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[SPARK-24379] BroadcastExchangeExec should catch SparkOutOfMemory and re-throw SparkFatalException, which wraps SparkOutOfMemory inside. #21424

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[SPARK-24379] BroadcastExchangeExec should catch SparkOutOfMemory and re-throw SparkFatalException, which wraps SparkOutOfMemory inside. #21424

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aa10470
[SPARK-24379] BroadcastExchangeExec should catch SparkOutOfMemory and…
May 24, 2018
3a9669c
move error message to place where hash relation is built
May 27, 2018
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15 changes: 3 additions & 12 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -22,15 +22,13 @@ import scala.concurrent.duration._
import scala.util.control.NonFatal

import org.apache.spark.{broadcast, SparkException}
import org.apache.spark.launcher.SparkLauncher
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.UnsafeRow
import org.apache.spark.sql.catalyst.plans.physical.{BroadcastMode, BroadcastPartitioning, Partitioning}
import org.apache.spark.sql.execution.{SparkPlan, SQLExecution}
import org.apache.spark.sql.execution.joins.HashedRelation
import org.apache.spark.sql.execution.metric.SQLMetrics
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.util.{SparkFatalException, ThreadUtils}

/**
Expand Down Expand Up @@ -112,17 +110,10 @@ case class BroadcastExchangeExec(
SQLMetrics.postDriverMetricUpdates(sparkContext, executionId, metrics.values.toSeq)
broadcasted
} catch {
// SPARK-24294: To bypass scala bug: https://github.com/scala/bug/issues/9554, we throw
// SparkFatalException, which is a subclass of Exception. ThreadUtils.awaitResult
// will catch this exception and re-throw the wrapped fatal throwable.
case oe: OutOfMemoryError =>
throw new SparkFatalException(
new OutOfMemoryError(s"Not enough memory to build and broadcast the table to " +
s"all worker nodes. As a workaround, you can either disable broadcast by setting " +
s"${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key} to -1 or increase the spark driver " +
s"memory by setting ${SparkLauncher.DRIVER_MEMORY} to a higher value")
.initCause(oe.getCause))
case e if !NonFatal(e) =>
// SPARK-24294: To bypass scala bug: https://github.com/scala/bug/issues/9554, we throw
// SparkFatalException, which is a subclass of Exception. ThreadUtils.awaitResult
// will catch this exception and re-throw the wrapped fatal throwable.
throw new SparkFatalException(e)
}
}
Expand Down
23 changes: 17 additions & 6 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
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Expand Up @@ -24,10 +24,12 @@ import com.esotericsoftware.kryo.io.{Input, Output}

import org.apache.spark.{SparkConf, SparkEnv, SparkException}
import org.apache.spark.internal.config.MEMORY_OFFHEAP_ENABLED
import org.apache.spark.memory.{MemoryConsumer, StaticMemoryManager, TaskMemoryManager}
import org.apache.spark.launcher.SparkLauncher
import org.apache.spark.memory.{MemoryConsumer, SparkOutOfMemoryError, StaticMemoryManager, TaskMemoryManager}
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.physical.BroadcastMode
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.LongType
import org.apache.spark.unsafe.Platform
import org.apache.spark.unsafe.map.BytesToBytesMap
Expand Down Expand Up @@ -106,11 +108,20 @@ private[execution] object HashedRelation {
1),
0)
}

if (key.length == 1 && key.head.dataType == LongType) {
LongHashedRelation(input, key, sizeEstimate, mm)
} else {
UnsafeHashedRelation(input, key, sizeEstimate, mm)
try {
if (key.length == 1 && key.head.dataType == LongType) {
LongHashedRelation(input, key, sizeEstimate, mm)
} else {
UnsafeHashedRelation(input, key, sizeEstimate, mm)
}
} catch {
case oe: SparkOutOfMemoryError =>
throw new SparkOutOfMemoryError(s"If this SparkOutOfMemoryError happens in Spark driver," +
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@cloud-fan cloud-fan May 28, 2018

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ah i see. So the SparkOutOfMemoryError is thrown by BytesToBytesMap, we need to catch and rethrow it to attach the error message anyway.

I also found that we may throw OOM when calling child.executeCollectIterator which calls RDD#collect, seems the previous code is corrected.

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@jinxing64 jinxing64 May 28, 2018

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So, I change back ?

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@cloud-fan cloud-fan May 28, 2018

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it seems we don't need to change anything, maybe just add some comments to say where OOM can occur, i.e. RDD#collect and BroadcastMode#transform

s" it could because there's not enough memory to build and broadcast the table to " +
s"all worker nodes. As a workaround, you can either disable the broadcast by setting " +
s"${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key} to -1 or increase the Spark driver " +
s"memory by setting ${SparkLauncher.DRIVER_MEMORY} to a higher value")
.initCause(oe.getCause)
}
}
}
Expand Down