Granular Index Defragmentation for Database I/O Cost Reduction
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Solution Overview
Problem
Current database index defragmentation methods are inefficient, often requiring entire index defragmentation and relying on rules of thumb, which can lead to unnecessary I/O costs and ineffective performance improvements.
Innovation Solution
Granular and workload-driven index defragmentation techniques allow for the defragmentation of individual index ranges, performing benefit analyses to estimate impact and provide automated recommendations for specific workloads and budgets, enabling targeted defragmentation without affecting the entire index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire index defragmentation is performed, then index performance is improved, but I/O costs increase significantly
Solution Approach 1:
The patent divides the index into multiple ranges and identifies only the fragmented ranges that require defragmentation. Instead of defragmenting the entire index, the system segments the work to focus only on problematic portions, thereby reducing I/O costs while maintaining performance improvements for the affected ranges.
Solution Approach 2:
The patent applies defragmentation selectively to specific index ranges that exhibit fragmentation, rather than uniformly treating the entire index. By identifying ranges with high fragmentation metrics and applying defragmentation only to those local areas, the system optimizes resource usage while achieving performance benefits where needed.
2Ease of operation
If defragmentation is performed based on rules of thumb, then defragmentation decisions are simple, but unnecessary defragmentation occurs
Solution Approach 1:
The patent implements a feedback mechanism that monitors index fragmentation metrics and query performance data. The system uses this feedback to automatically identify which index ranges require defragmentation, replacing simple rules of thumb with data-driven decisions that prevent unnecessary defragmentation while maintaining operational simplicity.
Solution Approach 2:
The system performs self-diagnosis by automatically analyzing its own index fragmentation state and query patterns. Instead of relying on external rules of thumb, the database system itself generates defragmentation recommendations based on its internal metrics, eliminating unnecessary operations while maintaining ease of operation through automated decision-making.
3Adaptability or versatility
If manual defragmentation selection is used, then defragmentation targets can be customized, but the process is time-consuming
Solution Approach 1:
The system automatically performs the defragmentation target selection process by analyzing its own fragmentation metrics and workload patterns. This self-service approach provides customized defragmentation recommendations without requiring manual intervention, thereby maintaining adaptability while eliminating the time-consuming aspect of manual selection.
Solution Approach 2:
The patent performs preliminary analysis of index fragmentation and workload characteristics to pre-identify candidate ranges for defragmentation. By preparing this information in advance, the system enables quick, informed decisions about which ranges to defragment, combining customized target selection with time efficiency.
Data Source
AI summary
This patent application relates to granular and workload driven database index defragmentation techniques. These techniques allow for defragmenting individual index ranges, performing benefit analysis to estimate the impact of defragmenting indexes or index ranges, and leveraging such benefit analysis to provide automated workload-driven recommendations of index(es) or index range(s) to defragment.