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https://issues.apache.org/jira/browse/STORM-297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13991581#comment-13991581
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ASF GitHub Bot commented on STORM-297:
--------------------------------------
Github user clockfly commented on a diff in the pull request:
https://github.com/apache/incubator-storm/pull/103#discussion_r12362548
--- Diff: storm-core/src/clj/backtype/storm/daemon/worker.clj ---
@@ -109,25 +111,30 @@
(defn mk-transfer-fn [worker]
(let [local-tasks (-> worker :task-ids set)
local-transfer (:transfer-local-fn worker)
- ^DisruptorQueue transfer-queue (:transfer-queue worker)]
+ ^DisruptorQueue transfer-queue (:transfer-queue worker)
+ task->node+port (:cached-task->node+port worker)]
(fn [^KryoTupleSerializer serializer tuple-batch]
(let [local (ArrayList.)
- remote (ArrayList.)]
+ remoteMap (HashMap.)]
(fast-list-iter [[task tuple :as pair] tuple-batch]
(if (local-tasks task)
(.add local pair)
- (.add remote pair)
- ))
+ (let [node+port (get @task->node+port task)]
--- End diff --
Move the message grouping(group by node+port) code from worker transfer
thread to executor send thread, as we only have ONE worker transfer thread, and
it can become the bottleneck.
> Storm Performance cannot be scaled up by adding more CPU cores
> --------------------------------------------------------------
>
> Key: STORM-297
> URL: https://issues.apache.org/jira/browse/STORM-297
> Project: Apache Storm (Incubating)
> Issue Type: Bug
> Reporter: Sean Zhong
> Labels: Performance, netty
> Fix For: 0.9.2-incubating
>
> Attachments: Storm_performance_fix.pdf, storm_performance_fix.patch
>
>
> We cannot scale up the performance by adding more CPU cores and increasing
> parallelism.
> For a 2 layer topology Spout ---shuffle grouping--> bolt, when message size
> is small (around 100 bytes), we can find in the below picture that neither
> the CPU nor the network is saturated. When message size is 100 bytes, only
> 40% of CPU is used, only 18% of network is used, although we have a high
> parallelism (overall we have 144 executors)
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