Spark执行失败时的一个错误分析

错误分析

堆栈信息中有一个错误信息：Job aborted due to stage failure: Task 1 in stage 2.0 failed 4 times, most recent failure: Lost task 1.3 in stage 2.0 (TID 264, idc-xx-xx-3-30.d.xx.com, executor 2): java.lang.OutOfMemoryError: Java heap space

根据提示信息可以得到以下几点

  • stage由一堆task组成，也就是taskset,编号1的task在stage2中失败了4次
  • executor 是实际执行task的节点，编号2的executor发生了Java heap space
  • executor 内存配置的是512M,没有配置 spark.executor.memoryOverhead，spark在计算executor最终需要分配多少内存时有以下机制
  1. 未配置spark.executor.memoryOverhead来直接控制off-heap时(堆外内存，将对象序列化后放在一大块gc不会直接管理的内存中，需要的时候再反序列化使用，就像放到磁盘上一样，此处堆外内存包含了方法区，直接内存，虚拟机栈，本地方法栈)
    realMem = executorMemory[heap] + (executorMemory * 0.10, with minimum of 384)[off-heap]
    2)配置spark.executor.memoryOverhead
    realMem = executorMemory[heap] + memoryOverhead[off-heap]

readMem表示java进程需要申请的总内存，如果超过container的内存容量，会被直接kill掉

异常种类

  • OutOfMemoryError: Java heap space，堆内存不足，溢出，需调整--executor-memory
  • OutOfMemoryError: Java permgen space，堆外内存不足，溢出，需调整spark.executor.memoryOverhead

下述异常属于Java heap space，调整--executor-memory

RDD的位置，根据MemoryMode可以选择是堆内或堆外

日志中查看到的异常信息

: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:147)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:121)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:121)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:121)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:101)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.Dataset.(Dataset.scala:185)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 2.0 failed 4 times, most recent failure: Lost task 1.3 in stage 2.0 (TID 264, idc-xx-xx-3-30.d.xx.com, executor 2): java.lang.OutOfMemoryError: Java heap space
at org.apache.parquet.hadoop.ParquetFileReader$ConsecutiveChunkList.readAll(ParquetFileReader.java:778)
at org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:511)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:270)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:225)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:137)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:109)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:184)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:109)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.hasNext(InMemoryRelation.scala:132)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1005)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:996)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:936)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:996)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:700)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)

导致异常的代码

/**
 * @param f file to read the chunks from
 * @return the chunks
 * @throws IOException
 */
public List<Chunk> readAll(FSDataInputStream f) throws IOException {
  List<Chunk> result = new ArrayList<Chunk>(chunks.size());
  f.seek(offset);
  byte[] chunksBytes = new byte[length];   //778行，分配长为length的byte[]时没有足够的可用内存导致heap space
  f.readFully(chunksBytes);
  // report in a counter the data we just scanned
  BenchmarkCounter.incrementBytesRead(length);
  int currentChunkOffset = 0;
  for (int i = 0; i < chunks.size(); i++) {
    ChunkDescriptor descriptor = chunks.get(i);
    if (i < chunks.size() - 1) {
      result.add(new Chunk(descriptor, chunksBytes, currentChunkOffset));
    } else {
      // because of a bug, the last chunk might be larger than descriptor.size
      result.add(new WorkaroundChunk(descriptor, chunksBytes, currentChunkOffset, f));
    }
    currentChunkOffset += descriptor.size;
  }
  return result ;
}
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原文地址：https://www.cnblogs.com/windliu/p/10941848.html