在Spark issue - python中读取大文件

Question

我已经在本地安装了spark，使用python，并运行以下代码:

data=sc.textFile('C:\Users\xxxx\Desktop\train.csv')
data.first()

我得到以下错误:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-11-fca93c6aedeb> in <module>()
----> 1 data.first()
C:Sparkpythonpysparkrdd.pyc in first(self)
   1313         ValueError: RDD is empty
   1314         """
-> 1315         rs = self.take(1)
   1316         if rs:
   1317             return rs[0]
C:Sparkpythonpysparkrdd.pyc in take(self, num)
   1295 
   1296             p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1297             res = self.context.runJob(self, takeUpToNumLeft, p)
   1298 
   1299             items += res
C:Sparkpythonpysparkcontext.pyc in runJob(self, rdd, partitionFunc, partitions, allowLocal)
    937         # SparkContext#runJob.
    938         mappedRDD = rdd.mapPartitions(partitionFunc)
--> 939         port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
    940         return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
    941 
C:Anaconda2libsite-packagespy4jjava_gateway.pyc in __call__(self, *args)
   1024         answer = self.gateway_client.send_command(command)
   1025         return_value = get_return_value(
-> 1026             answer, self.gateway_client, self.target_id, self.name)
   1027 
   1028         for temp_arg in temp_args:
C:Sparkpythonpysparksqlutils.pyc in deco(*a, **kw)
     43     def deco(*a, **kw):
     44         try:
---> 45             return f(*a, **kw)
     46         except py4j.protocol.Py4JJavaError as e:
     47             s = e.java_exception.toString()
C:Anaconda2libsite-packagespy4jprotocol.pyc in get_return_value(answer, gateway_client, target_id, name)
    314                 raise Py4JJavaError(
    315                     "An error occurred while calling {0}{1}{2}.n".
--> 316                     format(target_id, ".", name), value)
    317             else:
    318                 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 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2, 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.write(Unknown Source)
    at java.io.DataOutputStream.write(Unknown Source)
    at java.io.FilterOutputStream.write(Unknown Source)
    at org.apache.spark.api.python.PythonRDD$.writeUTF(PythonRDD.scala:622)
    at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:442)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:452)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:452)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:452)
    at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:280)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
    at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:239)
Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:393)
    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:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    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.write(Unknown Source)
    at java.io.DataOutputStream.write(Unknown Source)
    at java.io.FilterOutputStream.write(Unknown Source)
    at org.apache.spark.api.python.PythonRDD$.writeUTF(PythonRDD.scala:622)
    at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:442)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:452)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:452)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:452)
    at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:280)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
    at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:239)

我确信路径是正确的，因为我已经尝试过在同一文件夹中的其他文件。我想问题出在文件的大小上，大约3.4 gb。

请帮忙好吗?

Answer 1

无论您是在单机模式还是集群模式下使用Spark, spark.driver.memory和spark.executor.memory默认为1GB内存。您可以在启动Jupyter笔记本或在Spark Conf文件中更改此配置，从而为driver和executors添加更多内存。这样，您应该能够读取3.4GB的CSV文件，前提是您的机器上有必要的RAM。

Answer 2

在spark文档中可以看到，内存使用的默认最大值设置为1GB。

您可以在spark配置文件中看到默认值，如果在linux中可能位于:

/etc/spark/conf/spark-defaults.conf

spark.driver.memory
spark.executor.memory

但是，这个配置不能通过SparkConf直接在你的应用程序中设置，因为驱动程序JVM已经在那时启动了。

当启动spark主/从时，你应该添加内存集参数:

ex: ./sbin/start-master.sh --memory 2G