When should I use caching?

Use caching when data is read frequently but written infrequently, when computing the result is expensive, or when the data source has high latency. Avoid caching highly dynamic data where staleness is unacceptable.

What is cache invalidation?

Cache invalidation is the process of removing or updating cached data when the underlying source changes. Strategies include TTL-based expiry, event-driven purging, and versioned cache keys.

What is a cache stampede?

A cache stampede occurs when a popular cache entry expires and hundreds of concurrent requests all miss the cache simultaneously, overwhelming the database. Solutions include lock-based refresh, probabilistic early expiration, and pre-warming.

Caching Explained

The performance optimization technique that stores computed results for instant retrieval, operating across browser, CDN, application, and database layers.

Caching

Caching is the practice of storing frequently accessed data in a fast-access storage layer so that future requests can be served more quickly without recomputing or re-fetching the original data.

Explanation

Every time an application queries a database, calls an external API, or renders a complex page, it consumes time and resources. Caching stores the result so subsequent requests get an instant response. The key trade-off is freshness vs. speed: cached data may be stale, so cache invalidation — knowing when to refresh — is one of the hardest problems in computer science. Caching operates at multiple layers. Browser caches store static assets (CSS, JS, images) on the user device. CDN caches serve content from edge locations near users. Application caches (Redis, Memcached) store computed results, session data, and database query results. Database caches (query cache, buffer pool) optimize repeated queries. Each layer has different TTLs (time-to-live), invalidation strategies, and consistency guarantees. Common caching patterns include cache-aside (application checks cache first, falls back to database), write-through (writes go to cache and database simultaneously), write-behind (writes go to cache first, database is updated asynchronously), and read-through (cache transparently loads from database on miss). The right pattern depends on read/write ratios and consistency requirements.

Bookuvai Implementation

Bookuvai implements multi-layer caching strategies tailored to each project. We use Redis for application-level caching, CDN edge caching for static assets and API responses, and browser cache headers for client-side caching. Our standard cache invalidation strategy uses event-driven invalidation — when data changes, affected cache keys are purged immediately rather than waiting for TTL expiry.

Key Facts

Cache invalidation is considered one of the two hardest problems in CS
Redis and Memcached are the most popular application cache stores
CDN caching can reduce origin server load by 90% or more
Cache-aside is the most common pattern for read-heavy workloads
TTL (time-to-live) balances data freshness against cache hit rate

Related Terms

Frequently Asked Questions

When should I use caching?: Use caching when data is read frequently but written infrequently, when computing the result is expensive, or when the data source has high latency. Avoid caching highly dynamic data where staleness is unacceptable.
What is cache invalidation?: Cache invalidation is the process of removing or updating cached data when the underlying source changes. Strategies include TTL-based expiry, event-driven purging, and versioned cache keys.
What is a cache stampede?: A cache stampede occurs when a popular cache entry expires and hundreds of concurrent requests all miss the cache simultaneously, overwhelming the database. Solutions include lock-based refresh, probabilistic early expiration, and pre-warming.