如何在木星电池中重新运行星火(火星雨)代码？

Question

我在朱庇特中创建了一个“SparkSession”(使用pyspark)，然后读取一个.csv文件。

但是，当我试图重新运行第二次读取.csv文件的代码块时，我的代码在第一次运行时运行良好，我不知道为什么会出现以下错误：

   ---------------------------------------------------------------------------
    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)

这是我用来读取.csv文件的代码：

    # =========================  Setup Spark ============================#

import os
import sys

# NOTE: Please change the folder paths to your current setup.
#Windows
if sys.platform.startswith('win'):
    #Where you downloaded the resource bundle
    os.chdir("C:/Users/home")
    #Where you installed spark.    
    os.environ['SPARK_HOME'] = 'C:/Spark/spark-2.0.0-bin-hadoop2.7'

os.curdir

# Create a variable for our root path
SPARK_HOME = os.environ['SPARK_HOME']

#Add the following paths to the system path. Please check your installation
#to make sure that these zip files actually exist. The names might change
#as versions change.
sys.path.insert(0,os.path.join(SPARK_HOME,"python"))
sys.path.insert(0,os.path.join(SPARK_HOME,"python","lib"))
sys.path.insert(0,os.path.join(SPARK_HOME,"python","lib","pyspark.zip"))
sys.path.insert(0,os.path.join(SPARK_HOME,"python","lib","py4j-0.10.1-src.zip"))

#Initialize SparkSession and SparkContext
from pyspark.sql import SparkSession
from pyspark import SparkContext

#Create a Spark Session
SpSession2 = SparkSession \
    .builder \
    .master("local") \
    .appName("SparkPrjt1") \
    .config("spark.executor.memory", "1g") \
    .config("spark.driver.allowMultipleContexts","true")\
    .config("spark.cores.max","2") \
    .config("spark.sql.warehouse.dir", "file:///C:/tmp/spark-warehouse")\
    .getOrCreate()

#Get the Spark Context from Spark Session    
SpContext = SpSession2.sparkContext
from pyspark import SparkConf


# testData = SpContext.parallelize([3,6,4,2])
# testData.count()

 #---------------------------------------------------------------------
 #   Load Data from the data file
 #---------------------------------------------------------------------
 ccRaw = SpContext.textFile("C:\\Users\\home\\credit-card-default-1000.csv")

 ccRaw.take(3)

Answer 1

我会将初始化放在第一个单元中，所有其他单元都放在另一个单元中。每次您想要重新运行时，只需跳过初始化单元格。

好吧，让我看看

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

这个阶段很可能是因为记忆不足而失败的。该文件是否包含大量数据？

看上去就像别人和你一样有同样的麻烦

Apache Spark: pyspark crash for large dataset