Aggregation Execution Planning Strategy¶

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

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))