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Benchmarks

Vyacheslav Lukianov edited this page Feb 2, 2018 · 18 revisions

JMH Gradle Plugin 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 OpenJDK build 1.8.0_131-1-ojdkbuild-b11 with the following parameters: -Xms1g -Xmx1g. To get results in your environment, run in the project root directory:

./gradlew clean jar jmh

Tokyo Cabinet Benchmark

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

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

Currently, benchmark results are available for Xodus stores with key prefixing (Patricia tree) and without (BTree), MapDb tree map, Chronicle Map, H2 MVStore Map, LMDB JNI and Akiban PersistIt. All the scores are in seconds per single benchmark run for one million keys. Excerpt of the output of the build running benchmarks is as follows:

Benchmark                                                             Mode  Cnt    Score   Error   Units
chronicle.JMHChronicleMapTokyoCabinetReadBenchmark.randomRead           ss   10    0.553 ± 0.020    s/op
chronicle.JMHChronicleMapTokyoCabinetReadBenchmark.successiveRead       ss   10    0.180 ± 0.028    s/op
chronicle.JMHChronicleMapTokyoCabinetWriteBenchmark.randomWrite         ss   10    0.726 ± 0.037    s/op
chronicle.JMHChronicleMapTokyoCabinetWriteBenchmark.successiveWrite     ss   10    0.675 ± 0.008    s/op
env.JMHEnvTokyoCabinetReadBenchmark.randomRead                          ss   10    1.880 ± 0.016    s/op
env.JMHEnvTokyoCabinetReadBenchmark.successiveRead                      ss   10    0.156 ± 0.013    s/op
env.JMHEnvTokyoCabinetWriteBenchmark.randomWrite                        ss   10    1.888 ± 0.059    s/op
env.JMHEnvTokyoCabinetWriteBenchmark.successiveWrite                    ss   10    0.462 ± 0.022    s/op
env.JMHEnvWithPrefixingTokyoCabinetReadBenchmark.randomRead             ss   10    0.994 ± 0.004    s/op
env.JMHEnvWithPrefixingTokyoCabinetReadBenchmark.successiveRead         ss   10    0.240 ± 0.008    s/op
env.JMHEnvWithPrefixingTokyoCabinetWriteBenchmark.randomWrite           ss   10    1.486 ± 0.366    s/op
env.JMHEnvWithPrefixingTokyoCabinetWriteBenchmark.successiveWrite       ss   10    0.461 ± 0.050    s/op
h2.JMH_MVStoreTokyoCabinetReadBenchmark.randomRead                      ss   10   13.092 ± 0.029    s/op
h2.JMH_MVStoreTokyoCabinetReadBenchmark.successiveRead                  ss   10    0.109 ± 0.003    s/op
h2.JMH_MVStoreTokyoCabinetWriteBenchmark.randomWrite                    ss   10    1.860 ± 0.006    s/op
h2.JMH_MVStoreTokyoCabinetWriteBenchmark.successiveWrite                ss   10    0.517 ± 0.097    s/op
lmdb.JMH_LMDBTokyoCabinetReadBenchmark.randomRead                       ss   10    0.825 ± 0.004    s/op
lmdb.JMH_LMDBTokyoCabinetReadBenchmark.successiveRead                   ss   10    0.098 ± 0.001    s/op
lmdb.JMH_LMDBTokyoCabinetWriteBenchmark.randomWrite                     ss   10    0.831 ± 0.003    s/op
lmdb.JMH_LMDBTokyoCabinetWriteBenchmark.successiveWrite                 ss   10    0.178 ± 0.001    s/op
mapdb.JMHMapDbTokyoCabinetReadBenchmark.randomRead                      ss   10    7.154 ± 0.036    s/op
mapdb.JMHMapDbTokyoCabinetReadBenchmark.successiveRead                  ss   10    0.126 ± 0.004    s/op
mapdb.JMHMapDbTokyoCabinetWriteBenchmark.randomWrite                    ss   10    9.075 ± 0.030    s/op
mapdb.JMHMapDbTokyoCabinetWriteBenchmark.successiveWrite                ss   10   11.982 ± 0.132    s/op
persistit.JMHPersistItTokyoCabinetReadBenchmark.randomRead              ss   10    1.316 ± 0.020    s/op
persistit.JMHPersistItTokyoCabinetReadBenchmark.successiveRead          ss   10    0.857 ± 0.301    s/op
persistit.JMHPersistItTokyoCabinetWriteBenchmark.randomWrite            ss   10    1.763 ± 0.019    s/op
persistit.JMHPersistItTokyoCabinetWriteBenchmark.successiveWrite        ss   10    0.632 ± 0.059    s/op                                                                         

The same results in table form:

Random Read Successive Read Random Write Successive Write
Xodus store with key prefixing (Patricia) 0.994 0.240 1.486 0.461
Xodus store without key prefixing (BTree) 1.880 0.156 1.888 0.462
BDB JE database with key prefixing 3.493 1.307 5.937 3.416
BDB JE database without key prefixing 3.288 1.251 5.831 3.458
MapDB tree map 7.154 0.126 9.075 11.982
Chronicle Map 0.553 0.180 0.726 0.675
H2 MVStore Map 13.092 0.109 1.860 0.517
LMDB JNI 0.825 0.098 0.831 0.178
Akiban PersistIt 1.316 0.857 1.763 0.632

Results for BerkeleyDb JE are obtained in a similar environment as above (another JRE used), but the code of benchmark cannot be distributed under Apache 2.0 License.

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