Skip to content

Latest commit

 

History

History
570 lines (473 loc) · 24.6 KB

JuicefsFile.md

File metadata and controls

570 lines (473 loc) · 24.6 KB

JuicefsFile

Juicefs file source 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 juicefs-hadoop-x.x.x.jar in ${SEATUNNEL_HOME}/plugins/ dir.
  3. For juicefs hadoop configuration, please refer to Use JuiceFS on Hadoop Ecosystem

For SeaTunnel Zeta Engine

  1. You must ensure seatunnel-hadoop3-3.1.4-uber.jar, juicefs-hadoop-x.x.x.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 juicefs 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
jfs_name string yes - The jfs name of juicefs file system, for example: jfs://seatunnel-test
meta_url string yes - The juicefs meta url.
hadoop_properties Map no - Properties passed through to the Hadoop configuration. Hadoop Configurations
remote_user string no - The login user used to connect to hadoop login name. It is intended to be used for remote users in RPC, it won't have any credentials.
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.
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 jfs://seatunnel-test/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 Juicefs Data Synchronization Jobs

The following example demonstrates how to create a data synchronization job that reads data from juicefs 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 juicefs
source {
  JuicefsFile {
    path = "/seatunnel/orc"
    jfs_name = "jfs://sealtunneltest"
    meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
    hadoop_properties = {
      juicefs.cache-size = 0
    }
    file_format_type = "orc"
  }
}

# Console printing of the read juicefs 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 juicefs
source {
  JuicefsFile {
    path = "/seatunnel/json"
    jfs_name = "jfs://sealtunneltest"
    meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
    hadoop_properties = {
      juicefs.cache-size = 0
    }
    file_format_type = "json"
    schema {
      fields {
        id = int 
        name = string
      }
    }
  }
}

# Console printing of the read juicefs 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 {
  JuicefsFile {
    tables_configs = [
      {
          schema = {
              table = "fake01"
          }
          jfs_name = "jfs://sealtunneltest"
          meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
          hadoop_properties = {
            juicefs.cache-size = 0
          }
          path = "/test/seatunnel/read/orc"
          file_format_type = "orc"
      },
      {
          schema = {
              table = "fake02"
          }
          jfs_name = "jfs://sealtunneltest"
          meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
          hadoop_properties = {
            juicefs.cache-size = 0
          }
          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 {
  JuicefsFile {
    tables_configs = [
      {
          jfs_name = "jfs://sealtunneltest"
          meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
          hadoop_properties = {
            juicefs.cache-size = 0
          }
          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
              }
            }
          }
      },
      {
          jfs_name = "jfs://sealtunneltest"
          meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
          hadoop_properties = {
            juicefs.cache-size = 0
          }
          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 {
  JuicefsFile {
    path = "/seatunnel/orc"
    jfs_name = "jfs://sealtunneltest"
    meta_url = "mysql://xxxx:xxx@(127.0.0.1:3306)/juicefs"
    hadoop_properties = {
      juicefs.cache-size = 0
    }
    file_format_type = "orc"
    // file example abcD2024.csv
    file_filter_pattern = "abc[DX]*.*"
  }
}

sink {
  Console {
  }
}

Changelog

2.4.9-beta 2024-10-31

  • Add Juicefs File Source Connector

Tips

1.SeaTunnel Deployment Document.