Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task times, most recent failure: Lost task , hadoop7, executor ): ExecutorLostFailure (executor exited caused by one of the running tasks) Reason: Container killed by YARN…

19/08/12 14:15:35 ERROR cluster.YarnScheduler: Lost executor 5 on worker01.hadoop.mobile.cn: Container killed by YARN for exceeding memory limits. 5 GB of 5 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead. 在看这个问题之前,首先解释下…

Memory Limits for Windows and Windows Server Releases This topic describes the memory limits for supported Windows and Windows Server releases. Memory and Address Space Limits Physical Memory Limits: Windows 10 Physical Memory Limits: Windows 8 Physi…

实际遇到的真实问题,解决方法: 1.调整虚拟内存率yarn.nodemanager.vmem-pmem-ratio (这个hadoop默认是2.1) 2.调整map与reduce的在AM中的大小大于yarn里RM可分配的最小值yarn.scheduler.minimum-allocation-mb 大小因为在Container中计算使用的虚拟内存来自 map虚拟内大小=max(yarn.scheduler.minimum-allocation-mb,mapreduce.map.memory.mb…

insert overwrite table canal_amt1...... 2014-10-09 10:40:27,368 Stage-1 map = 100%, reduce = 32%, Cumulative CPU 2772.48 sec 2014-10-09 10:40:28,426 Stage-1 map = 100%, reduce = 32%, Cumulative CPU 2772.48 sec 2014-10-09 10:40:29,481 Stage-1 map = 10…

异常问题:Container is running beyond virtual memory limits. Current usage: 119.5 MB of 1 GB physical memory used; 2.2 GB of 2.1 GB virtual memory used. Killing container. spark-submit提交脚本: [spark@master work]$ more submit.sh #! /bin/bash jars="" for…

spark版本:1.6.0 scala版本:2.10 报错日志: Application application_1562341921664_2123 failed 2 times due to AM Container for appattempt_1562341921664_2123_000002 exited with exitCode: -104 For more detailed output, check the application tracking page: http://w…

当运行mapreduce的时候,有时候会出现异常信息,提示物理内存或者虚拟内存超出限制,默认情况下:虚拟内存是物理内存的2.1倍.异常信息类似如下: Container [pid=13026,containerID=container_1449820132317_0013_01_000012] is running beyond physical memory limits. Current usage: 1.0 GB of 1 GB physical memory used; 1.7 GB o…

以Spark-Client模式运行,Spark-Submit时出现了下面的错误: User: hadoop Name: Spark Pi Application Type: SPARK Application Tags: YarnApplicationState: FAILED FinalStatus Reported by AM: FAILED Started: 16-五月-2017 10:03:02 Elapsed: 14sec Tracking URL: History Diagnosti…

在spark yarn模式下跑yarn-client时出现无法初始化SparkContext错误. // :: INFO mapreduce.Job: Task Id : attempt_1428293579539_0001_m_000003_0, Status : FAILED Container [pid=,containerID=container_1428293579539_0001_01_000005] GB physical memory used; 2.6 GB of 2.1 GB…

原因分析 CDH 集群环境没有对 Container分配足够的运行环境(内存) 解决办法 需要修改的配置文件,将具体的配置项修改匹配集群环境资源.如下: 配置文件 配置设置 解释 计算值(参考) yarn-site.xml yarn.nodemanager.resource.memory-mb 分配给容器的物理内存数量 = 52 * 2 =104 G yarn-site.xml yarn.scheduler.minimum-allocation-mb 容器可以请求的最小物理内存量(以 MiB 为…

spark客户端提交任务至yarn,后台抛错,FinalStatus:UNDEFINED. ./spark-submit  --class org.apache.spark.examples.SparkPi --conf spark.eventLog.dir=hdfs://jenkintest/tmp/spark01 --master yarn --deploy-mode client --driver-memory 1g --principal sparkclient01 --keytab $…

今天在测试spark-sql运行在yarn上的过程中,无意间从日志中发现了一个问题: spark-sql --master yarn // :: INFO Client: Requesting a new application from cluster with NodeManagers // :: INFO Client: Verifying our application has not requested MB per container) // :: INFO Client: Will…

昨天使用hadoop跑五一的数据,发现报错: Container [pid=,containerID=container_1453101066555_4130018_01_000067] GB physical memory used; GB virtual memory used. Killing container. 发现是内存溢出了,遇到这种问题首先要判断是map阶段溢出还是reduce阶段溢出,然后分别设置其内存的大小,比如: 在运行hive sql前加上 : (map) 或者 (red…

单机搭建了2.6.5的伪分布式集群,写了一个tf-idf计算程序,分词用的是结巴分词,使用standalone模式运行没有任何问题,切换到伪分布式模式运行一直报错: hadoop is running beyond virtual memory limits 大概意思就是使用虚拟内存超出了限制. 网上参考了好几篇博客,几乎都是再说更改hadoop-env和mapred-site.xml hadoop-env直接更改堆大小 export HADOOP_HEAPSIZE=1000 mapred-si…

1 贴出完整日志信息 // :: INFO client.RMProxy: Connecting to ResourceManager at hdp1/ // :: INFO yarn.Client: Requesting a new application from cluster with NodeManagers // :: INFO yarn.Client: Verifying our application has not requested MB per container) //…

