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org.apache.spark.sql

RelationalGroupedDataset

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)
    Attributes
    protected[org.apache.spark.sql]

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def agg(expr: Column, exprs: Column*): DataFrame

    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

    (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

    (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

    (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
    Definition Classes
    Any
  9. def avg(colNames: String*): DataFrame

    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
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  11. def count(): DataFrame

    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
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
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  14. def finalize(): Unit
    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]
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    @native()
  16. def hashCode(): Int
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    Annotations
    @native()
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. def max(colNames: String*): DataFrame

    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

    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

    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
    Definition Classes
    AnyRef
  22. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  24. def pivot(pivotColumn: Column, values: List[Any]): RelationalGroupedDataset

    (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. This is an overloaded version of the pivot method with pivotColumn of the String type.

    pivotColumn

    the column to pivot.

    values

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

    Since

    2.4.0

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

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

    Pivots a column of the current DataFrame and performs the specified aggregation. This is an overloaded version of the pivot method with pivotColumn of the String type.

    // 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")
    pivotColumn

    the column to pivot.

    values

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

    Since

    2.4.0

  26. def pivot(pivotColumn: Column): RelationalGroupedDataset

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

    Pivots a column of the current DataFrame and performs the specified aggregation. This is an overloaded version of the pivot method with pivotColumn of the String type.

    // Or without specifying column values (less efficient)
df.groupBy($"year").pivot($"course").sum($"earnings");
    pivotColumn

    he column to pivot.

    Since

    2.4.0

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

    (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

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

    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")

    From Spark 3.0.0, values can be literal columns, for instance, struct. For pivoting by multiple columns, use the struct function to combine the columns and values:

    df.groupBy("year")
  .pivot("trainingCourse", Seq(struct(lit("java"), lit("Experts"))))
  .agg(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

  29. def pivot(pivotColumn: String): RelationalGroupedDataset

    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

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

    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

  31. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  32. def toString(): String
    Definition Classes
    RelationalGroupedDataset → AnyRef → Any
  33. final def wait(): Unit
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    @throws( ... )
  34. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  35. final def wait(arg0: Long): Unit
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    @native() @throws( ... )

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