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From Anthony Fox <adfaccu...@gmail.com>
Subject Re: Performance of table with large number of column families
Date Fri, 09 Nov 2012 18:29:09 GMT
Do you mean two partitions per server?  In my case, that would correspond
to 30 total rows which would make each row very large ... >1G/row.  Should
I increase the table.split.threshold in a corresponding way?


On Fri, Nov 9, 2012 at 1:09 PM, John Vines <vines@apache.org> wrote:

> Glad to hear. I typically advice a minimum of 2 shards per tserver. I
> would say the maximum is actually based on the tablet size. Others in the
> country may disagree/provide better reasoning.
>
> Sent from my phone, pardon the typos and brevity.
> On Nov 9, 2012 1:03 PM, "Anthony Fox" <adfaccuser@gmail.com> wrote:
>
>> Ok, I reingested with 1000 rows and performance for both single record
>> scans and index scans is much better.  I'm going to experiment a bit with
>> the optimal number of rows.  Thanks for the help, everyone.
>>
>>
>> On Fri, Nov 9, 2012 at 12:41 PM, John Vines <vines@apache.org> wrote:
>>
>>> The bloom filter checks only occur on a seek, and the way the column
>>> family filter works it's it seeks and then does a few scans to see if the
>>> appropriate families pop up in the short term. Bloom filter on the column
>>> family would be better if you had larger rows to encourage more
>>> seeks/minimize the number of rows to do bloom checks.
>>>
>>> The issue is that you are ultimately checking every single row for a
>>> column, which is sparse. It's not that different than doing a full table
>>> regex. If you had locality groups set up it would be more performant, until
>>> you create locality groups for everything.
>>>
>>> The intersecting iterators get their performance by being able to
>>> operate on large rows to avoid the penalty of checking each row. Minimize
>>> the number of partitions you have and it should clear up your issues.
>>>
>>> John
>>>
>>> Sent from my phone, pardon the typos and brevity.
>>> On Nov 9, 2012 12:24 PM, "William Slacum" <
>>> wilhelm.von.cloud@accumulo.net> wrote:
>>>
>>>> I'll ask for someone to verify this comment for me (look @ u John W
>>>> Vines), but the bloom filter helps when you have a discrete number of
>>>> column families that will appear across many rows.
>>>>
>>>> On Fri, Nov 9, 2012 at 12:18 PM, Anthony Fox <adfaccuser@gmail.com>wrote:
>>>>
>>>>> Ah, ok, I was under the impression that this would be really fast
>>>>> since I have a column family bloom filter turned on.  Is this not correct?
>>>>>
>>>>>
>>>>> On Fri, Nov 9, 2012 at 12:15 PM, William Slacum <
>>>>> wilhelm.von.cloud@accumulo.net> wrote:
>>>>>
>>>>>> When I said smaller of tablets, I really mean smaller number of rows
>>>>>> :) My apologies.
>>>>>>
>>>>>> So if you're searching for a random column family in a table, like
>>>>>> with a `scan -c <cf>` in the shell, it will start at row 0
and work
>>>>>> sequentially up to row 10000000 until it finds the cf.
>>>>>>
>>>>>>
>>>>>> On Fri, Nov 9, 2012 at 12:11 PM, Anthony Fox <adfaccuser@gmail.com>wrote:
>>>>>>
>>>>>>> This scan is without the intersecting iterator.  I'm just trying
to
>>>>>>> pull back a single data record at the moment which corresponds
to scanning
>>>>>>> for one column family.  I'll try with a smaller number of tablets,
but is
>>>>>>> the computation effort the same for the scan I am doing?
>>>>>>>
>>>>>>>
>>>>>>> On Fri, Nov 9, 2012 at 12:02 PM, William Slacum <
>>>>>>> wilhelm.von.cloud@accumulo.net> wrote:
>>>>>>>
>>>>>>>> So that means you have roughly 312.5k rows per tablet, which
means
>>>>>>>> about 725k column families in any given tablet. The intersecting
iterator
>>>>>>>> will work at a row per time, so I think at any given moment,
it will be
>>>>>>>> working through 32 at a time and doing a linear scan through
the RFile
>>>>>>>> blocks. With RFile indices, that check is usually pretty
fast, but you're
>>>>>>>> having go through 4 orders of magnitude more data sequentially
than you can
>>>>>>>> work on. If you can experiment and re-ingest with a smaller
number of
