由于 ExecutorLostFailure，无法使用 spark 读取镶木地板文件

Posted 2023-04-15

【中文标题】由于 ExecutorLostFailure，无法使用 spark 读取镶木地板文件

【英文标题】：Unable to read parquet file using spark due to ExecutorLostFailure

【发布时间】：2020-06-16 14:35:39

【问题描述】：

我想使用 Spark 读取 parquet 文件，但它会生成 Py4J 错误：无法创建执行程序。

这就是我生成镶木地板文件的方式：

import pandas as pd
df = pd.DataFrame(data='col1': [1, 2], 'col2': [3, 4])
df.to_parquet('df.parquet')

我是这样读的：

def create_spark(): # create spark session
    sc_conf = pyspark.SparkConf()
    sc_conf.setAppName("Iteration_3")
#     sc_conf.setMaster('spark://some_address:7077')
    sc_conf.setMaster('spark://A.B.C.D:7077')
    sc_conf.set('spark.executor.memory', '2g')
    sc_conf.set('spark.executor.cores', '2')
    sc_conf.set('spark.cores.max', '6')
    sc_conf.set('spark.logConf', True)
    sc_conf.set('spark.driver.host','A.B.C.D')
    sc_conf.set('spark.driver.port','42000')
    sc_conf.set('spark.blockManager.port','42100')
    sc_conf.set('spark.driver.bindAddress','0.0.0.0')
    print (sc_conf.getAll())

    spark_session = SparkSession.builder.config(conf=sc_conf).getOrCreate()
    return spark_session
spark = create_spark()
parquet_file = spark.read.parquet('df.parquet')

这是我得到的错误：

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-16-ba4073bfd9e9> in <module>
----> 1 parquet_file = spark.read.parquet('df.parquet')

/opt/app-root/lib/python3.6/site-packages/pyspark/sql/readwriter.py in parquet(self, *paths)
    314         [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
    315         """
--> 316         return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
    317 
    318     @ignore_unicode_prefix

/opt/app-root/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:

/opt/app-root/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()

/opt/app-root/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 012.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o90.parquet.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, 172.44.16.104, executor 68): ExecutorLostFailure (executor 68 exited caused by one of the running tasks) Reason: Unable to create executor due to Exception thrown in awaitResult: 
Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
    at org.apache.spark.util.EventLoop$$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$$anonfun$collect$1.apply(RDD.scala:945)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.mergeSchemasInParallel(ParquetFileFormat.scala:633)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.inferSchema(ParquetFileFormat.scala:241)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
    at scala.Option.orElse(Option.scala:289)
    at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:179)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)
    at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
    at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:641)
    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:238)
    at java.lang.Thread.run(Thread.java:748)

我试图去实现post 中提到的一些事情，但我不认为那里提到的问题，比如 OOM，是我的代码的问题。

【问题讨论】：

您是在本地模型还是在远程集群中运行 spark？这是 sc_conf.setMaster('spark://A.B.C.D:7077') 和实际运行的火花集群吗？如果您在本地运行它，无论哪种方式用于测试目的，我都会建议类似 sc_conf.setMaster('local[*]')

是集群。我不认为我可以在本地做，可以吗？

【参考方案1】：

版本不匹配。 Spark 为 2.3.0，PySpark 为 2.4.3。

在我的笔记本中，我运行了!pip install pyspark==2.3.0，然后推断出架构并且不再出现错误。