I remember running both RocksDB and LevelDB and it was definitely better (in that 1 test case, it was ~40K vs ~30K random writes/sec) - but I haven't done any exhaustive comparison.
Btw, I see that you're using 4 partitions ? Any reason you're not using like >= 128 and running with more containers ? On Tue, Jan 20, 2015 at 4:05 PM, Roger Hoover <[email protected]> wrote: > Thanks, Chris. > > I am not using a changelog for the store because the the bootstrap stream > is a master copy of the data and the job can recover from there. No need > to write out another copy. Is this the way you typically do it for > stream/table joins? > > Great to know you that you're looking into the performance issues. I love > the idea of local state for isolation and predictable throughput but the > current write throughput puts hard limits on the amount of local state that > a container can have without really long initialization/recovery times. > > Is my tests, LevelDB has about the same performance. Have you noticed that > as well? > > Cheers, > > Roger > > On Tue, Jan 20, 2015 at 9:28 AM, Chris Riccomini < > [email protected]> wrote: > > > Hey Roger, > > > > We did some benchmarking, and discovered very similar performance to what > > you've described. We saw ~40k writes/sec, and ~20 k reads/sec, > > per-container, on a Virident SSD. This was without any changelog. Are you > > using a changelog on the store? > > > > When we attached a changelog to the store, the writes dropped > > significantly (~1000 writes/sec). When we hooked up VisualVM, we saw that > > the container was spending > 99% of its time in > KafkaSystemProducer.send(). > > > > We're currently doing two things: > > > > 1. Working with our performance team to understand and tune RocksDB > > properly. > > 2. Upgrading the Kafka producer to use the new Java-based API. > (SAMZA-227) > > > > For (1), it seems like we should be able to get a lot higher throughput > > from RocksDB. Anecdotally, we've heard that RocksDB requires many threads > > in order to max out an SSD, and since Samza is single-threaded, we could > > just be hitting a RocksDB bottleneck. We won't know until we dig into the > > problem (which we started investigating last week). The current plan is > to > > start by benchmarking RocksDB JNI outside of Samza, and see what we can > > get. From there, we'll know our "speed of light", and can try to get > Samza > > as close as possible to it. If RocksDB JNI can't be made to go "fast", > > then we'll have to understand why. > > > > (2) should help with the changelog issue. I believe that the slowness > with > > the changelog is caused because the changelog is using a sync producer to > > send to Kafka, and is blocking when a batch is flushed. In the new API, > > the concept of a "sync" producer is removed. All writes are handled on an > > async writer thread (though we can still guarantee writes are safely > > written before checkpointing, which is what we need). > > > > In short, I agree, it seems slow. We see this behavior, too. We're > digging > > into it. > > > > Cheers, > > Chris > > > > On 1/17/15 12:58 PM, "Roger Hoover" <[email protected]> wrote: > > > > >Michael, > > > > > >Thanks for the response. I used VisualVM and YourKit and see the CPU is > > >not being used (0.1%). I took a few thread dumps and see the main > thread > > >blocked on the flush() method inside the KV store. > > > > > >On Sat, Jan 17, 2015 at 7:09 AM, Michael Rose <[email protected]> > > >wrote: > > > > > >> Is your process at 100% CPU? I suspect you're spending most of your > > >>time in > > >> JSON deserialization, but profile it and check. > > >> > > >> Michael > > >> > > >> On Friday, January 16, 2015, Roger Hoover <[email protected]> > > >>wrote: > > >> > > >> > Hi guys, > > >> > > > >> > I'm testing a job that needs to load 40M records (6GB in Kafka as > > >>JSON) > > >> > from a bootstrap topic. The topic has 4 partitions and I'm running > > >>the > > >> job > > >> > using the ProcessJobFactory so all four tasks are in one container. > > >> > > > >> > Using RocksDB, it's taking 19 minutes to load all the data which > > >>amounts > > >> to > > >> > 35k records/sec or 5MB/s based on input size. I ran iostat during > > >>this > > >> > time as see the disk write throughput is 14MB/s. > > >> > > > >> > I didn't tweak any of the storage settings. > > >> > > > >> > A few questions: > > >> > 1) Does this seem low? I'm running on a Macbook Pro with SSD. > > >> > 2) Do you have any recommendations for improving the load speed? > > >> > > > >> > Thanks, > > >> > > > >> > Roger > > >> > > > >> > > > > > -- Thanks and regards Chinmay Soman
