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Enhancing query optimization with hybrid ranking model and tree node dependency
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AI
DOI：10.1007/s11761-025-00473-8.png)
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Abstract
En 中文
With the rise of artificial intelligence in database query optimization, learning-based methods have become a promising approach to improving query performance. However, current learned optimizers struggle to model the complex structures of candidate plans and adapt to dynamic workloads. To address these issues, we propose Saro+, an enhanced learning-based optimizer building on our prior work, Saro. Saro+ introduces Tree Node Dependency Features to better capture execution plan structure and a Hybrid Ranking Model that combines listwise and pairwise strategies, balancing global and local optimization. Experiments on STATS and IMDb show that Saro+ outperforms both the PostgreSQL optimizer and single-strategy learned methods in query latency and stability, demonstrating strong robustness and adaptability under high concurrency and workload variability.
Keywords:
AI4DB
Learning-to-Rank
Learned Query optimizer
Hybrid Rank Model
Journal
S
IF:
1.7
Papers:
21
Citations:
0
