Hey Michael, I am relatively new to HBase, so do take my response with a grain of salt. I think, definitely your requirements are something that HBase should be able to handle easily(assuming you are not pulling inordinate amounts of data(payload) from HBase). Few things that you should look for to understand this better is, 1. What are your clients doing when you increase the number of threads? 2. How is the thread-connection mapping - 1 to 1? Are you creating a connection every time in your threads? 3. Do you see any one region server unduly getting more requests than the rest of them (region hotspot)? 4. What is your number of request handler count(hbase.regionserver.handler.count) on HBase? If it's too low, then, your connections on the client would wait before actually getting into the application layer(here, RS).
This is assuming you've given enough memory to your Region servers and your HDFS layer is stable. Hope this helps. On Mon, Nov 4, 2013 at 9:16 AM, <michael.grund...@high5games.com> wrote: > Hi all; I posted this as a question on StackOverflow as well but realized > I should have gone straight ot the horses-mouth with my question. Sorry for > the double post! > > We are running a series of HBase tests to see if we can migrate one of our > existing datasets from a RDBMS to HBase. We are running 15 nodes with 5 > zookeepers and HBase 0.94.12 for this test. > > We have a single table with three column families and a key that is > distributing very well across the cluster. All of our queries are running a > direct look-up; no searching or scanning. Since the HTablePool is now > frowned upon, we are using the Apache commons pool and a simple connection > factory to create a pool of connections and use them in our threads. Each > thread creates an HTableInstance as needed and closes it when done. There > are no leaks we can identify. > > If we run a single thread and just do lots of random calls sequentially, > the performance is quite good. Everything works great until we start trying > to scale the performance. As we add more threads and try and get more work > done in a single VM, we start seeing performance degrade quickly. The > client code is simply attempting to run either one of several gets or a > single put at a given frequency. It then waits until the next time to run, > we use this to simulate the workload from external clients. With a single > thread, we will see call times in the 2-3 milliseconds which is acceptable. > > As we add more threads, this call time starts increasing quickly. What > gets strange is if we add more VMs, the times hold steady across them all > so clearly it's a bottleneck in the running instance and not the cluster. > We can get a huge amount of processing happening across the cluster very > easily - it just has to use a lot of VMs on the client side to do it. We > know the contention isn't in the connection pool as we see the problem even > when we have more connections than threads. Unfortunately, the times are > spiraling out of control very quickly. We need it to support at least 128 > threads in practice, but most important I want to support 500 updates/sec > and 250 gets/sec. In theory, this should be a piece of cake for the cluster > as we can do FAR more work than that with a few VMs, but we don't even get > close to this with a single VM. > > So my question: how do people building high-performance apps with HBase > get around this? What approach are others using for connection pooling in a > multi-threaded environment? There seems to be a surprisingly little amount > of info about this on the web considering the popularity. Is there some > client setting we need to use that makes it perform better in a threaded > environment? We are going to try to cache HTable instances next but that's > a total guess. There are solutions to offloading work to other VMs but we > really want to avoid this as clearly the cluster can handle the load and it > will dramatically decrease the application performance in critical areas. > > Any help is greatly appreciated! Thanks! > -Mike > -- It's just about how deep your longing is!
