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From "Liyin Tang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-10659) [89-fb] Optimize the threading model in HBase write path
Date Thu, 06 Mar 2014 01:41:48 GMT

    [ https://issues.apache.org/jira/browse/HBASE-10659?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13921860#comment-13921860
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Liyin Tang commented on HBASE-10659:
------------------------------------

One of key motivations is to avoid handler waiting on the sync thread. This model requires
more IPC handler threads to reach the maximum QPS. I will share more detail numbers once it
is ready.

> [89-fb] Optimize the threading model in HBase write path
> --------------------------------------------------------
>
>                 Key: HBASE-10659
>                 URL: https://issues.apache.org/jira/browse/HBASE-10659
>             Project: HBase
>          Issue Type: New Feature
>            Reporter: Liyin Tang
>
> Recently, we have done multiple prototypes to optimize the HBase (0.89)write path. And
based on the simulator results, the following model is able to achieve much higher overall
throughput with less threads.
> IPC Writer Threads Pool: 
> IPC handler threads will prepare all Put requests, and append the WALEdit, as one transaction,
into a concurrent collection with a read lock. And then just return;
> HLogSyncer Thread:
> Each HLogSyncer thread is corresponding to one HLog stream. It swaps the concurrent collection
with a write lock, and then iterate over all the elements in the previous concurrent collection,
generate the sequence id for each transaction, and write to HLog. After the HLog sync is done,
append these transactions as a batch into a blocking queue. 
> Memstore Update Thread:
> The memstore update thread will poll the blocking queue and update the memstore for each
transaction by using the sequence id as MVCC. Once the memstore update is done, dispatch to
the responder thread pool to return to the client.
> Responder Thread Pool:
> Responder thread pool will return the RPC call in parallel. 
> We are still evaluating this model and will share more results/numbers once it is ready.
But really appreciate any comments in advance !



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