Learning to Optimize Join Queries With Deep Reinforcement Learning (1808.03196v2)

Abstract: Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space. The design and implementation of heuristics are well-understood when the cost model is roughly linear, and we find that these heuristics can be significantly suboptimal when there are non-linearities in cost. Ideally, instead of a fixed heuristic, we would want a strategy to guide the search space in a more data-driven way---tailoring the search to a specific dataset and query workload. Recognizing the link between classical Dynamic Programming enumeration methods and recent results in Reinforcement Learning (RL), we propose a new method for learning optimized join search strategies. We present our RL-based DQ optimizer, which currently optimizes select-project-join blocks. We implement three versions of DQ to illustrate the ease of integration into existing DBMSes: (1) A version built on top of Apache Calcite, (2) a version integrated into PostgreSQL, and (3) a version integrated into SparkSQL. Our extensive evaluation shows that DQ achieves plans with optimization costs and query execution times competitive with the native query optimizer in each system, but can execute significantly faster after learning (often by orders of magnitude).

• The paper introduces the DQ optimizer, which uses deep reinforcement learning to tackle the join ordering problem in query optimization.
• It models join query planning as a Markov Decision Process, employing Q-learning to estimate the long-term value of candidate plans.
• Empirical results show that DQ achieves significant speedups and adapts to non-linear cost models in systems like PostgreSQL and SparkSQL.

Overview of "Learning to Optimize Join Queries With Deep Reinforcement Learning"

The paper "Learning to Optimize Join Queries With Deep Reinforcement Learning" contributes a novel approach to query optimization by integrating deep reinforcement learning (RL) into the classical problem of join order selection in query processing systems. The paper leverages the algorithmic similarity between dynamic programming-based enumeration methods used in traditional query optimizers and the decision-making process in reinforcement learning. This connection enables the authors to propose and implement the DQ optimizer, an RL-based system designed to optimize select-project-join blocks by orchestrating data-driven join order strategies tailored specifically to the characteristics of the underlying dataset and query workload.

Key Contributions

1. Integration of Reinforcement Learning with Query Optimization: The authors propose a model where the sequential decision-making involved in join ordering is treated as a Markov Decision Process (MDP). The Q-learning algorithm, a reinforcement learning approach, estimates the long-term value of potential join plans, offering a stochastic optimization technique that can adapt via its learning model based on previous execution feedback.
2. Cost Model Adaptation: The paper outlines the limitations of fixed heuristic-based query optimizers, especially in the presence of non-linear cost models. DQ is intended to address these challenges by learning policies that consider the specific costs associated with each database instance and query pattern.
3. DQ Implementations: Three implementations of DQ illustrate its integration feasibility into existing systems:
   • DQ integrated with Apache Calcite.
   • DQ embedded within PostgreSQL.
   • A deployment prototype with SparkSQL.
4. Experimentation: The authors thoroughly evaluate DQ using the Join Order Benchmark and TPC-DS, demonstrating significant speedups in both optimization and execution time compared to native query planners across various cost models. Particularly in non-linear cost model scenarios, DQ shows robustness by approaching optimization time akin to heuristic methods while achieving plan quality close to exhaustive enumeration.
5. Learning from Execution Feedback: The paper also explores how DQ can be fine-tuned using real execution runtimes to correct inaccuracies in the estimated cost model, allowing the optimizer to adjust further and improve efficiency in evolving operational environments.

Implications and Future Directions

The research provides a strong indication that integrating learning techniques within query optimization can address some of the fundamental challenges associated with changing cost dynamics and heuristic limitations. By leveraging the advances in reinforcement learning, the paper puts forward a methodology that can theoretically extend beyond the traditional limitations of static cost models, potentially fostering a new era of adaptive query optimization systems capable of dynamically responding to complex database environments.

For future developments, the paper ignites several prospects:

• Expanding DQ's applicability across other relational operators and exploring its integration into more generalized query optimization frameworks.
• Enhancing feature engineering and deep network architectures to handle even broader scopes of query patterns and cost models, possibly exploring end-to-end learning techniques.
• Extending the research to accommodate increasingly complex real-world workloads and multi-query optimization scenarios, bridging the gap toward fully adaptive, self-optimizing database systems.

In conclusion, "Learning to Optimize Join Queries With Deep Reinforcement Learning" presents a compelling advancement in the convergence of machine learning and database systems, offering a data-driven, learning-based perspective on solving one of the core NP-hard problems within databases—the join ordering problem. Through substantial empirical evaluations and practical implementations, the work paves the path for further research and development in adaptive, AI-augmented query optimization solutions.