
# Create a source to product data
source {
  FakeSource {
    schema = {
      fields {
        name = string
        age = int
      }
    }
  }
}
# Write data to Oss
sink {
  OssFile {
    path = "/seatunnel/sink"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    have_partition = true
    partition_by = ["age"]
    partition_dir_expression = "${k0}=${v0}"
    is_partition_field_write_in_file = true
    file_format_type = "parquet"
    sink_columns = ["name","age"]
  }
}

For orc file format simple config

# Set the basic configuration of the task to be performed
env {
  parallelism = 1
  job.mode = "BATCH"
}
# Create a source to product data
source {
  FakeSource {
    schema = {
      fields {
        name = string
        age = int
      }
    }
  }
}
# Write data to Oss
sink {
  OssFile {
    path="/seatunnel/sink"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxx"
    access_secret = "xxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "orc"
  }
}

Multiple Table

For extract source metadata from upstream, you can use ${database_name}, ${table_name} and ${schema_name} in the path.

env {
  parallelism = 1
  spark.app.name = "SeaTunnel"
  spark.executor.instances = 2
  spark.executor.cores = 1
  spark.executor.memory = "1g"
  spark.master = local
  job.mode = "BATCH"
}
source {
  FakeSource {
    tables_configs = [
       {
        schema = {
          table = "fake1"
          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
            }
          }
        }
       },
       {
       schema = {
         table = "fake2"
         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
           }
         }
       }
      }
    ]
  }
}
sink {
  OssFile {
    bucket = "oss://whale-ops"
    access_key = "xxxxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxx"
    endpoint = "https://oss-accelerate.aliyuncs.com"
    path = "/tmp/fake_empty/text/${table_name}"
    row_delimiter = "\n"
    partition_dir_expression = "${k0}=${v0}"
    is_partition_field_write_in_file = true
    file_name_expression = "${transactionId}_${now}"
    file_format_type = "text"
    filename_time_format = "yyyy.MM.dd"
    is_enable_transaction = true
    compress_codec = "lzo"
  }
}

Changelog

2.2.0-beta 2022-09-26

• Add OSS Sink Connector

2.3.0-beta 2022-10-20

• [BugFix] Fix the bug of incorrect path in windows environment (2980)
• [BugFix] Fix filesystem get error (3117)
• [BugFix] Solved the bug of can not parse ‘\t’ as delimiter from config file (3083)

Next version

• [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
• [Improve] Support setting batch size for every file (3625)
• [Improve] Support file compress (3899)

Tips

1.SeaTunnel Deployment Document.