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From Bryan Beaudreault <bbeaudrea...@hubspot.com>
Subject Re: HBase Table Row Count Optimization - A Solicitation For Help
Date Sat, 21 Sep 2013 01:46:15 GMT
I could be wrong, but based on the info in your most recent emails and the
logs therein as well, I believe you may be running this job as a single
process.

Do you actually have a full hadoop setup running, with a jobtracker and
tasktrackers?  In the absence of proper configuration, the hadoop code will
simply launch a local, single-process job.  The LocalJobRunner referenced
in your logs points to that.

If this is the case you are likely only running a single mapper and
reducer, or at most running a few mappers at once in threads in your local
process. Either way this would obviously greatly limit the throughput.

If you have a full hadoop set-up, make sure the client (dev machine) you
are running this job from has access to a mapred-site.xml and hdfs-site.xml
configuration file, or at the very least set the mapred.job.tracker value
manually in your job configuration before submitting.

Let me know if I'm totally off base here.


On Fri, Sep 20, 2013 at 9:34 PM, James Birchfield <
jbirchfield@stumbleupon.com> wrote:

> Excellent!  Will do!
>
> Birchj
> On Sep 20, 2013, at 6:32 PM, Ted Yu <yuzhihong@gmail.com> wrote:
>
> > 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
> >>>>>
> >>>>>
> >>>>> 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 Notifications@carrieriq.com and
> >>>> delete or destroy any copy of this message and its attachments.
> >>>>
> >>
> >>
>
>

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