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From "ramkrishna.s.vasudevan (JIRA)" <>
Subject [jira] [Updated] (PHOENIX-180) Use stats to guide query parallelization
Date Wed, 13 Aug 2014 18:48:14 GMT


ramkrishna.s.vasudevan updated PHOENIX-180:

    Attachment: Phoenix-180_WIP.patch

A patch that does the things mentioned in the JIRA
-> Creates  a stats table
-> collects min, max keys and guide posts for a region.
-> Does not use STatsmanager.
-> PTable.getPTableStats() is getting used.

Needs more testing.  More test cases to prove things are working as expected.  Existing testcases
still go with the Bytes.split(). 
Ensure that we are able decide a proper configuration for the depth of the guide posts. Just
attaching a patch for initial feedback.

> Use stats to guide query parallelization
> ----------------------------------------
>                 Key: PHOENIX-180
>                 URL:
>             Project: Phoenix
>          Issue Type: Task
>            Reporter: James Taylor
>            Assignee: ramkrishna.s.vasudevan
>              Labels: enhancement
>         Attachments: Phoenix-180_WIP.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] ( 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:

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