Version: Next

OssFile

Oss file source connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Usage Dependency

For Spark/Flink Engine

You must ensure your spark/flink cluster already integrated hadoop. The tested hadoop version is 2.x.
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

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

Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot.

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:

code	data	success
200	get success	true

Text Or CSV File Type

If you assign file type to text csv, you can choose to specify the schema information or not.

For example, upstream data is the following:

tyrantlucifer#26#male

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

content
tyrantlucifer#26#male

If you assign data schema, you should also assign the option field_delimiter too except CSV file type

you should assign schema and delimiter as the following:

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

connector will generate data as the following:

name	age	gender
tyrantlucifer	26	male

Orc File Type

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

Orc Data type	SeaTunnel Data type
BOOLEAN	BOOLEAN
INT	INT
BYTE	BYTE
SHORT	SHORT
LONG	LONG
FLOAT	FLOAT
DOUBLE	DOUBLE
BINARY	BINARY
STRING VARCHAR CHAR	STRING
DATE	LOCAL_DATE_TYPE
TIMESTAMP	LOCAL_DATE_TIME_TYPE
DECIMAL	DECIMAL
LIST(STRING)	STRING_ARRAY_TYPE
LIST(BOOLEAN)	BOOLEAN_ARRAY_TYPE
LIST(TINYINT)	BYTE_ARRAY_TYPE
LIST(SMALLINT)	SHORT_ARRAY_TYPE
LIST(INT)	INT_ARRAY_TYPE
LIST(BIGINT)	LONG_ARRAY_TYPE
LIST(FLOAT)	FLOAT_ARRAY_TYPE
LIST(DOUBLE)	DOUBLE_ARRAY_TYPE
Map<K,V>	MapType, This type of K and V will transform to SeaTunnel type
STRUCT	SeaTunnelRowType

Parquet File Type

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

Orc Data type	SeaTunnel Data type
INT_8	BYTE
INT_16	SHORT
DATE	DATE
TIMESTAMP_MILLIS	TIMESTAMP
INT64	LONG
INT96	TIMESTAMP
BINARY	BYTES
FLOAT	FLOAT
DOUBLE	DOUBLE
BOOLEAN	BOOLEAN
FIXED_LEN_BYTE_ARRAY	TIMESTAMP DECIMAL
DECIMAL	DECIMAL
LIST(STRING)	STRING_ARRAY_TYPE
LIST(BOOLEAN)	BOOLEAN_ARRAY_TYPE
LIST(TINYINT)	BYTE_ARRAY_TYPE
LIST(SMALLINT)	SHORT_ARRAY_TYPE
LIST(INT)	INT_ARRAY_TYPE
LIST(BIGINT)	LONG_ARRAY_TYPE
LIST(FLOAT)	FLOAT_ARRAY_TYPE
LIST(DOUBLE)	DOUBLE_ARRAY_TYPE
Map<K,V>	MapType, This type of K and V will transform to SeaTunnel type
STRUCT	SeaTunnelRowType

Options

name	type	required	default value	Description
path	string	yes	-	The Oss path that needs to be read can have sub paths, but the sub paths need to meet certain format requirements. Specific requirements can be referred to "parse_partition_from_path" option
file_format_type	string	yes	-	File type, supported as the following file types: `text` `csv` `parquet` `orc` `json` `excel` `xml` `binary`
bucket	string	yes	-	The bucket address of oss file system, for example: `oss://seatunnel-test`.
endpoint	string	yes	-	fs oss endpoint
read_columns	list	no	-	The read column list of the data source, user can use it to implement field projection. The file type supported column projection as the following shown: `text` `csv` `parquet` `orc` `json` `excel` `xml` . If the user wants to use this feature when reading `text` `json` `csv` files, the "schema" option must be configured.
access_key	string	no	-
access_secret	string	no	-
delimiter	string	no	\001	Field delimiter, used to tell connector how to slice and dice fields when reading text files. Default `\001`, the same as hive's default delimiter.
parse_partition_from_path	boolean	no	true	Control whether parse the partition keys and values from file path. For example if you read a file from path `oss://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26`. Every record data from file will be added these two fields: name="tyrantlucifer", age=16
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`
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`
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
schema	config	no	-	The schema of upstream data.
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.
compress_codec	string	no	none	Which compress codec the files used.
encoding	string	no	UTF-8
file_filter_pattern	string	no		Filter pattern, which used for filtering files.
common-options	config	no	-	Source plugin common parameters, please refer to Source Common Options for details.

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.

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).

file_filter_pattern [string]

Filter pattern, which used for filtering files.

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

File Structure Example:

/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:

/data/seatunnel/20241001/.*\.txt

The result of this example matching is:

/data/seatunnel/20241001/report.txt

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

/data/seatunnel/20241002/abc.*

The result of this example matching is:

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

Example 3: Match all file starting with abc，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

schema [config]

Only need to be configured when the file_format_type are text, json, excel, xml or csv ( Or other format we can't read the schema from metadata).

fields [Config]

The schema of upstream data.

