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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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8 changes: 4 additions & 4 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala
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Expand Up @@ -166,10 +166,10 @@ case class FileSourceScanExec(
override val tableIdentifier: Option[TableIdentifier])
extends DataSourceScanExec with ColumnarBatchScan {

override val supportsBatch: Boolean = relation.fileFormat.supportBatch(
override lazy val supportsBatch: Boolean = relation.fileFormat.supportBatch(
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@mgaido91 mgaido91 Jul 19, 2018

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nit: can we maybe add a comment about the reason we are making them lazy?

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 +199,7 @@ 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 +270,7 @@ 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
36 changes: 36 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/FileSourceScanExecSuite.scala
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@@ -0,0 +1,36 @@
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.sql.execution

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

class FileSourceScanExecSuite extends SharedSQLContext {
test("FileSourceScanExec should be canonicalizable in executor side") {
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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nit: s/in/on

withTempPath { path =>
spark.range(1).toDF().write.parquet(path.getAbsolutePath)
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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Redundant toDF

val df = spark.read.parquet(path.getAbsolutePath)
val fileSourceScanExec =
df.queryExecution.sparkPlan.find(_.isInstanceOf[FileSourceScanExec]).get
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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This isInstanceOf is a bit non-Scala IMHO and I'd prefer collectFirst { case op: FileSourceScanExec => op } instead.

try {
spark.range(1).foreach(_ => fileSourceScanExec.canonicalized)
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@mgaido91 mgaido91 Jul 19, 2018

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not sure whether it is feasible (maybe in a followup?), but it would be great if we can test the canonicalization of all the Exec nodes in order to prevent such issue in the future... what do you think?

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@HyukjinKwon HyukjinKwon Jul 19, 2018

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Yes, I think of course it is.. it took me a while to make a small and simple test for it.. Hope leave it out of this PR's scope though.

} catch {
case e: Throwable => fail("FileSourceScanExec was not canonicalizable", e)
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@jaceklaskowski jaceklaskowski Jul 19, 2018

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It's a named test so I'd get rid of the try-catch block because:

  1. It's going to fail the test anyway
  2. The title of the test matches the fail message.

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@HyukjinKwon HyukjinKwon Jul 19, 2018

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Hm, this gives an explicit scope about which condition is a failure case though. I believe this is a rather pattern. If both are okay, let me just keep in this way.

}
}
}
}