QOVIS: Understanding and Diagnosing Query Optimizer via a Visualization-assisted Approach (Revision)

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Abstract

Understanding and diagnosing query optimizers is crucial to guarantee the correctness and efficiency of query processing in database systems. However, achieving this is non-trivial as there are three technical challenges: (i) hundreds and thousands of query plans are generated for each query during the query optimization procedure; (ii) the transformation logic among query plans is not easy to investigate even for expert database system developers; and (iii) navigating users to the root causes of the bugs/errors is inherently hard as the changes of the operators among query plans are missing in the query processing log. In this work, we propose QOVIS to overcome these challenges, which identifies the query optimization bugs/issues and investigates their root causes via a visualization-assisted approach. Specifically, QOVIS consists of data preprocessing layer, transformation logic computation layer, and visual analysis layer. We conduct extensive experimental studies (e.g., user study, case study, and performance study) to evaluate the efficiency and effectiveness of QOVIS . In particular, our user study (on 24 database developers and researchers) confirms that QOVIS significantly reduces the time required to investigate the bugs/errors in the query optimizer. Moreover, the generality of QOVIS is verified by utilizing it to understand and diagnose the real-world reported bugs/errors in different query optimizers of three widelyused systems: Apache Spark, Apache Hive, and DuckDB.