[
https://issues.apache.org/jira/browse/YARN-5936?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15706129#comment-15706129
]
Miklos Szegedi commented on YARN-5936:
--------------------------------------
Thank you for reporting this issue [~zhengchenyu]!
When you mentioned "But the cpu bandwidth of cgroup would lead to bad
performance in our experience.", do you mean that it is due to the design that
it limits the CPU usage to the vCore share affecting overall utilization, or do
you mean that the container got less resources than what was assigned to it? In
other words, is this a remark of the strict CPU cgroup design or the
implementation? Thank you!
> 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
> Fix For: 2.7.1
>
> 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}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
