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https://issues.apache.org/jira/browse/PHOENIX-180?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ramkrishna.s.vasudevan updated PHOENIX-180:
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Attachment: Phoenix-180_v3.patch
Updated patch. Corrects the test case failures and adds a delete for deleting
the entries in the stats table. Thanks to [~giacomotaylor] for helping me out
in solving all the test cases issues.
> Use stats to guide query parallelization
> ----------------------------------------
>
> Key: PHOENIX-180
> URL: https://issues.apache.org/jira/browse/PHOENIX-180
> Project: Phoenix
> Issue Type: Sub-task
> Reporter: James Taylor
> Assignee: ramkrishna.s.vasudevan
> Labels: enhancement
> Attachments: Phoenix-180_V1.patch, Phoenix-180_V2.patch,
> Phoenix-180_WIP.patch, Phoenix-180_v3.patch
>
>
> We're currently not using stats, beyond a table-wide min key/max key cached
> per client connection, to guide parallelization. If a query targets just a
> few regions, we don't know how to evenly divide the work among threads,
> because we don't know the data distribution. This other [issue]
> (https://github.com/forcedotcom/phoenix/issues/64) is targeting gather and
> maintaining the stats, while this issue is focused on using the stats.
> The main changes are:
> 1. Create a PTableStats interface that encapsulates the stats information
> (and implements the Writable interface so that it can be serialized back from
> the server).
> 2. Add a stats member variable off of PTable to hold this.
> 3. From MetaDataEndPointImpl, lookup the stats row for the table in the stats
> table. If the stats have changed, return a new PTable with the updated stats
> information. We may want to cache the stats row and have the stats gatherer
> invalidate the cache row when updated so we don't have to always do a scan
> for it. Additionally, it would be idea if we could use the same split policy
> on the stats table that we use on the system table to guarantee co-location
> of data (for the sake of caching).
> - modify the client-side parallelization (ParallelIterators.getSplits()) to
> use this information to guide how to chunk up the scans at query time.
> This should help boost query performance, especially in cases where the data
> is highly skewed. It's likely the cause for the slowness reported in this
> issue: https://github.com/forcedotcom/phoenix/issues/47.
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