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From Nate McCall <>
Subject Re: insert performance (1.2.8)
Date Tue, 20 Aug 2013 23:06:08 GMT
Ugh - sorry, I knew Sylvain and Michaƫl had worked on this recently but it
is only in 2.0 - I could have sworn it got marked for inclusion back into
1.2 but I was wrong:

This is indeed an issue if you don't know the column count before hand (or
had a very large number of them like in your case). Again, apologies, I
would not have recommended that route if I knew it was only in 2.0.

I would be willing to bet you could hit those insert numbers pretty easily
with thrift given the shape of your mutation.

On Tue, Aug 20, 2013 at 5:00 PM, Keith Freeman <> wrote:

>  So I tried inserting prepared statements separately (no batch), and my
> server nodes load definitely dropped significantly.  Throughput from my
> client improved a bit, but only a few %.  I was able to *almost* get 5000
> rows/sec (sort of) by also reducing the rows/insert-thread to 20-50 and
> eliminating all overhead from the timing, i.e. timing only the tight for
> loop of inserts.  But that's still a lot slower than I expected.
> I couldn't do batches because the driver doesn't allow prepared statements
> in a batch (QueryBuilder API).  It appears the batch itself could possibly
> be a prepared statement, but since I have 40+ columns on each insert that
> would take some ugly code to build so I haven't tried it yet.
> I'm using CL "ONE" on the inserts and RF 2 in my schema.
> On 08/20/2013 08:04 AM, Nate McCall wrote:
> John makes a good point re:prepared statements (I'd increase batch sizes
> again once you did this as well - separate, incremental runs of course so
> you can gauge the effect of each). That should take out some of the
> processing overhead of statement validation in the server (some - that load
> spike still seems high though).
>  I'd actually be really interested as to what your results were after
> doing so - i've not tried any A/B testing here for prepared statements on
> inserts.
>  Given your load is on the server, i'm not sure adding more async
> indirection on the client would buy you too much though.
>  Also, at what RF and consistency level are you writing?
> On Tue, Aug 20, 2013 at 8:56 AM, Keith Freeman <> wrote:
>>  Ok, I'll try prepared statements.   But while sending my statements
>> async might speed up my client, it wouldn't improve throughput on the
>> cassandra nodes would it?  They're running at pretty high loads and only
>> about 10% idle, so my concern is that they can't handle the data any
>> faster, so something's wrong on the server side.  I don't really think
>> there's anything on the client side that matters for this problem.
>> Of course I know there are obvious h/w things I can do to improve server
>> performance: SSDs, more RAM, more cores, etc.  But I thought the servers I
>> have would be able to handle more rows/sec than say Mysql, since write
>> speed is supposed to be one of Cassandra's strengths.
>> On 08/19/2013 09:03 PM, John Sanda wrote:
>> I'd suggest using prepared statements that you initialize at application
>> start up and switching to use Session.executeAsync coupled with Google
>> Guava Futures API to get better throughput on the client side.
>> On Mon, Aug 19, 2013 at 10:14 PM, Keith Freeman <> wrote:
>>>  Sure, I've tried different numbers for batches and threads, but
>>> generally I'm running 10-30 threads at a time on the client, each sending a
>>> batch of 100 insert statements in every call, using the
>>> QueryBuilder.batch() API from the latest datastax java driver, then calling
>>> the Session.execute() function (synchronous) on the Batch.
>>> I can't post my code, but my client does this on each iteration:
>>> -- divides up the set of inserts by the number of threads
>>> -- stores the current time
>>> -- tells all the threads to send their inserts
>>> -- then when they've all returned checks the elapsed time
>>> At about 2000 rows for each iteration, 20 threads with 100 inserts each
>>> finish in about 1 second.  For 4000 rows, 40 threads with 100 inserts each
>>> finish in about 1.5 - 2 seconds, and as I said all 3 cassandra nodes have a
>>> heavy CPU load while the client is hardly loaded.  I've tried with 10
>>> threads and more inserts per batch, or up to 60 threads with fewer, doesn't
>>> seem to make a lot of difference.
>>> On 08/19/2013 05:00 PM, Nate McCall wrote:
>>>  How big are the batch sizes? In other words, how many rows are you
>>> sending per insert operation?
>>>  Other than the above, not much else to suggest without seeing some
>>> example code (on pastebin, gist or similar, ideally).
>>> On Mon, Aug 19, 2013 at 5:49 PM, Keith Freeman <> wrote:
>>>> I've got a 3-node cassandra cluster (16G/4-core VMs ESXi v5 on 2.5Ghz
>>>> machines not shared with any other VMs).  I'm inserting time-series data
>>>> into a single column-family using "wide rows" (timeuuids) and have a 3-part
>>>> partition key so my primary key is something like ((a, b, day),
>>>> in-time-uuid), x, y, z).
>>>> My java client is feeding rows (about 1k of raw data size each) in
>>>> batches using multiple threads, and the fastest I can get it run reliably
>>>> is about 2000 rows/second.  Even at that speed, all 3 cassandra nodes are
>>>> very CPU bound, with loads of 6-9 each (and the client machine is hardly
>>>> breaking a sweat).  I've tried turning off compression in my table which
>>>> reduced the loads slightly but not much.  There are no other updates or
>>>> reads occurring, except the datastax opscenter.
>>>> I was expecting to be able to insert at least 10k rows/second with this
>>>> configuration, and after a lot of reading of docs, blogs, and google, can't
>>>> really figure out what's slowing my client down.  When I increase the
>>>> insert speed of my client beyond 2000/second, the server responses are just
>>>> too slow and the client falls behind.  I had a single-node Mysql database
>>>> that can handle 10k of these data rows/second, so I really feel like I'm
>>>> missing something in Cassandra.  Any ideas?
>>  --
>> - John

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