I have a cluster of 13 Linux RedHat machines (each one with 4 CPU cores and 8GB RAM) and I configured it to form a HBase cluster of 1 master and 12 region servers, with HBase-0.20.0 code base. For the cluster configuration, I followed what was described in the article “HBase-0.20.0 Performance Evaluation” by Anty Rao and Schubert Zhang on August 21, 2009 (accessible from: http://www.slideshare.net/schubertzhang/hbase-0200-performance-evaluation, which was also discussed in one of the HBase mailing list archives in the last several months). So I allocated 4GB heap for each HBase-related process, with the “hbase.regionserver.handler.count” set to be “20”. A small difference is that I used the HBase managed Zoo-keeper to manage the Hbase Masters.
I then set up an Hbase table with a row key of 48 bytes, and a column that holds about 20 Bytes data. For a single client, I was able to get in average, the write of 0.6 milliseconds per row (random write), and the read of 0.4 milliseconds per row (random read). Then I had each machine in the cluster to launch 1, or 2, or 3 client test applications, with each client test application read/write 100000 rows for each test run, for throughput testing. From my measurement results, I found that the random write will have best measured performance when each machine having 2 clients (totally 2*13=26 clients in the cluster), with 8500 rows/second; and the random read will have almost the same throughput for 2 or 3 clients, with 35000 rows/second. Since I designed the Hbase table to be accessed via the Web Service for random read or write data access, the HTable instance is created in a default mode, that is, I did not have customized settings for “setAutoFlush(false)”, “setWriteBufferSize(**)”, etc. I also incorporated the HTablePool so that each test client application will use the same HTable instance. But I observed little improvement on my random write testing from the above numbers, with or without table pooling. So the question that I have is that, following the original Google’s BigTable paper, should Random Write be always much faster than Random Read? If that is the case, what are the tunable parameters in terms of HBase setup that I can explore to improve the Random Write speed. I also downloaded the PerformanceEvaluation.java that is patched by Schubert Zhang (the link to the code is in the article mentioned above), and used it to test my cluster’s performance as well. In my cluster, to read/write 4,194,280 rows, I will need 274 seconds for random writes (translated to15307 row/second) and 305 seconds for random reads (translated to 13751 row/second). Notice that although random write is still faster than random read, but they are almost compatible. As a comparison, in the measurement result reported by Schubert Zhang (in the article mentioned above), with the smaller test environment that he had (1 master and 4 slaves, 4 CPU core/machine, 8GB RAM per machine), it was reported to obtain the random write of 11366 row/second and the random read of 4424 row/second. That is, the random read does get significantly improved in my case as I had more machines in the cluster, but not the random write. Please help on making comments and suggestions, for possible performance improvement on random write. Regards, Jun Li
