python - 创建虚拟变量时发生错误-SparkException：作业因阶段失败而中止：阶段 5.0 中的任务 59 失败 1 次
问题描述
在 Anaconda Python 3.6 上使用 pyspark 时出现错误。错误信息是 SparkException: Job aborted due to stage failure: Task 59 in stage 5.0 failed 1 次。
这是一个完整的追溯：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-9-aa2b9549c767> in <module>()
1 import pyspark.sql.functions as F
----> 2 categ = df.select('Event').distinct().rdd.flatMap(lambda x:x).collect()
~\AppData\Local\conda\conda\envs\xgboost\lib\site-packages\pyspark\rdd.py in collect(self)
812 """
813 with SCCallSiteSync(self.context) as css:
--> 814 sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
815 return list(_load_from_socket(sock_info, self._jrdd_deserializer))
816
~\AppData\Local\conda\conda\envs\xgboost\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
~\AppData\Local\conda\conda\envs\xgboost\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
~\AppData\Local\conda\conda\envs\xgboost\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
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 59 in stage 5.0 failed 1 times, most recent failure: Lost task 59.0 in stage 5.0 (TID 64, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\worker.py", line 253, in main
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\worker.py", line 248, in process
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 379, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 664, in load_stream
yield self.loads(stream)
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 658, in loads
s = stream.read(length)
File "C:\Users\SadeDorasamy\AppData\Local\conda\conda\envs\xgboost\lib\socket.py", line 586, in readinto
return self._sock.recv_into(b)
socket.timeout: timed out
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:330)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:470)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:453)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:284)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.agg_doAggregateWithKeys_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1651)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1639)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1638)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1638)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1872)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1821)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1810)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:165)
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:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\worker.py", line 253, in main
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\worker.py", line 248, in process
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 379, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 664, in load_stream
yield self.loads(stream)
File "C:\spark\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 658, in loads
s = stream.read(length)
File "C:\Users\SadeD\AppData\Local\conda\conda\envs\xgboost\lib\socket.py", line 586, in readinto
return self._sock.recv_into(b)
socket.timeout: timed out
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:330)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:470)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:453)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:284)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.agg_doAggregateWithKeys_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
错误发生在下面的代码中：
import pyspark.sql.functions as F
categ = df.select('Event').distinct().rdd.flatMap(lambda x:x).collect()
我正在尝试为事件中的分类数据创建虚拟变量。
exprs = [F.when(F.col('Event') == cat,1).otherwise(0).alias(str(cat)) for cat in categ]
df = df.select(exprs+df.columns)
除此之外，该代码
categ = df.select('Event').distinct().rdd.flatMap(lambda x:x).collect()
使用大量内存，因此需要时间来运行。pyspark 在本地运行。是什么导致了这个错误？如何解决它，或者是否有最佳方法来获得更便宜的虚拟变量？
解决方案
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