Python 为什么collect（）工作正常，但count（）和take（）在Spark中给了我错误？

上面的回复是：

rdd = sc.textFile("test_file.txt").cache()
rdd.collect()

然后

rdd.count（）

给我这个错误：

['my number is 0', 'my number is 1', 'my number is 2']

---------------------------------------------------------------------------
Py4JJavaError回溯（最近一次调用）
在里面
---->1 rdd.count（）
计数中的~\Anaconda3\envs\bigdata lab\lib\site packages\pyspark\rdd.py（self）
1053         3
1054         """
->1055返回self.mapPartitions（lambda i:[sum（1代表i中的u）]）.sum（）
1056
1057 def状态（自身）：
~\Anaconda3\envs\bigdata lab\lib\site packages\pyspark\rdd.py总和（self）
1044         6.0
1045         """
->1046返回self.mapPartitions（lambda x:[求和（x）]）.fold（0，运算符.add）
1047
1048 def计数（自身）：
~\Anaconda3\envs\bigdata lab\lib\site packages\pyspark\rdd.py折叠（self，zeroValue，op）
915#提供给每个分区的zeroValue与提供的分区是唯一的
916#到最后一次通话
-->917 VAL=self.mapPartitions（func.collect（））
918返回减少（op、VAL、零值）
919
收集中的~\Anaconda3\envs\bigdata lab\lib\site packages\pyspark\rdd.py（self）
814         """
815使用SCCallSiteSync（self.context）作为css：
-->816 sock_info=self.ctx.\u jvm.PythonRDD.collectAndServe（self.\u jrdd.rdd（））
817返回列表（_从_套接字加载（sock信息，self._jrdd_反序列化器））
818
~\Anaconda3\envs\bigdata lab\lib\site packages\py4j\java\u gateway.py在调用中（self，*args）
1255 answer=self.gateway\u client.send\u命令（command）
1256返回值=获取返回值(
->1257应答，self.gateway_客户端，self.target_id，self.name）
1258
1259对于临时参数中的临时参数：
获取返回值中的~\Anaconda3\envs\bigdata lab\lib\site packages\py4j\protocol.py（应答、网关客户端、目标id、名称）
326 raise Py4JJavaError(
327“调用{0}{1}{2}时出错。\n”。
-->328格式（目标id，“.”，名称），值）
329其他：
330升起Py4JError(
Py4JJavaError:调用z:org.apache.spark.api.python.PythonRDD.collectAndServe时出错。
：org.apache.SparkException:作业因阶段失败而中止：阶段17.0中的任务0失败1次，最近的失败：阶段17.0中的任务0.0丢失（TID 38，本地主机，执行器驱动程序）：org.apache.SparkException:Python worker无法连接回。
位于org.apache.spark.api.PythonWorkerFactory.createSimpleWorker（PythonWorkerFactory.scala:170）
位于org.apache.spark.api.python.PythonWorkerFactory.create（PythonWorkerFactory.scala:97）
位于org.apache.spark.SparkEnv.createPythonWorker（SparkEnv.scala:117）
位于org.apache.spark.api.python.BasePythonRunner.compute（PythonRunner.scala:108）
位于org.apache.spark.api.python.PythonRDD.compute（PythonRDD.scala:65）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:324）
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:288）
位于org.apache.spark.scheduler.ResultTask.runTask（ResultTask.scala:90）
位于org.apache.spark.scheduler.Task.run（Task.scala:121）
位于org.apache.spark.executor.executor$TaskRunner$$anonfun$10.apply（executor.scala:402）
位于org.apache.spark.util.Utils$.tryWithSafeFinally（Utils.scala:1360）
位于org.apache.spark.executor.executor$TaskRunner.run（executor.scala:408）
位于java.util.concurrent.ThreadPoolExecutor.runWorker（未知源）
位于java.util.concurrent.ThreadPoolExecutor$Worker.run（未知源）
位于java.lang.Thread.run（未知源）
原因：java.net.SocketTimeoutException:接受超时
位于java.net.DualStackPlainSocketImpl.waitForNewConnection（本机方法）
位于java.net.DualStackPlainSocketImpl.socketAccept（未知源）
位于java.net.AbstractPlainSocketImpl.accept（未知源）
位于java.net.PlainSocketImpl.accept（未知源）
位于java.net.ServerSocket.implacpt（未知源）
位于java.net.ServerSocket.accept（未知源）
位于org.apache.spark.api.PythonWorkerFactory.createSimpleWorker（PythonWorkerFactory.scala:164）
…还有14个
驱动程序堆栈跟踪：
位于org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages（DAGScheduler.scala:1887）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply（DAGScheduler.scala:1875）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply（DAGScheduler.scala:1874）
位于scala.collection.mutable.resizeblearray$class.foreach（resizeblearray.scala:59）
位于scala.collection.mutable.ArrayBuffer.foreach（ArrayBuffer.scala:48）
位于org.apache.spark.scheduler.DAGScheduler.abortStage（DAGScheduler.scala:1874）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply（DAGScheduler.scala:926）
位于org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply（DAGScheduler.scala:926）
位于scala.Option.foreach（Option.scala:257）
位于org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed（DAGScheduler.scala:926）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive（DAGScheduler.scala:2108）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive（DAGScheduler.scala:2057）
位于org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive（DAGScheduler.scala:2046）
位于org.apache.spark.util.EventLoop$$anon$1.run（EventLoop.scala:49）
位于org.apache.spark.scheduler.DAGScheduler.runJob（DAGScheduler.scala:737）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:2061）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:2082）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:2101）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:2126）
位于org.apache.spark.rdd.rdd$$anonfun$collect$1.apply（rdd.scala:9
---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-27-377a7789e04b> in <module>
----> 1 rdd.count()

