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From Nate McCall <n...@thelastpickle.com>
Subject Re: insert performance (1.2.8)
Date Tue, 20 Aug 2013 14:43:19 GMT
Thanks for putting this up - sorry I missed your post the other week. I
would be real curious as to your results if you added a prepared statement
for those inserts.


On Tue, Aug 20, 2013 at 9:14 AM, Przemek Maciolek <pmaciolek@gmail.com>wrote:

> I had similar issues (sent a note on the list few weeks ago but nobody
> responded). I think there's a serious bottleneck with using wide rows and
> composite keys. I made a trivial benchmark, which you check here:
> http://pastebin.com/qAcRcqbF  - it's written in cql-rb, but I ran the
> test using astyanax/cql3 enabled and the results were the same.
>
> In my case, inserting 10 000 entries took following time (seconds):
>
> Using composite keys
> Separetely: 12.892867
> Batch: 189.731306
>
> This means, I got 1000 rows/s when inserting them seperately and 52 (!!!)
> when inserting them in a huge batch.
>
> Using just partition key and wide row
> Separetely: 11.292507
> Batch: 0.093355
>
> Again, 1000 rows/s when inserting them one by one. But batch obviously
> improves thing and I easily got >10000 rows/s.
>
> Anyone else with similar experiences?
>
> Thanks,
> Przemek
>
>
> On Tue, Aug 20, 2013 at 4:04 PM, Nate McCall <nate@thelastpickle.com>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 <8forty@gmail.com> 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 <8forty@gmail.com>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 <8forty@gmail.com>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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