Jeremiah,
You were essentially correct. A "targeted" MR does not have to search
for the data, and does not slow down with database size. It is a
bucket-sweeping MR that currently has that behavior.
-Justin
On Fri, Jul 29, 2011 at 10:27 AM, Jeremiah Peschka
<jeremiah.pesc...@gmail.com> wrote:
> I would have suspected that an MR job where you supply a Bucket, Key pair
> would be just as fast as a Get request. Shows what I know.
> ---
> Jeremiah Peschka
> Founder, Brent Ozar PLF, LLC
>
> On Jul 29, 2011, at 1:37 AM, Antonio Rohman Fernandez wrote:
>
>> MapReduce ( or a simply Map ) gets really slow when database has a
>> significant amount of data ( or distributed over several servers ). Get
>> instead is always faster as Riak doesn't have to search for the key ( you
>> tell Riak exactly where to GET the data in your url )
>>
>> Rohman
>>
>> On Thu, 28 Jul 2011 23:43:06 +0400, m...@mawhrin.net wrote:
>>
>>> Hi,
>>>
>>> (I looked at various places for the information, however I could not
>>> find anything that would answer the question. It's not completely ruled
>>> out that not all places were checked though :))
>>>
>>> I use PB erlang interface to access the database. Given a bucket name
>>> and a key, the value can easily be extracted using:
>>>
>>> {ok, Object} = riakc_pb_socket:get(Conn, Bucket, Key),
>>> Value = riakc_obj:get_value(Object)
>>>
>>> Alternatively, a mapred (actually, just map) request could be issued:
>>>
>>> {ok, [{_, Value}]} = riakc_pb_socket:mapred(Conn, [
>>> {Bucket, Key}
>>> ], [
>>> {map, {modfun, riak_kv, map_object_value}, none, true}
>>> ])
>>>
>>> I would expect that the result is the same while in the second case, the
>>> amount of data transferred to the client is smaller (which might be good
>>> for certain situations).
>>>
>>> So the [open] question is: are there any reasons for using the first
>>> approach over the second?
>>>
>>> --
>>> Misha
>>>
>> --
>>
>> Antonio Rohman Fernandez
>> CEO, Founder & Lead Engineer
>> roh...@mahalostudio.com Projects
>> MaruBatsu.es
>> PupCloud.com
>> Wedding Album
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