Neo: A Learned Query Optimizer (1904.03711v1)

Abstract: Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.

• The paper introduces an end-to-end deep learning framework that replaces hand-crafted cost models with a value network to predict query execution times.
• The paper uses training from demonstration by leveraging PostgreSQL to accelerate learning optimal query strategies.
• The paper demonstrates Neo's robust performance and adaptability through a hybrid reinforcement learning and best-first search strategy that surpasses commercial systems.

An Expert Overview of "Neo: A Learned Query Optimizer"

The paper "Neo: A Learned Query Optimizer" explores the innovative integration of machine learning into the domain of database query optimization. Neo, or Neural Optimizer, is presented as a novel system that leverages deep neural networks to generate query execution plans, a paradigm shift from traditional optimizer approaches that rely heavily on manual tuning and heuristic-based techniques. This summary provides an expert perspective on the key contributions, methodologies, and implications of the research outlined in the paper.

Contributions and Methodology

Neo sets itself apart by striving to learn query execution plans end-to-end using deep learning, a task traditionally approached with rule-based systems. The paper highlights several key contributions:

1. Learning-Based Approach: Neo introduces an end-to-end learning framework for query optimization. It diverges from the convention of relying on hand-crafted cost models, instead employing a value network to predict query execution times.
2. Training from Demonstration: The system uses existing optimizers like PostgreSQL as training sources. This "learning from demonstration" technique accelerates Neo's training process towards discovering effective optimization strategies more swiftly than learning from scratch.
3. Framework Design: The architecture involves a combination of deep reinforcement learning and a best-first search strategy. Through multiple tree convolution layers and dynamic pooling, Neo captures rich information about query plans, enabling it to generalize better than conventional systems.
4. Focus on Core Challenges: Neo addresses essential aspects of the query optimization process, such as join ordering, physical operator selection, and index utilization, fundamentally replacing these heuristic-tuned strategies with learned decisions.
5. Performance and Scalability: The optimizer demonstrated in experiments that even when bootstrapped with relatively simple optimizers like PostgreSQL, Neo can achieve or surpass the performance of state-of-the-art commercial systems such as those from Oracle and Microsoft.

Implications and Future Directions

The introduction of Neo has significant implications for both practical database management and theoretical aspects of machine learning applications:

• Reduction in Human Effort: Neo's potential to reduce human engineering effort in optimizing database queries is profound. By learning optimal strategies dynamically, Neo can adapt to evolving database conditions and workloads, thus easing maintenance challenges entailed in conventional systems.
• Generalization Capacity: The optimizer's ability to generalize across unseen queries marks a considerable advancement, suggesting a future direction where database optimizers can adapt across different datasets and workloads with minimal human intervention.
• Robustness and Adaptability: Neo's design inherently allows it to adjust to inaccuracies in cardinality estimation, a notable concern in traditional systems. This adaptability makes it more robust in unpredictable operational environments.
• Inspirational Framework: Neo's success could inspire further research into machine learning-backed optimization for other database management challenges. Similar to how neural approaches have transformed fields like image recognition, Neo paves the way for renewed strategies in query optimization.

In summary, "Neo: A Learned Query Optimizer" represents a pivotal stride in reimagining how data management systems can harness machine learning for optimization tasks. The paper's exploration of using deep networks to automate and improve the highly complex process of query optimization is not only innovative but also sets a benchmark for future research directions in the field.