AWS-EMR 错误退出代码 143

Posted 2023-04-15

【中文标题】AWS-EMR 错误退出代码 143

【英文标题】：AWS-EMR error exit code 143

【发布时间】：2018-07-16 18:52:02

【问题描述】：

我正在对 AWS EMR 进行分析，但遇到了意外的 SIGTERM 错误。

一些背景：

我正在运行一个脚本，该脚本读取我存储在 S3 上的许多 csv 文件，然后执行分析。我的脚本是示意性的：

analysis_script.py

import pandas as pd
from pyspark.sql import SQLContext, DataFrame
from pyspark.sql.types import *
from pyspark import SparkContext
import boto3

#Spark context
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)

df = sqlContext.read.csv("s3n://csv_files/*", header = True)

def analysis(df):
    #do bunch of stuff. Create output dataframe
    return df_output

df_output = analysis(df)

我使用以下方式启动集群：

aws emr create-cluster 
--release-label emr-5.5.0 
--name "Analysis" 
--applications Name=Hadoop Name=Hive Name=Spark  Name=Ganglia  
--ec2-attributes KeyName=EMRB,InstanceProfile=EMR_EC2_DefaultRole 
--service-role EMR_DefaultRole 
--instance-groups InstanceGroupType=MASTER,InstanceCount=1,InstanceType=r3.xlarge InstanceGroupType=CORE,InstanceCount=4,InstanceType=r3.xlarge 
--region us-west-2 
--log-uri s3://emr-logs/ 
--bootstrap-actions Name="Install Python Packages",Path="s3://emr-bootstraps/install_python_packages_custom.bash",Args=["numpy pandas boto3 tqdm"] 
--auto-terminate

我可以从日志中看到 csv 文件的读取正常。但随后它以错误结束。以下行在 stderr 文件中：

18/07/16 12:02:26 ERROR ApplicationMaster: RECEIVED SIGNAL TERM
18/07/16 12:02:26 ERROR ApplicationMaster: User application exited with status 143
18/07/16 12:02:26 INFO ApplicationMaster: Final app status: FAILED, exitCode: 143, (reason: User application exited with status 143)
18/07/16 12:02:26 INFO SparkContext: Invoking stop() from shutdown hook
18/07/16 12:02:26 INFO SparkUI: Stopped Spark web UI at http://172.31.36.42:36169
18/07/16 12:02:26 INFO TaskSetManager: Starting task 908.0 in stage 1494.0 (TID 88112, ip-172-31-35-59.us-west-2.compute.internal, executor 27, partition 908, RACK_LOCAL, 7278 bytes)
18/07/16 12:02:26 INFO TaskSetManager: Finished task 874.0 in stage 1494.0 (TID 88078) in 16482 ms on ip-172-31-35-59.us-west-2.compute.internal (executor 27) (879/4805)
18/07/16 12:02:26 INFO BlockManagerInfo: Added broadcast_2328_piece0 in memory on ip-172-31-36-42.us-west-2.compute.internal:34133 (size: 28.8 KB, free: 2.8 GB)
18/07/16 12:02:26 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerBlockUpdated(BlockUpdatedInfo(BlockManagerId(20, ip-172-31-36-42.us-west-2.compute.internal, 34133, None),broadcast_2328_piece0,StorageLevel(memory, 1 replicas),29537,0))
18/07/16 12:02:26 INFO BlockManagerInfo: Added broadcast_2328_piece0 in memory on ip-172-31-47-55.us-west-2.compute.internal:45758 (size: 28.8 KB, free: 2.8 GB)
18/07/16 12:02:26 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerBlockUpdated(BlockUpdatedInfo(BlockManagerId(16, ip-172-31-47-55.us-west-2.compute.internal, 45758, None),broadcast_2328_piece0,StorageLevel(memory, 1 replicas),29537,0))
18/07/16 12:02:26 INFO DAGScheduler: Job 1494 failed: toPandas at analysis_script.py:267, took 479.895614 s
18/07/16 12:02:26 INFO DAGScheduler: ShuffleMapStage 1494 (toPandas at analysis_script.py:267) failed in 478.993 s due to Stage cancelled because SparkContext was shut down
18/07/16 12:02:26 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerSQLExecutionEnd(0,1531742546839)
18/07/16 12:02:26 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerStageCompleted(org.apache.spark.scheduler.StageInfo@28e5b10c)
18/07/16 12:02:26 INFO DAGScheduler: ShuffleMapStage 1495 (toPandas at analysis_script.py:267) failed in 479.270 s due to Stage cancelled because SparkContext was shut down
18/07/16 12:02:26 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerStageCompleted(org.apache.spark.scheduler.StageInfo@6b68c419)
18/07/16 12:02:26 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerJobEnd(1494,1531742546841,JobFailed(org.apache.spark.SparkException: Job 1494 cancelled because SparkContext was shut down))
18/07/16 12:02:26 INFO YarnAllocator: Driver requested a total number of 0 executor(s).
18/07/16 12:02:26 INFO YarnClusterSchedulerBackend: Shutting down all executors
18/07/16 12:02:26 INFO YarnSchedulerBackend$YarnDriverEndpoint: Asking each executor to shut down
18/07/16 12:02:26 INFO SchedulerExtensionServices: Stopping SchedulerExtensionServices(serviceOption=None, services=List(),started=false)
18/07/16 12:02:26 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped! 

我找不到太多关于退出代码 143 的有用信息。有人知道为什么会发生此错误吗？谢谢。

【问题讨论】：

您是否尝试过更新版本的 EMR 或查看 Spark UI？...请显示“做一堆东西”代码，因为我猜调用 toPandas 会杀死您的执行程序，因为它将近 500 秒后内存不足

是的，基本上我正在这个大 Spark 数据帧的所有列上运行聚合函数，这会缩小大小，然后我调用 toPandas

但是将其缩小到什么大小？该大小是否大于执行程序内存？

我很困惑为什么“toPandas”会成为问题？代码类似于'df.agg.("sum").toPandas()'。难道不是 agg 函数引起了问题，而不是 toPandas 吗？

因为 toPandas 正在将每个 RDD 分区下载到一台机器上。 agg 函数本身就是一个惰性操作

【参考方案1】：

当退出代码超过 128 时，Spark 会通过退出代码，这通常是 JVM 错误的情况。在退出代码 143 的情况下，它表示 JVM 收到了一个SIGTERM - 本质上是一个 unix 终止信号 (see this post for more exit codes and an explanation)。有关 Spark 退出代码 can be found in this question 的其他详细信息。

由于您自己没有终止此操作，因此我首先会怀疑外部的其他操作。鉴于在作业开始和发出SIGTERM 之间恰好经过了 8 分钟，EMR 本身似乎更有可能强制执行最大作业运行时间/集群年龄。尝试检查您的 EMR 设置以查看是否设置了任何此类超时 - 在我的情况下有一个（在 AWS Glue 上，但概念相同）。