Caching Strategies

Key Concepts

• Cache Invalidation
• Time-Based Expiration
• Content-Based Expiration
• Stale-While-Revalidate
• Cache Hierarchies
• Distributed Caching
• Client-Side Caching
• Server-Side Caching

Cache Invalidation

Cache invalidation is the process of removing or updating cached data when the original data changes. This ensures that users receive the most up-to-date information. Common strategies include invalidating specific cache entries or entire cache regions.

Example:

Imagine a news website where articles are cached. When a new article is published, the cached version of the homepage is invalidated to reflect the latest content.

Time-Based Expiration

Time-based expiration sets a fixed time limit for how long data can be cached. After the expiration time, the data is considered stale and must be revalidated or fetched again. This strategy is simple but may lead to outdated data if the expiration time is too long.

Example:

A weather app caches weather data for 10 minutes. After 10 minutes, the cached data is discarded, and new data is fetched from the server.

Content-Based Expiration

Content-based expiration uses metadata about the content to determine when it should expire. This can include file modification times, ETags, or other content signatures. This strategy ensures that cached data remains fresh based on its actual content.

Example:

A content management system uses ETags to cache web pages. When the content changes, the ETag changes, and the cache is invalidated automatically.

Stale-While-Revalidate

Stale-while-revalidate allows the cache to serve stale data while asynchronously revalidating it in the background. This strategy improves performance by reducing the time users wait for data, even if it is slightly outdated.

Example:

A social media app serves cached posts to users while checking for updates in the background. If new posts are available, they are fetched and cached for future requests.

Cache Hierarchies

Cache hierarchies involve multiple layers of caching, such as browser cache, proxy cache, and server cache. Each layer serves data from the nearest cache to the user, reducing latency and server load.

Example:

A user requests a webpage. The browser checks its cache first, then a proxy server, and finally the origin server if the data is not found in the previous layers.

Distributed Caching

Distributed caching uses multiple cache servers across a network to store and serve data. This strategy improves scalability and reliability by distributing the load and reducing the risk of cache failures.

Example:

An e-commerce site uses a distributed cache to store product information. Requests are routed to the nearest cache server, reducing latency and improving performance.

Client-Side Caching

Client-side caching stores data on the user's device, such as a web browser or mobile app. This reduces the need to fetch data from the server, improving performance and reducing bandwidth usage.

Example:

A news website uses client-side caching to store images and articles. When the user revisits the site, the browser serves the cached content, reducing load times.

Server-Side Caching

Server-side caching stores data on the server, such as in memory or on disk. This reduces the need to regenerate data for each request, improving response times and reducing server load.

Example:

A dynamic website uses server-side caching to store generated HTML pages. When a user requests a page, the server serves the cached version, reducing the time needed to generate the page from scratch.