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Aggregation Execution Planning Strategy

Aggregation is an execution planning strategy that SparkPlanner uses for planning Aggregate logical operators (in the order of preference):

  1. HashAggregateExec
  2. ObjectHashAggregateExec
  3. SortAggregateExec

Executing Rule

apply(
  plan: LogicalPlan): Seq[SparkPlan]

apply is part of the GenericStrategy abstraction.

apply works with Aggregate logical operators with all the aggregate expressions being either AggregateExpressions or PythonUDFs only. Otherwise, apply throws an AnalysisException.

apply destructures the Aggregate logical operator (into a four-element tuple) with the following:

  • Grouping Expressions
  • Aggregration Expressions
  • Result Expressions
  • Child Logical Operator

AggregateExpressions

For Aggregate logical operators with AggregateExpressions, apply splits them based on the isDistinct flag.

Without distinct aggregate functions (expressions), apply planAggregateWithoutDistinct. Otherwise, apply planAggregateWithOneDistinct.

In the end, apply creates one of the following physical operators based on whether there is distinct aggregate function or not.

Note

It is assumed that all the distinct aggregate functions have the same column expressions.

COUNT(DISTINCT foo), MAX(DISTINCT foo)

The following is not valid due to different column expressions

COUNT(DISTINCT bar), COUNT(DISTINCT foo)

PythonUDFs

For Aggregate logical operators with PythonUDFs (PySpark)...FIXME

AnalysisException

apply can throw an AnalysisException:

Cannot use a mixture of aggregate function and group aggregate pandas UDF

Demo

scala> :type spark
org.apache.spark.sql.SparkSession

// structured query with count aggregate function
val q = spark
  .range(5)
  .groupBy($"id" % 2 as "group")
  .agg(count("id") as "count")
val plan = q.queryExecution.optimizedPlan
scala> println(plan.numberedTreeString)
00 Aggregate [(id#0L % 2)], [(id#0L % 2) AS group#3L, count(1) AS count#8L]
01 +- Range (0, 5, step=1, splits=Some(8))

import spark.sessionState.planner.Aggregation
val physicalPlan = Aggregation.apply(plan)

// HashAggregateExec selected
scala> println(physicalPlan.head.numberedTreeString)
00 HashAggregate(keys=[(id#0L % 2)#12L], functions=[count(1)], output=[group#3L, count#8L])
01 +- HashAggregate(keys=[(id#0L % 2) AS (id#0L % 2)#12L], functions=[partial_count(1)], output=[(id#0L % 2)#12L, count#14L])
02    +- PlanLater Range (0, 5, step=1, splits=Some(8))

Last update: 2021-07-05
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