How to Create a Oss Data Synchronization Jobs

The following example demonstrates how to create a data synchronization job that reads data from Oss 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 Oss
source {
  OssFile {
    path = "/seatunnel/orc"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "orc"
  }
}

# Console printing of the read Oss data
sink {
  Console {
  }
}

# Set the basic configuration of the task to be performed
env {
  parallelism = 1
  job.mode = "BATCH"
}

# Create a source to connect to Oss
source {
  OssFile {
    path = "/seatunnel/json"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "json"
    schema {
      fields {
        id = int 
        name = string
      }
    }
  }
}

# Console printing of the read Oss data
sink {
  Console {
  }
}

Multiple Table

No need to config schema file type, eg: orc.

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 {
  OssFile {
    tables_configs = [
      {
          schema = {
              table = "fake01"
          }
          bucket = "oss://whale-ops"
          access_key = "xxxxxxxxxxxxxxxxxxx"
          access_secret = "xxxxxxxxxxxxxxxxxxx"
          endpoint = "https://oss-accelerate.aliyuncs.com"
          path = "/test/seatunnel/read/orc"
          file_format_type = "orc"
      },
      {
          schema = {
              table = "fake02"
          }
          bucket = "oss://whale-ops"
          access_key = "xxxxxxxxxxxxxxxxxxx"
          access_secret = "xxxxxxxxxxxxxxxxxxx"
          endpoint = "https://oss-accelerate.aliyuncs.com"
          path = "/test/seatunnel/read/orc"
          file_format_type = "orc"
      }
    ]
    result_table_name = "fake"
  }
}

sink {
  Assert {
    rules {
        table-names = ["fake01", "fake02"]
    }
  }
}

Need config schema file type, eg: json

env {
  execution.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 {
  OssFile {
    tables_configs = [
      {
          bucket = "oss://whale-ops"
          access_key = "xxxxxxxxxxxxxxxxxxx"
          access_secret = "xxxxxxxxxxxxxxxxxxx"
          endpoint = "https://oss-accelerate.aliyuncs.com"
          path = "/test/seatunnel/read/json"
          file_format_type = "json"
          schema = {
            table = "fake01"
            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
              }
            }
          }
      },
      {
          bucket = "oss://whale-ops"
          access_key = "xxxxxxxxxxxxxxxxxxx"
          access_secret = "xxxxxxxxxxxxxxxxxxx"
          endpoint = "https://oss-accelerate.aliyuncs.com"
          path = "/test/seatunnel/read/json"
          file_format_type = "json"
          schema = {
            table = "fake02"
            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
              }
            }
          }
      }
    ]
    result_table_name = "fake"
  }
}

sink {
  Assert {
    rules {
      table-names = ["fake01", "fake02"]
    }
  }
}

Filter File

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

source {
  OssFile {
    path = "/seatunnel/orc"
    bucket = "oss://tyrantlucifer-image-bed"
    access_key = "xxxxxxxxxxxxxxxxx"
    access_secret = "xxxxxxxxxxxxxxxxxxxxxx"
    endpoint = "oss-cn-beijing.aliyuncs.com"
    file_format_type = "orc"
    // file example abcD2024.csv
    file_filter_pattern = "abc[DX]*.*"
  }
}

sink {
  Console {
  }
}

Changelog

2.2.0-beta 2022-09-26

Add OSS File Source Connector

2.3.0-beta 2022-10-20

[BugFix] Fix the bug of incorrect path in windows environment (2980)
[Improve] Support extract partition from SeaTunnelRow fields (3085)
[Improve] Support parse field from file path (2985)

Tips

1.SeaTunnel Deployment Document.

OssFile

Support Those Engines​

Usage Dependency​

For Spark/Flink Engine​

For SeaTunnel Zeta Engine​

Key features​

Data Type Mapping​

JSON File Type​

Text Or CSV File Type​

Orc File Type​

Parquet File Type​

Options​

compress_codec [string]​

encoding [string]​

file_filter_pattern [string]​

schema [config]​

fields [Config]​

How to Create a Oss Data Synchronization Jobs​

Multiple Table​

Filter File​

Changelog​

2.2.0-beta 2022-09-26​

2.3.0-beta 2022-10-20​

Tips​

Support Those Engines

Usage Dependency

For Spark/Flink Engine

For SeaTunnel Zeta Engine

Key features

Data Type Mapping

JSON File Type

Text Or CSV File Type

Orc File Type

Parquet File Type

Options

compress_codec [string]

encoding [string]

file_filter_pattern [string]

schema [config]

fields [Config]

How to Create a Oss Data Synchronization Jobs

Multiple Table

Filter File

Changelog

2.2.0-beta 2022-09-26

2.3.0-beta 2022-10-20

Tips