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https://issues.apache.org/jira/browse/IGNITE-6699?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16420444#comment-16420444
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Ivan Fedotov commented on IGNITE-6699:
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Example for 7 threads in ignite-6699:
{noformat}
Benchmark Mode Cnt Score Error
Units
JmhStreamerAddDataBenchmark.addDataCollection avgt 11 50,990 ± 5,162 ms/op
{noformat}
{noformat}
Benchmark Mode Cnt Score Error Units
JmhStreamerAddDataBenchmark.addDataKeyValue avgt 11 49,230 ± 3,125 ms/op
{noformat}
> Optimize client-side data streamer performance
> ----------------------------------------------
>
> Key: IGNITE-6699
> URL: https://issues.apache.org/jira/browse/IGNITE-6699
> Project: Ignite
> Issue Type: Task
> Components: streaming
> Affects Versions: 2.3
> Reporter: Vladimir Ozerov
> Assignee: Ivan Fedotov
> Priority: Major
> Labels: iep-1, performance
> Fix For: 2.5
>
>
> Currently if a user has several server nodes and a single client node with
> single thread pushing data to streamer, he will not be able to load data at
> maximum speed. On the other hand, if he start several data loading threads,
> throughput will increase.
> One of root causes of this is bad data streamer design. Method
> {{IgniteDataStreamer.addData(K, V)}} returns new feature for every operation,
> this is too fine grained approach. Also it generates a lot of garbage and
> causes contention on streamer internals.
> Proposed implementation flow:
> 1) Compare performance of {{addData(K, V)}} vs {{addData(Collection)}}
> methods from one thread in distributed environment. The latter should show
> considerably higher throughput.
> 2) Users should receive per-batch features, rather than per-key.
> 3) Try caching thread data in some collection until it is large enough to
> avoid contention and unnecessary allocations.
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