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author | Sameer Agarwal <sameerag@cs.berkeley.edu> | 2016-09-11 17:35:27 +0200 |
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committer | Herman van Hovell <hvanhovell@databricks.com> | 2016-09-11 17:35:27 +0200 |
commit | 767d48076971f6f1e2c93ee540a9b2e5e465631b (patch) | |
tree | 49a4c899ddb83dcdb787399eec8d24fdfea33f6f /sql | |
parent | 883c7631847a95684534222c1b6cfed8e62710c8 (diff) | |
download | spark-767d48076971f6f1e2c93ee540a9b2e5e465631b.tar.gz spark-767d48076971f6f1e2c93ee540a9b2e5e465631b.tar.bz2 spark-767d48076971f6f1e2c93ee540a9b2e5e465631b.zip |
[SPARK-17415][SQL] Better error message for driver-side broadcast join OOMs
## What changes were proposed in this pull request?
This is a trivial patch that catches all `OutOfMemoryError` while building the broadcast hash relation and rethrows it by wrapping it in a nice error message.
## How was this patch tested?
Existing Tests
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes #14979 from sameeragarwal/broadcast-join-error.
Diffstat (limited to 'sql')
-rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala | 73 |
1 files changed, 42 insertions, 31 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala index a809076de5..7be5d31d4a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala @@ -21,6 +21,7 @@ import scala.concurrent.{ExecutionContext, Future} import scala.concurrent.duration._ 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 @@ -28,6 +29,7 @@ import org.apache.spark.sql.catalyst.plans.physical.{BroadcastMode, BroadcastPar import org.apache.spark.sql.execution.{SparkPlan, SQLExecution} import org.apache.spark.sql.execution.metric.SQLMetrics import org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates +import org.apache.spark.sql.internal.SQLConf import org.apache.spark.util.ThreadUtils /** @@ -70,38 +72,47 @@ case class BroadcastExchangeExec( // This will run in another thread. Set the execution id so that we can connect these jobs // with the correct execution. SQLExecution.withExecutionId(sparkContext, executionId) { - val beforeCollect = System.nanoTime() - // Note that we use .executeCollect() because we don't want to convert data to Scala types - val input: Array[InternalRow] = child.executeCollect() - if (input.length >= 512000000) { - throw new SparkException( - s"Cannot broadcast the table with more than 512 millions rows: ${input.length} rows") + try { + val beforeCollect = System.nanoTime() + // Note that we use .executeCollect() because we don't want to convert data to Scala types + val input: Array[InternalRow] = child.executeCollect() + if (input.length >= 512000000) { + throw new SparkException( + s"Cannot broadcast the table with more than 512 millions rows: ${input.length} rows") + } + val beforeBuild = System.nanoTime() + longMetric("collectTime") += (beforeBuild - beforeCollect) / 1000000 + val dataSize = input.map(_.asInstanceOf[UnsafeRow].getSizeInBytes.toLong).sum + longMetric("dataSize") += dataSize + if (dataSize >= (8L << 30)) { + throw new SparkException( + s"Cannot broadcast the table that is larger than 8GB: ${dataSize >> 30} GB") + } + + // Construct and broadcast the relation. + val relation = mode.transform(input) + val beforeBroadcast = System.nanoTime() + longMetric("buildTime") += (beforeBroadcast - beforeBuild) / 1000000 + + val broadcasted = sparkContext.broadcast(relation) + longMetric("broadcastTime") += (System.nanoTime() - beforeBroadcast) / 1000000 + + // There are some cases we don't care about the metrics and call `SparkPlan.doExecute` + // directly without setting an execution id. We should be tolerant to it. + if (executionId != null) { + sparkContext.listenerBus.post(SparkListenerDriverAccumUpdates( + executionId.toLong, metrics.values.map(m => m.id -> m.value).toSeq)) + } + + broadcasted + } catch { + case oe: OutOfMemoryError => + throw new OutOfMemoryError(s"Not enough memory to build and broadcast the table to " + + s"all worker nodes. As a workaround, you can either disable broadcast by setting " + + s"${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) } - val beforeBuild = System.nanoTime() - longMetric("collectTime") += (beforeBuild - beforeCollect) / 1000000 - val dataSize = input.map(_.asInstanceOf[UnsafeRow].getSizeInBytes.toLong).sum - longMetric("dataSize") += dataSize - if (dataSize >= (8L << 30)) { - throw new SparkException( - s"Cannot broadcast the table that is larger than 8GB: ${dataSize >> 30} GB") - } - - // Construct and broadcast the relation. - val relation = mode.transform(input) - val beforeBroadcast = System.nanoTime() - longMetric("buildTime") += (beforeBroadcast - beforeBuild) / 1000000 - - val broadcasted = sparkContext.broadcast(relation) - longMetric("broadcastTime") += (System.nanoTime() - beforeBroadcast) / 1000000 - - // There are some cases we don't care about the metrics and call `SparkPlan.doExecute` - // directly without setting an execution id. We should be tolerant to it. - if (executionId != null) { - sparkContext.listenerBus.post(SparkListenerDriverAccumUpdates( - executionId.toLong, metrics.values.map(m => m.id -> m.value).toSeq)) - } - - broadcasted } }(BroadcastExchangeExec.executionContext) } |