当运行中出现Container is running beyond physical memory这个问题出现主要是因为物理内存不足导致的,在执行mapreduce的时候,每个map和reduce都有自己分配到内存的最大值,当map函数需要的内存大于这个值就会报这个错误,解决方法: 在mapreduc-site.xml配置里面设置mapreduce的内存分配大小 <property> <name>mapreduce.map.memory.mb</name> <va…

启动Spark任务时,在没有配置spark.yarn.archive或者spark.yarn.jars时, 会看到不停地上传jar非常耗时:使用spark.yarn.archive可以大大地减少任务的启动时间,整个处理过程如下 1.在本地创建zip文件 hzlishuming@hadoop691:~/env/spark$ cd jars/ hzlishuming@hadoop691:~/env/spark/jars$ zip spark2.1.1-hadoop2.7.3.zip ./* 2.上传…

不多说,直接上干货! 福利 => 每天都推送 欢迎大家,关注微信扫码并加入我的4个微信公众号:   大数据躺过的坑      Java从入门到架构师      人工智能躺过的坑         Java全栈大联盟        每天都有大量的学习视频资料和精彩技术文章推送... 人生不易,唯有努力.        百家号 :九月哥快讯               快手号:  jiuyuege 问题详情 每次提交spark任务到yarn的时候,总会出现uploading resource(打包sp…

spark可以运行在standalone,yarn,mesos等多种模式下,当前我们用的最普遍的是yarn模式,在yarn模式下又分为client和cluster.本文接下来将分析yarn cluster下任务提交的过程.也就是回答,在yarn cluster模式下,任务是怎么提交的问题.在yarn cluster模式下,spark任务提交涉及四个角色(client, application, driver以及executor)之间的交互.接下来,将详细分析这四个角色在任务提交过程中都做了那些事…

一.参数说明 启动Spark任务时,在没有配置spark.yarn.archive或者spark.yarn.jars时, 会看到不停地上传jar,非常耗时:使用spark.yarn.archive可以大大地减少任务的启动时间,整个处理过程如下. 二.spark.yarn.archive使用 1.在本地创建zip文件 silent@bd01:~/env/spark$ cd jars/ silent@bd01:~/env/spark$ zip spark2.0.0.zip ./* 注:zip包为全量…

问题: 用  spark-submit --master yarn --deploy-mode cluster --driver-memory 2G --num-executors 6 --executor-memory 2G ~~~ 提交任务时,最后一个executor 执行时间 超过了 160s 导致 timeout而退出,造成任务重新执行造成用时过长.具体请看下面介绍: // :: WARN spark.HeartbeatReceiver: Removing executor with n…

来源:https://msdn.microsoft.com/en-us/library/windows/desktop/aa366778(v=vs.85).aspx Limits on memory and address space vary by platform, operating system, and by whether the IMAGE_FILE_LARGE_ADDRESS_AWARE value of the LOADED_IMAGEstructure and 4-gigab…

点击返回:自学Zabbix之路 1.Zabbix报错信息:It probably means that the systems requires more physical memory. 1.报错信息:It probably means that the systems requires more physical memory. 解决办法: 创建交换分区 root@zabbix-server:~# mkdir /swap root@zabbix-server:~# cd /swap/ roo…

参考文献: http://blog.csdn.net/lxhandlbb/article/details/54410644 每次提交Spark任务到yarn的时候,总会出现uploading resource(打包spark jars并上传)到hdfs上. 恶劣情况下,会在这里卡住很久. 解决: 在hdfs上创建目录: hdfs dfs -mkdir   /spark_jars 上传spark的jars(spark1.6 只需要上传spark-assembly-1.6.0-SNAPSHOT-ha…

文是超简单的spark yarn配置教程: yarn是hadoop的一个子项目,目的是用于管理分布式计算资源,在yarn上面搭建spark集群需要配置好hadoop和spark.我在搭建集群的时候有3台虚拟机,都是centos系统的.下面就开始一步一步地进行集群搭建. 一.配置hosts文件 hosts文件是主机名到ip的映射,目的是为了方便地查找主机,而不用去记各个主机的IP地址,比如配置master 10.218.20.210 就是为10.218.20.210地址取名为master,在以后的…

内存泄露(memory leak) VS 内存溢出(out of memory) 内存溢出 out of memory,是指程序在申请内存时,没有足够的内存空间供其使用,出现out of memory: 内存泄露 memory leak,是指程序在申请内存后,无法释放已申请的内存空间(指的是堆上的内存),一次内存泄露危害可以忽略,但内存泄露堆积后果很严重,无论多少内存,迟早会被占光. memory leak会最终会导致out of memory! GC为了能够正确释放对象,会监控每个对象的运行状…

[手动验证:任意2个节点间是否实现 双向 ssh免密登录] 弄懂通信原理和集群的容错性 任意2个节点间实现双向 ssh免密登录,默认在~目录下 [实现上步后,在其中任一节点安装\配置hadoop后,可以将整个安装目录scp复制到各个节点::::各个节点的文件内容是一样的!!!!] [hadoop@bigdata-server-03 ~]$ jps 9217 SecondaryNameNode 9730 Jps 9379 ResourceManager 9497 NodeManager 8895…

A processor including a virtualization system of the processor with a memory virtualization support system to map a reference to guest-physical memory made by guest software executable on a virtual machine which in turn is executable on a host machin…

A processor including a virtualization system of the processor with a memory virtualization support system to map a reference to guest-physical memory made by guest software executable on a virtual machine which in turn is executable on a host machin…