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Date: Thu, 1 Sep 2011 09:16:55 -0700
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Subject: Re: 15 seconds to increment 17k keys?
From: Ian Danforth <idanforth@numenta.com>
To: "user@cassandra.apache.org" <user@cassandra.apache.org>
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Does this scale with multiples of the replication factor or directly
with number of nodes? Or more succinctly, to double the writes per
second into the cluster how many more nodes would I need? (Thanks for
the note on pycassa, I've checked and it's not the limiting factor)

Ian

On Thu, Sep 1, 2011 at 3:36 AM, Richard Low <rlow@acunu.com> wrote:
> Assuming you have replicate_on_write enabled (which you almost
> certainly do for counters), you have to do a read on a write for each
> increment. =A0This means counter increments, even if all your data set
> fits in cache, are significantly slower than normal column inserts. =A0I
> would say ~1k increments per second is about right, although you can
> probably do some tuning to improve this.
>
> I've also found that the pycassa client uses significant amounts of
> CPU, so be careful you are not CPU bound on the client.
>
> --
> Richard Low
> Acunu | http://www.acunu.com | @acunu
>
> On Thu, Sep 1, 2011 at 2:31 AM, Yang <teddyyyy123@gmail.com> wrote:
>> 1ms per add operation is the general order of magnitude I have seen with=
 my
>> tests.
>>
>>
>> On Wed, Aug 31, 2011 at 6:04 PM, Ian Danforth <idanforth@numenta.com> wr=
ote:
>>>
>>> All,
>>>
>>> =A0I've got a 4 node cluster (ec2 m1.large instances, replication =3D 3=
)
>>> that has one primary counter type column family, that has one column
>>> in the family. There are millions of rows. Each operation consists of
>>> doing a batch_insert through pycassa, which increments ~17k keys. A
>>> majority of these keys are new in each batch.
>>>
>>> =A0Each operation is taking up to 15 seconds. For our system this is a
>>> significant bottleneck.
>>>
>>> =A0Does anyone know if this write speed is expected?
>>>
>>> Thanks in advance,
>>>
>>> =A0Ian
>>
>>
>