Thanks for the info. Right now the MapReduce Scan uses the FirstKeyOnlyFilter. From what I have read in the javadoc, FirstKeyFilter *should* be faster since it only grabs the first KV pair.
I will play around with setting the caching size to a much higher number and see how it performs. I do not think I have too much wiggle room to modify our cluster configurations, but will see what I can do. Thanks! Birch On Sep 20, 2013, at 5:39 PM, Bryan Beaudreault <[email protected]> wrote: > If your cells are extremely small try setting the caching even higher than > 10k. You want to strike a balance between MBs of response size and number > of calls needed, leaning towards larger response sizes as far as your > system can handle (account for RS max memory, and memory available to your > mappers). > > You could use the KeyOnlyFilter to further limit the sizes of responses > transferred. > > Another thing that may help would be increasing your block size. This > would speed up sequential read but slow down random access. It would be a > matter of making the config change and then running a major compaction to > re-write the data. > > We constantly run multiple MR jobs (often on the order of 10's) against the > same hbase cluster and don't often see issues. They are not full table > scans, but they do often overlap. I think it would be worth at least > attempting to run multiple jobs at once. > > > > > On Fri, Sep 20, 2013 at 8:09 PM, James Birchfield < > [email protected]> wrote: > >> I did not implement accurate timing, but the current table being counted >> has been running for about 10 hours, and the log is estimating the map >> portion at 10% >> >> 2013-09-20 23:40:24,099 INFO [main] Job : map >> 10% reduce 0% >> >> So a loooong time. Like I mentioned, we have billions, if not trillions >> of rows potentially. >> >> Thanks for the feedback on the approaches I mentioned. I was not sure if >> they would have any effect overall. >> >> I will look further into coprocessors. >> >> Thanks! >> Birch >> On Sep 20, 2013, at 4:58 PM, Vladimir Rodionov <[email protected]> >> wrote: >> >>> How long does it take for RowCounter Job for largest table to finish on >> your cluster? >>> >>> Just curious. >>> >>> On your options: >>> >>> 1. Not worth it probably - you may overload your cluster >>> 2. Not sure this one differs from 1. Looks the same to me but more >> complex. >>> 3. The same as 1 and 2 >>> >>> Counting rows in efficient way can be done if you sacrifice some >> accuracy : >>> >>> >> http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html >>> >>> Yeah, you will need coprocessors for that. >>> >>> Best regards, >>> Vladimir Rodionov >>> Principal Platform Engineer >>> Carrier IQ, www.carrieriq.com >>> e-mail: [email protected] >>> >>> ________________________________________ >>> From: James Birchfield [[email protected]] >>> Sent: Friday, September 20, 2013 3:50 PM >>> To: [email protected] >>> Subject: Re: HBase Table Row Count Optimization - A Solicitation For Help >>> >>> Hadoop 2.0.0-cdh4.3.1 >>> >>> HBase 0.94.6-cdh4.3.1 >>> >>> 110 servers, 0 dead, 238.2364 average load >>> >>> Some other info, not sure if it helps or not. >>> >>> Configured Capacity: 1295277834158080 (1.15 PB) >>> Present Capacity: 1224692609430678 (1.09 PB) >>> DFS Remaining: 624376503857152 (567.87 TB) >>> DFS Used: 600316105573526 (545.98 TB) >>> DFS Used%: 49.02% >>> Under replicated blocks: 0 >>> Blocks with corrupt replicas: 1 >>> Missing blocks: 0 >>> >>> It is hitting a production cluster, but I am not really sure how to >> calculate the load placed on the cluster. >>> On Sep 20, 2013, at 3:19 PM, Ted Yu <[email protected]> wrote: >>> >>>> How many nodes do you have in your cluster ? >>>> >>>> When counting rows, what other load would be placed on the cluster ? >>>> >>>> What is the HBase version you're currently using / planning to use ? >>>> >>>> Thanks >>>> >>>> >>>> On Fri, Sep 20, 2013 at 2:47 PM, James Birchfield < >>>> [email protected]> wrote: >>>> >>>>> 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 >>> >>> >>> Confidentiality Notice: The information contained in this message, >> including any attachments hereto, may be confidential and is intended to be >> read only by the individual or entity to whom this message is addressed. If >> the reader of this message is not the intended recipient or an agent or >> designee of the intended recipient, please note that any review, use, >> disclosure or distribution of this message or its attachments, in any form, >> is strictly prohibited. If you have received this message in error, please >> immediately notify the sender and/or [email protected] and >> delete or destroy any copy of this message and its attachments. >> >>
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