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https://issues.apache.org/jira/browse/HADOOP-4664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Matei Zaharia updated HADOOP-4664:
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Attachment: parallel-job-init-v1.patch
Here is a patch for this issue. The patch adds multiple job init threads in the
EagerTaskInitializationListener, which is used to initialize tasks by the
default scheduler (JobQueueTaskScheduler) and the fair scheduler. The capacity
scheduler actually initializes jobs in its assignTasks method, which happens in
an RPC handler thread, so it can already do this in parallel (although it may
be worth modifying it to have a separate set of job init threads so that the
RPC handlers don't block waiting for a job to initialize).
This patch also makes the CachedDNSToSwitchMap use a ConcurrentHashMap instead
of a TreeMap for its rack resolving cache to avoid errors caused by multiple
writes. (Cache-hit reads require no locks with ConcurrentHashMap.) Apart from
the possibility of multiple writes to the resolution cache, I think I saw no
other potentially conflict-inducing operations in initTasks, but I'd really
welcome a second pair of eyes to look at it.
The number of job init threads is configurable as mapred.jobinit.threads. I set
it to 4 by default, but let me know if there are any objections.
> Parallelize job initialization
> ------------------------------
>
> Key: HADOOP-4664
> URL: https://issues.apache.org/jira/browse/HADOOP-4664
> Project: Hadoop Core
> Issue Type: Improvement
> Components: mapred
> Reporter: Matei Zaharia
> Attachments: parallel-job-init-v1.patch
>
>
> The job init thread currently initializes one job at a time. However, this is
> a lengthy and partly IO-bound process because all of the job's block
> locations need to be resolved through the namenode and a map of them needs to
> be built. It can take tens of seconds. As a result, the cluster sometimes
> initializes jobs too slowly for full utilization to be achieved, if there are
> many small jobs queued up. It would be better to have a pool of threads that
> initialize multiple jobs in parallel. One thing to be careful of, however, is
> not causing deadlocks or holding locks for too long in these threads.
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