How do you implement caching strategies in cloud-native applications?

Implementing a caching strategy in cloud-native applications involves storing frequently accessed data in a high-speed storage layer (such as memory) to reduce backend data source access and latency, thereby improving application performance, scalability, and resource efficiency. It is crucial for handling scenarios with high concurrency and low latency requirements, such as e-commerce promotions and real-time recommendation systems.

Core strategies include hierarchical caching (combining local caches like Caffeine with distributed caches like Redis), cache location selection (application layer, before the database, or CDN), and invalidation strategies (e.g., TTL, Write-through). Features include automated deployment (managed via Kubernetes Operator), elastic scaling (HPA scaling based on cache metrics), and security integration (Istio traffic management). A multi-level cache architecture significantly reduces database pressure and enhances user experience.

Implementation steps: 1) Identify hot data and determine cache granularity (objects/query results). 2) Select cache tiers: local cache for burst traffic, Redis cluster as shared cache, and CDN for static content. 3) Deploy cache instances (Kubernetes deployment of Redis Sidecar or Operator-managed cluster). 4) Configure invalidation and consistency strategies (e.g., delayed double deletion to ensure eventual consistency). 5) Monitor hit rate/latency metrics via Prometheus, and continuously optimize TTL and eviction policies.