OssFile

Oss file sink connector

Key features

exactly-once
support multiple table write

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

Data Type Mapping

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

Orc File Type

Nexus Data Type

Orc Data Type

STRING

BOOLEAN

TINYINT

BYTE

SMALLINT

SHORT

INT

BIGINT

LONG

FLOAT

DOUBLE

DECIMAL

BYTES

BINARY

DATE

TIME TIMESTAMP

TIMESTAMP

ROW

STRUCT

NULL

UNSUPPORTED DATA TYPE

ARRAY

LIST

Map

Parquet File Type

Nexus Data Type

Parquet Data Type

STRING

BOOLEAN

TINYINT

INT_8

SMALLINT

INT_16

INT

INT32

BIGINT

INT64

FLOAT

DOUBLE

DECIMAL

BYTES

BINARY

DATE

TIME TIMESTAMP

TIMESTAMP_MILLIS

ROW

GroupType

NULL

UNSUPPORTED DATA TYPE

ARRAY

LIST

Map

Options

Name

Type

Required

Default

Description

path

string

yes

The oss path to write file in.

tmp_path

string

/tmp/nexus

The result file will write to a tmp path first and then use mv to submit tmp dir to target dir. Need a OSS dir.

bucket

string

yes

access_key

string

yes

access_secret

string

yes

endpoint

string

yes

custom_filename

boolean

false

Whether you need custom the filename

file_name_expression

string

"${transactionId}"

Only used when custom_filename is true

filename_time_format

string

"yyyy.MM.dd"

Only used when custom_filename is true

file_format_type

string

"csv"

field_delimiter

string

'\001'

Only used when file_format_type is text

row_delimiter

string

"\n"

Only used when file_format_type is text

have_partition

boolean

false

Whether you need processing partitions.

partition_by

array

Only used then have_partition is true

partition_dir_expression

string

"${k0}=${v0}/${k1}=${v1}/.../${kn}=${vn}/"

Only used then have_partition is true

is_partition_field_write_in_file

boolean

false

Only used then have_partition is true

sink_columns

array

When this parameter is empty, all fields are sink columns

is_enable_transaction

boolean

true

batch_size

int

1000000

compress_codec

string

none

common-options

object

max_rows_in_memory

int

Only used when file_format_type is excel.

sheet_name

string

Sheet${Random number}

Only used when file_format_type is excel.

xml_root_tag

string

RECORDS

Only used when file_format is xml.

xml_row_tag

string

RECORD

Only used when file_format is xml.

xml_use_attr_format

boolean

Only used when file_format is xml.

parquet_avro_write_timestamp_as_int96

boolean

false

Only used when file_format is parquet.

parquet_avro_write_fixed_as_int96

array

Only used when file_format is parquet.

encoding

string

"UTF-8"

Only used when file_format_type is json,text,csv,xml.

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:

Symbol

Description

Year

Month

Day of month

Hour in day (0-23)

Minute in hour

Second in minute

file_format_type [string]

We supported as the following file types:

text csv parquet orc json excel xml binary

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 Nexus 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.

parquet_avro_write_timestamp_as_int96 [boolean]

Support writing Parquet INT96 from a timestamp, only valid for parquet files.

parquet_avro_write_fixed_as_int96 [array]

Support writing Parquet INT96 from a 12-byte field, only valid for parquet files.

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="/nexus/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 = "/nexus/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="/nexus/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 = "Nexus"
  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"
  }
}

PreviousOracle NextOssJindoFile

Last updated 10 months ago

Key features​

Data Type Mapping​

Orc File Type​

Parquet File Type​

Options​

path [string]​

bucket [string]​

access_key [string]​

access_secret [string]​

endpoint [string]​

custom_filename [boolean]​

file_name_expression [string]​

filename_time_format [String]​

file_format_type [string]​

field_delimiter [string]​

row_delimiter [string]​

have_partition [boolean]​

partition_by [array]​

partition_dir_expression [string]​

is_partition_field_write_in_file [boolean]​

sink_columns [array]​

is_enable_transaction [boolean]​

batch_size [int]​

compress_codec [string]​

common options​

max_rows_in_memory [int]​

sheet_name [string]​

xml_root_tag [string]​

xml_row_tag [string]​

xml_use_attr_format [boolean]​

parquet_avro_write_timestamp_as_int96 [boolean]​

parquet_avro_write_fixed_as_int96 [array]​

encoding [string]​

How to Create an Oss Data Synchronization Jobs​

Multiple Table​

Key features

Data Type Mapping

Orc File Type

Parquet File Type

Options

path [string]

bucket [string]

access_key [string]

access_secret [string]

endpoint [string]

custom_filename [boolean]

file_name_expression [string]

filename_time_format [String]

file_format_type [string]

field_delimiter [string]

row_delimiter [string]

have_partition [boolean]

partition_by [array]

partition_dir_expression [string]

is_partition_field_write_in_file [boolean]

sink_columns [array]

is_enable_transaction [boolean]

batch_size [int]

compress_codec [string]

common options

max_rows_in_memory [int]

sheet_name [string]

xml_root_tag [string]

xml_row_tag [string]

xml_use_attr_format [boolean]

parquet_avro_write_timestamp_as_int96 [boolean]

parquet_avro_write_fixed_as_int96 [array]

encoding [string]

How to Create an Oss Data Synchronization Jobs

Multiple Table