Latest Caching Articles

Cache-aside is a caching pattern where the application manages the cache explicitly. Reads check the cache first, load data from the database on a cache miss, and then place that data into the cache for future requests.

Write-through caching is a caching strategy where application writes update the cache and the underlying database as part of the same write path. A write is considered successful only after the authoritative storage has been updated.

Cache invalidation is the process of removing, expiring, or replacing cached data when the authoritative data changes. It solves a fundamental caching problem: once data is copied into a cache, that copy can become stale .

Caching improves latency and reduces pressure on databases, APIs, storage systems, and expensive computations by keeping frequently needed data closer to where it is consumed. In production, however, adding a cache also creates another copy of data that can become stale, inconsistent, overloaded, or

Distributed applications depend on caching to achieve low latency, high throughput, and reasonable infrastructure costs. Without effective caching, every request competes for database connections, network bandwidth, and compute resources. As traffic grows, the database often becomes the first scalab

Caching significantly reduces database load, but poorly designed caches can become bottlenecks themselves. Two of the most common production problems are cache stampedes , where many requests simultaneously rebuild an expired value, and hot keys , where a single cache entry receives a disproportiona

Caching improves latency and reduces database load, but every cached value eventually becomes stale. Cache invalidation is the process of ensuring that cached data reflects authoritative state after changes occur. In production systems, invalidation is often more difficult than caching itself becaus

Large production systems rarely rely on a single cache. Modern architectures typically combine browser caches, CDNs, reverse proxies, local in-memory caches, distributed caches, and database caches. Each layer removes a different bottleneck and serves requests at a different point in the request pat

CDNs, reverse proxies, and application caches all reduce repeated work, but they operate at different locations and understand different parts of a request. A CDN serves users from geographically distributed edge locations, a reverse proxy protects and accelerates origin infrastructure, and an appli

Cache-aside, write-through, and write-behind define how applications coordinate cached data with authoritative storage. Each pattern changes read latency, write latency, consistency, durability, and failure recovery.

Caching reduces latency and protects databases from repeatedly performing the same expensive work. A well-designed cache can serve high-volume reads in milliseconds while lowering database CPU, storage I/O, connection usage, and infrastructure cost.

A Content Delivery Network (CDN) is a distributed network of servers that cache and deliver content closer to users. Instead of serving every request from a single origin server, a CDN uses geographically distributed edge locations to reduce latency and improve performance.

In Scalability for Dummies - Part 2: Database , we optimized the database using indexes, connection pools, replicas, and other techniques. But even a healthy database can become overloaded when users repeatedly request the same products, articles, profiles, and configuration data. This is where cach

Caching is one of the most effective techniques for improving performance, scalability, and availability in software systems. Instead of repeatedly fetching data from a slower source such as a database, API, object storage, or another service, a cache stores frequently used data in a faster layer.

Caching is one of the most important patterns for building scalable systems. It reduces latency, lowers database load, absorbs traffic spikes, and helps systems serve repeated requests faster.