Class

org.apache.spark.sql

RelationalGroupedDataset

Related Doc: package sql

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class RelationalGroupedDataset extends AnyRef

A set of methods for aggregations on a DataFrame, created by groupBy, cube or rollup (and also pivot).

The main method is the agg function, which has multiple variants. This class also contains some first-order statistics such as mean, sum for convenience.

Annotations
@Stable()
Since

2.0.0

Note

This class was named GroupedData in Spark 1.x.

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Instance Constructors

  1. new RelationalGroupedDataset(df: DataFrame, groupingExprs: Seq[Expression], groupType: GroupType)

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    Attributes
    protected[org.apache.spark.sql]

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def agg(expr: Column, exprs: Column*): DataFrame

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    Compute aggregates by specifying a series of aggregate columns.

    Compute aggregates by specifying a series of aggregate columns. Note that this function by default retains the grouping columns in its output. To not retain grouping columns, set spark.sql.retainGroupColumns to false.

    The available aggregate methods are defined in org.apache.spark.sql.functions.

    // Selects the age of the oldest employee and the aggregate expense for each department
    
    // Scala:
    import org.apache.spark.sql.functions._
    df.groupBy("department").agg(max("age"), sum("expense"))
    
    // Java:
    import static org.apache.spark.sql.functions.*;
    df.groupBy("department").agg(max("age"), sum("expense"));

    Note that before Spark 1.4, the default behavior is to NOT retain grouping columns. To change to that behavior, set config variable spark.sql.retainGroupColumns to false.

    // Scala, 1.3.x:
    df.groupBy("department").agg($"department", max("age"), sum("expense"))
    
    // Java, 1.3.x:
    df.groupBy("department").agg(col("department"), max("age"), sum("expense"));
    Annotations
    @varargs()
    Since

    1.3.0

  5. def agg(exprs: Map[String, String]): DataFrame

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    (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods.

    (Java-specific) Compute aggregates by specifying a map from column name to aggregate methods. The resulting DataFrame will also contain the grouping columns.

    The available aggregate methods are avg, max, min, sum, count.

    // Selects the age of the oldest employee and the aggregate expense for each department
    import com.google.common.collect.ImmutableMap;
    df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum"));
    Since

    1.3.0

  6. def agg(exprs: Map[String, String]): DataFrame

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    (Scala-specific) Compute aggregates by specifying a map from column name to aggregate methods.

    (Scala-specific) Compute aggregates by specifying a map from column name to aggregate methods. The resulting DataFrame will also contain the grouping columns.

    The available aggregate methods are avg, max, min, sum, count.

    // Selects the age of the oldest employee and the aggregate expense for each department
    df.groupBy("department").agg(Map(
      "age" -> "max",
      "expense" -> "sum"
    ))
    Since

    1.3.0

  7. def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame

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    (Scala-specific) Compute aggregates by specifying the column names and aggregate methods.

    (Scala-specific) Compute aggregates by specifying the column names and aggregate methods. The resulting DataFrame will also contain the grouping columns.

    The available aggregate methods are avg, max, min, sum, count.

    // Selects the age of the oldest employee and the aggregate expense for each department
    df.groupBy("department").agg(
      "age" -> "max",
      "expense" -> "sum"
    )
    Since

    1.3.0

  8. final def asInstanceOf[T0]: T0

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  9. def avg(colNames: String*): DataFrame

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    Compute the mean value for each numeric columns for each group.

    Compute the mean value for each numeric columns for each group. The resulting DataFrame will also contain the grouping columns. When specified columns are given, only compute the mean values for them.

    Annotations
    @varargs()
    Since

    1.3.0

  10. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  11. def count(): DataFrame

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    Count the number of rows for each group.

    Count the number of rows for each group. The resulting DataFrame will also contain the grouping columns.

    Since

    1.3.0

  12. final def eq(arg0: AnyRef): Boolean

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  13. def equals(arg0: Any): Boolean

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  14. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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  18. def max(colNames: String*): DataFrame

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    Compute the max value for each numeric columns for each group.

