Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation

TL;DR

This paper introduces a hybrid MILP-based approach for large-scale join order optimization, effectively scaling to queries with up to 100 relations and outperforming existing methods in plan quality.

Contribution

It presents a novel MILP model for bushy tree join structures and a hybrid framework that combines MILP with heuristic methods for scalable, high-quality query optimization.

Findings

Scales to join queries with up to 100 relations.

Achieves superior plan quality compared to existing methods.

Efficiently balances MILP and heuristic approaches for large queries.

Abstract

Finding optimal join orders is among the most crucial steps to be performed by query optimisers. Though extensively studied in data management research, the problem remains far from solved: While query optimisers rely on exhaustive search methods to determine ideal solutions for small problems, such methods reach their limits once queries grow in size. Yet, large queries become increasingly common in real-world scenarios, and require suitable methods to generate efficient execution plans. While a variety of heuristics have been proposed for large-scale query optimisation, they suffer from degrading solution quality as queries grow in size, or feature highly sub-optimal worst-case behavior, as we will show. We propose a novel method based on the paradigm of mixed integer linear programming (MILP): By deriving a novel MILP model capable of optimising arbitrary bushy tree structures, we address the limitations of existing MILP methods for join ordering, and can rely on highly optimised MILP solvers to derive efficient tree structures that elude competing methods. To ensure optimisation efficiency, we embed our MILP method into a hybrid framework, which applies MILP solvers precisely where they provide the greatest advantage over competitors, while relying on more efficient methods for less complex optimisation steps. Thereby, our approach gracefully scales to extremely large query sizes joining up to 100 relations, and consistently achieves the most robust plan quality among a large variety of competing join ordering methods.