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https://issues.apache.org/jira/browse/YARN-7386?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Misha Dmitriev updated YARN-7386:
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Attachment: YARN-7386.01.patch
> Duplicate Strings in various places in Yarn memory
> --------------------------------------------------
>
> Key: YARN-7386
> URL: https://issues.apache.org/jira/browse/YARN-7386
> Project: Hadoop YARN
> Issue Type: Improvement
> Reporter: Misha Dmitriev
> Assignee: Misha Dmitriev
> Attachments: YARN-7386.01.patch
>
>
> Using jxray (www.jxray.com) I've analyzed a Yarn RM heap dump obtained in a
> big cluster. The tool uncovered several sources of memory waste. One problem
> is duplicate strings:
> {code}
> Total strings Unique strings Duplicate values
> Overhead
> 361,506 86,672 5,928 22,886K (7.6%)
> {code}
> They are spread across a number of locations. The biggest source of waste is
> the following reference chain:
> {code}
> 7,416K (2.5%), 31292 / 62% dup strings (499 unique), 31292 dup backing arrays:
> ↖{j.u.HashMap}.values
> ↖org.apache.hadoop.yarn.api.records.impl.pb.ContainerLaunchContextPBImpl.environment
> ↖org.apache.hadoop.yarn.api.records.impl.pb.ApplicationSubmissionContextPBImpl.amContainer
> ↖org.apache.hadoop.yarn.server.resourcemanager.rmapp.RMAppImpl.submissionContext
> ↖{java.util.concurrent.ConcurrentHashMap}.values
> ↖org.apache.hadoop.yarn.server.resourcemanager.RMActiveServiceContext.applications
> ↖org.apache.hadoop.yarn.server.resourcemanager.RMContextImpl.activeServiceContext
> ↖org.apache.hadoop.yarn.server.resourcemanager.rmapp.monitor.RMAppLifetimeMonitor.rmContext
> ↖Java Local@3ed9ef820
> (org.apache.hadoop.yarn.server.resourcemanager.rmapp.monitor.RMAppLifetimeMonitor)
> {code}
> However, there are also many others. Mostly they are strings in proto buffer
> or proto buffer builder objects. I plan to get rid of at least the worst
> offenders by inserting String.intern() calls. String.intern() used to consume
> memory in PermGen and was not very scalable up until about the early JDK 7
> versions, but has greatly improved since then, and I've used it many times
> without any issues.
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