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[SPARK-23731][SQL] Make FileSourceScanExec canonicalizable after being (de)serialized #21815

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[SPARK-23731][SQL] Make FileSourceScanExec canonicalizable after being (de)serialized #21815

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22c7986
Make FileSourceScanExec canonicalizable in executor side
HyukjinKwon Jul 19, 2018
b488836
Address comments
HyukjinKwon Jul 19, 2018
7f531bd
Address comments
HyukjinKwon Jul 20, 2018
be6e594
Move test case to SparkPlanSuite
HyukjinKwon Jul 20, 2018
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10 changes: 6 additions & 4 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -166,10 +166,12 @@ case class FileSourceScanExec(
override val tableIdentifier: Option[TableIdentifier])
extends DataSourceScanExec with ColumnarBatchScan {

override val supportsBatch: Boolean = relation.fileFormat.supportBatch(
// Note that some vals referring the file-based relation are lazy intentionally
// so that this plan can be canonicalized on executor side too. See SPARK-23731.
override lazy val supportsBatch: Boolean = relation.fileFormat.supportBatch(
relation.sparkSession, StructType.fromAttributes(output))

override val needsUnsafeRowConversion: Boolean = {
override lazy val needsUnsafeRowConversion: Boolean = {
if (relation.fileFormat.isInstanceOf[ParquetSource]) {
SparkSession.getActiveSession.get.sessionState.conf.parquetVectorizedReaderEnabled
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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Since you mentioned SparkSession, that line caught my attention where the active SparkSession is accessed using SparkSession.getActiveSession.get not relation.sparkSession as is the case for other places. I think that's something worth considering changing since we're at it.

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@HyukjinKwon HyukjinKwon Jul 19, 2018

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Let's leave this out of this PR's scope. That's more like making the plan workable whereas this PR targets the plan can be canonicalized.

} else {
Expand Down Expand Up @@ -199,7 +201,7 @@ case class FileSourceScanExec(
ret
}

override val (outputPartitioning, outputOrdering): (Partitioning, Seq[SortOrder]) = {
override lazy val (outputPartitioning, outputOrdering): (Partitioning, Seq[SortOrder]) = {
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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That happens on the driver so no need for the lazy here.

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@mgaido91 mgaido91 Jul 19, 2018

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it'd be computed anyway, though, when we create a new FileSourceScanExec in the canonicalization process, if it is not lazy, so I'd say that this is needed, as well as all the others.

val bucketSpec = if (relation.sparkSession.sessionState.conf.bucketingEnabled) {
relation.bucketSpec
} else {
Expand Down Expand Up @@ -270,7 +272,7 @@ case class FileSourceScanExec(
private val pushedDownFilters = dataFilters.flatMap(DataSourceStrategy.translateFilter)
logInfo(s"Pushed Filters: ${pushedDownFilters.mkString(",")}")

override val metadata: Map[String, String] = {
override lazy val metadata: Map[String, String] = {
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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That's driver-only too, isn't it? Why is this lazy required?

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@HyukjinKwon HyukjinKwon Jul 19, 2018

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it can be in executor side actually:

	at org.apache.spark.sql.execution.FileSourceScanExec.<init>(DataSourceScanExec.scala:275)
	at org.apache.spark.sql.execution.FileSourceScanExec.doCanonicalize(DataSourceScanExec.scala:526)
	at org.apache.spark.sql.execution.FileSourceScanExec.doCanonicalize(DataSourceScanExec.scala:159)
	at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:211)
	at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:210)
	at org.apache.spark.sql.execution.FileSourceScanExecSuite$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(FileSourceScanExecSuite.scala:30)
	at org.apache.spark.sql.execution.FileSourceScanExecSuite$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(FileSourceScanExecSuite.scala:30)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
	at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:921)
	at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:921)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2083)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2083)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:109)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:367)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

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@jaceklaskowski jaceklaskowski Jul 20, 2018

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Ouch. I'd have never thought about any code with RDD and physical operators on the executor-side (!) Learnt it today.

def seqToString(seq: Seq[Any]) = seq.mkString("[", ", ", "]")
val location = relation.location
val locationDesc =
Expand Down
10 changes: 5 additions & 5 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
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Expand Up @@ -47,17 +47,15 @@ import org.apache.spark.util.ThreadUtils
abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializable {

/**
* A handle to the SQL Context that was used to create this plan. Since many operators need
* A handle to the SQL Context that was used to create this plan. Since many operators need
* access to the sqlContext for RDD operations or configuration this field is automatically
* populated by the query planning infrastructure.
*/
@transient
final val sqlContext = SparkSession.getActiveSession.map(_.sqlContext).orNull
@transient final val sqlContext = SparkSession.getActiveSession.map(_.sqlContext).orNull

protected def sparkContext = sqlContext.sparkContext

// sqlContext will be null when SparkPlan nodes are created without the active sessions.
// So far, this only happens in the test cases.
val subexpressionEliminationEnabled: Boolean = if (sqlContext != null) {
sqlContext.conf.subexpressionEliminationEnabled
} else {
Expand All @@ -69,7 +67,9 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ

/** Overridden make copy also propagates sqlContext to copied plan. */
override def makeCopy(newArgs: Array[AnyRef]): SparkPlan = {
SparkSession.setActiveSession(sqlContext.sparkSession)
if (sqlContext != null) {
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@mgaido91 mgaido91 Jul 19, 2018

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just curious, why wasn't the makeCopy problem discovered in the previous PR/investigation?

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@HyukjinKwon HyukjinKwon Jul 19, 2018

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Because it looks failed ahead. Once we go with lazy then it's discovered later (the exception message in the PR description).

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@mgaido91 mgaido91 Jul 19, 2018

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I see, thanks, I wondered because it seems a more generic issue, easier to happen, but probably we never met it as all the trials included FileSourceScanExec which caused an earlier failure... thanks.

SparkSession.setActiveSession(sqlContext.sparkSession)
}
super.makeCopy(newArgs)
}

Expand Down
17 changes: 17 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanSuite.scala
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Expand Up @@ -17,6 +17,7 @@

package org.apache.spark.sql.execution

import org.apache.spark.SparkEnv
import org.apache.spark.sql.QueryTest
import org.apache.spark.sql.test.SharedSQLContext

Expand All @@ -33,4 +34,20 @@ class SparkPlanSuite extends QueryTest with SharedSQLContext {
intercept[IllegalStateException] { plan.executeTake(1) }
}

test("SPARK-23731 plans should be canonicalizable after being (de)serialized") {
withTempPath { path =>
spark.range(1).write.parquet(path.getAbsolutePath)
val df = spark.read.parquet(path.getAbsolutePath)
val fileSourceScanExec =
df.queryExecution.sparkPlan.collectFirst { case p: FileSourceScanExec => p }.get
val serializer = SparkEnv.get.serializer.newInstance()
val readback =
serializer.deserialize[FileSourceScanExec](serializer.serialize(fileSourceScanExec))
try {
readback.canonicalized
} catch {
case e: Throwable => fail("FileSourceScanExec was not canonicalizable", e)
}
}
}
}