    Compute the max value for each numeric columns for each group. The resulting DataFrame will also contain the grouping columns. When specified columns are given, only compute the max values for them.

    Annotations
    @varargs()
    Since

    1.3.0

  19. def mean(colNames: String*): DataFrame

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    Compute the average value for each numeric columns for each group.

    Compute the average value for each numeric columns for each group. This is an alias for avg. The resulting DataFrame will also contain the grouping columns. When specified columns are given, only compute the average values for them.

    Annotations
    @varargs()
    Since

    1.3.0

  20. def min(colNames: String*): DataFrame

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    Compute the min value for each numeric column for each group.

    Compute the min value for each numeric column for each group. The resulting DataFrame will also contain the grouping columns. When specified columns are given, only compute the min values for them.

    Annotations
    @varargs()
    Since

    1.3.0

  21. final def ne(arg0: AnyRef): Boolean

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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  24. def pivot(pivotColumn: String, values: List[Any]): RelationalGroupedDataset

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    (Java-specific) Pivots a column of the current DataFrame and performs the specified aggregation.

    (Java-specific) Pivots a column of the current DataFrame and performs the specified aggregation.

    There are two versions of pivot function: one that requires the caller to specify the list of distinct values to pivot on, and one that does not. The latter is more concise but less efficient, because Spark needs to first compute the list of distinct values internally.

    // Compute the sum of earnings for each year by course with each course as a separate column
    df.groupBy("year").pivot("course", Arrays.<Object>asList("dotNET", "Java")).sum("earnings");
    
    // Or without specifying column values (less efficient)
    df.groupBy("year").pivot("course").sum("earnings");
    pivotColumn

    Name of the column to pivot.

    values

    List of values that will be translated to columns in the output DataFrame.

    Since

    1.6.0

  25. def pivot(pivotColumn: String, values: Seq[Any]): RelationalGroupedDataset

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    Pivots a column of the current DataFrame and performs the specified aggregation.

    Pivots a column of the current DataFrame and performs the specified aggregation. There are two versions of pivot function: one that requires the caller to specify the list of distinct values to pivot on, and one that does not. The latter is more concise but less efficient, because Spark needs to first compute the list of distinct values internally.

    // Compute the sum of earnings for each year by course with each course as a separate column
    df.groupBy("year").pivot("course", Seq("dotNET", "Java")).sum("earnings")
    
    // Or without specifying column values (less efficient)
    df.groupBy("year").pivot("course").sum("earnings")
    pivotColumn

    Name of the column to pivot.

    values

    List of values that will be translated to columns in the output DataFrame.

    Since

    1.6.0

  26. def pivot(pivotColumn: String): RelationalGroupedDataset

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    Pivots a column of the current DataFrame and performs the specified aggregation.

    Pivots a column of the current DataFrame and performs the specified aggregation.

    There are two versions of pivot function: one that requires the caller to specify the list of distinct values to pivot on, and one that does not. The latter is more concise but less efficient, because Spark needs to first compute the list of distinct values internally.

    // Compute the sum of earnings for each year by course with each course as a separate column
    df.groupBy("year").pivot("course", Seq("dotNET", "Java")).sum("earnings")
    
    // Or without specifying column values (less efficient)
    df.groupBy("year").pivot("course").sum("earnings")
    pivotColumn

    Name of the column to pivot.

    Since

    1.6.0

  27. def sum(colNames: String*): DataFrame

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    Compute the sum for each numeric columns for each group.

    Compute the sum for each numeric columns for each group. The resulting DataFrame will also contain the grouping columns. When specified columns are given, only compute the sum for them.

    Annotations
    @varargs()
    Since

    1.3.0

  28. final def synchronized[T0](arg0: ⇒ T0): T0

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  29. def toString(): String

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    RelationalGroupedDataset → AnyRef → Any
  30. final def wait(): Unit

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    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit

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  32. final def wait(arg0: Long): Unit

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