jsonpath.md

JsonPath

JsonPath transform plugin

Description

Support use jsonpath select data

Options

name	type	required	default value
columns	Array	Yes
row_error_handle_way	Enum	No	FAIL

common options [string]

Transform plugin common parameters, please refer to Transform Plugin for details

row_error_handle_way [Enum]

This option is used to specify the processing method when an error occurs in the row, the default value is FAIL.

• FAIL: When FAIL is selected, data format error will block and an exception will be thrown.
• SKIP: When SKIP is selected, data format error will skip this row data.

columns[array]

option

name	type	required	default value
src_field	String	Yes
dest_field	String	Yes
path	String	Yes
dest_type	String	No	String
column_error_handle_way	Enum	No

src_field

the json source field you want to parse

Support SeatunnelDateType

• STRING
• BYTES
• ARRAY
• MAP
• ROW

dest_field

after use jsonpath output field

dest_type

the type of dest field

path

Jsonpath

column_error_handle_way [Enum]

This option is used to specify the processing method when an error occurs in the column.

• FAIL: When FAIL is selected, data format error will block and an exception will be thrown.
• SKIP: When SKIP is selected, data format error will skip this column data.
• SKIP_ROW: When SKIP_ROW is selected, data format error will skip this row data.

Read Json Example

The data read from source is a table like this json:

{
  "data": {
    "c_string": "this is a string",
    "c_boolean": true,
    "c_integer": 42,
    "c_float": 3.14,
    "c_double": 3.14,
    "c_decimal": 10.55,
    "c_date": "2023-10-29",
    "c_datetime": "16:12:43.459",
    "c_array":["item1", "item2", "item3"]
  }
}

Assuming we want to use JsonPath to extract properties.

transform {
  JsonPath {
    source_table_name = "fake"
    result_table_name = "fake1"
    columns = [
     {
        "src_field" = "data"
        "path" = "$.data.c_string"
        "dest_field" = "c1_string"
     },
     {
        "src_field" = "data"
        "path" = "$.data.c_boolean"
        "dest_field" = "c1_boolean"
        "dest_type" = "boolean"
     },
     {
        "src_field" = "data"
        "path" = "$.data.c_integer"
        "dest_field" = "c1_integer"
        "dest_type" = "int"
     },
     {
        "src_field" = "data"
        "path" = "$.data.c_float"
        "dest_field" = "c1_float"
        "dest_type" = "float"
     },
     {
        "src_field" = "data"
        "path" = "$.data.c_double"
        "dest_field" = "c1_double"
        "dest_type" = "double"
     },
      {
         "src_field" = "data"
         "path" = "$.data.c_decimal"
         "dest_field" = "c1_decimal"
         "dest_type" = "decimal(4,2)"
      },
      {
         "src_field" = "data"
         "path" = "$.data.c_date"
         "dest_field" = "c1_date"
         "dest_type" = "date"
      },
      {
         "src_field" = "data"
         "path" = "$.data.c_datetime"
         "dest_field" = "c1_datetime"
         "dest_type" = "time"
      },
			{
         "src_field" = "data"
         "path" = "$.data.c_array"
         "dest_field" = "c1_array"
         "dest_type" = "array<string>"        
      }
    ]
  }
}

Then the data result table fake1 will like this

data	c1_string	c1_boolean	c1_integer	c1_float	c1_double	c1_decimal	c1_date	c1_datetime	c1_array
too much content not to show	this is a string	true	42	3.14	3.14	10.55	2023-10-29	16:12:43.459	["item1", "item2", "item3"]

Read SeatunnelRow Example

Suppose a column in a row of data is of type SeatunnelRow and that the name of the column is col

SeatunnelRow(col)		other
name	age	....
a	18	....

The JsonPath transform converts the values of seatunnel into an array,

transform {
  JsonPath {
    source_table_name = "fake"
    result_table_name = "fake1"
  
    row_error_handle_way = FAIL
    columns = [
     {
        "src_field" = "col"
        "path" = "$[0]"
        "dest_field" = "name"
        "dest_type" = "string"
     },
     {
        "src_field" = "col"
        "path" = "$[1]"
        "dest_field" = "age"
        "dest_type" = "int"
     }
    ]
  }
}

Then the data result table fake1 will like this

name	age	col	other
a	18	["a",18]	...

Configure error data handle way

You can configure row_error_handle_way and column_error_handle_way to handle abnormal data. Both are optional.

row_error_handle_way is used to handle all data anomalies in the row data, while column_error_handle_way is used to handle data anomalies in a column. It has a higher priority than row_error_handle_way.

Skip error data rows

Configure to skip row data with exceptions in any column

transform {
  JsonPath {

    row_error_handle_way = SKIP
    
    columns = [
     {
        "src_field" = "json_data"
        "path" = "$.f1"
        "dest_field" = "json_data_f1"
     },
     {
        "src_field" = "json_data"
        "path" = "$.f2"
        "dest_field" = "json_data_f2"
     }
    ]
  }
}

Skip error data column

Configure only json_data_f1 column data exceptions to skip and fill in null values, other column data exceptions will continue to throw exception interrupt handlers

transform {
  JsonPath {

    row_error_handle_way = FAIL
    
    columns = [
     {
        "src_field" = "json_data"
        "path" = "$.f1"
        "dest_field" = "json_data_f1"
        
        "column_error_handle_way" = "SKIP"
     },
     {
        "src_field" = "json_data"
        "path" = "$.f2"
        "dest_field" = "json_data_f2"
     }
    ]
  }
}

Skip the row for specified column error

Configure to skip the row of data only for json_data_f1 column data exceptions, and continue to throw exceptions to interrupt the handler for other column data exceptions

transform {
  JsonPath {

    row_error_handle_way = FAIL
    
    columns = [
     {
        "src_field" = "json_data"
        "path" = "$.f1"
        "dest_field" = "json_data_f1"
        
        "column_error_handle_way" = "SKIP_ROW"
     },
     {
        "src_field" = "json_data"
        "path" = "$.f2"
        "dest_field" = "json_data_f2"
     }
    ]
  }
}

Changelog

• Add JsonPath Transform