在 Apache Spark 中通过管道运行 Windows 批处理文件

Posted 2023-02-16

【中文标题】在 Apache Spark 中通过管道运行 Windows 批处理文件

【英文标题】：Running a Windows Batch File through Piping in Apache Spark

【发布时间】：2017-01-23 00:20:20

【问题描述】：

我有一个要求，我必须在 Spark 集群的多个节点上使用 Apache Spark 运行 Windows 批处理文件。

那么是否可以使用 Apache Spark 的管道概念来做同样的事情？

我之前在 Ubuntu 机器上使用 Spark 中的 Piping 运行了一个 shell 文件。我下面的代码做同样的事情运行良好：

data = ["hi","hello","how","are","you"]
distScript = "/home/aawasthi/echo.sh"
distScriptName = "echo.sh"
sc.addFile(distScript)
RDDdata = sc.parallelize(data)
print RDDdata.pipe(SparkFiles.get(distScriptName)).collect()

我尝试修改相同的代码以在安装了 Spark（为 Hadoop 2.6 预构建的 1.6）的 Windows 机器上运行 Windows 批处理文件。但它给了我sc.addFile 步骤的错误。代码如下：

batchFile = "D:/spark-1.6.2-bin-hadoop2.6/data/OpenCV/runOpenCv"
batchFileName = "runOpenCv"
sc.addFile(batchFile)

Spark 抛出的错误如下：

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-11-9e13c265cbae> in <module>()
----> 1 sc.addFile(batchFile)`

Py4JJavaError: An error occurred while calling o160.addFile.
: java.io.FileNotFoundException: Added file D:/spark-1.6.2-bin-hadoop2.6/data/OpenCV/runOpenCv does not exist.
    at org.apache.spark.SparkContext.addFile(SparkContext.scala:1364)
    at org.apache.spark.SparkContext.addFile(SparkContext.scala:1340)
    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:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)

虽然批处理文件存在于给定位置。

更新： 在文件路径开头的batchFile & batchFileName & file:/// 中添加了.bat 作为扩展名。修改后的代码为：

from pyspark import SparkFiles
from pyspark import SparkContext    
sc    
batchFile = "file:///D:/spark-1.6.2-bin-hadoop2.6/data/OpenCV/runOpenCv.bat"
batchFileName = "runOpenCv.bat"
sc.addFile(batchFile)
RDDdata = sc.parallelize(["hi","hello"])
print SparkFiles.get("runOpenCv.bat")
print RDDdata.pipe(SparkFiles.get(batchFileName)).collect()

现在它在addFile 步骤中不会出错，并且print SparkFiles.get("runOpenCv.bat") 打印路径C:\Users\abhilash.awasthi\AppData\Local\Temp\spark-c0f383b1-8365-4840-bd0f-e7eb46cc6794\userFiles-69051066-f18c-45dc-9610-59cbde0d77fe\runOpenCv.bat 所以文件被添加。但在代码的最后一步，它会抛出以下错误：

    Py4JJavaError                             Traceback (most recent call last)
<ipython-input-6-bf2b8aea3ef0> in <module>()
----> 1 print RDDdata.pipe(SparkFiles.get(batchFileName)).collect()

D:\spark-1.6.2-bin-hadoop2.6\python\pyspark\rdd.pyc in collect(self)
    769         """
    770         with SCCallSiteSync(self.context) as css:
--> 771             port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    772         return list(_load_from_socket(port, self._jrdd_deserializer))
    773 

D:\spark-1.6.2-bin-hadoop2.6\python\lib\py4j-0.9-src.zip\py4j\java_gateway.py in __call__(self, *args)
    811         answer = self.gateway_client.send_command(command)
    812         return_value = get_return_value(
--> 813             answer, self.gateway_client, self.target_id, self.name)
    814 
    815         for temp_arg in temp_args:

D:\spark-1.6.2-bin-hadoop2.6\python\pyspark\sql\utils.pyc in deco(*a, **kw)
     43     def deco(*a, **kw):
     44         try:
---> 45             return f(*a, **kw)
     46         except py4j.protocol.Py4JJavaError as e:
     47             s = e.java_exception.toString()

D:\spark-1.6.2-bin-hadoop2.6\python\lib\py4j-0.9-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
    306                 raise Py4JJavaError(
    307                     "An error occurred while calling 012.\n".
--> 308                     format(target_id, ".", name), value)
    309             else:
    310                 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 1 in stage 0.0 failed 1 times, most recent failure: Lost task 1.0 in stage 0.0 (TID 1, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "D:\spark-1.6.2-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\worker.py", line 111, in main
  File "D:\spark-1.6.2-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\worker.py", line 106, in process
  File "D:\spark-1.6.2-bin-hadoop2.6\python\pyspark\rdd.py", line 317, in func
    return f(iterator)
  File "D:\spark-1.6.2-bin-hadoop2.6\python\pyspark\rdd.py", line 715, in func
    shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
  File "C:\Anaconda2\lib\subprocess.py", line 710, in __init__
    errread, errwrite)
  File "C:\Anaconda2\lib\subprocess.py", line 958, in _execute_child
    startupinfo)
WindowsError: [Error 2] The system cannot find the file specified

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
    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)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:926)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:405)
    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:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "D:\spark-1.6.2-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\worker.py", line 111, in main
  File "D:\spark-1.6.2-bin-hadoop2.6\python\lib\pyspark.zip\pyspark\worker.py", line 106, in process
  File "D:\spark-1.6.2-bin-hadoop2.6\python\pyspark\rdd.py", line 317, in func
    return f(iterator)
  File "D:\spark-1.6.2-bin-hadoop2.6\python\pyspark\rdd.py", line 715, in func
    shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
  File "C:\Anaconda2\lib\subprocess.py", line 710, in __init__
    errread, errwrite)
  File "C:\Anaconda2\lib\subprocess.py", line 958, in _execute_child
    startupinfo)
WindowsError: [Error 2] The system cannot find the file specified

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

【问题讨论】：

在 windows 批处理文件有 .cmd 或 .bat 扩展名。您是否尝试过包含它？

@MCND 我真傻……是的，名称中应该有扩展名。在batchFile 和batchFileName 中添加.bat 后，我没有收到文件不存在错误。但我得到了不同的错误，如更新的答案所示。

No FileSystem for scheme: D，所以 D: 没有按需要处理，也许（对不起，如果这是愚蠢的，我知道一些关于批处理文件，但 java 不是我的领域）你需要一个 URI 所以需要file:///D:/... 之类的东西

【参考方案1】：

请逃跑/

batchFile = "D://spark-1.6.2-bin-hadoop2.6//data//OpenCV//runOpenCv"

此外，正如上面 AA 所建议的，它可能具有 .cmd 或 .bat 扩展名。

【讨论】：

转义字符是\，所以不需要转义/