• 【错误记录】Python 中使用 PySpark 数据计算报错 ( SparkException: Python worker failed to connect back. )

    文章目录


    错误原因 : 没有为 PySpark 配置 Python 解释器 , 将下面的代码卸载 Python 数据分析代码的最前面即可 ;

    # 为 PySpark 配置 Python 解释器
import os
os.environ['PYSPARK_PYTHON'] = "Y:/002_WorkSpace/PycharmProjects/pythonProject/venv/Scripts/python.exe"

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    os.environ['PYSPARK_PYTHON'] 的值设置为 你自己电脑上的 python.exe 绝对路径即可 , 不要按照我电脑上的 Python 解释器路径设置 ;





    一、报错信息


    Python 中使用 PySpark 数据计算 ,

    # 创建一个包含整数的 RDD
rdd = sparkContext.parallelize([1, 2, 3, 4, 5])


# 为每个元素执行的函数
def func(element):
    return element * 10


# 应用 map 操作，将每个元素乘以 10
rdd2 = rdd.map(func)

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    执行时 , 报如下错误 :

    Y:\002_WorkSpace\PycharmProjects\pythonProject\venv\Scripts\python.exe Y:/002_WorkSpace/PycharmProjects/HelloPython/hello.py
23/07/30 21:24:54 WARN Shell: Did not find winutils.exe: java.io.FileNotFoundException: java.io.FileNotFoundException: HADOOP_HOME and hadoop.home.dir are unset. -see https://wiki.apache.org/hadoop/WindowsProblems
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
23/07/30 21:24:54 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
PySpark 版本号 :  3.4.1
23/07/30 21:25:07 ERROR Executor: Exception in task 9.0 in stage 0.0 (TID 9)
org.apache.spark.SparkException: Python worker failed to connect back.
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:192)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:109)
	at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:124)
	at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:166)
	at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
	at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
	at org.apache.spark.scheduler.Task.run(Task.scala:139)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
Caused by: java.net.SocketTimeoutException: Accept timed out
	at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method)
	at java.net.DualStackPlainSocketImpl.socketAccept(DualStackPlainSocketImpl.java:135)
	at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
	at java.net.PlainSocketImpl.accept(PlainSocketImpl.java:199)
	at java.net.ServerSocket.implAccept(ServerSocket.java:545)
	at java.net.ServerSocket.accept(ServerSocket.java:513)
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:179)
	... 15 more
23/07/30 21:25:07 WARN TaskSetManager: Lost task 9.0 in stage 0.0 (TID 9) (windows10.microdone.cn executor driver): org.apache.spark.SparkException: Python worker failed to connect back.
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:192)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:109)
	at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:124)
	at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:166)
	at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
	at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
	at org.apache.spark.scheduler.Task.run(Task.scala:139)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
Caused by: java.net.SocketTimeoutException: Accept timed out
	at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method)
	at java.net.DualStackPlainSocketImpl.socketAccept(DualStackPlainSocketImpl.java:135)
	at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
	at java.net.PlainSocketImpl.accept(PlainSocketImpl.java:199)
	at java.net.ServerSocket.implAccept(ServerSocket.java:545)
	at java.net.ServerSocket.accept(ServerSocket.java:513)
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:179)
	... 15 more

23/07/30 21:25:07 ERROR TaskSetManager: Task 9 in stage 0.0 failed 1 times; aborting job
Traceback (most recent call last):
  File "Y:\002_WorkSpace\PycharmProjects\HelloPython\hello.py", line 33, in <module>
    print(rdd2.collect())
  File "Y:\002_WorkSpace\PycharmProjects\pythonProject\venv\lib\site-packages\pyspark\rdd.py", line 1814, in collect
    sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
  File "Y:\002_WorkSpace\PycharmProjects\pythonProject\venv\lib\site-packages\py4j\java_gateway.py", line 1322, in __call__
    return_value = get_return_value(
  File "Y:\002_WorkSpace\PycharmProjects\pythonProject\venv\lib\site-packages\py4j\protocol.py", line 326, in get_return_value
    raise Py4JJavaError(
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 9 in stage 0.0 failed 1 times, most recent failure: Lost task 9.0 in stage 0.0 (TID 9) (windows10.microdone.cn executor driver): org.apache.spark.SparkException: Python worker failed to connect back.
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:192)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:109)
	at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:124)
	at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:166)
	at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
	at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
	at org.apache.spark.scheduler.Task.run(Task.scala:139)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
Caused by: java.net.SocketTimeoutException: Accept timed out
	at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method)
	at java.net.DualStackPlainSocketImpl.socketAccept(DualStackPlainSocketImpl.java:135)
	at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
	at java.net.PlainSocketImpl.accept(PlainSocketImpl.java:199)
	at java.net.ServerSocket.implAccept(ServerSocket.java:545)
	at java.net.ServerSocket.accept(ServerSocket.java:513)
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:179)
	... 15 more

