我有一台机器的EMR集群" c3.8xlarge",在阅读了几个资源之后,我明白我必须在堆外允许相当数量的内存,因为我正在使用pyspark,所以我有将群集配置如下:
一位遗嘱执行人:
驱动程序:
当我cache()
DataFrame时,它需要大约3.6GB的内存。
现在,当我在DataFrame上调用collect()
或toPandas()
时,进程崩溃了。
我知道我将大量数据带入驱动程序,但我认为它不是那么大,而且我无法弄清楚崩溃的原因。
当我致电collect()
或toPandas()
时,我收到此错误:
Py4JJavaError: An error occurred while calling o181.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 6.0 failed 4 times, most recent failure: Lost task 5.3 in stage 6.0 (TID 110, ip-10-0-47-207.prod.eu-west-1.hs.internal, executor 9): ExecutorLostFailure (executor 9 exited caused by one of the running tasks) Reason: Container marked as failed: container_1511879540686_0005_01_000016 on host: ip-10-0-47-207.prod.eu-west-1.hs.internal. Exit status: 137. Diagnostics: Container killed on request. Exit code is 137
Container exited with a non-zero exit code 137
Killed by external signal
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1690)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1678)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1677)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1677)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1905)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1860)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1849)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:671)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:278)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2803)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2800)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2800)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2823)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2800)
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:748)
====更新====
正如@ user6910411建议的那样,我尝试了here提到的解决方案,在这种情况下我收到以下错误:
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 2.0 failed 4 times, most recent failure: Lost task 7.3 in stage 2.0 (TID 41, ip-10-0-33-57.prod.eu-west-1.hs.internal, executor 5): ExecutorLostFailure (executor 5 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 13.5 GB of 12 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1690)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1678)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1677)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1677)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1905)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1860)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1849)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:671)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:458)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
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:748)
有关此处发生的事情的任何提示?
答案 0 :(得分:10)
TL; DR 我认为你严重低估了内存需求。
即使假设数据已完全缓存,存储信息也只显示将数据带回驱动程序所需的峰值内存的一小部分。
List[Row]
。后者还存储列名,进一步增加了内存使用量。普通toPandas
实施首先收集Rows
,then creates Pandas DataFrame
locally。这进一步增加(可能是两倍)内存使用量。幸运的是,这部分已经在master(Spark 2.3)上得到了解决,使用了箭头序列化(SPARK-13534 - Implement Apache Arrow serializer for Spark DataFrame for use in DataFrame.toPandas)更直接的方法。
对于独立于Apache Arrow的可能解决方案,您可以在Apache Spark开发人员列表上查看Faster and Lower memory implementation toPandas。
由于数据实际上非常大,我会考虑将它写入Parquet并使用PyArrow(Reading and Writing the Apache Parquet Format)直接在Python中读取它,完全跳过所有中间阶段。
答案 1 :(得分:1)
通过使用箭头设置,您会看到加速
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
答案 2 :(得分:0)
如上所述,当调用toPandas()时,DataFrame的所有记录都收集到驱动程序中,因此应在数据的一小部分上完成。 (https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html)