abstract class TableValuedFunction extends AnyRef
Interface for invoking table-valued functions in Spark SQL.
- Source
- TableValuedFunction.scala
- Since
4.0.0
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Instance Constructors
- new TableValuedFunction()
Abstract Value Members
- abstract def collations(): Dataset[Row]
Gets all of the Spark SQL string collations.
Gets all of the Spark SQL string collations.
- Since
4.0.0
- abstract def explode(collection: Column): Dataset[Row]
Creates a
DataFramecontaining a new row for each element in the given array or map column.Creates a
DataFramecontaining a new row for each element in the given array or map column. Uses the default column namecolfor elements in the array andkeyandvaluefor elements in the map unless specified otherwise.- Since
4.0.0
- abstract def explode_outer(collection: Column): Dataset[Row]
Creates a
DataFramecontaining a new row for each element in the given array or map column.Creates a
DataFramecontaining a new row for each element in the given array or map column. Uses the default column namecolfor elements in the array andkeyandvaluefor elements in the map unless specified otherwise. Unlike explode, if the array/map is null or empty then null is produced.- Since
4.0.0
- abstract def inline(input: Column): Dataset[Row]
Creates a
DataFramecontaining a new row for each element in the given array of structs.Creates a
DataFramecontaining a new row for each element in the given array of structs.- Since
4.0.0
- abstract def inline_outer(input: Column): Dataset[Row]
Creates a
DataFramecontaining a new row for each element in the given array of structs.Creates a
DataFramecontaining a new row for each element in the given array of structs. Unlike inline, if the array is null or empty then null is produced for each nested column.- Since
4.0.0
- abstract def json_tuple(input: Column, fields: Column*): Dataset[Row]
Creates a
DataFramecontaining a new row for a json column according to the given field names.Creates a
DataFramecontaining a new row for a json column according to the given field names.- Annotations
- @varargs()
- Since
4.0.0
- abstract def posexplode(collection: Column): Dataset[Row]
Creates a
DataFramecontaining a new row for each element with position in the given array or map column.Creates a
DataFramecontaining a new row for each element with position in the given array or map column. Uses the default column nameposfor position, andcolfor elements in the array andkeyandvaluefor elements in the map unless specified otherwise.- Since
4.0.0
- abstract def posexplode_outer(collection: Column): Dataset[Row]
Creates a
DataFramecontaining a new row for each element with position in the given array or map column.Creates a
DataFramecontaining a new row for each element with position in the given array or map column. Uses the default column nameposfor position, andcolfor elements in the array andkeyandvaluefor elements in the map unless specified otherwise. Unlike posexplode, if the array/map is null or empty then the row (null, null) is produced.- Since
4.0.0
- abstract def range(start: Long, end: Long, step: Long, numPartitions: Int): Dataset[Long]
Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range fromstarttoend(exclusive) with a step value, with partition number specified.Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range fromstarttoend(exclusive) with a step value, with partition number specified.- Since
4.0.0
- abstract def range(start: Long, end: Long, step: Long): Dataset[Long]
Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range fromstarttoend(exclusive) with a step value.Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range fromstarttoend(exclusive) with a step value.- Since
4.0.0
- abstract def range(start: Long, end: Long): Dataset[Long]
Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range fromstarttoend(exclusive) with step value 1.Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range fromstarttoend(exclusive) with step value 1.- Since
4.0.0
- abstract def range(end: Long): Dataset[Long]
Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range from 0 toend(exclusive) with step value 1.Creates a
Datasetwith a singleLongTypecolumn namedid, containing elements in a range from 0 toend(exclusive) with step value 1.- Since
4.0.0
- abstract def sql_keywords(): Dataset[Row]
Gets Spark SQL keywords.
Gets Spark SQL keywords.
- Since
4.0.0
- abstract def stack(n: Column, fields: Column*): Dataset[Row]
Separates
col1, ...,colkintonrows.Separates
col1, ...,colkintonrows. Uses column names col0, col1, etc. by default unless specified otherwise.- Annotations
- @varargs()
- Since
4.0.0
- abstract def variant_explode(input: Column): Dataset[Row]
Separates a variant object/array into multiple rows containing its fields/elements.
Separates a variant object/array into multiple rows containing its fields/elements. Its result schema is
struct<pos int, key string, value variant>.posis the position of the field/element in its parent object/array, andvalueis the field/element value.keyis the field name when exploding a variant object, or is NULL when exploding a variant array. It ignores any input that is not a variant array/object, including SQL NULL, variant null, and any other variant values.- Since
4.0.0
- abstract def variant_explode_outer(input: Column): Dataset[Row]
Separates a variant object/array into multiple rows containing its fields/elements.
Separates a variant object/array into multiple rows containing its fields/elements. Its result schema is
struct<pos int, key string, value variant>.posis the position of the field/element in its parent object/array, andvalueis the field/element value.keyis the field name when exploding a variant object, or is NULL when exploding a variant array. Unlike variant_explode, if the given variant is not a variant array/object, including SQL NULL, variant null, and any other variant values, then NULL is produced.- Since
4.0.0
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- Deprecated
(Since version 9)