Package org.apache.spark.sql

Class RelationalGroupedDataset

Object
org.apache.spark.sql.RelationalGroupedDataset
public class RelationalGroupedDataset extends Object
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.

Since:
2.0.0
Note:
This class was named GroupedData in Spark 1.x.

  • Method Details

    • apply

      public static RelationalGroupedDataset apply(Dataset<Row> df, scala.collection.immutable.Seq<org.apache.spark.sql.catalyst.expressions.Expression> groupingExprs, RelationalGroupedDataset.GroupType groupType)

    • agg

      public Dataset<Row> agg(Column expr, Column... exprs)
      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 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"));

      Parameters:
      expr - (undocumented)
      exprs - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • mean

      public Dataset<Row> mean(String... colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • max

      public Dataset<Row> max(String... colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • avg

      public Dataset<Row> avg(String... colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • min

      public Dataset<Row> min(String... colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • sum

      public Dataset<Row> sum(String... colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • as

      public <K, T> KeyValueGroupedDataset<K,T> as(Encoder<K> evidence$1, Encoder<T> evidence$2)
      Returns a KeyValueGroupedDataset where the data is grouped by the grouping expressions of current RelationalGroupedDataset.

      Parameters:
      evidence$1 - (undocumented)
      evidence$2 - (undocumented)
      Returns:
      (undocumented)
      Since:
      3.0.0

    • agg

      public Dataset<Row> agg(scala.Tuple2<String,String> aggExpr, scala.collection.immutable.Seq<scala.Tuple2<String,String>> aggExprs)
      (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"
   )

      Parameters:
      aggExpr - (undocumented)
      aggExprs - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • agg

      public Dataset<Row> agg(scala.collection.immutable.Map<String,String> exprs)
      (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"
   ))

      Parameters:
      exprs - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • agg

      public Dataset<Row> agg(Map<String,String> exprs)
      (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"));

      Parameters:
      exprs - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • agg

      public Dataset<Row> agg(Column expr, scala.collection.immutable.Seq<Column> exprs)
      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 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"));

      Parameters:
      expr - (undocumented)
      exprs - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • count

      public Dataset<Row> count()
      Count the number of rows for each group. The resulting DataFrame will also contain the grouping columns.

      Returns:
      (undocumented)
      Since:
      1.3.0

    • mean

      public Dataset<Row> mean(scala.collection.immutable.Seq<String> colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • max

      public Dataset<Row> max(scala.collection.immutable.Seq<String> colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • avg

      public Dataset<Row> avg(scala.collection.immutable.Seq<String> colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • min

      public Dataset<Row> min(scala.collection.immutable.Seq<String> colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • sum

      public Dataset<Row> sum(scala.collection.immutable.Seq<String> colNames)
      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.

      Parameters:
      colNames - (undocumented)
      Returns:
      (undocumented)
      Since:
      1.3.0

    • pivot

      public RelationalGroupedDataset pivot(String pivotColumn)
      Pivots a column of the current DataFrame and performs the specified aggregation.

      Spark will eagerly compute the distinct values in pivotColumn so it can determine the resulting schema of the transformation. To avoid any eager computations, provide an explicit list of values via pivot(pivotColumn: String, values: Seq[Any]). 

      
   // Compute the sum of earnings for each year by course with each course as a separate column
   df.groupBy("year").pivot("course").sum("earnings")

      Parameters:
      pivotColumn - Name of the column to pivot.
      Returns:
      (undocumented)
      Since:
      1.6.0
      See Also:
      • org.apache.spark.sql.Dataset.unpivot for the reverse operation, except for the aggregation.

    • pivot

      public RelationalGroupedDataset pivot(String pivotColumn, scala.collection.immutable.Seq<Object> values)
      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"))

      Parameters:
      pivotColumn - Name of the column to pivot.
      values - List of values that will be translated to columns in the output DataFrame.
      Returns:
      (undocumented)
      Since:
      1.6.0
      See Also:
      • org.apache.spark.sql.Dataset.unpivot for the reverse operation, except for the aggregation.

    • pivot

      public RelationalGroupedDataset pivot(String pivotColumn, List<Object> values)
      (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");

      Parameters:
      pivotColumn - Name of the column to pivot.
      values - List of values that will be translated to columns in the output DataFrame.
      Returns:
      (undocumented)
      Since:
      1.6.0
      See Also:
      • org.apache.spark.sql.Dataset.unpivot for the reverse operation, except for the aggregation.

    • pivot

      public RelationalGroupedDataset pivot(Column pivotColumn)
      Pivots a column of the current DataFrame and performs the specified aggregation.

      Spark will eagerly compute the distinct values in pivotColumn so it can determine the resulting schema of the transformation. To avoid any eager computations, provide an explicit list of values via pivot(pivotColumn: Column, values: Seq[Any]). 

      
   // Compute the sum of earnings for each year by course with each course as a separate column
   df.groupBy($"year").pivot($"course").sum($"earnings");

      Parameters:
      pivotColumn - he column to pivot.
      Returns:
      (undocumented)
      Since:
      2.4.0
      See Also:
      • org.apache.spark.sql.Dataset.unpivot for the reverse operation, except for the aggregation.

    • pivot

      public RelationalGroupedDataset pivot(Column pivotColumn, scala.collection.immutable.Seq<Object> values)
      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")

      Parameters:
      pivotColumn - the column to pivot.
      values - List of values that will be translated to columns in the output DataFrame.
      Returns:
      (undocumented)
      Since:
      2.4.0
      See Also:
      • org.apache.spark.sql.Dataset.unpivot for the reverse operation, except for the aggregation.

    • pivot

      public RelationalGroupedDataset pivot(Column pivotColumn, List<Object> values)
      (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.

      Parameters:
      pivotColumn - the column to pivot.
      values - List of values that will be translated to columns in the output DataFrame.
      Returns:
      (undocumented)
      Since:
      2.4.0
      See Also:
      • org.apache.spark.sql.Dataset.unpivot for the reverse operation, except for the aggregation.

    • toString

      public String toString()
      Overrides:
      toString in class Object