Simple Log Service:Table functions

Builds a table from text in CSV format.

• Syntax

  tab_parse_csv(
      data, sep=',', quote='"', lstrip=True,
      headers=None, case_insensitive=True, primary_keys=None,
  )

• Parameters

  Parameter name	Data type	Required	Description
  data	String	Yes	The text in CSV format.
  sep	String	No	The separator for the CSV format. The default value is a comma (,).
  quote	String	No	The quote character. If a value contains the separator, you must enclose the value in quotes. The default value is a double quotation mark (").
  lstrip	Boolean	No	Whether to remove leading spaces from each keyword. Default value: True.
  headers	String, String List	No	The header names for each parsed field. By default, headers are read from the first row. If the first row contains data instead of headers, specify the headers by using this parameter.
  case_insensitive	Boolean	No	Whether matching is case-insensitive. Default value: True.
  primary_keys	String, String List	No	The primary key in the data. After you set this parameter, if you use a mapping enrichment function, the field specified in the field parameter of the function must be the same as this primary key. For more information about mapping enrichment functions, see Mapping enrichment functions.

• Response

  Returns the mapped table data.

• Examples

  • Example 1: Build a table and map the value of a field.

    • Raw log

      city:nanjing

    • Transformation rule

      e_table_map(
          tab_parse_csv(
              "province,city,pop,gdp\nshanghai,shanghai,2000,1000\njiangsu,nanjing,800,500"
          ),
          "city",
          "province",
      )

    • Result

      city:nanjing
      province:jiangsu

  • Example 2: Build a table and map the values of multiple fields.

    • Raw log

      city:nanjing
      province:jiangsu

    • Transformation rule

      e_table_map(
          tab_parse_csv(
              "province,city,pop,gdp\nshanghai,shanghai,2000,1000\njiangsu,nanjing,800,500"
          ),
          ["province", "city"],
          ["pop", "gdp"],
      )

    • Result

      city:nanjing
      gdp:500
      pop:800
      province:jiangsu

  • Example 3: Map fields with names that differ from the table column names. In each source field tuple, the first element is the source field and the second is the table field. In each destination field tuple, the first element is the table field and the second is the new field.

    • Raw log

      city:nanjing
      province:jiangsu

    • Transformation rule

      e_table_map(
          tab_parse_csv(
              "prov,city,pop,gdp\nshanghai,shanghai,2000,1000\njiangsu,nanjing,800,500"
          ),
          [("province", "prov"), "city"],
          [("pop", "population"), ("gdp", "GDP")],
      )

    • Result

      GDP:500
      city:nanjing
      population:800
      province:jiangsu

  • Example 4: Map fields with names that differ from the table column names, with primary keys specified. In each source field tuple, the first element is the source field and the second is the table field. In each destination field tuple, the first element is the table field and the second is the new field. The table field in the source field tuple must match the primary key.

    • Raw log

      city:nanjing
      province:jiangsu

    • Transformation rule

      e_table_map(
          tab_parse_csv(
              "prov,city,pop,gdp\nshanghai,shanghai,2000,1000\njiangsu,nanjing,800,500",
              primary_keys=["prov", "city"],
          ),
          [("province", "prov"), "city"],
          [("pop", "population"), ("gdp", "GDP")],
      )

    • Result

      GDP:500
      city:nanjing
      population:800
      province:jiangsu

Builds a dictionary from a table.

• Syntax

  tab_to_dict(table, key_field, value_field, key_join=",", value_join=",")

• Parameters

  Parameter Name	Data type	Required	Description
  table	table	Yes	The table data.
  key_field	String, String List	Yes	The table column or columns used to build the dictionary keys. If you specify multiple columns, they are joined by the string specified in key_join.
  value_field	String, String List	Yes	The table column or columns used to build the dictionary values. If you specify multiple columns, they are joined by the string specified in value_join.
  key_join	String	No	The string used to join multiple columns to form a key. The default value is a comma (,).
  value_join	String	No	The string used to join multiple columns to form a value. The default value is a comma (,).

• Response

  Returns the mapped dictionary data.

• Examples

  • Example 1

    • Raw log

      k1:v1
      city:nj

    • Transformation rule

      e_dict_map(
          tab_to_dict(tab_parse_csv("city,pop\nsh,2000\nnj,800"), "city", "pop"),
          "city",
          "popu",
      )

    • Result

      k1:v1
      city:nj
      popu:800

  • Example 2

    • Raw log

      k1:v1
      city:js,nj

    • Transformation rule

      e_dict_map(
          tab_to_dict(
              tab_parse_csv("province,city,pop\nsh,sh,2000\njs,nj,800"),
              ["province", "city"],
              "pop",
          ),
          "city",
          "popu",
      )

    • Result

      k1:v1
      city:js,nj
      popu:800