在jupyter notebook

时间：2018-04-09 21:37:39

标签： python python-3.x apache-spark pyspark spark-streaming

我正在尝试使用代码为

的python中的spark流式传输的简单网络字数统计程序 
from pyspark import SparkContext
from pyspark.streaming import StreamingContext


sc = SparkContext("local[2]", "NetworkWordCount")
ssc = StreamingContext(sc, 1)


lines = ssc.socketTextStream("localhost", 9999)

words = lines.flatMap(lambda line: line.split(" "))

pairs = words.map( lambda word : (word,1))
wordCount = pairs.reduceByKey( lambda x, y : (x+y))
wordCount.pprint()
ssc.start()
ssc.awaitTermination()

直到ssc.start() 但它在ssc.awaitTermination()

给出错误 
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-2-18f3db416f1c> in <module>()
      1 ssc.start()
----> 2 ssc.awaitTermination()

/usr/local/lib/python3.5/dist-packages/pyspark/streaming/context.py in awaitTermination(self, timeout)
    204         """
    205         if timeout is None:
--> 206             self._jssc.awaitTermination()
    207         else:
    208             self._jssc.awaitTerminationOrTimeout(int(timeout * 1000))

/usr/local/lib/python3.5/dist-packages/py4j/java_gateway.py in __call__(self, *args)
   1158         answer = self.gateway_client.send_command(command)
   1159         return_value = get_return_value(
-> 1160             answer, self.gateway_client, self.target_id, self.name)
   1161 
   1162         for temp_arg in temp_args:

/usr/local/lib/python3.5/dist-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    318                 raise Py4JJavaError(
    319                     "An error occurred while calling {0}{1}{2}.\n".
--> 320                     format(target_id, ".", name), value)
    321             else:
    322                 raise Py4JError(

Py4JJavaError: An error occurred while calling o22.awaitTermination.
: org.apache.spark.SparkException: An exception was raised by Python:
Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/pyspark/streaming/util.py", line 65, in call
    r = self.func(t, *rdds)
  File "/usr/local/lib/python3.5/dist-packages/pyspark/streaming/dstream.py", line 171, in takeAndPrint
    taken = rdd.take(num + 1)
  File "/usr/local/lib/python3.5/dist-packages/pyspark/rdd.py", line 1358, in take
    res = self.context.runJob(self, takeUpToNumLeft, p)
  File "/usr/local/lib/python3.5/dist-packages/pyspark/context.py", line 1001, in runJob
    port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
  File "/usr/local/lib/python3.5/dist-packages/py4j/java_gateway.py", line 1160, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/local/lib/python3.5/dist-packages/py4j/protocol.py", line 320, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.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 1, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/blaze/spark/spark-2.2.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 123, in main
    ("%d.%d" % sys.version_info[:2], version))
Exception: Python in worker has different version 2.7 than that in driver 3.5, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
    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$$failJobAndIndependentStages(DAGScheduler.scala:1517)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
    at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:455)
    at org.apache.spark.api.python.PythonRDD.runJob(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)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/blaze/spark/spark-2.2.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 123, in main
    ("%d.%d" % sys.version_info[:2], version))
Exception: Python in worker has different version 2.7 than that in driver 3.5, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more


    at org.apache.spark.streaming.api.python.TransformFunction.callPythonTransformFunction(PythonDStream.scala:95)
    at org.apache.spark.streaming.api.python.TransformFunction.apply(PythonDStream.scala:78)
    at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
    at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
    at scala.util.Try$.apply(Try.scala:192)
    at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256)
    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)

spark版本：2.2.1 python版本：3.5 java版本：1.8.0_162 pyspark版本：2.3.0

感谢。

​

2 个答案:

答案 0 :(得分：1)

您使用的是独立火花吗？

您的错误是：异常：worker中的Python与驱动程序3.5中的版本不同，PySpark无法使用不同的次要版本运行。请检查环境变量PYSPARK_PYTHON和PYSPARK_DRIVER_PYTHON是否已正确设置。

您的错误已在此处解决： How do I set the driver's python version in spark?

答案 1 :(得分：0)

更新SPARK环境以使用PYTHON 3.7：

打开一个新终端并键入以下命令：export PYSPARK_PYTHON=python3.7这将确保辅助节点使用Python 3.7（与驱动程序相同）而不是默认的Python 3.4

根据您使用的PYTHON版本，您可能需要安装/更新ANACONDA：

（要安装，请参见：https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart）

确保您具有anaconda 4.1.0或更高版本。打开一个新终端，然后在新终端中输入以下内容检查您的conda版本：

conda --version

检查conda版本

如果您低于anaconda 4.1.0，请输入conda update conda

  1. 接下来，我们通过输入来检查是否具有库nb_conda_kernels

conda list

检查我们是否有nb_conda_kernels

  1. 如果看不到nb_conda_kernels，请输入

conda install nb_conda_kernels

安装nb_conda_kernels

  1. 如果您使用的是Python 2，并且想要一个单独的Python 3环境，请输入以下内容

conda create -n py36 python=3.6 ipykernel

py35是环境的名称。您可以随意命名它。

或者，如果您使用的是Python 3，并且想要一个单独的Python 2环境，则可以键入以下内容。

conda create -n py27 python=2.7 ipykernel

py27是环境的名称。它使用python 2.7。

  1. 确保已成功安装python版本并关闭终端。打开一个新终端，然后输入pyspark。您应该看到出现了新的环境。
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