【Python】Jupyter Notebook报错 SparkException: Python worker failed to connect back.


报错

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-24-bafca16b0526> in <module>
      8     return jobitem, ratingsRDD
      9 jobitem, jobRDD = preparJobdata(sc)
---> 10 jobRDD.collect() #岗位信息特征展示

G:\Projects\python-3.6.4-amd64\lib\site-packages\pyspark\rdd.py in collect(self)
    947         """
    948         with SCCallSiteSync(self.context) as css:
--> 949             sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    950         return list(_load_from_socket(sock_info, self._jrdd_deserializer))
    951 

G:\Projects\python-3.6.4-amd64\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
   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 
   1307         for temp_arg in temp_args:

G:\Projects\python-3.6.4-amd64\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

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 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0) (192.168.101.68 executor driver): org.apache.spark.SparkException: Python worker failed to connect back.
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:182)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)
	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.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$TaskRunner.run(Executor.scala:500)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
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:174)
	... 14 more

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$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2202)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2223)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2242)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
	at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:1029)
	at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:180)
	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: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:745)
Caused by: org.apache.spark.SparkException: Python worker failed to connect back.
	at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:182)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)
	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.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$TaskRunner.run(Executor.scala:500)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	... 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:174)
	... 14 more

解决

配置了以下变量环境:

# Windows下Hadoop环境配置
HADOOP_HOME = F:\hadoop-common-2.2.0-bin-master\hadoop-common-2.2.0-bin-master

# Windows下JDK环境配置
JAVA_HOME = F:\jdk-8u121-windows-x64_8.0.1210.13

# Windows下Pyspark环境配置
PYSPARK_DRIVER_PYTHON = jupyter
PYSPARK_DRIVER_PYTHON_OPTS = notebook
PYSPARK_PYTHON = python

配置完成后记得重启一下电脑!


版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。

相关推荐


学习编程是顺着互联网的发展潮流,是一件好事。新手如何学习编程?其实不难,不过在学习编程之前你得先了解你的目的是什么?这个很重要,因为目的决定你的发展方向、决定你的发展速度。
IT行业是什么工作做什么?IT行业的工作有:产品策划类、页面设计类、前端与移动、开发与测试、营销推广类、数据运营类、运营维护类、游戏相关类等,根据不同的分类下面有细分了不同的岗位。
女生学Java好就业吗?女生适合学Java编程吗?目前有不少女生学习Java开发,但要结合自身的情况,先了解自己适不适合去学习Java,不要盲目的选择不适合自己的Java培训班进行学习。只要肯下功夫钻研,多看、多想、多练
Can’t connect to local MySQL server through socket \'/var/lib/mysql/mysql.sock问题 1.进入mysql路径
oracle基本命令 一、登录操作 1.管理员登录 # 管理员登录 sqlplus / as sysdba 2.普通用户登录
一、背景 因为项目中需要通北京网络,所以需要连vpn,但是服务器有时候会断掉,所以写个shell脚本每五分钟去判断是否连接,于是就有下面的shell脚本。
BETWEEN 操作符选取介于两个值之间的数据范围内的值。这些值可以是数值、文本或者日期。
假如你已经使用过苹果开发者中心上架app,你肯定知道在苹果开发者中心的web界面,无法直接提交ipa文件,而是需要使用第三方工具,将ipa文件上传到构建版本,开...
下面的 SQL 语句指定了两个别名,一个是 name 列的别名,一个是 country 列的别名。**提示:**如果列名称包含空格,要求使用双引号或方括号:
在使用H5混合开发的app打包后,需要将ipa文件上传到appstore进行发布,就需要去苹果开发者中心进行发布。​
+----+--------------+---------------------------+-------+---------+
数组的声明并不是声明一个个单独的变量,比如 number0、number1、...、number99,而是声明一个数组变量,比如 numbers,然后使用 nu...
第一步:到appuploader官网下载辅助工具和iCloud驱动,使用前面创建的AppID登录。
如需删除表中的列,请使用下面的语法(请注意,某些数据库系统不允许这种在数据库表中删除列的方式):
前不久在制作win11pe,制作了一版,1.26GB,太大了,不满意,想再裁剪下,发现这次dism mount正常,commit或discard巨慢,以前都很快...
赛门铁克各个版本概览:https://knowledge.broadcom.com/external/article?legacyId=tech163829
实测Python 3.6.6用pip 21.3.1,再高就报错了,Python 3.10.7用pip 22.3.1是可以的
Broadcom Corporation (博通公司,股票代号AVGO)是全球领先的有线和无线通信半导体公司。其产品实现向家庭、 办公室和移动环境以及在这些环境...
发现个问题,server2016上安装了c4d这些版本,低版本的正常显示窗格,但红色圈出的高版本c4d打开后不显示窗格,
TAT:https://cloud.tencent.com/document/product/1340