This project is the source code of TS-Benchmark.

The general steps to complete the test are:

1. Data generation cd data_generation

2. Train DCGAN model python DCGAN.py Run the encoder_dc.py file to train the encoder, python encoder_dc.py Finally execute the test python test_dc.py

3. Data Import Since each database have different build-in tools for data import, we have defined some tools related to data import in the tsdb-test/data/load directory

4. build project cd Tsdb-benchmark/ts-benchmark/ sh build.sh

5. config parametes of database and run the benchmark cd Tsdb-benchmark/ts-benchmark/ vim run.sh (choose database and test mode) sh run.sh

The configuration of TSDBs is shown as follows:

• InfluxDB. We enlarge the default values of some important parameters of the TSM engine for better performance of the system. For example, the parameter wal-fsync- delay is set as "0s", the parameter cache-max-memory-size is set to 1,048,576,000 bytes, and the parameter cache-snapshot-memory-size is enlarged to “100M” and so on. Maximum memory size is sufficient. The parameter max-values-per-tag is set as 0 to allow an unlimited number of tag values.

• TimescaleDB. The parameter shared-buffers is set as 8GB, maintenance-work-mem is set as 2GB, checkpoint-completion-target is set as 0.7, min_wal size is set as 1GB, and max wal size is 2GB. Parameters of PostgreSQL is set based on PgTune.

• Druid. The parameter Roll-up is set as true, and Granu-larity is set as hour. For local batch import, the parameter maxRowsPerSegment is set as 10M, maxRowsInMemory is set as 20M, and maxTotalRows is set as 100M.

• OpenTSDB. The parameter tsd-http-request-enable-chunked is enabled, tsd-http-request-max-chunk is set as 32KB, tsd-core-auto-create-metrics is set as true, and the parameter tsd-storage-enable-compaction is set to be false to improve the write performance.

More information please ref to for detail