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From Chris Anderson <jch...@apache.org>
Subject Re: Comparison of MongoDB & CouchDB: MongoDB seems better on insert
Date Tue, 21 Dec 2010 00:13:26 GMT
On Mon, Dec 20, 2010 at 2:41 PM, Brian Mitchell <binary42@gmail.com> wrote:

[snip]

>
> You did all I can ask for in the sense of constructive response... though I do think
the CouchDB community seems shy on providing people with simple ranges of data to help them
decide to invest time into more refined measurement.
>
> The truth is that couchdb does have it's strengths that don't tend to show well in many
cases. On the other hand, some applications do need performance numbers that don't easily
come from a standard couched setup. Knowing this before diving into dangerous comparisons
will help avoid the awkwardness as people try to measure things that don't really represent
the priorities of a project well. Right now you have to dig deeper than most people will ever
look to find these numbers. They are hidden whether that is done intentionally or not.
>
> I'd think that it'd be in the community's favor to be as clear as possible with what
kind of ballpark the DB is in. People who miss out on the great parts of CouchDB because they
only focus on speed (so they chose something else) are probably not the prime targets of this
project anyway. If people here disagree, then some serious work needs to be done on improving
these numbers (IMO, there is a lot of speed to gain).
>

I think the performance question is a good one. It's hard to answer
because we've seen so many different performance #s depending on
workload.

As far as rough #s, this bash script inserts about 2500 documents a
second into CouchDB on my MacBook Air.

https://github.com/apache/couchdb/blob/trunk/test/bench/benchbulk.sh

Generally the question that tends to matter is: Can view generation
keep up with inserts, on average? Once the index is built, responses
to it are fast, so the index build time matters. On desktop-class
hardware, I tend to expect between 500-1000 documents per second can
be processed (for documents about as complex and large as a tweet). Of
course this # will vary widely depending on hardware and the # of keys
you emit.

BigCouch also makes partitioning this view generation load easier, so
that more of the mapping can be done in parallel.

Chris



> Brian.
>
>



-- 
Chris Anderson
http://jchrisa.net
http://couch.io

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