APOLLO: Automatic detection and diagnosis of performance regressions in database systems

Jinho Jung, Hong Hu, Joy Arulraj, Taesoo Kim, Woonhak Kang

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

Abstract

The practical art of constructing database management systems (DBMSs) involves a morass of trade-offs among query execution speed, query optimization speed, standards compliance, feature parity, modularity, portability, and other goals. It is no surprise that DBMSs, like all complex software systems, contain bugs that can adversely affect their performance. The performance of DBMSs is an important metric as it determines how quickly an application can take in new information and use it to make new decisions. Both developers and users face challenges while dealing with performance regression bugs. First, developers usually find it challenging to manually design test cases to uncover performance regressions since DBMS components tend to have complex interactions. Second, users encountering performance regressions are often unable to report them, as the regression-triggering queries could be complex and database-dependent. Third, developers have to expend a lot of effort on localizing the root cause of the reported bugs, due to the system complexity and software development complexity. Given these challenges, this paper presents the design of APOLLO, a toolchain for automatically detecting, reporting, and diagnosing performance regressions in DBMSs. We demonstrate that APOLLO automates the generation of regression-triggering queries, simplifies the bug reporting process for users, and enables developers to quickly pinpoint the root cause of performance regressions. By automating the detection and diagnosis of performance regressions, APOLLO reduces the labor cost of developing efficient DBMSs.

Original languageEnglish (US)
Pages (from-to)57-70
Number of pages14
JournalProceedings of the VLDB Endowment
Volume13
Issue number1
DOIs
StatePublished - 2020
Event46th International Conference on Very Large Data Bases, VLDB 2020 - Virtual, Japan
Duration: Aug 31 2020Sep 4 2020

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

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