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https://issues.apache.org/jira/browse/IGNITE-16424?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sergey Korotkov updated IGNITE-16424:
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Labels: ducktests (was: )
> Generate test data in several threads in DataGenerationApplication
> ------------------------------------------------------------------
>
> Key: IGNITE-16424
> URL: https://issues.apache.org/jira/browse/IGNITE-16424
> Project: Ignite
> Issue Type: Improvement
> Reporter: Sergey Korotkov
> Assignee: Sergey Korotkov
> Priority: Minor
> Labels: ducktests
> Time Spent: 10m
> Remaining Estimate: 0h
>
> DataGenerationApplication app is used to fill ignite cluster with test data.
> Now It accepts as parameters:
> * number of caches to create
> * number of entries to put to each cache (interval of integer keys in fact)
> * size of each cache entry
> * number of backup partitions for caches created
> Currently application creates and fills caches one by one in one thread. For
> huge caches it is a very time-consuming operation. On the other hand it's
> known that the parallell load in several threads via the Ignite Streamer
> works fine and can speed up the process significantly.
> It's also known that such parallell operations are very heap-memory
> intensive. Special attention should be paid to this issue.
> ---
> So, the tasks looks like:
> # Modify the DataGenerationApplication as it would load data in several
> threads. It should accept new integer parameter: _*threads*_ (1 by default)
> and use this number of threads to load data trying to spread work evenly
> between them.
> # Try to figure out some heuristic of the dependence of the required heap
> memory on the number of threads, caches and data size. It may require to
> implement some tool to get actual heap usage metrics from
> DataGenerationApplication after test run.
> # Implement this heuristic in python (to start DataGenerationApplication
> with the appropriate heap memory JVM options)
>
>
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