RelationalGroupedDataset — Untyped Row-based Grouping

RelationalGroupedDataset is an interface to calculate aggregates over groups of rows in a DataFrame.

KeyValueGroupedDataset is used for typed aggregates using custom Scala objects (not Rows).

RelationalGroupedDataset is a result of executing the following grouping operators:

Table 1. RelationalGroupedDataset’s Aggregate Operators (in alphabetical order)
Operator Description








Pivots on a column (with new columns per distinct value)



spark.sql.retainGroupColumns Spark property controls whether to retain columns used for aggregation or not (in RelationalGroupedDataset operators).

spark.sql.retainGroupColumns is turned on by default.

scala> spark.version
res0: String = 2.3.0-SNAPSHOT

scala> spark.conf.get("spark.sql.retainGroupColumns")
res1: String = true

// Use dataFrameRetainGroupColumns method for type-safe access to the current value
import spark.sessionState.conf
scala> conf.dataFrameRetainGroupColumns
res2: Boolean = true

Computing Aggregates Using Aggregate Column Expressions — agg Operator

agg(expr: Column, exprs: Column*): DataFrame
agg(exprs: Map[String, String]): DataFrame
agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame

agg creates a DataFrame with the rows being the result of executing grouping expressions (specified using columns or names) over row groups.

You can use untyped or typed column expressions.
val countsAndSums = spark.
  range(10).  // <-- 10-element Dataset
  withColumn("group", 'id % 2).  // <-- define grouping column
  groupBy("group"). // <-- group by groups
  agg(count("id") as "count", sum("id") as "sum")
|    0|    5| 20|
|    1|    5| 25|

Internally, agg creates a DataFrame with Aggregate or Pivot logical operators.

// groupBy above
scala> println(countsAndSums.queryExecution.logical.numberedTreeString)
00 'Aggregate [group#179L], [group#179L, count('id) AS count#188, sum('id) AS sum#190]
01 +- Project [id#176L, (id#176L % cast(2 as bigint)) AS group#179L]
02    +- Range (0, 10, step=1, splits=Some(8))

// rollup operator
val rollupQ = spark.range(2).rollup('id).agg(count('id))
scala> println(rollupQ.queryExecution.logical.numberedTreeString)
00 'Aggregate [rollup('id)], [unresolvedalias('id, None), count('id) AS count(id)#267]
01 +- Range (0, 2, step=1, splits=Some(8))

// cube operator
val cubeQ = spark.range(2).cube('id).agg(count('id))
scala> println(cubeQ.queryExecution.logical.numberedTreeString)
00 'Aggregate [cube('id)], [unresolvedalias('id, None), count('id) AS count(id)#280]
01 +- Range (0, 2, step=1, splits=Some(8))

// pivot operator
val pivotQ = spark.
  withColumn("group", 'id % 2).
scala> println(pivotQ.queryExecution.logical.numberedTreeString)
00 'Pivot [group#296L], group#296: bigint, [0, 1], [count('id)]
01 +- Project [id#293L, (id#293L % cast(2 as bigint)) AS group#296L]
02    +- Range (0, 10, step=1, splits=Some(8))

Creating DataFrame from Aggregate Expressions — toDF Internal Method

toDF(aggExprs: Seq[Expression]): DataFrame

Internally, toDF branches off per group type.


For PivotType, toDF creates a DataFrame with Pivot unary logical operator.

Creating RelationalGroupedDataset Instance

RelationalGroupedDataset takes the following when created:

pivot Operator

pivot(pivotColumn: String): RelationalGroupedDataset  (1)
pivot(pivotColumn: String, values: Seq[Any]): RelationalGroupedDataset  (2)
  1. Selects distinct and sorted values on pivotColumn and calls the other pivot (that results in 3 extra "scanning" jobs)

  2. Preferred as more efficient because the unique values are aleady provided

pivot pivots on a pivotColumn column, i.e. adds new columns per distinct values in pivotColumn.

pivot is only supported after groupBy operation.
Only one pivot operation is supported on a RelationalGroupedDataset.
val visits = Seq(
  (0, "Warsaw", 2015),
  (1, "Warsaw", 2016),
  (2, "Boston", 2017)
).toDF("id", "city", "year")

val q = visits
  .groupBy("city")  // <-- rows in pivot table
  .pivot("year")    // <-- columns (unique values queried)
  .count()          // <-- values in cells
|  city|2015|2016|2017|
|Warsaw|   1|   1|null|
|Boston|null|null|   1|

scala> q.explain
== Physical Plan ==
HashAggregate(keys=[city#8], functions=[pivotfirst(year#9, count(1) AS `count`#222L, 2015, 2016, 2017, 0, 0)])
+- Exchange hashpartitioning(city#8, 200)
   +- HashAggregate(keys=[city#8], functions=[partial_pivotfirst(year#9, count(1) AS `count`#222L, 2015, 2016, 2017, 0, 0)])
      +- *HashAggregate(keys=[city#8, year#9], functions=[count(1)])
         +- Exchange hashpartitioning(city#8, year#9, 200)
            +- *HashAggregate(keys=[city#8, year#9], functions=[partial_count(1)])
               +- LocalTableScan [city#8, year#9]

scala> visits
  .pivot("year", Seq("2015")) // <-- one column in pivot table
|  city|2015|
|Warsaw|   1|
Use pivot with a list of distinct values to pivot on so Spark does not have to compute the list itself (and run three extra "scanning" jobs).
spark sql pivot webui.png
Figure 1. pivot in web UI (Distinct Values Defined Explicitly)
spark sql pivot webui scanning jobs.png
Figure 2. pivot in web UI — Three Extra Scanning Jobs Due to Unspecified Distinct Values
spark.sql.pivotMaxValues (default: 10000) controls the maximum number of (distinct) values that will be collected without error (when doing pivot without specifying the values for the pivot column).

Internally, pivot creates a RelationalGroupedDataset with PivotType group type and pivotColumn resolved using the DataFrame’s columns with values as Literal expressions.


toDF internal method maps PivotType group type to a DataFrame with Pivot unary logical operator.

scala> q.queryExecution.logical
res0: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Pivot [city#8], year#9: int, [2015, 2016, 2017], [count(1) AS count#24L]
+- Project [_1#3 AS id#7, _2#4 AS city#8, _3#5 AS year#9]
   +- LocalRelation [_1#3, _2#4, _3#5]

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