SciTS：科学实验和工业物联网中时序数据库的基准
SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things
摘要：
时间序列数据在工业物联网（IIoT）和大规模科学实验中的使用越来越多。管理时间序列数据需要一个存储引擎，它可以跟上不断增长的数据量，同时提供可接受的查询延迟。虽然传统的ACID数据库更倾向于一致性而不是性能，但已经开发了许多具有新颖存储引擎的时间序列数据库，以提供更好的摄取性能和更低的查询延迟。为了了解时间序列数据库的独特设计如何影响其性能，我们设计了SciTS，这是一个高度可扩展和可参数化的时间序列数据基准。该基准测试研究了时间序列数据库的数据摄取能力，特别是当它们的规模越来越大时。并从科学实验用例出发，研究了5个实用查询的语义。我们使用SciTS来评估4个不同存储引擎的4个数据库的性能：ClickHouse，InfluxDB，TimescaleDB和PostgreSQL。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
