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[SPARK-22122][SQL] Use analyzed logical plans to count input rows in TPCDSQueryBenchmark #19344

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[SPARK-22122][SQL] Use analyzed logical plans to count input rows in TPCDSQueryBenchmark #19344

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f147497
Respect With in TPCDSQueryBenchmark
maropu Sep 25, 2017
489f2a2
Fix
maropu Sep 28, 2017
5691cf6
Fix
maropu Sep 29, 2017
8d8a9ff
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maropu Sep 30, 2017
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32 changes: 11 additions & 21 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/TPCDSQueryBenchmark.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -20,11 +20,10 @@ package org.apache.spark.sql.execution.benchmark
import org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
import org.apache.spark.sql.catalyst.expressions.SubqueryExpression
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.catalyst.catalog.HiveTableRelation
import org.apache.spark.sql.catalyst.plans.logical.SubqueryAlias
import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.execution.datasources.LogicalRelation
import org.apache.spark.util.Benchmark

/**
Expand Down Expand Up @@ -66,24 +65,15 @@ object TPCDSQueryBenchmark extends Logging {
classLoader = Thread.currentThread().getContextClassLoader)

// This is an indirect hack to estimate the size of each query's input by traversing the
// logical plan and adding up the sizes of all tables that appear in the plan. Note that this
// currently doesn't take WITH subqueries into account which might lead to fairly inaccurate
// per-row processing time for those cases.
// logical plan and adding up the sizes of all tables that appear in the plan.
val queryRelations = scala.collection.mutable.HashSet[String]()
spark.sql(queryString).queryExecution.logical.map {
case UnresolvedRelation(t: TableIdentifier) =>
queryRelations.add(t.table)
case lp: LogicalPlan =>
lp.expressions.foreach { _ foreach {
case subquery: SubqueryExpression =>
subquery.plan.foreach {
case UnresolvedRelation(t: TableIdentifier) =>
queryRelations.add(t.table)
case _ =>
}
case _ =>
}
}
spark.sql(queryString).queryExecution.analyzed.foreach {
case SubqueryAlias(alias, _: LogicalRelation) =>
queryRelations.add(alias)
case LogicalRelation(_, _, Some(catalogTable), _) =>
queryRelations.add(catalogTable.identifier.table)
case HiveTableRelation(tableMeta, _, _) =>
queryRelations.add(tableMeta.identifier.table)
case _ =>
}
val numRows = queryRelations.map(tableSizes.getOrElse(_, 0L)).sum
Expand Down