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From "Benedict (JIRA)" <>
Subject [jira] [Commented] (CASSANDRA-1632) Thread workflow and cpu affinity
Date Thu, 21 Nov 2013 11:20:41 GMT


Benedict commented on CASSANDRA-1632:

Thanks for the full write up.

To expand a little on this, on the paths tested by stress there are currently between 3 and
5 threads a request goes through, depending on the route taken. Requests that go to the "wrong"
server (and are re-routed), which is a majority for stress as it stands, go Thrift/Netty->OTC;
and on the correct server go ITC->RS/WS->OTC. There isn't a lot that can be done to
reduce the hand-off here, although if one day we had an in-process cache, we might be able
to skip the RS for requests that can be handled from the cache. It's also possible we could
do this for the WS once we have a non-blocking write path.

For requests going to the "correct" server we do often have an unnecessary step of Thrift/Netty->RS/WS->OTC
. I have a patch that skips the middle stage, by using a TPE that permits same-thread execution
if it has idle threads and the calling thread is registered to support it (and forbids a thread
in the pool from activating until the execution completes). This gives a 15% bump in single
node performance, but without smart routing this is rapidly lost amongst the cluster. However
since we have "smart" routing in the Java driver, this may be worth reconsidering.

> Thread workflow and cpu affinity
> --------------------------------
>                 Key: CASSANDRA-1632
>                 URL:
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Chris Goffinet
>            Assignee: Jason Brown
>              Labels: performance
>         Attachments: 1632_batchRead-v1.diff, threadAff_reads.txt, threadAff_writes.txt
> Here are some thoughts I wanted to write down, we need to run some serious benchmarks
to see the benefits:
> 1) All thread pools for our stages use a shared queue per stage. For some stages we could
move to a model where each thread has its own queue. This would reduce lock contention on
the shared queue. This workload only suits the stages that have no variance, else you run
into thread starvation. Some stages that this might work: ROW-MUTATION.
> 2) Set cpu affinity for each thread in each stage. If we can pin threads to specific
cores, and control the workflow of a message from Thrift down to each stage, we should see
improvements on reducing L1 cache misses. We would need to build a JNI extension (to set cpu
affinity), as I could not find anywhere in JDK where it was exposed. 
> 3) Batching the delivery of requests across stage boundaries. Peter Schuller hasn't looked
deep enough yet into the JDK, but he thinks there may be significant improvements to be had
there. Especially in high-throughput situations. If on each consumption you were to consume
everything in the queue, rather than implying a synchronization point in between each request.

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