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https://issues.apache.org/jira/browse/GIRAPH-300?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13434507#comment-13434507
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Eli Reisman commented on GIRAPH-300:
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We are using tons more workers, I rarely use less than 200. But our
memory/resource profile is much lower per-worker and we are competing with
existing jobs coming on and off the grid as we run pretty much all the time. So
it takes a few runs of the same job to get a real baseline of how it will
behave in any generalized way (threads and network resources in use during a
given run vary widely here etc.)
Anyway, this will definitely be helpful for us right away, looking forward to
running it.
> Improve netty reliability with retrying failed connections, tracking
> requests, thread-safe hash partitioning
> ------------------------------------------------------------------------------------------------------------
>
> Key: GIRAPH-300
> URL: https://issues.apache.org/jira/browse/GIRAPH-300
> Project: Giraph
> Issue Type: Improvement
> Reporter: Avery Ching
> Assignee: Avery Ching
> Attachments: GIRAPH-300.patch
>
>
> * Upgrade to the most recent stable version of Netty (3.5.3.Final)
> * Try multiple connection attempts up to n failures
> * Track requests throughout the system by keeping track of the request id and
> then matching the request id to the response (minor refactoring of
> WritableRequest to make requests simpler and support the request id)
> * Improved handling of netty exceptions by dumping the exception stack to
> help debug failures
> * Fixes bug in HashWorkerPartitioner by making partitionList thread-safe
> (this causes divide by zero exceptions in real life)
> Currently, netty connection failures causes issues with more than 75 workers
> in my setup. This allows us to reach over 200+ in a reasonably reliable
> network that doesn't kill connections.
> This code passes the local Hadoop regressions and the single node Hadoop
> instance regressions. It also succeeded on large runs (200+ workers) on a
> real Hadoop cluster.
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