【hadoop is running beyond virtual memory limits问题解决】的更多相关文章

hadoop is running beyond virtual memory limits问题解决

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…

hadoop的job执行在yarn中内存分配调节————Container [pid=108284,containerID=container_e19_1533108188813_12125_01_000002] is running beyond virtual memory limits. Current usage: 653.1 MB of 2 GB physical memory used

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…

Container [pid=6263,containerID=container_1494900155967_0001_02_000001] is running beyond virtual memory limits

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…

【hadoop】 running beyond virtual memory错误原因及解决办法

问题描述: 在hadoop中运行应用,出现了running beyond virtual memory错误.提示如下: Container [pid=28920,containerID=container_1389136889967_0001_01_000121] is running beyond virtual memory limits. Current usage: 1.2 GB of 1 GB physical memory used; 2.2 GB of 2.1 GB virtual…

Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十三）kafka+spark streaming打包好的程序提交时提示虚拟内存不足（Container is running beyond virtual memory limits. Current usage: 119.5 MB of 1 GB physical memory used; 2.2 GB of 2.1 G）

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…

[hadoop] - Container [xxxx] is running beyond physical/virtual memory limits.

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…

spark运行任务报错：Container [...] is running beyond physical memory limits. Current usage: 3.0 GB of 3 GB physical memory used; 5.0 GB of 6.3 GB virtual memory used. Killing container.

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…

is running beyond physical memory limits. Current usage: 2.0 GB of 2 GB physical memory used; 2.6 GB of 40 GB virtual memory used

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…

hadoop yarn running beyond physical memory used

老是报物理内存越界,kill container,然后把yarn.scheduler.minimum-allocation-mb设成2048就好了 跟这个yarn.nodemanager.pmem-check-enabled参数应该也有关系 在这篇文章中得到启发:http://dongxicheng.org/mapreduce-nextgen/hadoop-yarn-memory-cpu-scheduling/ 调度和隔离 Hadoop YARN同时支持内存和CPU两种资源的调度(默认只支持内存…

运行hadoop的时候提示物理内存或虚拟内存溢出的解决方案running beyond physical memory或者beyond vitual memory limits

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…