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From Ted Yu <yuzhih...@gmail.com>
Subject Re: HBase Table Row Count Optimization - A Solicitation For Help
Date Sat, 21 Sep 2013 01:32:53 GMT
Please take a look at the javadoc
for src/main/java/org/apache/hadoop/hbase/client/coprocessor/AggregationClient.java

As long as the machine can reach your HBase cluster, you should be able to
run AggregationClient and utilize the AggregateImplementation endpoint in
the region servers.

Cheers


On Fri, Sep 20, 2013 at 6:26 PM, James Birchfield <
jbirchfield@stumbleupon.com> wrote:

> Thanks Ted.
>
> That was the direction I have been working towards as I am learning today.
>  Much appreciation to all the replies to this thread.
>
> Whether I keep the MapReduce job or utilize the Aggregation coprocessor
> (which is turning out that it should be possible for me here), I need to
> make sure I am running the client in an efficient manner.  Lars may have
> hit upon the core problem.  I am not running the map reduce job on the
> cluster, but rather from a stand alone remote java client executing the job
> in process.  This may very well turn out to be the number one issue.  I
> would love it if this turns out to be true.  Would make this a great
> learning lesson for me as a relative newcomer to working with HBase, and
> potentially allow me to finish this initial task much quicker than I was
> thinking.
>
> So assuming the MapReduce jobs need to be run on the cluster instead of
> locally, does a coprocessor endpoint client need to be run the same, or is
> it safe to run it on a remote machine since the work gets distributed out
> to the region servers?  Just wondering if I would run into the same issues
> if what I said above holds true.
>
> Thanks!
> Birch
> On Sep 20, 2013, at 6:17 PM, Ted Yu <yuzhihong@gmail.com> wrote:
>
> > In 0.94, we have AggregateImplementation, an endpoint coprocessor, which
> > implements getRowNum().
> >
> > Example is in AggregationClient.java
> >
> > Cheers
> >
> >
> > On Fri, Sep 20, 2013 at 6:09 PM, lars hofhansl <larsh@apache.org> wrote:
> >
> >> From your numbers below you have about 26k regions, thus each region is
> >> about 545tb/26k = 20gb. Good.
> >>
> >> How many mappers are you running?
> >> And just to rule out the obvious, the M/R is running on the cluster and
> >> not locally, right? (it will default to a local runner when it cannot
> use
> >> the M/R cluster).
> >>
> >> Some back of the envelope calculations tell me that assuming 1ge network
> >> cards, the best you can expect for 110 machines to map through this
> data is
> >> about 10h. (so way faster than what you see).
> >> (545tb/(110*1/8gb/s) ~ 40ks ~11h)
> >>
> >>
> >> We should really add a rowcounting coprocessor to HBase and allow using
> it
> >> via M/R.
> >>
> >> -- Lars
> >>
> >>
> >>
> >> ________________________________
> >> From: James Birchfield <jbirchfield@stumbleupon.com>
> >> To: user@hbase.apache.org
> >> Sent: Friday, September 20, 2013 5:09 PM
> >> Subject: Re: HBase Table Row Count Optimization - A Solicitation For
> Help
> >>
> >>
> >> 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 <vrodionov@carrieriq.com
> >
> >> 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: vrodionov@carrieriq.com
> >>>
> >>> ________________________________________
> >>> From: James Birchfield [jbirchfield@stumbleupon.com]
> >>> Sent: Friday, September 20, 2013 3:50 PM
> >>> To: user@hbase.apache.org
> >>> 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 <yuzhihong@gmail.com> 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 <
> >>>> jbirchfield@stumbleupon.com> 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
> >>>
> >>>
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> >>
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