  Time-series data has an increasingly growing usage in Industrial Internet of
Things (IIoT) and large-scale scientific experiments. Managing time-series data
needs a storage engine that can keep up with their constantly growing volumes
while providing an acceptable query latency. While traditional ACID databases
favor consistency over performance, many time-series databases with novel
storage engines have been developed to provide better ingestion performance and
lower query latency. To understand how the unique design of a time-series
database affects its performance, we design SciTS, a highly extensible and
parameterizable benchmark for time-series data. The benchmark studies the data
ingestion capabilities of time-series databases especially as they grow larger
in size. It also studies the latencies of 5 practical queries from the
scientific experiments use case. We use SciTS to evaluate the performance of 4
databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and
PostgreSQL.