~\Anaconda3\envs\bigdata-lab\lib\site-packages\pyspark\rdd.py in count(self)
   1053         3
   1054         """
-> 1055         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1056 
   1057     def stats(self):

~\Anaconda3\envs\bigdata-lab\lib\site-packages\pyspark\rdd.py in sum(self)
   1044         6.0
   1045         """
-> 1046         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
   1047 
   1048     def count(self):

~\Anaconda3\envs\bigdata-lab\lib\site-packages\pyspark\rdd.py in fold(self, zeroValue, op)
    915         # zeroValue provided to each partition is unique from the one provided
    916         # to the final reduce call
--> 917         vals = self.mapPartitions(func).collect()
    918         return reduce(op, vals, zeroValue)
    919 

~\Anaconda3\envs\bigdata-lab\lib\site-packages\pyspark\rdd.py in collect(self)
    814         """
    815         with SCCallSiteSync(self.context) as css:
--> 816             sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    817         return list(_load_from_socket(sock_info, self._jrdd_deserializer))
    818 

~\Anaconda3\envs\bigdata-lab\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

~\Anaconda3\envs\bigdata-lab\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 17.0 failed 1 times, most recent failure: Lost task 0.0 in stage 17.0 (TID 38, localhost, executor driver): org.apache.spark.SparkException: Python worker failed to connect back.
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:170)
    at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:97)
    at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
    at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
    at java.lang.Thread.run(Unknown Source)
Caused by: java.net.SocketTimeoutException: Accept timed out
    at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method)
    at java.net.DualStackPlainSocketImpl.socketAccept(Unknown Source)
    at java.net.AbstractPlainSocketImpl.accept(Unknown Source)
    at java.net.PlainSocketImpl.accept(Unknown Source)
    at java.net.ServerSocket.implAccept(Unknown Source)
    at java.net.ServerSocket.accept(Unknown Source)
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:164)
    ... 14 more

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1887)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1875)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1874)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
    at java.lang.reflect.Method.invoke(Unknown Source)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Unknown Source)
Caused by: org.apache.spark.SparkException: Python worker failed to connect back.
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:170)
    at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:97)
    at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
    at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
    ... 1 more
Caused by: java.net.SocketTimeoutException: Accept timed out
    at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method)
    at java.net.DualStackPlainSocketImpl.socketAccept(Unknown Source)
    at java.net.AbstractPlainSocketImpl.accept(Unknown Source)
    at java.net.PlainSocketImpl.accept(Unknown Source)
    at java.net.ServerSocket.implAccept(Unknown Source)
    at java.net.ServerSocket.accept(Unknown Source)
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:164)
    ... 14 more

my number is 0
my number is 1
my number is 2

rdd = sc.textFile("/FileStore/tables/test-4.txt")
rdd.count()  // this gives me output 3