Py4JJavaError：调用 o1670.collectToPython 时出错

Posted 2023-04-14

【中文标题】Py4JJavaError：调用 o1670.collectToPython 时出错

【英文标题】：Py4JJavaError: An error occurred while calling o1670.collectToPython

【发布时间】：2020-08-06 16:52:53

【问题描述】：

我正在尝试将 spark RDD 转换为 Pandas DataFrame。

我以 csv 文件为例。该文件有 10 以下是前 3 行：

“Eldon 可堆叠储物架底座，铂金”，Muhammed MacIntyre,3,-213.25,38.94,35,Nunavut,Storage & Organization,0.8

"1.7 立方英尺紧凑型 ""Cube"" 办公室冰箱",Barry French,293,457.81,208.16,68.02,Nunavut,Appliances,0.58

“Cardinal Slant-D� Ring 活页夹，重型乙烯基”，Barry French，293,46.71,8.69,2.99，Nunavut，活页夹和活页夹配件，0.39

我的代码在这里：

import pandas as pd
import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("HelloWorld").getOrCreate()
sc = spark.sparkContext


from pyspark.sql.types import StructType
from pyspark.sql.types import StructField
from pyspark.sql.types import StringType
from pyspark.sql.context import SQLContext

schema = StructType([StructField(str(i), StringType(), True) for i in range(10)])

text = sc.textFile('data_53000kb.csv')
text = text.map(lambda x: [c.strip() for c in x.split(',')])
df = spark.createDataFrame(text, schema)
df.toPandas()

此时我收到以下错误：

Py4JJavaError: An error occurred while calling o1670.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 40.0 failed 1 times, most recent failure: Lost task 0.0 in stage 40.0 (TID 72, localhost, executor driver): java.net.SocketException: Connection reset by peer: socket write error
    at java.net.SocketOutputStream.socketWrite0(Native Method)
    at java.net.SocketOutputStream.socketWrite(Unknown Source)
    at java.net.SocketOutputStream.write(Unknown Source)
    at java.io.BufferedOutputStream.flushBuffer(Unknown Source)
    at java.io.BufferedOutputStream.write(Unknown Source)
    at java.io.DataOutputStream.write(Unknown Source)
    at java.io.FilterOutputStream.write(Unknown Source)
    at org.apache.spark.api.python.PythonRDD$.writeUTF(PythonRDD.scala:394)
    at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:214)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224)
    at scala.collection.Iterator$class.foreach(Iterator.scala:891)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:224)
    at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:561)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread$$anonfun$run$1.apply(PythonRunner.scala:346)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:195)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
    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$$anonfun$collect$1.apply(RDD.scala:990)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:385)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:989)
    at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:299)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3263)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3260)
    at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
    at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3260)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
    at java.lang.reflect.Method.invoke(Unknown Source)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Unknown Source)
Caused by: java.net.SocketException: Connection reset by peer: socket write error
    at java.net.SocketOutputStream.socketWrite0(Native Method)
    at java.net.SocketOutputStream.socketWrite(Unknown Source)
    at java.net.SocketOutputStream.write(Unknown Source)
    at java.io.BufferedOutputStream.flushBuffer(Unknown Source)
    at java.io.BufferedOutputStream.write(Unknown Source)
    at java.io.DataOutputStream.write(Unknown Source)
    at java.io.FilterOutputStream.write(Unknown Source)
    at org.apache.spark.api.python.PythonRDD$.writeUTF(PythonRDD.scala:394)
    at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:214)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224)
    at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224)
    at scala.collection.Iterator$class.foreach(Iterator.scala:891)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:224)
    at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:561)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread$$anonfun$run$1.apply(PythonRunner.scala:346)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945)
    at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:195)

我现在能做什么？

【问题讨论】：

我也遇到了这个错误，但是发现我输入的文件名是错误的：/

【参考方案1】：

df.toPandas() 将所有数据收集到驱动节点，因此这是非常昂贵的操作。还有一个名为 maxResultSize 的 spark 属性

spark.driver.maxResultSize (default 1G) --> 每个 Spark 操作（例如收集）的所有分区的序列化结果的总大小限制（以字节为单位）。应至少为 1M，或 0 表示无限制。如果总大小超过此限制，作业将被中止。具有上限可能会导致驱动程序中的内存不足错误（取决于 spark.driver.memory 和 JVM 中对象的内存开销）。设置适当的限制可以防止驱动程序出现内存不足错误。

如果估计的数据大小大于 maxResultSize 给定的作业将被中止。这里的目标是保护您的应用程序免受驱动程序丢失，仅此而已。

您可能需要增加 maxResultSize