>>>>>>>> tablets, anywhere between 15 and 45, I think you will see
better
>>>>>>>> performance.
>>>>>>>>
>>>>>>>> On Fri, Nov 9, 2012 at 11:53 AM, Anthony Fox <adfaccuser@gmail.com>wrote:
>>>>>>>>
>>>>>>>>> Failed to answer the original question - 15 tablet servers,
32
>>>>>>>>> tablets/splits.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Fri, Nov 9, 2012 at 11:52 AM, Anthony Fox <adfaccuser@gmail.com
>>>>>>>>> > wrote:
>>>>>>>>>
>>>>>>>>>> I've tried a number of different settings of
>>>>>>>>>> table.split.threshold.  I started at 1G and bumped
it down to 128M and the
>>>>>>>>>> cf scan is still ~30 seconds for both.  I've also
used less rows - 00000 to
>>>>>>>>>> 99999 and still see similar performance numbers.
 I thought the column
>>>>>>>>>> family bloom filter would help deal with large row
space but sparsely
>>>>>>>>>> populated column space.  Is that correct?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Fri, Nov 9, 2012 at 11:49 AM, William Slacum <
>>>>>>>>>> wilhelm.von.cloud@accumulo.net> wrote:
>>>>>>>>>>
>>>>>>>>>>> I'm more inclined to believe it's because you
have to search
>>>>>>>>>>> across 10M different rows to find any given column
family, since they're
>>>>>>>>>>> randomly, and possibly uniformly, distributed.
How many tablets are you
>>>>>>>>>>> searching across?
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:45 AM, Anthony Fox
<
>>>>>>>>>>> adfaccuser@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Yes, there are 10M possible partitions. 
I do not have a hash
>>>>>>>>>>>> from value to partition, the data is essentially
randomly balanced across
>>>>>>>>>>>> all the tablets.  Unlike the bloom filter
and intersecting iterator
>>>>>>>>>>>> examples, I do not have locality groups turned
on and I have data in the cq
>>>>>>>>>>>> and the value for both index entries and
record entries.  Could this be the
>>>>>>>>>>>> issue?  Each record entry has approximately
30 column qualifiers with data
>>>>>>>>>>>> in the value for each.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:41 AM, William
Slacum <
>>>>>>>>>>>> wilhelm.von.cloud@accumulo.net> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> I guess assuming you have 10M possible
partitions, if you're
>>>>>>>>>>>>> using a relatively uniform hash to generate
your IDs, you'll average about
>>>>>>>>>>>>> 2 per partition. Do you have any index
for term/value to partition? This
>>>>>>>>>>>>> will help you narrow down your search
space to a subset of your partitions.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:39 AM, William
Slacum <
>>>>>>>>>>>>> wilhelm.von.cloud@accumulo.net> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> That shouldn't be a huge issue. How
many rows/partitions do
>>>>>>>>>>>>>> you have? How many do you have to
scan to find the specific column
>>>>>>>>>>>>>> family/doc id you want?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:26 AM,
Anthony Fox <
>>>>>>>>>>>>>> adfaccuser@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I have a table set up to use
the intersecting iterator pattern.  The
>>>>>>>>>>>>>>> table has about 20M records which
leads to 20M column families for the
>>>>>>>>>>>>>>> data section - 1 unique column
family per record.  The index section of
>>>>>>>>>>>>>>> the table is not quite as large
as the data section.  The rowkey is a
>>>>>>>>>>>>>>> random padded integer partition
between 0000000 and 9999999.  I turned
>>>>>>>>>>>>>>> bloom filters on and used the
ColumnFamilyFunctor to get performant
>>>>>>>>>>>>>>> column family scans without specifying
a range like in the bloom filter
>>>>>>>>>>>>>>> examples in the README.  However,
my column family scans (without any
>>>>>>>>>>>>>>> custom iterator) are still fairly
slow - ~30 seconds for a column family
>>>>>>>>>>>>>>> batch scan of one record. I've
also tried RowFunctor but I see similar
>>>>>>>>>>>>>>> performance.  Can anyone shed
any light on the performance metrics I'm
>>>>>>>>>>>>>>> seeing?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>> Anthony
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>

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