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

SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things