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 counting). -- Lars ________________________________ From: James Birchfield <[email protected]> To: [email protected] 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! Thanks, Birch
