Py4JJavaError：調用 o37.showString 時出錯。 Spark & anaconda3

Question

我是一名學生，我真的被這個Py4JJavaError問題困擾了兩個星期，在互聯網上沒有太多； 我真的需要幫助：

我遵循本教程： https://docs.microsoft.com/fr-fr/azure/hdinsight/spark/apache-spark-machine-learning-mllib-ipython ://docs.microsoft.com/fr-fr/azure/hdinsight/spark/apache-spark-machine-learning-mllib-ipython

當我從 RDD 中檢索一行以便能夠觀察數據模式時，例如inspections.take(1)或df.show(5)我遇到了這個錯誤

> Py4JJavaError                             Traceback (most recent call
> last) <ipython-input-13-eb589bae8d4b> in <module>()
> ----> 1 df.show(5)
> 
> ~/anaconda3/lib/python3.6/site-packages/pyspark/sql/dataframe.py in
> show(self, n, truncate, vertical)
>     376         """
>     377         if isinstance(truncate, bool) and truncate:
> --> 378             print(self._jdf.showString(n, 20, vertical))
>     379         else:
>     380             print(self._jdf.showString(n, int(truncate), vertical))
> 
> ~/anaconda3/lib/python3.6/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/lib/python3.6/site-packages/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()
> 
> ~/anaconda3/lib/python3.6/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 o37.showString. :
> 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
> "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py",
> line 372, in main
>     process()   File "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py",
> line 367, in process
>     serializer.dump_stream(func(split_index, iterator), outfile)   File
> "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/serializers.py",
> line 390, in dump_stream
>     vs = list(itertools.islice(iterator, batch))   File "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/util.py",
> line 100, in wrapper
>     return f(*args, **kwargs)   File "<ipython-input-10-9aa45565a8c1>", line 3, in csvParse
> ModuleNotFoundError: No module named 'StringIO'
> 
>   at
> org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
>   at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
>   at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
>   at
> org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
>   at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
>   at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)   at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)    at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)    at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)    at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)   at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
>   at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
>   at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
>   at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
>   at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
>   at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
>   at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)     at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)     at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)    at
> org.apache.spark.scheduler.Task.run(Task.scala:121)   at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
>   at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
>   at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
>   at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>   at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>   at java.lang.Thread.run(Thread.java:745)
> 
> Driver stacktrace:    at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala: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$$anon$1.run(EventLoop.scala:49)
>   at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
>   at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)    at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)     at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)     at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
>   at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
>   at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3384)
>   at
> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
>   at
> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
>   at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
>   at
> org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
>   at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
>   at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
>   at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3364)  at
> org.apache.spark.sql.Dataset.head(Dataset.scala:2545)     at
> org.apache.spark.sql.Dataset.take(Dataset.scala:2759)     at
> org.apache.spark.sql.Dataset.getRows(Dataset.scala:255)   at
> org.apache.spark.sql.Dataset.showString(Dataset.scala:292)    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:483)     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.api.python.PythonException: Traceback (most recent
> call last):   File
> "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py",
> line 372, in main
>     process()   File "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py",
> line 367, in process
>     serializer.dump_stream(func(split_index, iterator), outfile)   File
> "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/serializers.py",
> line 390, in dump_stream
>     vs = list(itertools.islice(iterator, batch))   File "/Users/sabbar/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/util.py",
> line 100, in wrapper
>     return f(*args, **kwargs)   File "<ipython-input-10-9aa45565a8c1>", line 3, in csvParse
> ModuleNotFoundError: No module named 'StringIO'
> 
>   at
> org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
>   at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
>   at
> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
>   at
> org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
>   at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
>   at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)   at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)    at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)    at
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)    at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)   at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
>   at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
>   at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
>   at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
>   at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
>   at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
>   at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)     at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)     at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)    at
> org.apache.spark.scheduler.Task.run(Task.scala:121)   at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
>   at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
>   at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
>   at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>   at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>   ... 1 more

這是代碼：

from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.sql import Row
from pyspark.sql.functions import UserDefinedFunction
from pyspark.sql.types import *
import pyspark 
#from pyspark import SparkContext
#sc = SparkContext("local", "Simple App")
from pyspark.context import SparkContext
from pyspark.sql.session import SparkSession
from py4j.protocol import Py4JJavaError

def csvParse(s):
    import csv
    from StringIO import StringIO
    sio = StringIO(s)
    value = csv.reader(sio).next()
    sio.close()
    return value

inspections = sc.textFile('Chicago_Street_Names.csv').map(csvParse)

inspections.take(1)

請幫我這是下周要做的項目

Answer 1

正如@pault 在評論中建議的那樣，您不需要編寫自己的函數來解析簡單的 csv 文件。 您可以使用sc.read.csv(FILEPATH) 。

如果您想按原樣繼續執行您的函數，則可以將from StringIO import StringIO替換為from io import StringIO 。 在較新的 Python 3 版本中， StringIO package已替換為io package 。

Answer 2

我在運行 3.1 Spark 版本時遇到了同樣的問題，我只是在 Anaconda 使用 Spark 2.4.0 和 Python 3.7 創建了一個新環境。

Answer 3

我在嘗試從數據湖中讀取 cdm 數據時遇到了類似的問題

readDf = spark.read.format("com.microsoft.cdm")\
  .option("storage", storage)\
  .option("manifestPath", manifestPath)\
  .option("entity", "account")\
  .option("appId", appId)\
  .option("appKey", serviceCredentialKeyName)\
  .option("tenantId", directoryId)\
  .load()

Error Message: 
java.lang.StringIndexOutOfBoundsException: String index out of range: -1

raise Py4JJavaError("An error occurred while calling {0}{1}{2}.\n".