[Experiment, Analysis, and Benchmark] Systematic Evaluation of Plan-based Adaptive Query Processing
Pei Mu, Anderson Chaves Carniel, Antonio Barbalace, Amir Shaikhha

TL;DR
This paper systematically evaluates plan-based adaptive query processing, revealing environment-specific performance sources and guiding future DBMS optimization strategies.
Contribution
It provides the first comprehensive analysis of plan-based AQP across different storage architectures, highlighting the distinct sources of performance improvements.
Findings
In on-disk DBMS, plan reordering yields most speedups.
In in-memory DBMS, cardinality refinement significantly improves performance.
Plan-based AQP outperforms state-of-the-art related methods.
Abstract
Unreliable cardinality estimation remains a critical performance bottleneck in database management systems (DBMSs). Adaptive Query Processing (AQP) strategies address this limitation by providing a more robust query execution mechanism. Specifically, plan-based AQP achieves this by incrementally refining cardinality using feedback from the execution of sub-plans. However, the actual reason behind the improvements of plan-based AQP, especially across different storage architectures (on-disk vs. in-memory DBMSs), remains unexplored. This paper presents the first comprehensive analysis of state-of-the-art plan-based AQP. We implement and evaluate this strategy on both on-disk and in-memory DBMSs across two benchmarks. Our key findings reveal that while plan-based AQP provides overall speedups in both environments, the sources of improvement differ significantly. In the on-disk DBMS,…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Data Management and Algorithms · Cloud Computing and Resource Management
