RelationalGroupedDataset — Untyped Row-based Grouping

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

Note	KeyValueGroupedDataset is used for typed aggregates over groups of custom Scala objects (not Rows).

RelationalGroupedDataset is a result of executing the following grouping operators:

Table 1. RelationalGroupedDataset’s Aggregate Operators
Operator	Description
agg
`avg`
`count`
`max`
`mean`
`min`
pivot	`pivot(pivotColumn: String): RelationalGroupedDataset pivot(pivotColumn: String, values: Seq[Any]): RelationalGroupedDataset pivot(pivotColumn: Column): RelationalGroupedDataset (1) pivot(pivotColumn: Column, values: Seq[Any]): RelationalGroupedDataset (1)` New in 2.4.0 Pivots on a column (with new columns per distinct value)
`sum`

Note

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

spark.sql.retainGroupColumns is enabled by default.

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 or Function Names — `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.

Note	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")
scala> countsAndSums.show
+-----+-----+---+
|group|count|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.
  range(10).
  withColumn("group", 'id % 2).
  groupBy("group").
  pivot("group").
  agg(count("id"))
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

Caution

FIXME

Internally, toDF branches off per group type.

Caution

FIXME

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

Note	`toDF` is used when the following `RelationalGroupedDataset` operators are used: agg and count mean, max, avg, min and sum (indirectly through aggregateNumericColumns)

`aggregateNumericColumns` Internal Method

aggregateNumericColumns(colNames: String*)(f: Expression => AggregateFunction): DataFrame

aggregateNumericColumns…FIXME

Note	`aggregateNumericColumns` is used when the following `RelationalGroupedDataset` operators are used: mean, max, avg, min and sum.

Creating RelationalGroupedDataset Instance

RelationalGroupedDataset takes the following when created:

DataFrame
Grouping expressions
Group type (to indicate the "source" operator)
- GroupByType for groupBy
- CubeType
- RollupType
- PivotType

`pivot` Operator

pivot(pivotColumn: String): RelationalGroupedDataset (1)
pivot(pivotColumn: String, values: Seq[Any]): RelationalGroupedDataset (2)
pivot(pivotColumn: Column): RelationalGroupedDataset (3)
pivot(pivotColumn: Column, values: Seq[Any]): RelationalGroupedDataset (3)

Selects distinct and sorted values on pivotColumn and calls the other pivot (that results in 3 extra "scanning" jobs)
Preferred as more efficient because the unique values are aleady provided
New in 2.4.0

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

Note	`pivot` is only supported after groupBy operation.

Note	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
scala> q.show
+------+----+----+----+
|  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
  .groupBy('city)
  .pivot("year", Seq("2015")) // <-- one column in pivot table
  .count
  .show
+------+----+
|  city|2015|
+------+----+
|Warsaw|   1|
|Boston|null|
+------+----+

Important

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).

Figure 1. pivot in web UI (Distinct Values Defined Explicitly)

Figure 2. pivot in web UI — Three Extra Scanning Jobs Due to Unspecified Distinct Values

Note	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.

Note

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]

`strToExpr` Internal Method

strToExpr(expr: String): (Expression => Expression)

strToExpr…FIXME

Note	`strToExpr` is used exclusively when `RelationalGroupedDataset` is requested to agg with aggregation functions specified by name

`alias` Method

alias(expr: Expression): NamedExpression

alias…FIXME

Note	`alias` is used exclusively when `RelationalGroupedDataset` is requested to create a DataFrame from aggregate expressions.

RelationalGroupedDataset — Untyped Row-based Grouping