Cache Inconsistency Detection via Key-Based Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inconsistent data caching across multiple data centers can result in users being served stale data, indicating potential software or hardware issues, and existing methods are inadequate for detecting cache inconsistencies in large-scale systems.
Innovation Solution
A system and method for detecting cache inconsistencies using key-based sampling, which captures the complete history of a key to compare cache values across data centers, involving three phases: reverse chronological comparison of operation logs, near real-time evaluation of candidate keys, and real-time verification of cache values to eliminate false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If cache is used to store recently retrieved data, then data retrieval speed is improved, but cache consistency across data centers deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring cache operations across data centers and detecting inconsistencies. The system logs cache operations, compares cache values across data centers, and identifies when caches diverge from the authoritative database, providing feedback to maintain consistency awareness.
Solution Approach 2:
The patent introduces an intermediary detection system that acts as a mediator between the cache system and data centers. This intermediary component (inconsistency detector) compares cache values across data centers and identifies inconsistencies without directly interfering with the primary cache retrieval function.
2Measurement precision
If complete history of all keys is captured for comparison, then cache inconsistency detection accuracy is improved, but system complexity and resource consumption worsen
Solution Approach 1:
The patent extracts only the necessary information for inconsistency detection by capturing specific cache operation logs and key-value pairs rather than complete history of all keys. The system selectively logs operations that are relevant to detecting inconsistencies, reducing data volume while maintaining detection accuracy.
Solution Approach 2:
The patent segments the inconsistency detection process into distinct phases: logging cache operations, comparing cache values across data centers, and identifying inconsistent keys. This segmentation allows the system to process data in manageable stages rather than handling complete key histories simultaneously.
3Reliability
If real-time verification of all candidate keys is performed, then false positive elimination is improved, but processing time and computational resources worsen
Solution Approach 1:
The patent applies partial verification by focusing computational resources on verifying only the candidate keys identified as potentially inconsistent, rather than verifying all keys in the system. This partial action approach eliminates false positives for the critical subset of keys while avoiding the prohibitive cost of universal verification.
Data Source
AI summary
A system and method of detecting cache inconsistencies among distributed data centers is described. Key-based sampling captures a complete history of a key for comparing cache values across data centers. In one phase of a cache inconsistency detection algorithm, a log of operations performed on a sampled key is compared in reverse chronological order for inconsistent cache values. In another phase, a log of operations performed on a candidate key having inconsistent cache values as identified in the previous phase is evaluated in near real time in forward chronological order for inconsistent cache values. In a confirmation phase, a real time comparison of actual cache values stored in the data centers is performed on the candidate keys identified by both the previous phases as having inconsistent cache values. An alert is issued that identifies the data centers in which the inconsistent cache values were reported.