Debunking the Myth of Join Ordering: Toward Robust SQL Analytics (2502.15181v2)

Abstract: Join order optimization is critical in achieving good query performance. Despite decades of research and practice, modern query optimizers could still generate inferior join plans that are orders of magnitude slower than optimal. Existing research on robust query processing often lacks theoretical guarantees on join-order robustness while sacrificing query performance. In this paper, we rediscover the recent Predicate Transfer technique from a robustness point of view. We introduce two new algorithms, LargestRoot and SafeSubjoin, and then propose Robust Predicate Transfer (RPT) that is provably robust against arbitrary join orders of an acyclic query. We integrated Robust Predicate Transfer with DuckDB, a state-of-the-art analytical database, and evaluated against all the queries in TPC-H, JOB, and TPC-DS benchmarks. Our experimental results show that RPT improves join-order robustness by orders of magnitude compared to the baseline. With RPT, the largest ratio between the maximum and minimum execution time out of random join orders for a single acyclic query is only 1.6x (the ratio is close to 1 for most evaluated queries). Meanwhile, applying RPT also improves the end-to-end query performance by 1.5x (per-query geometric mean). We hope that this work sheds light on solving the practical join ordering problem.

• The paper introduces the Robust Predicate Transfer (RPT) method and two algorithms, LargestRoot and SafeSubjoin, to improve the robustness of acyclic join order optimization against cardinality estimation errors.
• Integrated into DuckDB, the RPT framework demonstrated significant performance improvements, averaging around 1.5 imes faster on benchmarks like TPC-H and TPC-DS for acyclic queries.
• The findings have practical implications for database optimizer design by reducing dependency on precise cardinality estimates and theoretical implications for extending robust methods to cyclic queries.

Insights into "Debunking the Myth of Join Ordering: Toward Robust SQL Analytics"

The paper, "Debunking the Myth of Join Ordering: Toward Robust SQL Analytics," addresses a significant challenge in the field of query optimization: the optimization of join orders, which remains problematic due to its inherent complexity and the persistent inaccuracies in cardinality estimations. Despite advancements in query processing, today's optimizers often generate suboptimal join plans, which can be vastly slower than the ideal scenario. The authors propose a novel approach through their exploration of the Robust Predicate Transfer (RPT) method, which promises to enhance robustness against arbitrary join orders in acyclic queries.

Methodological Advancements

The paper primarily revisits and extends the Predicate Transfer (PT) technique by focusing on its robustness to various join orders. It introduces two new algorithms, LargestRoot and SafeSubjoin, enhancing the robustness guarantees associated with PT in the context of acyclic joins—an area previously not fully addressed by conventional methods. The paper grounds its theoretical claims in Yannakakis’s algorithm, which provides a complexity of $O(N + OUT)$ for acyclic queries.

LargestRoot Algorithm: This algorithm constructs a maximum spanning tree on the join graph to guarantee full semi-join reductions by placing the largest relation as the root of the join tree, optimizing the evaluation order from the root downwards.

SafeSubjoin Algorithm: This technique focuses on ensuring that subjoins within a query plan are safe, meaning they are connected within at least one join tree of the query, thus preventing intermediate result blowup during execution.

Experimental Validation

The authors integrated their RPT framework within DuckDB, a modern analytical database management system, and evaluated its performance across TPC-H, JOB, and TPC-DS benchmarks. Robust Predicate Transfer demonstrated substantial improvements in execution time and join order robustness for acyclic queries, consistently outperforming conventional joins without the proposed optimizations. Performance improvements averaged around $1.5\times$ over baseline methods, underlining the efficiency gains while maintaining theoretical robustness.

Practical and Theoretical Implications

Practically, the advancements in join optimization detailed in this paper can influence the design and implementation of query optimizers in database systems by reducing the dependency on precise cardinality estimations, thus broadening the scope for employing machine learning methods without the punitive cost of significant errors.

Theoretically, the work paves the way for further investigations into robust query processing beyond acyclic joins, looking at potential strategies to extend these guarantees to cyclic queries. The strength of RPT lies in its ability to avoid catastrophic performance losses due to suboptimal cardinality predictions, a common pitfall in existing systems.

Future Directions

While RPT significantly advances the robustness of acyclic query processing, it does highlight existing challenges in cyclic query environments, opening up an area for future research. Moreover, the paper suggests potential integration with worst-case optimal join algorithms to cater to cyclic query patterns, hinting at a hybrid future where robust and worst-case approaches are combined for broader applicability.

Overall, the contributions made in this paper succinctly address one of the persisting challenges in database management systems, offering a robust solution grounded in sound theoretical foundations and validated through extensive empirical evaluation. The methodology adopted by the authors is both compelling and practical, setting a benchmark for future work in query optimization.