SortExec Unary Physical Operator¶

SortExec is a unary physical operator (that, among other use cases, represents Sort logical operators at execution).

Creating Instance¶

SortExec takes the following to be created:

• SortOrder expressions
• global flag
• Child physical operator
• testSpillFrequency (default: 0)

SortExec is created when:

• BasicOperators execution planning strategy is executed (with a Sort logical operator)
• FileFormatWriter utility is used to write out a query result
• EnsureRequirements physical optimization is executed

Performance Metrics¶

Radix Sort¶

SortExec operator uses the spark.sql.sort.enableRadixSort configuration property when creating an UnsafeExternalRowSorter.

BlockingOperatorWithCodegen¶

SortExec is a BlockingOperatorWithCodegen.

CodegenSupport¶

SortExec supports Java code generation (indirectly as a BlockingOperatorWithCodegen).

Output Data Ordering Requirements¶

outputOrdering: Seq[SortOrder]

outputOrdering is the given SortOrder expressions.

outputOrdering is part of the SparkPlan abstraction.

Required Child Output Distribution¶

requiredChildDistribution: Seq[Distribution]

requiredChildDistribution is a OrderedDistribution (with the SortOrder expressions) with the global flag enabled or a UnspecifiedDistribution.

requiredChildDistribution is part of the SparkPlan abstraction.

Physical Optimizations¶

OptimizeSkewedJoin¶

OptimizeSkewedJoin physical optimization is used to optimize skewed SortMergeJoinExecs (with SortExec operators) in Adaptive Query Execution.

RemoveRedundantSorts¶

SortExec operators can be removed from a physical query plan by RemoveRedundantSorts physical optimization (with spark.sql.execution.removeRedundantSorts enabled).

Creating UnsafeExternalRowSorter¶

createSorter(): UnsafeExternalRowSorter

createSorter...FIXME

createSorter is used when:

• SortExec is requested to execute
• FileFormatWriter utility is used to write out a query result

Demo¶

val q = Seq((0, "zero"), (1, "one")).toDF("id", "name").sort('id)
val qe = q.queryExecution

val logicalPlan = qe.analyzed
scala> println(logicalPlan.numberedTreeString)
00 Sort [id#72 ASC NULLS FIRST], true
01 +- Project [_1#69 AS id#72, _2#70 AS name#73]
02    +- LocalRelation [_1#69, _2#70]

// BasicOperators does the conversion of Sort logical operator to SortExec
val sparkPlan = qe.sparkPlan
scala> println(sparkPlan.numberedTreeString)
00 Sort [id#72 ASC NULLS FIRST], true, 0
01 +- LocalTableScan [id#72, name#73]

// SortExec supports Whole-Stage Code Generation
val executedPlan = qe.executedPlan
scala> println(executedPlan.numberedTreeString)
00 *(1) Sort [id#72 ASC NULLS FIRST], true, 0
01 +- Exchange rangepartitioning(id#72 ASC NULLS FIRST, 200)
02    +- LocalTableScan [id#72, name#73]

import org.apache.spark.sql.execution.SortExec
val sortExec = executedPlan.collect { case se: SortExec => se }.head
assert(sortExec.isInstanceOf[SortExec])