LocalFile

Local file source connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key features

• batch
• stream
• exactly-once

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

• column projection
• parallelism
• support user-defined split
• file format type
  • text
  • csv
  • parquet
  • orc
  • json
  • excel
  • xml
  • binary

Description

Read data from local file system.

tip

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.

Options

name	type	required	default value
path	string	yes	-
file_format_type	string	yes	-
read_columns	list	no	-
delimiter/field_delimiter	string	no	\001
parse_partition_from_path	boolean	no	true
date_format	string	no	yyyy-MM-dd
datetime_format	string	no	yyyy-MM-dd HH:mm:ss
time_format	string	no	HH:mm:ss
skip_header_row_number	long	no	0
schema	config	no	-
sheet_name	string	no	-
xml_row_tag	string	no	-
xml_use_attr_format	boolean	no	-
file_filter_pattern	string	no	-
compress_codec	string	no	none
encoding	string	no	UTF-8
common-options		no	-
tables_configs	list	no	used to define a multiple table task

path [string]

The source file path.

file_format_type [string]

File type, supported as the following file types:

text csv parquet orc json excel xml binary

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

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

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

If you assign file type to binary, SeaTunnel can synchronize files in any format, such as compressed packages, pictures, etc. In short, any files can be synchronized to the target place. Under this requirement, you need to ensure that the source and sink use binary format for file synchronization at the same time. You can find the specific usage in the example below.

read_columns [list]

The read column list of the data source, user can use it to implement field projection.

delimiter/field_delimiter [string]

delimiter parameter will deprecate after version 2.3.5, please use field_delimiter instead.

Only need to be configured when file_format is text.

Field delimiter, used to tell connector how to slice and dice fields.

default \001, the same as hive's default delimiter

parse_partition_from_path [boolean]

Control whether parse the partition keys and values from file path

For example if you read a file from path file://hadoop-cluster/tmp/seatunnel/parquet/name=tyrantlucifer/age=26

Every record data from file will be added these two fields:

name	age
tyrantlucifer	26

Tips: Do not define partition fields in schema option

date_format [string]

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]

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

default yyyy-MM-dd HH:mm:ss

time_format [string]

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

default HH:mm:ss

skip_header_row_number [long]

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]

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 information of upstream data.

sheet_name [string]

Only need to be configured when file_format is excel.

Reader the sheet of the workbook.

xml_row_tag [string]

Only need to be configured when file_format is xml.

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

xml_use_attr_format [boolean]

Only need to be configured when file_format is xml.

Specifies Whether to process data using the tag attribute format.

file_filter_pattern [string]

Filter pattern, which used for filtering files.

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).

common options

Source plugin common parameters, please refer to Source Common Options for details

tables_configs

Used to define a multiple table task, when you have multiple tables to read, you can use this option to define multiple tables.

Example

One Table

LocalFile {
  path = "/apps/hive/demo/student"
  file_format_type = "parquet"
}

LocalFile {
  schema {
    fields {
      name = string
      age = int
    }
  }
  path = "/apps/hive/demo/student"
  file_format_type = "json"
}

For json, text or csv file format with encoding

LocalFile {
    path = "/tmp/hive/warehouse/test2"
    file_format_type = "text"
    encoding = "gbk"
}

Multiple Table

LocalFile {
  tables_configs = [
    {
      schema {
        table = "student"
      }
      path = "/apps/hive/demo/student"
      file_format_type = "parquet"
    },
    {
      schema {
        table = "teacher"
      }
      path = "/apps/hive/demo/teacher"
      file_format_type = "parquet"
    }
  ]
}

LocalFile {
  tables_configs = [
    {
      schema {
        fields {
          name = string
          age = int
        }
      }
      path = "/apps/hive/demo/student"
      file_format_type = "json"
    },
    {
      schema {
        fields {
          name = string
          age = int
        }
      }
      path = "/apps/hive/demo/teacher"
      file_format_type = "json"
    }
}

Transfer Binary File

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

source {
  LocalFile {
    path = "/seatunnel/read/binary/"
    file_format_type = "binary"
  }
}
sink {
  // you can transfer local file to s3/hdfs/oss etc.
  LocalFile {
    path = "/seatunnel/read/binary2/"
    file_format_type = "binary"
  }
}

Changelog

2.2.0-beta 2022-09-26

• Add Local 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)