Caching via Base Version Values
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Solution Overview
Problem
Current caching strategies face challenges in efficiently managing cache invalidation, leading to unnecessary coupling and high costs, especially in complex systems where frequent changes require costly mass-invalidation or manual subscription to change events, causing developers to avoid caching despite its benefits.
Innovation Solution
A caching architecture where consuming subsystems cache calculated information along with base version values from multiple subsystems, allowing for efficient cache validation by checking if the base version values have changed, thus eliminating the need for explicit cache invalidation and reducing unnecessary recalculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit cache invalidation is used when data changes, then cache correctness is maintained, but coupling between changed items and consumers increases and circular dependencies occur
Solution Approach 1:
A base version value acts as an intermediary between setup information and consuming subsystems. Instead of direct coupling where consumers must know about all changes, the base version value mediates by encoding the state of setup information. Consumers simply compare their cached base version value with the current one to determine if recalculation is needed, eliminating the need for complex invalidation logic and circular dependencies.
2Reliability
If event subscriptions are created for all possible changes, then cache invalidation is handled, but system complexity increases and new change types require additional subscriptions
Solution Approach 1:
Multiple change events and invalidation triggers are merged into a single base version value. Instead of subscribing to numerous individual change events, the consuming subsystem only needs to check whether the base version value has changed. This consolidation reduces subscription management complexity while maintaining reliable cache invalidation handling.
3Reliability
If mass-invalidation of all caches is performed on any change, then cache correctness is ensured, but computational cost and performance degradation occur
Solution Approach 1:
Cache invalidation is applied locally rather than globally. The base version value enables fine-grained invalidation by allowing consumers to determine precisely which cached values need updating based on whether their specific base version value has changed. This prevents unnecessary mass-invalidation of unrelated caches, reducing computational overhead and maintaining system performance.
4Productivity
If caching is used to improve performance, then computational overhead is reduced, but ensuring cache correctness becomes expensive and difficult
Solution Approach 1:
The cache validation mechanism becomes self-service through automatic base version value comparison. When a consuming subsystem needs to use cached information, it automatically retrieves the current base version value, compares it with its cached version, and determines whether recalculation is needed. This eliminates the need for complex manual cache management while maintaining correctness, allowing performance benefits to be realized without proportional increases in management complexity.
Data Source
AI summary
A consuming subsystem calculates information based on setup information from one or more other subsystems. Each of the one or more other subsystems generates a base version value that changes every time any of the setup information changes. The consuming subsystem caches information, including the base version values at the time the information was calculated by the consuming subsystem.