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HBase >> mail # user >> HBase Client Performance Bottleneck in a Single Virtual Machine


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Re: HBase Client Performance Bottleneck in a Single Virtual Machine
Hi Micheal,

can you try to create a single HConnection in your client:
HConnectionManager.createConnection(Configuration conf) or
HConnectionManager.createConnection(Configuration conf, ExecutorService pool)

Then use HConnection.getTable(...) each time you need to do an operation.

I.e.
Configuration conf = ...;
ExecutorService pool = ...;
// create a single HConnection for you vm.
HConnection con = HConnectionManager.createConnection(Configuration conf, ExecutorService pool);
// reuse the connection for many tables, even in different threads
HTableInterface table = con.getTable(...);
// use table even for only a few operation.
table.close();
...
HTableInterface table = con.getTable(...);
// use table even for only a few operation.
table.close();
...
// at the end close the connection
con.close();

-- Lars

________________________________
 From: "[EMAIL PROTECTED]" <[EMAIL PROTECTED]>
To: [EMAIL PROTECTED]
Sent: Sunday, November 3, 2013 7:46 PM
Subject: HBase Client Performance Bottleneck in a Single Virtual Machine
 

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