pyspark 不是环境问题：Py4Java错误：调用z：org.apache.spark.api.python. Python RDD.collectAndServe时发生错误

a0x5cqrl  于 2022-11-01  发布在  Spark
关注(0)|答案(1)|浏览(394)

我试着处理来自“www.example.com”的数据http://download.geonames.org/export/dump/allCountries.zip。


# the session declearation in order to use toDF()

import pyspark 
from pyspark.sql import SparkSession
from pyspark.storagelevel import StorageLevel 
spark = SparkSession.builder \
        .appName("dbscan") \
        .config("master","local[4]") \
        .enableHiveSupport() \
        .getOrCreate()

sc = spark.sparkContext

# read the data

from pyspark import SparkContext, SparkConf
sc = SparkContext("local","GeoProcessor")
testFile = sc.textFile("allCountries_sample.txt")

# split the data and transform rdd to dataframe

df=testFile.map(lambda x:x.split('\t')).toDF(['geonameid','name','asciiname','alternatenames','latitude','longitude','feature class','feature code','country code','cc2','admin1 code','admin2 code','admin3 code','admin4 code','population','elevation','dem','timezone','modification date'])

# remain only three columns

columns_remain=['name','country code','dem']
df=df[columns_remain]

# tried to transform df to rdd and the using collect() to validate what's inside the rdd

df.rdd.collect()

然后我得到了一个错误“Py4JJavaError：调用时发生错误。”
错误的详细信息如下：（我可以成功地使用collect（）的一个简单的rdd建立自己从列表像一个例子显示从一个教程）

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-49-44126f674c44> in <module>
----> 1 df.rdd.collect()

/usr/local/spark/python/pyspark/rdd.py in collect(self)
    887         """
    888         with SCCallSiteSync(self.context) as css:
--> 889             sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    890         return list(_load_from_socket(sock_info, self._jrdd_deserializer))
    891 

/usr/local/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1302 
   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 

/usr/local/spark/python/pyspark/sql/utils.py in deco(*a,**kw)
    129     def deco(*a,**kw):
    130         try:
--> 131             return f(*a,**kw)
    132         except py4j.protocol.Py4JJavaError as e:
    133             converted = convert_exception(e.java_exception)

/usr/local/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    324             value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
    325             if answer[1] == REFERENCE_TYPE:
--> 326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
    328                     format(target_id, ".", name), value)

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 25.0 failed 1 times, most recent failure: Lost task 0.0 in stage 25.0 (TID 25, jupyter-magician996-40163-2ecom, executor driver): java.lang.IllegalStateException: Input row doesn't have expected number of values required by the schema. 19 fields are required while 37 values are provided.
    at org.apache.spark.sql.execution.python.EvaluatePython$$anonfun$$nestedInanonfun$makeFromJava$16$1.applyOrElse(EvaluatePython.scala:186)
    at org.apache.spark.sql.execution.python.EvaluatePython$.nullSafeConvert(EvaluatePython.scala:211)
    at org.apache.spark.sql.execution.python.EvaluatePython$.$anonfun$makeFromJava$16(EvaluatePython.scala:180)
    at org.apache.spark.sql.SparkSession.$anonfun$applySchemaToPythonRDD$2(SparkSession.scala:738)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
    at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:156)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:148)
    at scala.collection.Iterator.foreach(Iterator.scala:941)
    at scala.collection.Iterator.foreach$(Iterator.scala:941)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:148)
    at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
    at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
    at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
    at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.to(SerDeUtil.scala:148)
    at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
    at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.toBuffer(SerDeUtil.scala:148)
    at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
    at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.toArray(SerDeUtil.scala:148)
    at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
    at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:127)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
    at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
    at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
    at java.base/java.lang.Thread.run(Thread.java:834)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2023)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1972)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1971)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1971)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:950)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:950)
    at scala.Option.foreach(Option.scala:407)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:950)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2203)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2152)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2141)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:752)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2158)
    at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1004)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:1003)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:168)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:566)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.base/java.lang.Thread.run(Thread.java:834)
Caused by: java.lang.IllegalStateException: Input row doesn't have expected number of values required by the schema. 19 fields are required while 37 values are provided.
    at org.apache.spark.sql.execution.python.EvaluatePython$$anonfun$$nestedInanonfun$makeFromJava$16$1.applyOrElse(EvaluatePython.scala:186)
    at org.apache.spark.sql.execution.python.EvaluatePython$.nullSafeConvert(EvaluatePython.scala:211)
    at org.apache.spark.sql.execution.python.EvaluatePython$.$anonfun$makeFromJava$16(EvaluatePython.scala:180)
    at org.apache.spark.sql.SparkSession.$anonfun$applySchemaToPythonRDD$2(SparkSession.scala:738)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:459)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
    at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:156)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:148)
    at scala.collection.Iterator.foreach(Iterator.scala:941)
    at scala.collection.Iterator.foreach$(Iterator.scala:941)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:148)
    at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
    at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
    at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
    at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.to(SerDeUtil.scala:148)
    at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
    at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.toBuffer(SerDeUtil.scala:148)
    at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
    at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
    at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.toArray(SerDeUtil.scala:148)
    at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
    at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:127)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
    at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
    at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
    ... 1 more
pyspark

1条答案

按热度按时间
ajsxfq5m

ajsxfq5m1#

最后得出的结论是，我使用的Docker容器缺乏计算资源，尝试分配更多的资源将有助于解决这个问题。

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