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From lars hofhansl <la...@apache.org>
Subject Re: HBase Table Row Count Optimization - A Solicitation For Help
Date Fri, 20 Sep 2013 23:56:50 GMT
Hi James,

do you need that many tables? "Table" in HBase should have been call "KeySpace" instead. 600
is lot.

But anyway... Did you enabled scanner caching for your M/R job (if you didn't every next()
will be a roundtrip to the RegionServer and you end up measuring your networks RTT)?
Are you IO bound?

Lastly instead of doing it as M/R (which has to bring all the data back to the mapper just
to count the returned rows), you could use a coprocessor, which do the counting on the server
(or use Phoenix, search back in the archives for an example that James Taylor gave for row

-- Lars

 From: James Birchfield <jbirchfield@stumbleupon.com>
To: user@hbase.apache.org 
Sent: Friday, September 20, 2013 2:47 PM
Subject: HBase Table Row Count Optimization - A Solicitation For Help

    After reading the documentation and scouring the mailing list archives, I understand
there is no real support for fast row counting in HBase unless you build some sort of tracking
logic into your code.  In our case, we do not have such logic, and have massive amounts of
data already persisted.  I am running into the issue of very long execution of the RowCounter
MapReduce job against very large tables (multi-billion for many is our estimate).  I understand
why this issue exists and am slowly accepting it, but I am hoping I can solicit some possible
ideas to help speed things up a little.
    My current task is to provide total row counts on about 600 tables, some extremely
large, some not so much.  Currently, I have a process that executes the MapRduce job in process
like so:
            Job job = RowCounter.createSubmittableJob(
                    ConfigManager.getConfiguration(), new String[]{tableName});
            boolean waitForCompletion = job.waitForCompletion(true);
            Counters counters = job.getCounters();
            Counter rowCounter = counters.findCounter(hbaseadminconnection.Counters.ROWS);
            return rowCounter.getValue();
    At the moment, each MapReduce job is executed in serial order, so counting one table
at a time.  For the current implementation of this whole process, as it stands right now,
my rough timing calculations indicate that fully counting all the rows of these 600 tables
will take anywhere between 11 to 22 days.  This is not what I consider a desirable timeframe.

    I have considered three alternative approaches to speed things up.

    First, since the application is not heavily CPU bound, I could use a ThreadPool and
execute multiple MapReduce jobs at the same time looking at different tables.  I have never
done this, so I am unsure if this would cause any unanticipated side effects.  

    Second, I could distribute the processes.  I could find as many machines that can
successfully talk to the desired cluster properly, give them a subset of tables to work on,
and then combine the results post process.

    Third, I could combine both the above approaches and run a distributed set of multithreaded
process to execute the MapReduce jobs in parallel.

    Although it seems to have been asked and answered many times, I will ask once again. 
Without the need to change our current configurations or restart the clusters, is there a
faster approach to obtain row counts?  FYI, my cache size for the Scan is set to 1000. 
I have experimented with different numbers, but nothing made a noticeable difference.  Any
advice or feedback would be greatly appreciated!

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