Project: SeaTunnel (App Definition and Development / Streaming & Messaging)
File: LocalFile.md

Local file source connector Spark<br/> Flink<br/> SeaTunnel Zeta<br/> Read all the data in a split in a pollNext call. What splits are read will be saved in snapshot. [x] file format type [x] text [x] csv [x] parquet [x] orc [x] json [x] excel [x] xml 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. ::: | name | type | required | default value | |||-|--| | path | string | yes | - | | fileformattype | string | yes | - | | read_columns | list | no | - | | delimiter/field_delimiter | string | no | \001 | | parsepartitionfrom_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 | | skipheaderrow_number | long | no | 0 | | schema | config | no | - | | sheet_name | string | no | - | | xmlrowtag | string | no | - | | xmluseattr_format | boolean | no | - | | filefilterpattern | 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 | The source file path. File type, supported as the following file types: `text` `csv` `parquet` `orc` `json` `excel` `xml` 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: ```json {"code": 200, "data": "get success", "success": true} ``` You can also save multiple pieces of data in one file and split them by newline: ```json lines {"code": 200, "data": "get success", "success": true} {"code": 300, "data": "get failed", "success": false} ``` you should assign schema as the following: ```hocon 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: ```text 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: ```hocon field_delimiter

"#" schema { fields { name = string age = int gender = string } } ``` connector will generate data as the following: | name | age | gender | ||--|--| | tyrantlucifer | 26 | male | The read column list of the data source, user can use it to implement field projection. 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 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 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 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 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 the first few lines, but only for the txt and csv. For example, set like following: `skipheaderrow_number = 2` then SeaTunnel will skip the first 2 lines from source files Only need to be configured when the fileformattype are text, json, excel, xml or csv ( Or other format we can't read the schema from metadata). The schema information of upstream data. Only need to be configured when file_format is excel. Reader the sheet of the workbook. Only need to be configured when file_format is xml. Specifies the tag name of the data rows within the XML file. Only need to be configured when file_format is xml. Specifies Whether to process data using the tag attribute format. Filter pattern, which used for filtering files. 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. Only used when fileformattype is json,text,csv,xml. The encoding of the file to read. This param will be parsed by `Charset.forName(encoding)`. Source plugin common parameters, please refer to for details Used to define a multiple table task, when you have multiple tables to read, you can use this option to define multiple tables. ```hocon LocalFile { path = "/apps/hive/demo/student" fileformattype = "parquet" } ``` ```hocon LocalFile { schema { fields { name = string age = int } } path = "/apps/hive/demo/student" fileformattype = "json" } ``` For json, text or csv file format with `encoding` ```hocon LocalFile { path = "/tmp/hive/warehouse/test2" fileformattype = "text" encoding = "gbk" } ``` ```hocon LocalFile { tables_configs = [ { schema { table = "student" } path = "/apps/hive/demo/student" fileformattype = "parquet" }, { schema { table = "teacher" } path = "/apps/hive/demo/teacher" fileformattype = "parquet" } ] } ``` ```hocon LocalFile { tables_configs = [ { schema { fields { name = string age = int } } path = "/apps/hive/demo/student" fileformattype = "json" }, { schema { fields { name = string age = int } } path = "/apps/hive/demo/teacher" fileformattype = "json" } } ``` Add Local File Source Connector ) ) )