Snowpark Scala API Reference 1.20.0 - com.snowflake.snowpark.tableFunctions
Keywords: Snowpark Scala API Reference 1.20.0 com.snowflake.snowpark.tableFunctions

Snowpark Scala API Reference1.20.0 < Back
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- tableFunctions

o

com.snowflake.snowpark

# tableFunctions

###

#### object tableFunctions

Provides utility functions that generate table function expressions that can be passed to DataFrame join method and Session tableFunction method.

This object also provides functions that correspond to Snowflake system-defined table functions.

The following examples demonstrate the use of some of these functions:

    import com.snowflake.snowpark.functions.parse_json

    // Creates DataFrame from Session.tableFunction
    session.tableFunction(tableFunctions.flatten, Map("input" -> parse_json(lit("[1,2]"))))
    session.tableFunction(tableFunctions.split_to_table, "split by space", " ")

    // DataFrame joins table function
    df.join(tableFunctions.flatten, Map("input" -> parse_json(df("a"))))
    df.join(tableFunctions.split_to_table, df("a"), ",")

    // Invokes any table function including user-defined table function
     df.join(tableFunctions.tableFunction("flatten"), Map("input" -> parse_json(df("a"))))
     session.tableFunction(tableFunctions.tableFunction("split_to_table"), "split by space", " ")

Since

  0.4.0

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8.  def explode(input: Column): Column

   Flattens a given array or map type column into individual rows.

   Flattens a given array or map type column into individual rows. The output column(s) in case of array input column is `VALUE`, and are `KEY` and `VALUE` in case of amp input column.

   Example

       import com.snowflake.snowpark.functions._

       val df = Seq("""{"a":1, "b": 2}""").toDF("a")
       val df1 = df.select(
         parse_json(df("a"))
         .cast(types.MapType(types.StringType, types.IntegerType))
         .as("a"))
       df1.select(lit(1), tableFunctions.explode(df1("a")), df1("a")("a")).show()

   input

     The expression that will be unseated into rows. The expression must be either MapType or ArrayType data.

   returns

     The result Column reference

   Since

     1.10.0

9.  def flatten(input: Column, path: String, outer: Boolean, recursive: Boolean, mode: String): Column

   Flattens (explodes) compound values into multiple rows.

   Flattens (explodes) compound values into multiple rows.

   Example

       import com.snowflake.snowpark.functions._
       import com.snowflake.snowpark.tableFunctions._

       df.join(
         tableFunctions.flatten(parse_json(df("a")), "path", true, true, "both")
       )

   input

     The expression that will be unseated into rows. The expression must be of data type VariantType, MapType or ArrayType.

   path

     The path to the element within a VariantType data structure which needs to be flattened. Can be a zero-length string (i.e. empty path) if the outermost element is to be flattened.

   outer

     Optional boolean value. If FALSE, any input rows that cannot be expanded, either because they cannot be accessed in the path or because they have zero fields or entries, are completely omitted from the output. If TRUE, exactly one row is generated for zero-row expansions (with NULL in the KEY, INDEX, and VALUE columns).

   recursive

     If FALSE, only the element referenced by PATH is expanded. If TRUE, the expansion is performed for all sub-elements recursively.

   mode

     ("object", "array", or "both") Specifies whether only objects, arrays, or both should be flattened.

   returns

     The result Column reference

   Since

     1.10.0

10.  def flatten(input: Column): Column

    Flattens (explodes) compound values into multiple rows.

    Flattens (explodes) compound values into multiple rows.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        df.join(
          tableFunctions.flatten(parse_json(df("a")))
        )

    input

      The expression that will be unseated into rows. The expression must be of data type VariantType, MapType or ArrayType.

    returns

      The result Column reference

    Since

      1.10.0

11.  lazy val flatten: TableFunction

    Flattens (explodes) compound values into multiple rows.

    Flattens (explodes) compound values into multiple rows.

    Argument List:

    input: Required. The expression that will be unseated into rows. The expression must be of data type VariantType, MapType or ArrayType.

    path: Optional. The path to the element within a VariantType data structure which needs to be flattened. Can be a zero-length string (i.e. empty path) if the outermost element is to be flattened. Default: Zero-length string (i.e. empty path)

    outer: Optional boolean value. If FALSE, any input rows that cannot be expanded, either because they cannot be accessed in the path or because they have zero fields or entries, are completely omitted from the output. If TRUE, exactly one row is generated for zero-row expansions (with NULL in the KEY, INDEX, and VALUE columns). Default: FALSE

    recursive: Optional boolean value If FALSE, only the element referenced by PATH is expanded. If TRUE, the expansion is performed for all sub-elements recursively. Default: FALSE

    mode: Optional String ("object", "array", or "both") Specifies whether only objects, arrays, or both should be flattened. Default: both

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        df.join(
          tableFunctions.flatten,
          Map("input" -> parse_json(df("a"), "outer" -> lit(true)))
        )

        session.tableFunction(
          tableFunctions.flatten,
          Map("input" -> parse_json(lit("[1,2]"), "mode" -> lit("array")))
        )

    Since

      0.4.0

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18.  def split_to_table(str: Column, delimiter: String): Column

    This table function splits a string (based on a specified delimiter) and flattens the results into rows.

    This table function splits a string (based on a specified delimiter) and flattens the results into rows.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        df.join(tableFunctions.split_to_table(df("a"), lit(",")))

    str

      Text to be split.

    delimiter

      Text to split string by.

    returns

      The result Column reference

    Since

      1.10.0

19.  lazy val split_to_table: TableFunction

    This table function splits a string (based on a specified delimiter) and flattens the results into rows.

    This table function splits a string (based on a specified delimiter) and flattens the results into rows.

    Argument List:

    First argument (no name): Required. Text to be split.

    Second argument (no name): Required. Text to split string by.

    Example

        import com.snowflake.snowpark.functions._
        import com.snowflake.snowpark.tableFunctions._

        df.join(tableFunctions.split_to_table, df("a"), lit(","))
        session.tableFunction(
          tableFunctions.split_to_table,
          lit("split by space"),
          lit(" ")
        )

    Since

      0.4.0

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