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Jason Brown commented on CASSANDRA-1632:
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[~stuhood] Thanks. I looked into the FJP again (it's been awhile), and while
there is the work-stealing aspect, it's mostly, if I understand correctly, in
the context of a task that can be broken down into smaller parts (like a
recursive problem). Most of the behaviors that happen at our SEDA stages (via
StageManager's ThreadPoolExecutors) don't really fall into that category, I
think. You can make the argument that batch mutates and multi-gets could be
broken down that way (and I'm seriously considering FJP or something similar
for those cases), but I think the main use cases the individual gets or mutates
are not served as well by the FJP paradigm.
> Thread workflow and cpu affinity
> --------------------------------
>
> Key: CASSANDRA-1632
> URL: https://issues.apache.org/jira/browse/CASSANDRA-1632
> Project: Cassandra
> Issue Type: Improvement
> Components: Core
> Reporter: Chris Goffinet
> Assignee: Jason Brown
> Labels: performance
>
> 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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