Deep Reinforcement Learning for Join Order Enumeration (1803.00055v2)

Abstract: Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for "learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency.

• The paper introduces ReJOIN, which leverages deep reinforcement learning to adaptively optimize join order selection based on past query performance.
• It demonstrates that DRL can reduce query plan costs by approximately 20% compared to traditional PostgreSQL optimizers.
• The study shows that ReJOIN maintains near-constant optimization time regardless of query complexity, underscoring its scalability in database systems.

Deep Reinforcement Learning for Join Order Enumeration: An Expert Overview

Join order selection is a critical factor in query optimization, profoundly impacting the performance of database systems. The paper "Deep Reinforcement Learning for Join Order Enumeration" addresses the limitations of traditional query optimizers, which often utilize static, heuristic-based approaches without the ability to improve based on prior outcomes. The authors propose ReJOIN, a novel approach leveraging deep reinforcement learning (DRL) to dynamically enhance the join order enumeration process.

Core Contributions

The paper introduces ReJOIN, a proof-of-concept join order enumerator leveraging DRL algorithms. This approach marks a methodological shift from the static nature of conventional query optimizers, enabling the system to adaptively learn from historical queries. The primary contributions of this research include:

1. Dynamic Join Order Optimization: ReJOIN employs DRL to optimize join orders by learning from previous query execution results, thereby overcoming the stateless nature of existing optimizers. The trained model can adaptively generate more cost-effective execution plans by factoring in past performance feedback.
2. Proof of Concept: The authors demonstrate through ReJOIN that DRL can be effectively applied to join order optimization. Notably, it provides evidence that neural networks, when integrated properly, do not necessarily introduce prohibitive computational overhead into database management systems.
3. Preliminary Results: The empirical evaluation shows that ReJOIN can match or exceed the performance of PostgreSQL's built-in optimizer in terms of both the quality and execution cost of plans. Importantly, ReJOIN achieves superior planning efficiency, maintaining near-constant optimization time regardless of query complexity measured by the number of joined relations.

Technical Approach

The paper formulates the join order selection problem as a DRL task where each query corresponds to an episode. States represent subtrees in join trees with associated predicates, and actions involve combining subtrees into a binary join tree, with each episode culminating in a complete join order. Rewards are computed based on the cost model of the database, which the authors argue as a proxy for performance due to practical constraints on using actual query execution latency for scaling learning.

For reinforcement learning, ReJOIN adopts the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art technique that optimizes policy parameters through gradient ascent methods. This allows the system to incrementally refine its decision-making by balancing exploration of new strategies and exploitation of known reward-enhancing actions.

Results and Analysis

The experiments reveal that ReJOIN, once adequately trained, generates query plans with costs average 20% lower than those from PostgreSQL's optimizer. Additionally, the latency of executing ReJOIN-produced plans consistently outperforms PostgreSQL, confirming the practical benefits of DRL in join order selection.

From an efficiency standpoint, ReJOIN maintains an advantage in optimization time, notably exhibiting slight increases in planning time regardless of query complexity. This capability underlines the practicality of deploying DRL methods within real-world query optimization processes.

Implications and Future Directions

This research opens multiple avenues for further investigation:

• Latency-driven Rewards: Transitioning from cost-based to actual latency-based rewards could further refine the accuracy of the optimization process, albeit at the expense of increased training time due to the need for full query execution.
• Expanded Optimization Scope: Currently focused on join order, ReJOIN's DRL framework could be expanded to address broader optimization tasks, including index selection and operator-level decisions, moving toward a more comprehensive end-to-end query optimization approach.

In summary, the paper presents a promising application of DRL to a well-established problem in database systems, setting the stage for more adaptive and efficient query optimization techniques. As the field progresses, integrating learning-based optimizers holds the potential to drastically improve database management and pave the way for increasingly intelligent systems.