expressions

Type Members

1. abstract class Aggregator[-IN, BUF, OUT] extends Serializable

   

   :: Experimental :: A base class for user-defined aggregations, which can be used in Dataset operations to take all of the elements of a group and reduce them to a single value.

   :: Experimental :: A base class for user-defined aggregations, which can be used in Dataset operations to take all of the elements of a group and reduce them to a single value.

   For example, the following aggregator extracts an int from a specific class and adds them up:

   case class Data(i: Int)
   
   val customSummer =  new Aggregator[Data, Int, Int] {
     def zero: Int = 0
     def reduce(b: Int, a: Data): Int = b + a.i
     def merge(b1: Int, b2: Int): Int = b1 + b2
     def finish(r: Int): Int = r
   }.toColumn()
   
   val ds: Dataset[Data] = ...
   val aggregated = ds.select(customSummer)

   Based loosely on Aggregator from Algebird: https://github.com/twitter/algebird

   IN

   The input type for the aggregation.

   BUF

   The type of the intermediate value of the reduction.

   OUT

   The type of the final output result.

   Annotations

   @Experimental() @Evolving()

   Since

   1.6.0

2. abstract class MutableAggregationBuffer extends Row

   

   A Row representing a mutable aggregation buffer.

   A Row representing a mutable aggregation buffer.

   This is not meant to be extended outside of Spark.

   Annotations

   @Stable()

   Since

   1.5.0

3. abstract class UserDefinedAggregateFunction extends Serializable

   

   The base class for implementing user-defined aggregate functions (UDAF).

   The base class for implementing user-defined aggregate functions (UDAF).

   Annotations

   @Stable()

   Since

   1.5.0

4. case class UserDefinedFunction(f: AnyRef, dataType: DataType, inputTypes: Option[Seq[DataType]]) extends Product with Serializable

   

   A user-defined function.

   A user-defined function. To create one, use the udf functions in functions.

   As an example:

   // Defined a UDF that returns true or false based on some numeric score.
   val predict = udf((score: Double) => if (score > 0.5) true else false)
   
   // Projects a column that adds a prediction column based on the score column.
   df.select( predict(df("score")) )

   Annotations

   @Stable()

   Since

   1.3.0

   Note

   The user-defined functions must be deterministic. Due to optimization, duplicate invocations may be eliminated or the function may even be invoked more times than it is present in the query.

5. class Window extends AnyRef

   

   Utility functions for defining window in DataFrames.

   Utility functions for defining window in DataFrames.

   // PARTITION BY country ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
   Window.partitionBy("country").orderBy("date")
     .rowsBetween(Window.unboundedPreceding, Window.currentRow)
   
   // PARTITION BY country ORDER BY date ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING
   Window.partitionBy("country").orderBy("date").rowsBetween(-3, 3)

   Annotations

   @Stable()

   Since

   1.4.0

6. class WindowSpec extends AnyRef

   

   A window specification that defines the partitioning, ordering, and frame boundaries.

   A window specification that defines the partitioning, ordering, and frame boundaries.

   Use the static methods in Window to create a WindowSpec.

   Annotations

   @Stable()

   Since

   1.4.0