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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2016/01/23 06:22:39 UTC

[jira] [Updated] (SPARK-11095) Simplify Netty RPC implementation by using a separate thread pool for each endpoint

     [ https://issues.apache.org/jira/browse/SPARK-11095?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Reynold Xin updated SPARK-11095:
--------------------------------
    Issue Type: Improvement  (was: Sub-task)
        Parent:     (was: SPARK-5293)

> Simplify Netty RPC implementation by using a separate thread pool for each endpoint
> -----------------------------------------------------------------------------------
>
>                 Key: SPARK-11095
>                 URL: https://issues.apache.org/jira/browse/SPARK-11095
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>            Reporter: Reynold Xin
>            Assignee: Shixiong Zhu
>
> The dispatcher class and the inbox class of the current Netty-based RPC implementation is fairly complicated. It uses a single, shared thread pool to execute all the endpoints. This is similar to how Akka does actor message dispatching. The benefit of this design is that this RPC implementation can support a very large number of endpoints, as they are all multiplexed into a single thread pool for execution. The downside is the complexity resulting from synchronization and coordination.
> An alternative implementation is to have a separate message queue and thread pool for each endpoint. The dispatcher simply routes the messages to the appropriate message queue, and the threads poll the queue for messages to process.
> If the endpoint is single threaded, then the thread pool should contain only a single thread. If the endpoint supports concurrent execution, then the thread pool should contain more threads.
> Two additional things we need to be careful with are:
> 1. An endpoint should only process normal messages after OnStart is called. This can be done by having the thread that starts the endpoint processing OnStart.
> 2. An endpoint should process OnStop after all normal messages have been processed. I think this can be done by having a busy loop to spin until the size of the message queue is 0.



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