Torben Sorensen
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ADR-009: Caching Strategy for Read-Heavy Operations
Date: 2025-12-12
Status: Proposed
Context
The application has several read-heavy endpoints (e.g., getting flyer items, recipes, brands). As traffic increases, these endpoints will put significant load on the database, even for data that changes infrequently.
Decision
We will implement a multi-layered caching strategy using an in-memory data store like Redis.
- Define Cacheable Data: Identify data suitable for caching (e.g., flyer data, recipe details, brand lists).
- Define Invalidation Strategy: Determine the cache invalidation strategy (e.g., time-to-live (TTL), event-based invalidation on data update).
- Implement Cache-Aside Pattern: The repository layer will be updated to implement a "Cache-Aside" pattern, where methods first check Redis for data before falling back to the database.
Consequences
Positive: Directly addresses application performance and scalability. Reduces database load and improves API response times for common requests. Negative: Introduces Redis as a dependency if not already used. Adds complexity to the data-fetching logic and requires careful management of cache invalidation to prevent stale data.