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2785)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2721)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2720)
	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2720)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1206)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1206)
	at scala.Option.foreach(Option.scala:407)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1206)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2984)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2923)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2912)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:971)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2263)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2284)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2303)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2328)
	at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1019)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:405)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:1018)
	at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:193)
	at org.apache.spark.api.python.PythonRDD.collectAndServe(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:374)
	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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
	at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Python worker failed to connect back.
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:192)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:109)
	at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:124)
	at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:166)
	at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
	at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
	at org.apache.spark.scheduler.Task.run(Task.scala:139)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more
Caused by: java.net.SocketTimeoutException: Accept timed out
	at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method)
	at java.net.DualStackPlainSocketImpl.socketAccept(DualStackPlainSocketImpl.java:135)
	at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)
	at java.net.PlainSocketImpl.accept(PlainSocketImpl.java:199)
	at java.net.ServerSocket.implAccept(ServerSocket.java:545)
	at java.net.ServerSocket.accept(ServerSocket.java:513)
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:179)
	... 15 more

[Stage 0:>                                                        (0 + 11) / 12]
Process finished with exit code 1


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    在这里插入图片描述
    在这里插入图片描述

    核心报错信息如下 :
    org.apache.spark.SparkException: Python worker failed to connect back.
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:192)
    at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:109)
    at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:124)





    二、问题分析


    执行的代码如下 :

    """
PySpark 数据处理
"""

# 导入 PySpark 相关包
from pyspark import SparkConf, SparkContext

# 创建 SparkConf 实例对象 , 该对象用于配置 Spark 任务
# setMaster("local[*]") 表示在单机模式下 本机运行
# setAppName("hello_spark") 是给 Spark 程序起一个名字
sparkConf = SparkConf() \
    .setMaster("local[*]") \
    .setAppName("hello_spark")

# 创建 PySpark 执行环境 入口对象
sparkContext = SparkContext(conf=sparkConf)

# 打印 PySpark 版本号
print("PySpark 版本号 : ", sparkContext.version)

# 创建一个包含整数的 RDD
rdd = sparkContext.parallelize([1, 2, 3, 4, 5])


# 为每个元素执行的函数
def func(element):
    return element * 10


# 应用 map 操作，将每个元素乘以 10
rdd2 = rdd.map(func)

# 打印新的 RDD 中的内容
print(rdd2.collect())

# 停止 PySpark 程序
sparkContext.stop()


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    执行的代码 , 没有任何错误 ;

    报错原因是 Python 代码没有准确地找到 Python 解释器 ;

    在 PyCharm 中 , 已经配置了 Python 3.10 版本的解释器 , 该解释器可以被 Python 程序识别到 , 但是不能被 PySpark 识别到 ;
    在这里插入图片描述

    因此 , 这里需要手动为 PySpark 设置 Python 解释器 ;

    设置 PySpark 的 Python 解释器环境变量 ;





    三、解决方案


    在 PyCharm 中 , 选择 " 菜单栏 / File / Settings " 选项 ,

    在这里插入图片描述

    在 Settings 窗口中 , 选择 Python 解释器面板 , 查看 配置的 Python 解释器安装在哪个路径中 ;

    在这里插入图片描述

    记录 Python 解释器位置 :

    Y:/002_WorkSpace/PycharmProjects/pythonProject/venv/Scripts/python.exe

    在这里插入图片描述

    在 代码 的开始位置 , 添加如下代码 :

    import os
os.environ['PYSPARK_PYTHON'] = "Y:/002_WorkSpace/PycharmProjects/pythonProject/venv/Scripts/python.exe"

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    将 os.environ['PYSPARK_PYTHON'] = 后的 Python.exe 路径换成你自己电脑上的路径即可 ;


    修改后的完整代码如下 :

    """
PySpark 数据处理
"""

# 导入 PySpark 相关包
from pyspark import SparkConf, SparkContext
# 为 PySpark 配置 Python 解释器
import os
os.environ['PYSPARK_PYTHON'] = "Y:/002_WorkSpace/PycharmProjects/pythonProject/venv/Scripts/python.exe"

# 创建 SparkConf 实例对象 , 该对象用于配置 Spark 任务
# setMaster("local[*]") 表示在单机模式下 本机运行
# setAppName("hello_spark") 是给 Spark 程序起一个名字
sparkConf = SparkConf() \
    .setMaster("local[*]") \
    .setAppName("hello_spark")

# 创建 PySpark 执行环境 入口对象
sparkContext = SparkContext(conf=sparkConf)

# 打印 PySpark 版本号
print("PySpark 版本号 : ", sparkContext.version)

# 创建一个包含整数的 RDD
rdd = sparkContext.parallelize([1, 2, 3, 4, 5])


# 为每个元素执行的函数
def func(element):
    return element * 10


# 应用 map 操作，将每个元素乘以 10
rdd2 = rdd.map(func)

# 打印新的 RDD 中的内容
print(rdd2.collect())

# 停止 PySpark 程序
sparkContext.stop()


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    执行结果 :

    Y:\002_WorkSpace\PycharmProjects\pythonProject\venv\Scripts\python.exe Y:/002_WorkSpace/PycharmProjects/HelloPython/hello.py
23/07/30 21:39:59 WARN Shell: Did not find winutils.exe: java.io.FileNotFoundException: java.io.FileNotFoundException: HADOOP_HOME and hadoop.home.dir are unset. -see https://wiki.apache.org/hadoop/WindowsProblems
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
23/07/30 21:39:59 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
PySpark 版本号 :  3.4.1
[10, 20, 30, 40, 50]

Process finished with exit code 0


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    在这里插入图片描述

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  • 原文地址：https://blog.csdn.net/han1202012/article/details/132011806