【发布时间】：2020-05-04 13:47:18
【问题描述】：
我对 Spark 完全陌生。我使用的是 Spark 2.3 版和 Python 3.7 版。顺便说一句，在 Windows 10 上。 我正在启动一个 Jupyter Notebook 来执行 PySpark 操作。我正在学习 Pluralsight 课程（Spark 2.0 入门）
我正在使用 Anaconda 命令提示符中的以下命令在 Jupyter 中启动 pyspark：
设置 PYSPARK_DRIVER_PYTHON=jupyter 设置 PYSPARK_DRIVER_PYTHON_OPTS=笔记本 pyspark
笔记本打开后：
我运行以下命令：
sc
from pyspark.sql.types import Row
from datetime import datetime
simple_data = sc.parallelize([1, "Alice", 50])
simple_data
simple_data.count()
simple_data.first()
现在，它失败了：simple_data.first() 并出现以下错误：
Py4JJavaError Traceback (most recent call last)
<ipython-input-5-cc577dea1d9b> in <module>
----> 1 simple_data.first()
C:\spark\python\pyspark\rdd.py in first(self)
1374 ValueError: RDD is empty
1375 """
-> 1376 rs = self.take(1)
1377 if rs:
1378 return rs[0]
C:\spark\python\pyspark\rdd.py in take(self, num)
1356
1357 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1358 res = self.context.runJob(self, takeUpToNumLeft, p)
1359
1360 items += res
C:\spark\python\pyspark\context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
999 # SparkContext#runJob.
1000 mappedRDD = rdd.mapPartitions(partitionFunc)
-> 1001 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
1002 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
1003
C:\spark\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
C:\spark\python\pyspark\sql\utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
C:\spark\python\lib\py4j-0.10.6-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
318 raise Py4JJavaError(
319 "An error occurred while calling {0}{1}{2}.\n".
--> 320 format(target_id, ".", name), value)
321 else:
322 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 4, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\spark\python\pyspark\rdd.py", line 1354, in takeUpToNumLeft
yield next(iterator)
StopIteration
错误日志比我在这里粘贴的要多。我查找了可能的解决方案，并使用 conda install -c cyclus java-jdk 更新了 Java jdk，但即使在那之后，也没有任何改变。
我有点卡住了，无法继续我的课程。为什么它适用于.count() 但不适用于.first() 如何解决此错误？我错过了什么？
在回答中尝试@Sparker0i 的建议后添加完整的错误消息：
Py4JJavaError Traceback (most recent call last)
<ipython-input-3-4dbbd81a7c5c> in <module>
2 #simple_data
3
----> 4 simple_data = sc.parallelize([[1, "Alice", 50]]).toDF()
5 simple_data.count()
6 simple_data.first()
C:\spark\python\pyspark\sql\session.py in toDF(self, schema, sampleRatio)
56 [Row(name=u'Alice', age=1)]
57 """
---> 58 return sparkSession.createDataFrame(self, schema, sampleRatio)
59
60 RDD.toDF = toDF
C:\spark\python\pyspark\sql\session.py in createDataFrame(self, data, schema, samplingRatio, verifySchema)
685
686 if isinstance(data, RDD):
--> 687 rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
688 else:
689 rdd, schema = self._createFromLocal(map(prepare, data), schema)
C:\spark\python\pyspark\sql\session.py in _createFromRDD(self, rdd, schema, samplingRatio)
382 """
383 if schema is None or isinstance(schema, (list, tuple)):
--> 384 struct = self._inferSchema(rdd, samplingRatio, names=schema)
385 converter = _create_converter(struct)
386 rdd = rdd.map(converter)
C:\spark\python\pyspark\sql\session.py in _inferSchema(self, rdd, samplingRatio, names)
353 :return: :class:`pyspark.sql.types.StructType`
354 """
--> 355 first = rdd.first()
356 if not first:
357 raise ValueError("The first row in RDD is empty, "
C:\spark\python\pyspark\rdd.py in first(self)
1374 ValueError: RDD is empty
1375 """
-> 1376 rs = self.take(1)
1377 if rs:
1378 return rs[0]
C:\spark\python\pyspark\rdd.py in take(self, num)
1356
1357 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1358 res = self.context.runJob(self, takeUpToNumLeft, p)
1359
1360 items += res
C:\spark\python\pyspark\context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
999 # SparkContext#runJob.
1000 mappedRDD = rdd.mapPartitions(partitionFunc)
-> 1001 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
1002 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
1003
C:\spark\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
C:\spark\python\pyspark\sql\utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
C:\spark\python\lib\py4j-0.10.6-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
318 raise Py4JJavaError(
319 "An error occurred while calling {0}{1}{2}.\n".
--> 320 format(target_id, ".", name), value)
321 else:
322 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: 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, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\spark\python\pyspark\rdd.py", line 1354, in takeUpToNumLeft
yield next(iterator)
StopIteration
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 229, in main
File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 224, in process
File "C:\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 372, in dump_stream
vs = list(itertools.islice(iterator, batch))
RuntimeError: generator raised StopIteration
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:438)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:421)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.api.python.PythonRDD$$anonfun$1.apply(PythonRDD.scala:141)
at org.apache.spark.api.python.PythonRDD$$anonfun$1.apply(PythonRDD.scala:141)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2048)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:141)
at org.apache.spark.api.python.PythonRDD.runJob(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:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\spark\python\pyspark\rdd.py", line 1354, in takeUpToNumLeft
yield next(iterator)
StopIteration
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 229, in main
File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 224, in process
File "C:\spark\python\lib\pyspark.zip\pyspark\serializers.py", line 372, in dump_stream
vs = list(itertools.islice(iterator, batch))
RuntimeError: generator raised StopIteration
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:438)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:421)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.api.python.PythonRDD$$anonfun$1.apply(PythonRDD.scala:141)
at org.apache.spark.api.python.PythonRDD$$anonfun$1.apply(PythonRDD.scala:141)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
【问题讨论】：
-
您是在本地还是在集群上运行 Spark？你对 Spark 的配置是什么？什么版本的 Spark？你可以试试 simple_data.show() 吗？您是否看到 UI 上正在运行 Spark 作业？
-
我在本地运行 Spark。版本是 2.3。我不知道您所说的 Spark 配置是什么意思。我跑了
simple_data.show()，它抛出了一个错误：AttributeError: 'RDD' object has no attribute 'show' -
你能在 UI 上看到你的工作吗？数据帧通常比 RDD 更受欢迎。你可以试试 simple_data.collect() 吗？然后打印出来。因为您原始问题中的错误指向一个空 RDD，这对我来说意味着 spark 无法做任何事情。您能否在 Spark UI 上验证作业已完成？
-
simple_data.collect()有效。它打印了[1, 'Alice', 50]和simple_data.count()打印 3。但是simple_data.first()失败了。 -
@LearnerR 也许您应该尝试将 Spark 更新到最新版本或执行重新安装。在 Jupyter 上使用 Spark 2.4.3 我无法重现您的问题。否则，您可能应该闲逛，直到出现有更多洞察力的人。可能是也可能不是根本原因的一件事是使用 sc。我认为在 Spark 2.X 中，标准连接设置是使用 spark=SparkSession.builder.config(conf=conf).getOrCreate() 然后 spark.sparkContext.parallelize(*)。
标签： python apache-spark pyspark jupyter-notebook