import ChangeLog from '../changelog/connector-file-local.md';

LocalFile

Local file source connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key features

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 for text and , for csv
row_delimiter	string	no	\n
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	-
excel_engine	string	no	POI
xml_row_tag	string	no	-
xml_use_attr_format	boolean	no	-
csv_use_header_line	boolean	no	false
file_filter_pattern	string	no	-
filename_extension	string	no	-
compress_codec	string	no	none
archive_compress_codec	string	no	none
encoding	string	no	UTF-8
null_format	string	no	-
binary_chunk_size	int	no	1024
binary_complete_file_mode	boolean	no	false
common-options		no	-
tables_configs	list	no	used to define a multiple table task
file_filter_modified_start	string	no	-
file_filter_modified_end	string	no	-
enable_file_split	boolean	no	false
file_split_size	long	no	134217728

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 markdown

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.

If you assign file type to markdown, SeaTunnel can parse markdown files and extract structured data. The markdown parser extracts various elements including headings, paragraphs, lists, code blocks, tables, and more. Each element is converted to a row with the following schema:

element_id: Unique identifier for the element
element_type: Type of the element (Heading, Paragraph, ListItem, etc.)
heading_level: Level of heading (1-6, null for non-heading elements)
text: Text content of the element
page_number: Page number (default: 1)
position_index: Position index within the document
parent_id: ID of the parent element
child_ids: Comma-separated list of child element IDs

Note: Markdown format only supports reading, not writing.

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

row_delimiter [string]

Only need to be configured when file_format is text

Row delimiter, used to tell connector how to slice and dice rows

default \n

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.

excel_engine [string]

Only need to be configured when file_format is excel.

supported as the following file types: POI EasyExcel

The default excel reading engine is POI, but POI can easily cause memory overflow when reading Excel with more than 65,000 rows, so you can switch to EasyExcel as the reading engine.

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.

csv_use_header_line [boolean]

Whether to use the header line to parse the file, only used when the file_format is csv and the file contains the header line that match RFC 4180

file_filter_pattern [string]

Filter pattern, which used for filtering files. If you only want to filter based on file names, simply write the regular file names; If you want to filter based on the file directory at the same time, the expression needs to start with path.

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

If the path is /data/seatunnel, and the file structure example is:

/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:

.*.txt

The result of this example matching is:

/data/seatunnel/20241001/report.txt

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

abc.*

The result of this example matching is:

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

Example 3: Match all files starting with abc in folder 20241007，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

filename_extension [string]

Filter filename extension, which used for filtering files with specific extension. Example: csv .txt json .xml.

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.

archive_compress_codec [string]

The compress codec of archive files and the details that supported as the following shown:

archive_compress_codec	file_format	archive_compress_suffix
ZIP	txt,json,excel,xml	.zip
TAR	txt,json,excel,xml	.tar
TAR_GZ	txt,json,excel,xml	.tar.gz
GZ	txt,json,excel,xml	.gz
NONE	all	.*

Note: gz compressed excel file needs to compress the original file or specify the file suffix, such as e2e.xls ->e2e_test.xls.gz

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).

null_format [string]

Only used when file_format_type is text. null_format to define which strings can be represented as null.

e.g: \N

binary_chunk_size [int]

Only used when file_format_type is binary.

The chunk size (in bytes) for reading binary files. Default is 1024 bytes. Larger values may improve performance for large files but use more memory.

binary_complete_file_mode [boolean]

Only used when file_format_type is binary.

Whether to read the complete file as a single chunk instead of splitting into chunks. When enabled, the entire file content will be read into memory at once. Default is false.

file_filter_modified_start [string]

File modification time filter. The connector will filter some files base on the last modification start time (include start time). The default data format is yyyy-MM-dd HH:mm:ss.

file_filter_modified_end [string]

File modification time filter. The connector will filter some files base on the last modification end time (not include end time). The default data format is yyyy-MM-dd HH:mm:ss.

enable_file_split [string]

Turn on the file splitting function, the default is false。It can be selected when the file type is csv, text, json and non-compressed format.

file_split_size [long]

File split size, which can be filled in when the enable_file_split parameter is true. The unit is the number of bytes. The default value is the number of bytes of 128MB, which is 134217728.

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"
    binary_chunk_size = 2048
    binary_complete_file_mode = false
  }
}
sink {
  // you can transfer local file to s3/hdfs/oss etc.
  LocalFile {
    path = "/seatunnel/read/binary2/"
    file_format_type = "binary"
  }
}

Filter File

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

source {
  LocalFile {
    path = "/data/seatunnel/"
    file_format_type = "csv"
    skip_header_row_number = 1
    // file example abcD2024.csv
    file_filter_pattern = "abc[DX]*.*"
  }
}

sink {
  Console {
  }
}

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LocalFile

Support Those Engines

Key features

Description

Options

path [string]

file_format_type [string]

read_columns [list]

delimiter/field_delimiter [string]

row_delimiter [string]

parse_partition_from_path [boolean]

date_format [string]

datetime_format [string]

time_format [string]

skip_header_row_number [long]

schema [config]

fields [Config]

sheet_name [string]

excel_engine [string]

xml_row_tag [string]

xml_use_attr_format [boolean]

csv_use_header_line [boolean]

file_filter_pattern [string]

filename_extension [string]

compress_codec [string]

archive_compress_codec [string]

encoding [string]

null_format [string]

binary_chunk_size [int]

binary_complete_file_mode [boolean]

file_filter_modified_start [string]

file_filter_modified_end [string]

enable_file_split [string]

file_split_size [long]

common options

tables_configs

Example

One Table

Multiple Table

Transfer Binary File

Filter File

Changelog