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Redis is a fast in-memory data store used for caching and low-latency applications.

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

Database per Service ensures each microservice owns its data, reducing dependencies.

Read-after-write consistency ensures users see their latest updates immediately.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Cache penetration occurs when requests for non-existent data overload the database.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Connection pooling reuses database connections but can cause issues if misconfigured.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Distributed caching spreads data across multiple servers for better performance and reliability.

Cache invalidation is about knowing when to refresh cached data.

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

Fallbacks allow systems to handle failures safely and maintain user experience.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Graceful degradation allows apps to function partially during service failures.