Query Statistics for Selective Index Fragmentation Optimization
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Solution Overview
Problem
Conventional database systems face inefficiencies in search operations due to index fragmentation and resource-intensive optimization processes, particularly as the index size grows and the number of tokens increases, leading to suboptimal performance and resource consumption.
Innovation Solution
The proposed solution involves selectively optimizing frequently queried tokens and queries by using query statistics to identify and prioritize tokens and index portions for optimization, thereby reducing resource consumption and improving search efficiency without de-fragmenting the entire index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the entire index is de-fragmented to improve search efficiency, then search performance is improved, but system resource consumption (memory, processor cycles) increases undesirably
Solution Approach 1:
The patent applies local quality by de-fragmenting only specific portions of the index that contain frequently accessed tokens rather than the entire index. The system identifies high-frequency tokens and selectively de-fragments only the index segments containing these tokens, thereby improving search efficiency for commonly queried data while minimizing system resource consumption on less critical index portions.
Solution Approach 2:
The patent segments the index into multiple portions based on token frequency and access patterns. By dividing the index into high-frequency and low-frequency segments, the system can apply de-fragmentation operations only to the high-frequency segments that most impact search performance, rather than uniformly processing the entire index.
2Productivity
If all tokens are optimized to improve search performance, then search efficiency is improved, but the cost becomes uneconomical as the number of tokens increases
Solution Approach 1:
The patent applies partial action by optimizing only a subset of tokens rather than all tokens in the index. The system identifies and optimizes tokens that appear most frequently in search queries, accepting that some less frequent tokens will remain unoptimized. This partial optimization approach achieves significant performance improvement while avoiding the uneconomical cost of optimizing every single token.
Solution Approach 2:
The patent applies local quality by assigning different optimization levels to different tokens based on their frequency. High-frequency tokens receive full optimization attention, while low-frequency tokens receive minimal or no optimization. This differentiated approach ensures resources are concentrated on tokens that provide the most value for search performance.
3Productivity
If conventional systems optimize all search requests to improve efficiency, then potential performance gains are achieved, but undesired amounts of system resources are consumed on potentially irrelevant queries
Solution Approach 1:
The patent applies partial action by optimizing only the most relevant and frequently executed search requests rather than all search requests. The system identifies high-value queries based on frequency and resource consumption patterns, and applies optimization techniques selectively to these queries. This avoids wasting system resources on optimizing rare or irrelevant queries that would provide minimal performance benefit.
Data Source
AI summary
Systems, methods, and other embodiments associated with selectively acquiring and utilizing query statistics are described. One example method includes selectively acquiring statistics associated with search queries submitted to a search process during a sample period. The statistics may be associated with an index and tokens associated with the index and search queries. The method may also include selecting tokens and/or queries to optimize based, at least in part, on the statistics. In one example, whether and/or how the tokens and/or queries are optimized may depend on estimating index fragmentation based, at least in part, on the statistics.