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SortAggregateExec Aggregate Unary Physical Operator

SortAggregateExec is an AggregateCodegenSupport physical operator for Sort-Based Aggregation that uses SortBasedAggregationIterator (to iterate over UnsafeRows in partitions) when executed.

SortAggregateExec in web UI (Details for Query)

SortAggregateExec is an OrderPreservingUnaryExecNode.

Creating Instance

SortAggregateExec takes the following to be created:

SortAggregateExec is created when:

Performance Metrics

number of output rows

Whole-Stage Code Generation

As an AggregateCodegenSupport physical operator, SortAggregateExec supports Whole-Stage Code Generation only when supportCodegen flag is enabled.

Executing Physical Operator

doExecute(): RDD[InternalRow]

doExecute is part of the SparkPlan abstraction.

doExecute requests the child physical operator to execute (that triggers physical query planning and generates an RDD[InternalRow]).

doExecute transforms the RDD[InternalRow] with the transformation f function for every partition (using RDD.mapPartitionsWithIndex transformation).


This is exactly as in HashAggregateExec.

Transformation Function

doExecute uses RDD.mapPartitionsWithIndex to process partition InternalRows (with a partition ID).

// (partition ID, partition rows)
f: (Int, Iterator[T]) => Iterator[U]

For every partition, mapPartitionsWithIndex...FIXME


This is exactly as in HashAggregateExec except that SortAggregateExec uses SortBasedAggregationIterator.


supportCodegen: Boolean

supportCodegen is part of the AggregateCodegenSupport abstraction.

supportCodegen is enabled (true) when all the following hold:

Required Child Ordering

requiredChildOrdering: Seq[Seq[SortOrder]]

requiredChildOrdering is part of the SparkPlan abstraction.

requiredChildOrdering is SortOrders in Ascending direction for every grouping key.

Ordering Expressions

orderingExpressions: Seq[SortOrder]

orderingExpressions is part of the AliasAwareQueryOutputOrdering abstraction.

orderingExpressions is SortOrders in Ascending direction for every grouping key.


Let's disable preference for ObjectHashAggregateExec physical operator (using the spark.sql.execution.useObjectHashAggregateExec configuration property).

spark.conf.set("spark.sql.execution.useObjectHashAggregateExec", false)
assert(spark.sessionState.conf.useObjectHashAggregation == false)
val names = Seq(
  (0, "zero"),
  (1, "one"),
  (2, "two")).toDF("num", "name")

Let's use immutable data types for aggregateBufferAttributes (so HashAggregateExec physical operator will not be selected).

val q = names
  .withColumn("initial", substring('name, 0, 1))
== Physical Plan ==
SortAggregate(key=[initial#160], functions=[collect_set(initial#160, 0, 0)])
+- *(2) Sort [initial#160 ASC NULLS FIRST], false, 0
   +- Exchange hashpartitioning(initial#160, 200), ENSURE_REQUIREMENTS, [id=#122]
      +- SortAggregate(key=[initial#160], functions=[partial_collect_set(initial#160, 0, 0)])
         +- *(1) Sort [initial#160 ASC NULLS FIRST], false, 0
            +- *(1) LocalTableScan [initial#160]
import q.queryExecution.optimizedPlan
import org.apache.spark.sql.catalyst.plans.logical.Aggregate
val aggLog = optimizedPlan.asInstanceOf[Aggregate]
import org.apache.spark.sql.catalyst.planning.PhysicalAggregation
import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
val (_, aggregateExpressions: Seq[AggregateExpression], _, _) = PhysicalAggregation.unapply(aggLog).get
val aggregateBufferAttributes =
import org.apache.spark.sql.execution.aggregate.HashAggregateExec
assert(HashAggregateExec.supportsAggregate(aggregateBufferAttributes) == false)