Dynamic Query Optimizer Using Reinforcement Learning
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Solution Overview
Problem
Existing join optimization techniques in distributed file systems face challenges such as outdated statistical information, inability to capture complex data relationships, and high computational overhead, leading to suboptimal query execution plans.
Innovation Solution
A dynamic query optimizer system that incorporates a transfer learning engine and a reinforcement learning engine to learn from historical query execution data, predict optimal join orders, algorithms, and types, and adapt to changing workloads and data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional join optimizers use statistical information for cost estimation, then query execution plans can be generated, but the statistical information becomes outdated and leads to suboptimal execution plans
Solution Approach 1:
The patent implements dynamic query optimization by continuously updating statistical information about data distributions and access patterns. The system transitions from static, outdated statistics to dynamic, real-time statistics that adapt to changing workloads and data characteristics, ensuring accurate cost estimation and optimal execution plans.
Solution Approach 2:
The system incorporates feedback mechanisms where actual query execution results are used to update and refine statistical information. This closed-loop approach ensures that the optimizer learns from past executions and continuously improves its cost estimation accuracy, preventing the statistical information from becoming outdated.
2Adaptability or versatility
If rule-based configurations are used for join optimization, then optimization decisions can be made, but complex relationships between data tables and query characteristics cannot be captured
Solution Approach 1:
The patent replaces rule-based mechanical optimization systems with machine learning models that can automatically learn and capture complex relationships between data tables and query characteristics. The ML-based optimizer substitutes rigid rule-based configurations with adaptive, data-driven decision-making capabilities.
Solution Approach 2:
The system changes the fundamental parameters of the optimization approach by transitioning from fixed rule-based configurations to dynamic parameters derived from machine learning models. This allows the optimizer to adapt to complex relationships that cannot be expressed through traditional rule-based configurations.
3Productivity
If sophisticated join optimization techniques are employed, then query execution efficiency can be improved, but optimization overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching optimization results from machine learning models for common query patterns. This allows the system to quickly retrieve pre-analyzed execution plans without performing full optimization calculations each time, reducing optimization overhead while maintaining high execution efficiency.
Solution Approach 2:
The system discards redundant optimization calculations by identifying and reusing previously computed optimization results. When similar queries are detected, the system recovers and reuses the cached optimization plans, avoiding重复 computation and reducing overall optimization overhead.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for dynamic query optimization. An example method includes receiving, by communications hardware, a first query and determining, by a query analysis engine, whether the first query corresponds to a stored query of a historical query repository. The method also includes determining, in response to determining that the first query does not correspond to the stored query, determining, by the query analysis engine, a parameter set indicating a current state associated with a system involved in an execution of the first query, processing, by a reinforcement learning engine, the first query and the parameter set in accordance with a plurality of policies, selecting, by the reinforcement learning engine and based on the processing, a first policy from the plurality of policies, and generating, by the reinforcement learning engine and based on the first policy, a query execution plan for the first query.