Constrained V-Optimal Histogram for Database Query Optimization
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Solution Overview
Problem
Current database histogram techniques, such as V-Optimal histograms, face challenges in computation cost and fail to account for application workload characteristics, leading to inefficiencies in summarizing data statistics.
Innovation Solution
The Constrained V-Optimal Histogram (CVOH) method arranges database values and their indicator values into buckets, combining adjacent buckets based on criteria like frequency proximity to reduce the number of buckets while minimizing error, and can be tailored to consider workload characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If V-Optimal histogram techniques are used to summarize database statistics, then measurement precision is improved, but computation cost increases
Solution Approach 1:
The patent segments the database values into multiple buckets, where each bucket represents a range of values. This segmentation allows the system to summarize statistics at a coarser granularity level, reducing the computational complexity while maintaining acceptable accuracy for query optimization purposes.
Solution Approach 2:
The patent changes the parameter of bucket count from a fixed large number to a configurable parameter. By adjusting the number of buckets based on specific needs, the system can balance between summary accuracy and computation cost, avoiding the always-high computational overhead of V-Optimal histograms with many buckets.
2Use of energy by moving object
If traditional histogram methods are used, then computation cost is reduced, but adaptability to application workload characteristics deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the histogram parameters (such as number of buckets and value ranges) to be adjusted based on application workload characteristics. This enables the system to adapt to different query patterns and data distributions without incurring high computation costs for reanalysis.
Solution Approach 2:
The patent applies different bucketing strategies to different parts of the data distribution based on local characteristics. By identifying regions with different data densities or importance, the system can allocate more buckets to critical regions and fewer to less important regions, achieving better adaptability with reduced overall computation.
3Measurement precision
If the number of buckets is increased to improve summary accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies partial action by using a moderate number of buckets that provides sufficient accuracy for the specific application needs, rather than using the maximum possible number of buckets. This partial approach achieves acceptable summary accuracy while avoiding the complexity associated with managing a large number of buckets.
Data Source
AI summary
Database values and their associated indicators can be arranged in multiple “buckets.” Adjacent buckets can be combined into a single bucket successively based one or more criteria associated with the indicators to effectively reduce the number of buckets until a desired number is reached.