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https://issues.apache.org/jira/browse/YARN-5936?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16250021#comment-16250021
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Miklos Szegedi commented on YARN-5936:
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Another option for the future is the use of the cgroup pids subsystem on newer
kernels. The main reason fairness is not enforced in non-strict mode, is that
it allows the container to run as many threads with the same cgroup and weight
as needed. You can limit the amount of threads with the pids namespace, so that
the effective overall container weight becomes weight*pids_limit. The drawback
of this approach is that it limits multitasking and the number of launcher
processes. The possible ideal value of pids_limit is the <number of
cores>/<desired thread count>, so that we do not starve single threaded
containers.
> when cpu strict mode is closed, yarn couldn't assure scheduling fairness
> between containers
> -------------------------------------------------------------------------------------------
>
> Key: YARN-5936
> URL: https://issues.apache.org/jira/browse/YARN-5936
> Project: Hadoop YARN
> Issue Type: Bug
> Components: nodemanager
> Affects Versions: 2.7.1
> Environment: CentOS7.1
> Reporter: zhengchenyu
> Priority: Critical
> Original Estimate: 1m
> Remaining Estimate: 1m
>
> When using LinuxContainer, the setting that
> "yarn.nodemanager.linux-container-executor.cgroups.strict-resource-usage" is
> true could assure scheduling fairness with the cpu bandwith of cgroup. But
> the cpu bandwidth of cgroup would lead to bad performance in our experience.
> Without cpu bandwidth of cgroup, cpu.share of cgroup is our only way to
> assure scheduling fairness, but it is not completely effective. For example,
> There are two container that have same vcore(means same cpu.share), one
> container is single-threaded, the other container is multi-thread. the
> multi-thread will have more CPU time, It's unreasonable!
> Here is my test case, I submit two distributedshell application. And two
> commmand are below:
> {code}
> hadoop jar
> share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar
> org.apache.hadoop.yarn.applications.distributedshell.Client -jar
> share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar
> -shell_script ./run.sh -shell_args 10 -num_containers 1 -container_memory
> 1024 -container_vcores 1 -master_memory 1024 -master_vcores 1 -priority 10
> hadoop jar
> share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar
> org.apache.hadoop.yarn.applications.distributedshell.Client -jar
> share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar
> -shell_script ./run.sh -shell_args 1 -num_containers 1 -container_memory
> 1024 -container_vcores 1 -master_memory 1024 -master_vcores 1 -priority 10
> {code}
> here show the cpu time of the two container:
> {code}
> PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
> 15448 yarn 20 0 9059592 28336 9180 S 998.7 0.1 24:09.30 java
> 15026 yarn 20 0 9050340 27480 9188 S 100.0 0.1 3:33.97 java
> 13767 yarn 20 0 1799816 381208 18528 S 4.6 1.2 0:30.55 java
> 77 root rt 0 0 0 0 S 0.3 0.0 0:00.74
> migration/1
> {code}
> We find the cpu time of Muliti-Thread are ten times than the cpu time of
> Single-Thread, though the two container have same cpu.share.
> notes:
> run.sh
> {code}
> java -cp /home/yarn/loop.jar:$CLASSPATH loop.loop $1
> {code}
> loop.java
> {code}
> package loop;
> public class loop {
> public static void main(String[] args) {
> // TODO Auto-generated method stub
> int loop = 1;
> if(args.length>=1) {
> System.out.println(args[0]);
> loop = Integer.parseInt(args[0]);
> }
> for(int i=0;i<loop;i++){
> System.out.println("start thread " + i);
> new Thread(new Runnable() {
> @Override
> public void run() {
> // TODO Auto-generated method stub
> int j=0;
> while(true){j++;}
> }
> }).start();
> }
> }
> }
> {code}
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