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HBase >> mail # user >> Essential column family performance


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Re: Essential column family performance
Good idea, Sergey. We'll rerun with larger non essential column family
values and see if there's a crossover point. One other difference for us
is that we're using FAST_DIFF encoding. We'll try with no encoding too.
Our table has 20 million rows across four regions servers.

Regarding the parallelization we do, we run multiple scans in parallel
instead of one single scan over the table. We use the region boundaries
of the table to divide up the work evenly, adding a start/stop key for
each scan that corresponds to the region boundaries. Our client then
does a final merge/aggregation step (i.e. adding up the count it gets
back from the scan for each region).

On 04/08/2013 01:34 PM, Sergey Shelukhin wrote:
> IntegrationTestLazyCfLoading uses randomly distributed keys with the
> following condition for filtering:
> 1 == (Long.parseLong(Bytes.toString(rowKey, 0, 4), 16) & 1); where rowKey
> is hex string of MD5 key.
> Then, there are 2 "lazy" CFs, each of which has a value of 4-64k.
> This test also showed significant improvement IIRC, so random distribution
> and high %%ge of values selected should not be a problem as such.
>
> My hunch would be that the additional cost of seeks/merging the results
> from two CFs outweights the benefit of lazy loading on such small values
> for the "lazy" CF with lots of data selected. This feature definitely makes
> no sense if you are selecting all values, because then extra work is being
> done for no benefit (everything is read anyway).
> So the use cases would be larger "lazy" CFs or/and low percentage of values
> selected.
>
> Can you try to increase the 2nd CF values' size and rerun the test?
>
>
> On Mon, Apr 8, 2013 at 10:38 AM, James Taylor <[EMAIL PROTECTED]>wrote:
>
>> In the TestJoinedScanners.java, is the 40% randomly distributed or
>> sequential?
>>
>> In our test, the % is randomly distributed. Also, our custom filter does
>> the same thing that SingleColumnValueFilter does.  On the client-side, we'd
>> execute the query in parallel, through multiple scans along the region
>> boundaries. Would that have a negative impact on performance for this
>> "essential column family" feature?
>>
>> Thanks,
>>
>>      James
>>
>>
>> On 04/08/2013 10:10 AM, Anoop John wrote:
>>
>>> Agree here. The effectiveness depends on what % of data satisfies the
>>> condition, how it is distributed across HFile blocks. We will get
>>> performance gain when the we will be able to skip some HFile blocks (from
>>> non essential CFs). Can test with different HFile block size (lower
>>> value)?
>>>
>>> -Anoop-
>>>
>>>
>>> On Mon, Apr 8, 2013 at 8:19 PM, Ted Yu <[EMAIL PROTECTED]> wrote:
>>>
>>>   I made the following change in TestJoinedScanners.java:
>>>> -      int flag_percent = 1;
>>>> +      int flag_percent = 40;
>>>>
>>>> The test took longer but still favors joined scanner.
>>>> I got some new results:
>>>>
>>>> 2013-04-08 07:46:06,959 INFO  [main] regionserver.**
>>>> TestJoinedScanners(157):
>>>> Slow scanner finished in 7.424388 seconds, got 2050 rows
>>>> ...
>>>> 2013-04-08 07:46:12,010 INFO  [main] regionserver.**
>>>> TestJoinedScanners(157):
>>>> Joined scanner finished in 5.05063 seconds, got 2050 rows
>>>>
>>>> 2013-04-08 07:46:18,358 INFO  [main] regionserver.**
>>>> TestJoinedScanners(157):
>>>> Slow scanner finished in 6.348517 seconds, got 2050 rows
>>>> ...
>>>> 2013-04-08 07:46:22,946 INFO  [main] regionserver.**
>>>> TestJoinedScanners(157):
>>>> Joined scanner finished in 4.587545 seconds, got 2050 rows
>>>>
>>>> Looks like effectiveness of joined scanner is affected by distribution of
>>>> data.
>>>>
>>>> Cheers
>>>>
>>>> On Sun, Apr 7, 2013 at 8:52 PM, lars hofhansl <[EMAIL PROTECTED]> wrote:
>>>>
>>>>   Looking at the joined scanner test code, it sets it up such that 1% of
>>>> the
>>>>
>>>>> rows match, which would somewhat be in line with James' results.
>>>>>
>>>>> In my own testing a while ago I found a 100% improvement with 0% match.
>>>>>
>>>>>
>>>>> -- Lars
>>
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB