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HBase, mail # user - Optimizing Multi Gets in hbase


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Re: Optimizing Multi Gets in hbase
Varun Sharma 2013-02-19, 15:52
I have another question, if I am running a scan wrapped around multiple
rows in the same region, in the following way:

Scan scan = new scan(getWithMultipleRowsInSameRegion);

Now, how does execution occur. Is it just a sequential scan across the
entire region or does it seek to hfile blocks containing the actual values.
What I truly mean is, lets say the multi get is on following rows:

Row1 : HFileBlock1
Row2 : HFileBlock20
Row3 : Does not exist
Row4 : HFileBlock25
Row5 : HFileBlock100

The efficient way to do this would be to determine the correct blocks using
the index and then searching within the blocks for, say Row1. Then, seek to
HFileBlock20 and then look for Row2. Elimininate Row3 and then keep on
seeking to + searching within HFileBlocks as needed.

I am wondering if a scan wrapped around a Get with multiple rows would do
the same ?

Thanks
Varun

On Tue, Feb 19, 2013 at 12:37 AM, Nicolas Liochon <[EMAIL PROTECTED]> wrote:

> Looking at the code, it seems possible to do this server side within the
> multi invocation: we could group the get by region, and do a single scan.
> We could also add some heuristics if necessary...
>
>
>
> On Tue, Feb 19, 2013 at 9:02 AM, lars hofhansl <[EMAIL PROTECTED]> wrote:
>
> > I should qualify that statement, actually.
> >
> > I was comparing scanning 1m KVs to getting 1m KVs when all KVs are
> > returned.
> >
> > As James Taylor pointed out to me privately: A fairer comparison would
> > have been to run a scan with a filter that lets x% of the rows pass (i.e.
> > the selectivity of the scan would be x%) and compare that to a multi Get
> of
> > the same x% of the row.
> >
> > There we found that a Scan+Filter is more efficient that issuing multi
> > Gets if x is >= 1-2%.
> >
> >
> > Or in other words, translating many Gets into a Scan+Filter is beneficial
> > if the Scan would return at least 1-2% of the rows to the client.
> > For example:
> > if you are looking for less than 10-20k rows in 1m rows, using muli Gets
> > is likely more efficient.
> > if you are looking for more than 10-20k rows in 1m rows, using a
> > Scan+Filter is likely more efficient.
> >
> >
> > Of course this is predicated on whether you have an efficient way to
> > represent the rows you are looking for in a filter, so that would
> probably
> > shift this slightly more towards Gets (just imaging a Filter that to
> encode
> > 100k random row keys to be matched; since Filters are instantiated store
> > there is another natural limit there).
> >
> >
> > As I said below, the crux of the matter is having some histograms of your
> > data, so that such a decision could be made automatically.
> >
> >
> > -- Lars
> >
> >
> >
> > ________________________________
> >  From: lars hofhansl <[EMAIL PROTECTED]>
> > To: "[EMAIL PROTECTED]" <[EMAIL PROTECTED]>
> > Sent: Monday, February 18, 2013 5:48 PM
> > Subject: Re: Optimizing Multi Gets in hbase
> >
> > As it happens we did some tests around last week.
> > Turns out doing Gets in batches instead of a scan still gives you 1/3 of
> > the performance.
> >
> > I.e. when you have a table with, say, 10m rows and scanning take N
> > seconds, then calling 10m Gets in batches of 1000 take ~3N, which is
> pretty
> > impressive.
> >
> > Now, this is with all data in the cache!
> > When the data is not in the cache and the Gets are random it is many
> > orders of magnitude slower, as the Gets are sprayed all over the disk. In
> > that case sorting the Gets and issuing scans would indeed be much more
> > efficient.
> >
> >
> > The Gets in a batch are already sorted on the client, but as N. says it
> is
> > hard to determine when to turn many Gets into a Scan with filters
> > automatically. Without statistics/histograms I'd even wager a guess that
> > would be impossible to do.
> > Imagine you issue 10000 random Gets, but your table has 10bn rows, in
> that
> > case it is almost certain that the Gets are faster than a scan.
> > Now image the Gets only cover a small key range. With statistics we could