# 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**.

1. **Define Cacheable Data**: Identify data suitable for caching (e.g., flyer data, recipe details, brand lists).
2. **Define Invalidation Strategy**: Determine the cache invalidation strategy (e.g., time-to-live (TTL), event-based invalidation on data update).
3. **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.
