ML Query Optimization for Federated Database Efficiency
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Solution Overview
Problem
Existing data federation techniques face challenges in optimizing queries in federated databases due to static definitions and difficulties in integrating with query optimizers, leading to inefficient data movement and execution plans.
Innovation Solution
The implementation of machine learning-based query optimization techniques that generate predictive data movement instructions and query execution plans by calculating the cost of execution for multiple potential target data sources, selecting the most efficient one based on predefined criteria, and employing a dynamic federated query schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static definitions are used to map data representation to original data location, then the system structure is simple, but query execution efficiency deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static data mapping definitions to dynamic query optimization. The system now dynamically determines data location and movement strategies based on query characteristics, data distribution, and system state, allowing the optimization approach to adapt to varying query patterns and workload conditions rather than relying on fixed predefined mappings
Solution Approach 2:
The patent changes parameters by introducing cost-based metrics and machine learning models that evaluate multiple factors including data location, data volume to be moved, query selectivity, and system resource availability. These parameter changes enable the system to make informed decisions about data movement and query execution plans based on quantifiable performance criteria rather than static definitions
2Productivity
If data is moved to optimize query execution, then query performance improves, but data movement overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching cost estimates for data movement operations and query execution plans. The system prepares optimization strategies in advance by evaluating potential data movement scenarios and caching the results, so that when queries are executed, the system can quickly retrieve pre-analyzed optimization paths without performing costly real-time data movement analysis
Solution Approach 2:
The patent uses copying by creating virtual representations of data locations and cost models that allow the system to simulate and evaluate different query execution strategies without actually moving data. The cost-based optimizer uses copied metadata and statistical information to predict execution costs and determine optimal data movement decisions before any physical data transfer occurs
3Measurement precision
If machine learning models are used for query optimization, then optimization accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies this principle by using lightweight machine learning models that can be quickly trained and discarded. The system employs cost estimation models and predictive algorithms that are computationally efficient and can be rapidly retrained on new data patterns without requiring extensive computational resources, allowing the system to adapt to changing workload characteristics while maintaining manageable complexity
Solution Approach 2:
The patent uses intermediaries by introducing cost-based estimation functions and machine learning models as intermediary layers between the query parser and the execution engine. These intermediaries translate complex query requirements into optimized execution plans by evaluating cost metrics and making intelligent decisions about data movement and query rewriting, thereby reducing the computational burden on the overall system
Data Source
AI summary
Techniques are provided for machine learning based query optimization for federated databases. An exemplary method comprises obtaining a query to be processed in a federated database; generating at least one predictive data movement instruction to move data to a target data source when the target data source satisfies one or more of a predefined efficiency criteria with respect to a query type of the query and a predefined capacity criteria at an expected execution time of the query; and generating a query execution plan for the query by calculating a cost of execution for a plurality of potential target data sources and selecting a target data source for the query based on the calculated cost of execution. The federated database optionally employs a dynamic federated query schema.