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From Julian Moritz <maili...@julianmoritz.de>
Subject Re: performance issues
Date Mon, 05 Apr 2010 19:19:36 GMT
Hi J Chris,

J Chris Anderson schrieb:
> On Apr 5, 2010, at 11:52 AM, Julian Moritz wrote:
> 
>> Hi,
>>
>> Julian Moritz schrieb:
>>> Hi,
>>>
>> I've just found this via google:
>>
>>>> We don't parallelize view index creation yet, so this is not an
>>>> additional problem for you. You can however build two views in
>>>> parallel and make use of two cores that way.
>> If this is (still) true, view index creation is the bottleneck of my
>> application. Hence I'm just playing around and yet using 100% of my
>> core, I cannot use CouchDB anymore.
>>
> 
> We rarely see view generation that is actually limited by view-function execution speed.
The majority of the time the actual bottleneck is disk IO. To parallelize view generation
the best option is to run a CouchDB-Lounge cluster.
> 

Hm, at the moment I have access to two computers. This isn't what you
mean with a "couchdb-lounge cluster", right?

> It looks like you might be better of removing your reduce function, which might also
speed things up.
> 

But I need it for making my list unique. This is an important feature
for my application.

Thanks, I'll think about how to set up a couchdb cluster and do more
testing.

Regards
Julian

> Chris
> 
> 
>> Regards
>> Julian
>>
>>> I've developed a (in my eyes) simple view. I have a wordlist which does
>>> not  contain unique words. I want to store it in a view, with every word
>>> occurring once and ordered by random. Therefore I have a simple view
>>> function:
>>>
>>> function(doc){
>>> emit([hash(doc.word), doc.word], null);
>>> }
>>>
>>> and a simple reduce:
>>>
>>> function(key, values, rereduce){
>>> return true;
>>> }
>>>
>>> calling that view with group=true it does what I want.
>>>
>>> When storing plenty of words to the database, one of my two cpu cores is
>>> used completely by couchjs.
>>>
>>> Isn't the view built using two (or all) cpu cores? I thought (obviously
>>> I'm wrong) that it would be calculated in parallel and using a
>>> quadro-core (or more cores) would make storing faster.
>>>
>>> Is there a solution for that? Should I use another query-server?
>>>
>>> Regards
>>> Julian
>>>
> 
> 

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