ce8e88d2d7
Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222 |
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.. | ||
advisor | ||
dump | ||
rdb | ||
auto_sanity_test.sh | ||
benchmark_leveldb.sh | ||
benchmark.sh | ||
blob_dump.cc | ||
check_format_compatible.sh | ||
CMakeLists.txt | ||
db_bench_tool_test.cc | ||
db_bench_tool.cc | ||
db_bench.cc | ||
db_crashtest.py | ||
db_repl_stress.cc | ||
db_sanity_test.cc | ||
db_stress.cc | ||
dbench_monitor | ||
Dockerfile | ||
generate_random_db.sh | ||
ingest_external_sst.sh | ||
ldb_cmd_impl.h | ||
ldb_cmd_test.cc | ||
ldb_cmd.cc | ||
ldb_test.py | ||
ldb_tool.cc | ||
ldb.cc | ||
pflag | ||
reduce_levels_test.cc | ||
regression_test.sh | ||
report_lite_binary_size.sh | ||
rocksdb_dump_test.sh | ||
run_flash_bench.sh | ||
run_leveldb.sh | ||
sample-dump.dmp | ||
sst_dump_test.cc | ||
sst_dump_tool_imp.h | ||
sst_dump_tool.cc | ||
sst_dump.cc | ||
trace_analyzer_test.cc | ||
trace_analyzer_tool.cc | ||
trace_analyzer_tool.h | ||
trace_analyzer.cc | ||
verify_random_db.sh | ||
write_external_sst.sh | ||
write_stress_runner.py | ||
write_stress.cc |