From 90cec34d360e77af9c665d7835c45b7748ee536c Mon Sep 17 00:00:00 2001 From: gatorsmile Date: Tue, 17 Apr 2018 21:03:57 -0700 Subject: [PATCH] [SPARK-24002][SQL] Task not serializable caused by org.apache.parquet.io.api.Binary$ByteBufferBackedBinary.getBytes ``` Py4JJavaError: An error occurred while calling o153.sql. : org.apache.spark.SparkException: Job aborted. at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:223) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:189) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68) at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:79) at org.apache.spark.sql.Dataset$$anonfun$6.apply(Dataset.scala:190) at org.apache.spark.sql.Dataset$$anonfun$6.apply(Dataset.scala:190) at org.apache.spark.sql.Dataset$$anonfun$59.apply(Dataset.scala:3021) at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:89) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:127) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3020) at org.apache.spark.sql.Dataset.(Dataset.scala:190) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74) at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:646) at sun.reflect.GeneratedMethodAccessor153.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380) at py4j.Gateway.invoke(Gateway.java:293) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:226) at java.lang.Thread.run(Thread.java:748) Caused by: org.apache.spark.SparkException: Exception thrown in Future.get: at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:190) at org.apache.spark.sql.execution.InputAdapter.doExecuteBroadcast(WholeStageCodegenExec.scala:267) at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec.doConsume(BroadcastNestedLoopJoinExec.scala:530) at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:155) at org.apache.spark.sql.execution.ProjectExec.consume(basicPhysicalOperators.scala:37) at org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:69) at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:155) at org.apache.spark.sql.execution.FilterExec.consume(basicPhysicalOperators.scala:144) ... at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:190) ... 23 more Caused by: java.util.concurrent.ExecutionException: org.apache.spark.SparkException: Task not serializable at java.util.concurrent.FutureTask.report(FutureTask.java:122) at java.util.concurrent.FutureTask.get(FutureTask.java:206) at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:179) ... 276 more Caused by: org.apache.spark.SparkException: Task not serializable at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:340) at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:330) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:156) at org.apache.spark.SparkContext.clean(SparkContext.scala:2380) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:850) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:849) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:371) at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:849) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:417) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:123) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:118) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$3.apply(SparkPlan.scala:152) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:149) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:118) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.prepareShuffleDependency(ShuffleExchangeExec.scala:89) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:125) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:116) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52) at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.doExecute(ShuffleExchangeExec.scala:116) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:123) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:118) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$3.apply(SparkPlan.scala:152) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:149) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:118) at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:271) at org.apache.spark.sql.execution.aggregate.HashAggregateExec.inputRDDs(HashAggregateExec.scala:181) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:414) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:123) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:118) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$3.apply(SparkPlan.scala:152) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:149) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:118) at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:61) at org.apache.spark.sql.execution.collect.Collector$.collect(Collector.scala:70) at org.apache.spark.sql.execution.SparkPlan.executeCollectResult(SparkPlan.scala:264) at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anon$1$$anonfun$call$1.apply(BroadcastExchangeExec.scala:93) at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anon$1$$anonfun$call$1.apply(BroadcastExchangeExec.scala:81) at org.apache.spark.sql.execution.SQLExecution$.withExecutionId(SQLExecution.scala:150) at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anon$1.call(BroadcastExchangeExec.scala:80) at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anon$1.call(BroadcastExchangeExec.scala:76) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) ... 1 more Caused by: java.nio.BufferUnderflowException at java.nio.HeapByteBuffer.get(HeapByteBuffer.java:151) at java.nio.ByteBuffer.get(ByteBuffer.java:715) at org.apache.parquet.io.api.Binary$ByteBufferBackedBinary.getBytes(Binary.java:405) at org.apache.parquet.io.api.Binary$ByteBufferBackedBinary.getBytesUnsafe(Binary.java:414) at org.apache.parquet.io.api.Binary$ByteBufferBackedBinary.writeObject(Binary.java:484) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1128) at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496) ``` The Parquet filters are serializable but not thread safe. SparkPlan.prepare() could be called in different threads (BroadcastExchange will call it in a thread pool). Thus, we could serialize the same Parquet filter at the same time. This is not easily reproduced. The fix is to avoid serializing these Parquet filters in the driver. This PR is to avoid serializing these Parquet filters by moving the parquet filter generation from the driver to executors. Having two queries one is a 1000-line SQL query and a 3000-line SQL query. Need to run at least one hour with a heavy write workload to reproduce once. Author: gatorsmile Closes #21086 from gatorsmile/taskNotSerializable. --- .../parquet/ParquetFileFormat.scala | 27 ++++++++++--------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala index a6129dafd2f77..b0ba21e47df45 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala @@ -321,19 +321,6 @@ class ParquetFileFormat SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, sparkSession.sessionState.conf.isParquetINT96AsTimestamp) - // Try to push down filters when filter push-down is enabled. - val pushed = - if (sparkSession.sessionState.conf.parquetFilterPushDown) { - filters - // Collects all converted Parquet filter predicates. Notice that not all predicates can be - // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` - // is used here. - .flatMap(ParquetFilters.createFilter(requiredSchema, _)) - .reduceOption(FilterApi.and) - } else { - None - } - val broadcastedHadoopConf = sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf)) @@ -350,12 +337,26 @@ class ParquetFileFormat sparkSession.sessionState.conf.parquetRecordFilterEnabled val timestampConversion: Boolean = sparkSession.sessionState.conf.isParquetINT96TimestampConversion + val enableParquetFilterPushDown: Boolean = + sparkSession.sessionState.conf.parquetFilterPushDown // Whole stage codegen (PhysicalRDD) is able to deal with batches directly val returningBatch = supportBatch(sparkSession, resultSchema) (file: PartitionedFile) => { assert(file.partitionValues.numFields == partitionSchema.size) + // Try to push down filters when filter push-down is enabled. + val pushed = if (enableParquetFilterPushDown) { + filters + // Collects all converted Parquet filter predicates. Notice that not all predicates can be + // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` + // is used here. + .flatMap(ParquetFilters.createFilter(requiredSchema, _)) + .reduceOption(FilterApi.and) + } else { + None + } + val fileSplit = new FileSplit(new Path(new URI(file.filePath)), file.start, file.length, Array.empty)