\# ADR-001: Adopt Redis for Caching Layer \*\*Status:\*\* Accepted\\ \*\*Date:\*\* 2025-08-17\\ \*\*Author:\*\* John Doe\\ \*\*Reviewers:\*\* Backend Team \------------------------------------------------------------------------ \## Context Our web application frequently queries the database for user session data and configuration settings.\\ This has led to \*\*performance bottlenecks\*\* under high traffic. We need a caching solution that:\\ \- Reduces database load\\ \- Provides sub-millisecond reads\\ \- Supports distributed deployment for future scaling \------------------------------------------------------------------------ \## Considered Options 1\.  \*\*Rely only on PostgreSQL\*\*     -   Pros: Simple, no new infrastructure\\     -   Cons: High latency under load, risk of DB saturation 2\.  \*\*Use Memcached\*\*     -   Pros: Very fast, simple key-value store\\     -   Cons: Limited feature set, no persistence, weaker ecosystem 3\.  \*\*Use Redis (chosen)\*\*     -   Pros: Fast, supports persistence, pub/sub, wide adoption, rich         ecosystem\\     -   Cons: Slightly more complex setup and maintenance \------------------------------------------------------------------------ \## Decision We decided to \*\*adopt Redis as the primary caching layer\*\* because it balances performance, reliability, and ecosystem maturity.\\ Redis will be used for:\\ \- Session storage\\ \- Frequently accessed configuration values\\ \- Future use cases (rate limiting, queues) \------------------------------------------------------------------------ \## Consequences \-   \*\*Positive:\*\* Reduced DB load, faster response times, scalability     for traffic spikes\\ \-   \*\*Negative:\*\* Adds infrastructure dependency (Redis cluster must be     monitored and maintained)\\ \-   \*\*Mitigations:\*\* High-availability Redis setup with monitoring and     alerting \------------------------------------------------------------------------ \## Implementation Roadmap 1\.  Deploy Redis cluster in staging environment\\ 2\.  Update application code to read/write session data to Redis\\ 3\.  Configure monitoring and failover strategies\\ 4\.  Roll out changes to production \------------------------------------------------------------------------ \## Success Metrics \-   Average response time reduced by 30%\\ \-   Database CPU utilization \\< 60% under peak load\\ \-   99.9% cache hit rate for session data \------------------------------------------------------------------------ \## Future Considerations Evaluate Redis Streams for event-driven features.\\ Consider managed Redis services (e.g., AWS ElastiCache) if operational overhead grows.