RelationalGroupedDataset
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package sql
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"));
1.3.0
(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"));
1.3.0
(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" ))
1.3.0
(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" )
1.3.0
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.
1.3.0
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.
1.3.0
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.
1.3.0
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.
1.3.0
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.
1.3.0
Pivots a column of the current DataFrame and perform the specified aggregation.
Pivots a column of the current DataFrame and perform 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");
Name of the column to pivot.
List of values that will be translated to columns in the output DataFrame.
1.6.0
Pivots a column of the current DataFrame and perform the specified aggregation.
Pivots a column of the current DataFrame and perform 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")
Name of the column to pivot.
List of values that will be translated to columns in the output DataFrame.
1.6.0
Pivots a column of the current DataFrame and perform the specified aggregation.
Pivots a column of the current DataFrame and perform 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")
Name of the column to pivot.
1.6.0
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.
1.3.0
A set of methods for aggregations on a
DataFrame, created byDataset.groupBy.The main method is the agg function, which has multiple variants. This class also contains convenience some first order statistics such as mean, sum for convenience.
This class was named
GroupedDatain Spark 1.x.2.0.0