我在Jupyter中创建了一个'SparkSession'（使用pyspark），然后读取.csv文件。
我的代码在第一次运行时工作正常，但是，当我尝试重新运行第二次读取.csv文件的代码块时，我不知道为什么会出现以下错误：
value这是我用来读取.csv文件的代码：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-14-f65a29e5e6d3> in <module>()
----> 1 ccRaw.take(3)
C:\Spark\spark-2.0.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\rdd.py in take(self, num)
1308
1309 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1310 res = self.context.runJob(self, takeUpToNumLeft, p)
1311
1312 items += res
C:\Spark\spark-2.0.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
939 # SparkContext#runJob.
940 mappedRDD = rdd.mapPartitions(partitionFunc)
--> 941 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
942 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
943
C:\Spark\spark-2.0.0-bin-hadoop2.7\python\lib\py4j-0.10.1-src.zip\py4j\java_gateway.py in __call__(self, *args)
931 answer = self.gateway_client.send_command(command)
932 return_value = get_return_value(
--> 933 answer, self.gateway_client, self.target_id, self.name)
934
935 for temp_arg in temp_args:
C:\Spark\spark-2.0.0-bin-hadoop2.7\python\lib\pyspark.zip\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()
C:\Spark\spark-2.0.0-bin-hadoop2.7\python\lib\py4j-0.10.1-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
310 raise Py4JJavaError(
311 "An error occurred while calling {0}{1}{2}.\n".
--> 312 format(target_id, ".", name), value)
313 else:
314 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8.0 failed 1 times, most recent failure: Lost task 0.0 in stage 8.0 (TID 8, localhost): java.net.SocketException: Connection reset by peer: socket write error
at java.net.SocketOutputStream.socketWrite0(Native Method)
at java.net.SocketOutputStream.socketWrite(Unknown Source)
at java.net.SocketOutputStream.write(Unknown Source)
at java.io.BufferedOutputStream.flushBuffer(Unknown Source)
at java.io.BufferedOutputStream.flush(Unknown Source)
at java.io.DataOutputStream.flush(Unknown Source)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:331)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1857)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:441)
at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:211)
at java.lang.Thread.run(Unknown Source)
Caused by: java.net.SocketException: Connection reset by peer: socket write error
at java.net.SocketOutputStream.socketWrite0(Native Method)
at java.net.SocketOutputStream.socketWrite(Unknown Source)
at java.net.SocketOutputStream.write(Unknown Source)
at java.io.BufferedOutputStream.flushBuffer(Unknown Source)
at java.io.BufferedOutputStream.flush(Unknown Source)
at java.io.DataOutputStream.flush(Unknown Source)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:331)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1857)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.
scala:269)
答案 0 :(得分：3)
我会将初始化放在第一个单元格中，而所有其他单元格放在另一个单元格中。 每次要重新运行时，只需跳过初始化单元格。
好的，让我们看看
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8.0 failed 1 times, most recent failure: Lost task 0.0 in stage 8.0 (TID 8, localhost): java.net.SocketException: Connection reset by peer: socket write error
很可能因为内存不足导致舞台失败。该文件是否包含大量数据？
看起来像其他人一样有同样的麻烦 Apache Spark: pyspark crash for large dataset