Benchmarking and Performance Analysis of Event Sequence Queries on Relational Database
The relational database has been the fundamental technology for data-driven decision making based on the histories of event occurrences about the analysis target. Thus the performance of analytical workloads in relational databases has been studied intensively. As a common language for performance analysis, decision support benchmarks such as TPC-H have been widely used. These benchmarks focus on summarization of the event occurrence information. Individual event occurrences or inter-occurrence associations are rarely examined in these benchmarks. However, this type of query, called an event sequence query in this paper, is becoming important in various real-world applications. Typically, an event sequence query extracts event sequences starting from a small number of interesting event occurrences. In a relational database, these queries are described by multiple self-joins on the whole sequence of events. Furthermore, each pair of events to be joined tends to have a strong correlation in the timestamp attribute, resulting in heavily skewed join workloads. Despite the usefulness in real-world data analysis, very little work has been done on performance analysis of event sequence queries.
In this paper, we present the initial design of ESQUE benchmark, a benchmark for event sequence queries. We then give experimental results of the comparison of database system implementations: PostgreSQL v.s. MySQL, and the comparison of historical versions of PostgreSQL. Conducted performance analysis shows that ESQUE benchmark allows us to discover performance problems which had been overlooked in existing benchmarks.
KeywordsEvent sequence query Relational database Performance analysis Benchmark Data analytics
This paper is in part based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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