将大 RDD 写入 Hive - 将展开内存传输到存储内存失败

Posted 2023-02-16

【中文标题】将大 RDD 写入 Hive - 将展开内存传输到存储内存失败

【英文标题】：Writing big RDD to Hive - transferring unroll memory to storage memory failed

【发布时间】：2017-11-30 10:36:12

【问题描述】：

我正在尝试在 PySpark 中进行一些连接并将结果保存在 Hive 中。 sn-p 适用于小型数据集，但当数据大小增加时，我得到以下错误

以下是软件版本

HDFS 2.7.3 Hive 1.2.1000 纱线 2.7.3 MapReduce2 2.7.3 Spark2 2.1.1 Python 3.5.2 Hortonworks 2.6.2.0-205

代码：

hdfs_df.write.mode("append").format("orc").save("HIVE PATH")

例外：

  File "/grid/0/hadoop/yarn/local/usercache/pentaho/appcache/application_1512030580416_0001/container_e16_1512030580416_0001_01_000001/pyspark.zip/pyspark/sql/readwriter.py", line 550, in save
  File "/grid/0/hadoop/yarn/local/usercache/pentaho/appcache/application_1512030580416_0001/container_e16_1512030580416_0001_01_000001/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/grid/0/hadoop/yarn/local/usercache/pentaho/appcache/application_1512030580416_0001/container_e16_1512030580416_0001_01_000001/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
  File "/grid/0/hadoop/yarn/local/usercache/pentaho/appcache/application_1512030580416_0001/container_e16_1512030580416_0001_01_000001/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o439.save.
: 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:117)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
    at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:484)
    at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:520)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:215)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:198)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:280)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 21 in stage 25.0 failed 4 times, most recent failure: Lost task 21.3 in stage 25.0 (TID 6957, dgsddevhdp14.mcs.local, executor 2): java.lang.AssertionError: assertion failed: transferring unroll memory to storage memory failed
    at scala.Predef$.assert(Predef.scala:170)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsBytes(MemoryStore.scala:382)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1032)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1007)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:947)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1007)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:711)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:99)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

【参考方案1】：

也许在保存之前尝试 .coalesce(200)（或其他数字）。

【讨论】：

试过 hdfs_df.coalesce(100).write.mode("append").format("orc").save("HIVE_PATH") 没有运气:(

你有这个：引起：.....java.lang.AssertionError：断言失败：将展开内存转移到存储内存失败。在此处检查此问题：github.com/apache/spark/blob/master/core/src/main/scala/org/…。看起来你需要更多的内存给工人......

遇到了同样的问题，增加worker内存解决了这个问题。