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HBase >> mail # user >> HBase Table Row Count Optimization - A Solicitation For Help

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Re: HBase Table Row Count Optimization - A Solicitation For Help
>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 <[EMAIL PROTECTED]>
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.

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
> ________________________________________
> From: James Birchfield [[EMAIL PROTECTED]]
> Sent: Friday, September 20, 2013 3:50 PM
> 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