java - Pyspark 3.1.1 Py4JjavaError

Question

我在我的 Ubuntu 机器上运行带有 java openjdk 11.0.11 的 pyspark 3.1.1。我在数据框中创建了一些随机数据

import numpy as np
n, p = (10, 4)
data = np.random.rand(n, p)

# create the dataframe
schem = StructType([StructField('col_%d'%i, FloatType(), False) for i in range(p)])
randData = spark.createDataFrame(data.tolist(), schema=schem)
randData.show()

这在我的带有 pyspark 3.0.1 的 Windows 10 机器上运行良好，但在上述系统上，我得到了这个堆栈跟踪:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-21-6f093bb5efc1> in <module>
      7 # create the dataframe
      8 randData = spark.createDataFrame(data.tolist(), schema=schem)
----> 9 randData.show()

~/spark-3.1.1-bin-hadoop2.7/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
    482         """
    483         if isinstance(truncate, bool) and truncate:
--> 484             print(self._jdf.showString(n, 20, vertical))
    485         else:
    486             print(self._jdf.showString(n, int(truncate), vertical))

~/spark-3.1.1-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1302 
   1303         answer = self.gateway_client.send_command(command)
-> 1304         return_value = get_return_value(
   1305             answer, self.gateway_client, self.target_id, self.name)
   1306 

~/spark-3.1.1-bin-hadoop2.7/python/pyspark/sql/utils.py in deco(*a, **kw)
    109     def deco(*a, **kw):
    110         try:
--> 111             return f(*a, **kw)
    112         except py4j.protocol.Py4JJavaError as e:
    113             converted = convert_exception(e.java_exception)

~/spark-3.1.1-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    324             value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
    325             if answer[1] == REFERENCE_TYPE:
--> 326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
    328                     format(target_id, ".", name), value)

Py4JJavaError: An error occurred while calling o221.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 34.0 failed 1 times, most recent failure: Lost task 0.0 in stage 34.0 (TID 619) (192.168.150.128 executor driver): org.apache.spark.SparkException: 
Bad data in pyspark.daemon's standard output. Invalid port number:
  458961458 (0x1b5b3232)
Python command to execute the daemon was:
  ipython3 -m pyspark.daemon
Check that you don't have any unexpected modules or libraries in
your PYTHONPATH:
  /home/ahowe42/spark-3.1.1-bin-hadoop2.7/python/lib/pyspark.zip:/home/ahowe42/spark-3.1.1-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip:/home/ahowe42/spark-3.1.1-bin-hadoop2.7/jars/spark-core_2.12-3.1.1.jar:/home/ahowe42/spark-3.1.1-bin-hadoop2.7/python:
Also, check if you have a sitecustomize.py module in your python path,
or in your python installation, that is printing to standard output
    at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:238)
    at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:132)
    at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:105)
    at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)
    at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:131)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2253)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2202)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2201)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2201)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1078)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1078)
    at scala.Option.foreach(Option.scala:407)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1078)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2440)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2382)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2371)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2202)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2223)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2242)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
    at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)
    at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)
    at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2722)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2929)
    at org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:338)
    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.SparkException: 
Bad data in pyspark.daemon's standard output. Invalid port number:
  458961458 (0x1b5b3232)
Python command to execute the daemon was:
  ipython3 -m pyspark.daemon
Check that you don't have any unexpected modules or libraries in
your PYTHONPATH:
  /home/ahowe42/spark-3.1.1-bin-hadoop2.7/python/lib/pyspark.zip:/home/ahowe42/spark-3.1.1-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip:/home/ahowe42/spark-3.1.1-bin-hadoop2.7/jars/spark-core_2.12-3.1.1.jar:/home/ahowe42/spark-3.1.1-bin-hadoop2.7/python:
Also, check if you have a sitecustomize.py module in your python path,
or in your python installation, that is printing to standard output
    at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:238)
    at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:132)
    at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:105)
    at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)
    at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:131)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

我用这个简单的命令得到了同样的堆栈跟踪: spark.CreateDataFrame([[1, 2, 3], [4, 5, 6]], ['a', 'b', 'c']).show() .
知道这是怎么回事吗？
我使用从 spark.apache.org 下载的内容安装了 pyspark。 JAVA_HOME设置为 /usr/lib/jvm/java-11-openjdk-amd64 .我的 bashrc 中有以下设置:

export PYTHONPATH=/home/ahowe42/anaconda3/bin
export SPARK_HOME=/home/ahowe42/spark-3.1.1-bin-hadoop2.7
export PYSPARK_PYTHON=ipython3
export PYTHONPATH=$PYTHONPATH:$SPARK_HOME/python
export PATH=$PATH:$SPARK_HOME/bin:$PYTHONPATH:$JAVA_HOME/jre/bin

我使用加载和初始化 pyspark

import findspark
findspark.init()
import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.functions import *#avg, count, expr
from pyspark.sql.types import *
sc = pyspark.SparkContext()
spark = SparkSession(sc)
spark.sparkContext.appName = 'exploreReadWrite'
spark

Answer 1

这确实是一条评论，但我还没有代表发表评论。
你试过which python ? spark 端口号的类似错误是指库之间的 python 版本不匹配。关于您的$PYTHONPATH ，这将在多个位置设置，它可以由您的 shell 调用的任何进程更新 - 所以您知道它已从您的 .bashrc 中的字符串更改由您的服务器。但是，$PYTHONPATH我想会从左到右搜索，所以如果你的anaconda目录中有python运行时，我认为它会在spark版本之前被调用，如果你只是说py .会不会是你的anaconda版本和spark版本使用了不同版本的python？