Caching
Storing frequently needed data in a fast, easily accessible location to reduce access times and offload backend systems.
Caching is one of the most effective ways to improve application performance. Instead of reading from the database or re-running heavy calculations on every request, results are stored and served from cache on repeat requests. From browser caches and CDN caching to distributed in-memory caches like Redis – caching is an essential tool at every layer of the IT stack.
What is Caching?
Caching is the temporary storage of data in a faster storage medium (cache) to reduce access to slower sources (database, API, file system). The cache acts as a middle layer: if the requested information is in the cache (cache hit), it is returned immediately. If not (cache miss), it is loaded from the source and stored in the cache. Central challenges are cache invalidation (when to remove stale data?) and eviction strategy (what to remove when the cache is full?). Common strategies are LRU (Least Recently Used), TTL (Time to Live) and LFU (Least Frequently Used).
How does Caching work?
On an incoming request the system checks the cache first. On a cache hit, data is returned from fast storage (e.g. Redis in RAM) – typical response under 1 ms. On a cache miss the source (e.g. PostgreSQL) is queried, the result is returned to the client and also stored in the cache (cache-aside pattern). Alternatively the cache can be filled proactively (write-through or write-behind). TTL values define how long an entry is valid before it expires.
Practical Examples
A news site caches the homepage in Redis for 60 seconds: 10 million hits per day hit the database only every 60 seconds.
An e-commerce shop caches product catalog data with a 5-minute TTL and invalidates on price changes – page load drops from 800 ms to 50 ms.
An API service uses response caching with ETags: clients get HTTP 304 (Not Modified) when content is unchanged, saving bandwidth and load time.
A SaaS app caches complex dashboard calculations (aggregations over millions of rows) and refreshes the cache in the background every 5 minutes.
A social media service uses a distributed Redis cluster to serve user profiles and feeds to millions of concurrent users with sub-millisecond latency.
Typical Use Cases
Database offload: Cache frequently queried data (product lists, config, user profiles) in RAM
API performance: Cache external API responses to reduce latency and stay within rate limits
Session management: Store user sessions in Redis for fast access in distributed systems
Content delivery: Serve static and semi-static content via CDN caches worldwide
Computation caching: Cache expensive results (reports, aggregations, ML inference)
Advantages and Disadvantages
Advantages
- Major performance gains: Response times from seconds down to milliseconds
- Scalability: Backend systems are offloaded and can serve more concurrent users
- Cost savings: Fewer database queries and API calls lower infrastructure cost
- Resilience: Cached data can still be served during short backend outages
- Better UX: Faster load times improve satisfaction and conversion
Disadvantages
- Cache invalidation is complex: Stale data can lead to inconsistent state
- Extra infrastructure: Redis cluster or Memcached need operation and monitoring
- Memory cost: RAM is more expensive than disk, especially for large datasets
- Cold start: After a flush or restart nothing is cached (cache stampede risk)
Frequently Asked Questions about Caching
When should I use Redis vs Memcached?
How do I avoid stale data in the cache?
What is a cache stampede and how do I prevent it?
Related Terms
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