Ugh - sorry, I knew Sylvain and Michaël 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: https://issues.apache.org/jira/browse/CASSANDRA-4693
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 <8fo...@gmail.com> 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 <8fo...@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 <8fo...@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 <8fo...@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 >> >> >> > >
