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From Michael Segel <michael_se...@hotmail.com>
Subject Re: Rowkey design question
Date Wed, 08 Apr 2015 11:45:40 GMT

First, I’d suggest you rethink your schema by adding an additional dimension. 
You’ll end up with more rows, but a narrower table. 

In terms of compaction… if the data is relatively static, you won’t have compactions because
nothing changed. 
But if your data is that static… why not put the data in sequence files and use HBase as
the index. Could be faster. 



> On Apr 8, 2015, at 3:26 AM, Kristoffer Sjögren <stoffe@gmail.com> wrote:
> I just read through HBase MOB design document and one thing that caught my
> attention was the following statement.
> "When HBase deals with large numbers of values > 100kb and up to ~10MB of
> data, it encounters performance degradations due to write amplification
> caused by splits and compactions."
> Is there any chance to run into this problem in the read path for data that
> is written infrequently and never changed?
> On Wed, Apr 8, 2015 at 9:30 AM, Kristoffer Sjögren <stoffe@gmail.com> wrote:
>> A small set of qualifiers will be accessed frequently so keeping them in
>> block cache would be very beneficial. Some very seldom. So this sounds very
>> promising!
>> The reason why i'm considering a coprocessor is that I need to provide
>> very specific information in the query request. Same thing with the
>> response. Queries are also highly parallelizable across rows and each
>> individual query produce a valid result that may or may not be aggregated
>> with other results in the client, maybe even inside the region if it
>> contained multiple rows targeted by the query.
>> So it's a bit like Phoenix but with a different storage format and query
>> engine.
>> On Wed, Apr 8, 2015 at 12:46 AM, Nick Dimiduk <ndimiduk@gmail.com> wrote:
>>> Those rows are written out into HBase blocks on cell boundaries. Your
>>> column family has a BLOCK_SIZE attribute, which you may or may have no
>>> overridden the default of 64k. Cells are written into a block until is it
>>>> = the target block size. So your single 500mb row will be broken down
>>> into
>>> thousands of HFile blocks in some number of HFiles. Some of those blocks
>>> may contain just a cell or two and be a couple MB in size, to hold the
>>> largest of your cells. Those blocks will be loaded into the Block Cache as
>>> they're accessed. If your careful with your access patterns and only
>>> request cells that you need to evaluate, you'll only ever load the blocks
>>> containing those cells into the cache.
>>>> Will the entire row be loaded or only the qualifiers I ask for?
>>> So then, the answer to your question is: it depends on how you're
>>> interacting with the row from your coprocessor. The read path will only
>>> load blocks that your scanner requests. If your coprocessor is producing
>>> scanner with to seek to specific qualifiers, you'll only load those
>>> blocks.
>>> Related question: Is there a reason you're using a coprocessor instead of
>>> a
>>> regular filter, or a simple qualified get/scan to access data from these
>>> rows? The "default stuff" is already tuned to load data sparsely, as would
>>> be desirable for your schema.
>>> -n
>>> On Tue, Apr 7, 2015 at 2:22 PM, Kristoffer Sjögren <stoffe@gmail.com>
>>> wrote:
>>>> Sorry I should have explained my use case a bit more.
>>>> Yes, it's a pretty big row and it's "close" to worst case. Normally
>>> there
>>>> would be fewer qualifiers and the largest qualifiers would be smaller.
>>>> The reason why these rows gets big is because they stores aggregated
>>> data
>>>> in indexed compressed form. This format allow for extremely fast queries
>>>> (on local disk format) over billions of rows (not rows in HBase speak),
>>>> when touching smaller areas of the data. If would store the data as
>>> regular
>>>> HBase rows things would get very slow unless I had many many region
>>>> servers.
>>>> The coprocessor is used for doing custom queries on the indexed data
>>> inside
>>>> the region servers. These queries are not like a regular row scan, but
>>> very
>>>> specific as to how the data is formatted withing each column qualifier.
>>>> Yes, this is not possible if HBase loads the whole 500MB each time i
>>> want
>>>> to perform this custom query on a row. Hence my question :-)
>>>> On Tue, Apr 7, 2015 at 11:03 PM, Michael Segel <
>>> michael_segel@hotmail.com>
>>>> wrote:
>>>>> Sorry, but your initial problem statement doesn’t seem to parse …
>>>>> Are you saying that you a single row with approximately 100,000
>>> elements
>>>>> where each element is roughly 1-5KB in size and in addition there are
>>> ~5
>>>>> elements which will be between one and five MB in size?
>>>>> And you then mention a coprocessor?
>>>>> Just looking at the numbers… 100K * 5KB means that each row would end
>>> up
>>>>> being 500MB in size.
>>>>> That’s a pretty fat row.
>>>>> I would suggest rethinking your strategy.
>>>>>> On Apr 7, 2015, at 11:13 AM, Kristoffer Sjögren <stoffe@gmail.com>
>>>>> wrote:
>>>>>> Hi
>>>>>> I have a row with around 100.000 qualifiers with mostly small values
>>>>> around
>>>>>> 1-5KB and maybe 5 largers ones around 1-5 MB. A coprocessor do
>>> random
>>>>>> access of 1-10 qualifiers per row.
>>>>>> I would like to understand how HBase loads the data into memory.
>>> Will
>>>> the
>>>>>> entire row be loaded or only the qualifiers I ask for (like pointer
>>>>> access
>>>>>> into a direct ByteBuffer) ?
>>>>>> Cheers,
>>>>>> -Kristoffer
>>>>> The opinions expressed here are mine, while they may reflect a
>>> cognitive
>>>>> thought, that is purely accidental.
>>>>> Use at your own risk.
>>>>> Michael Segel
>>>>> michael_segel (AT) hotmail.com

The opinions expressed here are mine, while they may reflect a cognitive thought, that is
purely accidental. 
Use at your own risk. 
Michael Segel
michael_segel (AT) hotmail.com

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