Benchmarks

JMH is used to build and run benchmarks. Benchmark results are obtained on a PC running under Windows 7 with Intel(R) Core(TM) i7-3770 3.4 GHz CPU and 64-bit JRE build 1.8.0_77-b03 with the following parameters: -Xms1g -Xmx1g. Nothing else is configured, tests run with default settings. To get results in your environment, run in the project root directory:

./gradlew testBenchmarksJMH

Tokyo Cabinet Benchmark

Tokyo Cabinet Benchmark is useful for comparison of performance of key/value storages. For one million 8-char string keys ('00000000', '00000001', etc.) and values equal to the keys, 4 operations are measured:

1. writing all key/value pairs in ascedning order;
2. writing all key/value pairs in random order;
3. reading all key/value pairs in ascedning order;
4. reading all key/value pairs in random order.

Currently benchmark results are available for Xodus stores with and without key prefixing, Berkeley DB JE databases with and without key prefixing, MapDb tree map, and Chronicle Map.

# Run complete. Total time: 02:13:06

Benchmark                                                             Mode  Cnt   Score   Error   Units
chronicle.JMHChronicleMapTokyoCabinetReadBenchmark.randomRead           ss   16   0.829 - 0.075    s/op
chronicle.JMHChronicleMapTokyoCabinetReadBenchmark.successiveRead       ss   16   0.098 - 0.005    s/op
chronicle.JMHChronicleMapTokyoCabinetWriteBenchmark.randomWrite         ss   16   1.238 - 0.133    s/op
chronicle.JMHChronicleMapTokyoCabinetWriteBenchmark.successiveWrite     ss   16   1.184 - 0.099    s/op
env.JMHEnvTokyoCabinetReadBenchmark.randomRead                          ss   60   2.692 - 0.045    s/op
env.JMHEnvTokyoCabinetReadBenchmark.successiveRead                      ss   60   0.117 - 0.005    s/op
env.JMHEnvTokyoCabinetWriteBenchmark.randomWrite                        ss   60   2.208 - 0.046    s/op
env.JMHEnvTokyoCabinetWriteBenchmark.successiveWrite                    ss   60   0.699 - 0.018    s/op
env.JMHEnvWithPrefixingTokyoCabinetReadBenchmark.randomRead             ss   60   1.289 - 0.020    s/op
env.JMHEnvWithPrefixingTokyoCabinetReadBenchmark.successiveRead         ss   60   0.292 - 0.048    s/op
env.JMHEnvWithPrefixingTokyoCabinetWriteBenchmark.randomWrite           ss   60   1.661 - 0.074    s/op
env.JMHEnvWithPrefixingTokyoCabinetWriteBenchmark.successiveWrite       ss   60   0.777 - 0.064    s/op
je.JMH_JETokyoCabinetReadBenchmark.randomRead                           ss   60   3.208 - 0.027    s/op
je.JMH_JETokyoCabinetReadBenchmark.successiveRead                       ss   60   1.252 - 0.189    s/op
je.JMH_JETokyoCabinetWriteBenchmark.randomWrite                         ss   60   5.844 - 0.145    s/op
je.JMH_JETokyoCabinetWriteBenchmark.successiveWrite                     ss   60   3.441 - 0.187    s/op
je.JMH_JEWithPrefixingTokyoCabinetReadBenchmark.randomRead              ss   60   3.504 - 0.070    s/op
je.JMH_JEWithPrefixingTokyoCabinetReadBenchmark.successiveRead          ss   60   1.302 - 0.200    s/op
je.JMH_JEWithPrefixingTokyoCabinetWriteBenchmark.randomWrite            ss   60   5.905 - 0.205    s/op
je.JMH_JEWithPrefixingTokyoCabinetWriteBenchmark.successiveWrite        ss   60   3.411 - 0.209    s/op
mapdb.JMHMapDbTokyoCabinetReadBenchmark.randomRead                      ss   16   6.705 - 0.196    s/op
mapdb.JMHMapDbTokyoCabinetReadBenchmark.successiveRead                  ss   16   0.274 - 0.091    s/op
mapdb.JMHMapDbTokyoCabinetWriteBenchmark.randomWrite                    ss   16  21.822 - 0.380    s/op
mapdb.JMHMapDbTokyoCabinetWriteBenchmark.successiveWrite                ss   16  26.588 - 0.517    s/op