LocalFile

Local file source connector

Description

Read data from local file system.

提示

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.

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

Options

name	type	required	default value
path	string	yes	-
file_format_type	string	yes	-
read_columns	list	no	-
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	-
common-options		no	-
sheet_name	string	no	-
file_filter_pattern	string	no	-

path [string]

The source file path.

delimiter [string]

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]

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

file_format_type [string]

File type, supported as the following file types:

text csv parquet orc json excel

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 delimiter too except CSV file type

you should assign schema and delimiter as the following:

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

connector will generate data as the following:

name	age	gender
tyrantlucifer	26	male

schema [config]

fields [Config]

The schema information of upstream data.

read_columns [list]

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
• json
• csv
• orc
• parquet
• excel

Tips: If the user wants to use this feature when reading text json csv files, the schema option must be configured

common options

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

sheet_name [string]

Reader the sheet of the workbook,Only used when file_format_type is excel.

file_filter_pattern [string]

Filter pattern, which used for filtering files.

Example

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"
}

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)