What strategies do you use for cache invalidation?

They say that there are only two hard things in Computer Science: Cache Invalidation and naming things. Caching improves application speed by storing data locally, but it requires cache invalidation strategies to keep data accurate as changes occur. Let’s explore why cache invalidation is important and examine some effective strategies.

This blog is written by Jeremy Rivera at KushoAI. We're building the fastest way to test your APIs. It's completely free and you can sign up here.

Why Is Cache Invalidation Needed?

Caches reduce the need to access slower data stores, improving speed and reducing server load. However, cached data can become outdated. Without invalidation, users might see stale data, leading to inconsistencies, security risks, and inefficient resource use.

Common Cache Invalidation Strategies

1. Time-Based Expiry (TTL) In a time-to-live (TTL) strategy, cached data expires after a defined interval, removing or refreshing old data automatically. TTL is simple to set up and is useful when data freshness isn’t critical but periodic updates are necessary. Example: A weather app could cache forecast data every 30 minutes, ensuring users see current information without frequent API calls. Best Use: TTL is suited for data with predictable update intervals, such as metrics, weather data, or static website content.
2. Manual Invalidation In manual invalidation, developers or applications trigger cache invalidation directly when data changes, providing precise control over cache contents. Example: An e-commerce platform can invalidate a cached product entry when the product’s price or availability changes, ensuring users see correct details. Best Use: Ideal for infrequent but critical updates, like pricing data, configurations, or content management systems (CMS).
3. Write-Through Cache A write-through cache updates both the cache and database on any data modification, keeping the cache in sync with real-time changes.Example: A banking app could use write-through caching for account balances, preventing users from seeing outdated information after transactions. Best Use: Effective for frequently read-and-updated data, such as user profiles, financial records, or configuration data.
4. Cache-Aside (Lazy Loading) With cache-aside, or lazy loading, data is cached only upon request. If the data is absent in the cache (a “cache miss”), it is fetched from the database, cached, and returned to the requester. Example: A blog application could cache articles on the first read, reducing database calls on future requests. Best Use: This method is best for data accessed sporadically, like blog articles or user-generated content that doesn’t change often.
5. Event-Driven Invalidation Event-driven invalidation uses data updates as triggers for invalidating related cache entries. It’s a popular strategy in distributed systems or microservices architectures. Example: In a retail platform, a product update event might invalidate the product’s cache across multiple services, ensuring all views reflect the update immediately. Best Use: Ideal for applications that need real-time updates, such as collaborative tools or data-sensitive applications with dependencies on other services.
6. Versioned Cache Entries With versioning, cache entries are tagged with a version number that increments with each data update. Outdated versions are then discarded. Example: A documentation website could serve different versions of a guide, allowing users to access specific versions while seeing the most recent updates. Best Use: Ideal for data with multiple versions or where backwards compatibility matters, like API documentation or CMS content.

Choosing the Right Strategy

When choosing a cache invalidation strategy, consider your data's update frequency, criticality, and access patterns. For example, finance and e-commerce platforms benefit from real-time cache updates (e.g., write-through or event-driven invalidation), while content-heavy sites can often rely on TTL or cache-aside.

This blog is written by Jeremy Rivera at KushoAI. We're building an AI agent that tests your APIs for you. Bring in API information and watch KushoAI turn it into fully functional and exhaustive test suites in minutes.