2 Commits (e9d6a0d7ce763392b695851924100b09540a70a1)

Author	SHA1	Message	Date
Alan Paxton	c1ec0b28eb	java / jni io_uring support (#9224) ... Summary: Existing multiGet() in java calls multi_get_helper() which then calls DB::std::vector MultiGet(). This doesn't take advantage of io_uring. This change adds another JNI level method that runs a parallel code path using the DB::void MultiGet(), using ByteBuffers at the JNI level. We call it multiGetDirect(). In addition to using the io_uring path, this code internally returns pinned slices which we can copy out of into our direct byte buffers; this should reduce the overall number of copies in the code path to/from Java. Some jmh benchmark runs (100k keys, 1000 key multiGet) suggest that for value sizes > 1k, we see about a 20% performance improvement, although performance is slightly reduced for small value sizes, there's a little bit more overhead in the JNI methods. Closes https://github.com/facebook/rocksdb/issues/8407 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9224 Reviewed By: mrambacher Differential Revision: D32951754 Pulled By: jay-zhuang fbshipit-source-id: 1f70df7334be2b6c42a9c8f92725f67c71631690	3 years ago
Adam Retter	6477075f2c	JMH microbenchmarks for RocksJava (#6241) ... Summary: This is the start of some JMH microbenchmarks for RocksJava. Such benchmarks can help us decide on performance improvements of the Java API. At the moment, I have only added benchmarks for various Comparator options, as that is one of the first areas where I want to improve performance. I plan to expand this to many more tests. Details of how to compile and run the benchmarks are in the `README.md`. A run of these on a XEON 3.5 GHz 4vCPU (QEMU Virtual CPU version 2.5+) / 8GB RAM KVM with Ubuntu 18.04, OpenJDK 1.8.0_232, and gcc 8.3.0 produced the following: ``` # Run complete. Total time: 01:43:17 REMEMBER: The numbers below are just data. To gain reusable insights, you need to follow up on why the numbers are the way they are. Use profilers (see -prof, -lprof), design factorial experiments, perform baseline and negative tests that provide experimental control, make sure the benchmarking environment is safe on JVM/OS/HW level, ask for reviews from the domain experts. Do not assume the numbers tell you what you want them to tell. Benchmark (comparatorName) Mode Cnt Score Error Units ComparatorBenchmarks.put native_bytewise thrpt 25 122373.920 ± 2200.538 ops/s ComparatorBenchmarks.put java_bytewise_adaptive_mutex thrpt 25 17388.201 ± 1444.006 ops/s ComparatorBenchmarks.put java_bytewise_non-adaptive_mutex thrpt 25 16887.150 ± 1632.204 ops/s ComparatorBenchmarks.put java_direct_bytewise_adaptive_mutex thrpt 25 15644.572 ± 1791.189 ops/s ComparatorBenchmarks.put java_direct_bytewise_non-adaptive_mutex thrpt 25 14869.601 ± 2252.135 ops/s ComparatorBenchmarks.put native_reverse_bytewise thrpt 25 116528.735 ± 4168.797 ops/s ComparatorBenchmarks.put java_reverse_bytewise_adaptive_mutex thrpt 25 10651.975 ± 545.998 ops/s ComparatorBenchmarks.put java_reverse_bytewise_non-adaptive_mutex thrpt 25 10514.224 ± 930.069 ops/s ``` Indicating a ~7x difference between comparators implemented natively (C++) and those implemented in Java. Let's see if we can't improve on that in the near future... Pull Request resolved: https://github.com/facebook/rocksdb/pull/6241 Differential Revision: D19290410 Pulled By: pdillinger fbshipit-source-id: 25d44bf3a31de265502ed0c5d8a28cf4c7cb9c0b	5 years ago