Caching is one of those concepts that seems straightforward until you start implementing it. Put frequently accessed data in fast storage so you don’t have to hit slower storage as often. Simple enough. But the devil is in the details. How do you decide what goes in the cache? When do you populate it?
Caching is fundamentally about optimizing reads. There are two core strategies for handling cache reads, plus optimization techniques you can layer on top of either approach.
Core Read Strategies
Cache-Aside (Lazy Loading)
Cache-aside is probably the most common pattern because it’s intuitive. Your application code manages the cache directly. When you need data, you check the cache first. If it’s there, great. If not, you fetch from the primary data store and populate the cache for next time.
The flow looks like this: check cache, miss, fetch from database, write to cache, return data. On subsequent requests: check cache, hit, return cached data.
Cache-aside works well when you have predictable access patterns and can tolerate the latency hit on cache misses. The application has full control over what gets cached and when. You can implement sophisticated eviction policies, cache warming strategies, or selective caching based on business logic.
The downside is complexity. Every piece of code that reads data needs cache logic. You’re responsible for cache invalidation when data changes. And there’s a race condition window between fetching from the database and updating the cache where other requests might also miss and duplicate the work.
Cache-aside shines when your read patterns are predictable but your data changes infrequently. Web applications serving mostly static content, product catalogs, or user profiles are good fits.
Read-Through
Read-through caches sit between your application and the data store, automatically handling cache misses. When you request data that’s not cached, the cache itself fetches from the backing store, populates the entry, and returns the data. From your application’s perspective, you always read from the cache.
This simplifies application code significantly. No cache miss logic, no manual population, no race conditions on reads. The cache becomes a transparent performance layer that your application doesn’t need to think about.
The trade-off is less control. You can’t easily implement custom caching logic or business-specific cache population strategies. The cache makes all the decisions about what to fetch and when.
Read-through works best when you have straightforward data access patterns and want to keep caching concerns separate from application logic. It’s particularly useful for applications where cache misses are acceptable and the backing store can handle the load.
Optimization Techniques
Both cache-aside and read-through handle cache misses reactively. You can improve performance by being more proactive about when data gets cached.
Cache Warming
Cache warming pre-loads data into the cache before it’s requested. Instead of waiting for cache misses to populate entries, you proactively fetch and cache data you expect to be accessed soon.
This eliminates the cold start problem where the first users after a cache flush experience slow response times. You can warm caches during deployment, at startup, or during low-traffic periods based on historical access patterns or business logic.
The challenge is predicting what data will actually be needed. Warming the wrong data wastes resources and might evict truly useful entries. Cache warming works best when access patterns are predictable, like popular product pages or user data for active sessions.
Cache warming can be implemented with either core strategy. With cache-aside, your application code handles the warming. With read-through, you might configure the cache layer to warm specific keys.
Refresh-Ahead
Refresh-ahead proactively updates cache entries before they expire. The cache tracks access patterns and refreshes frequently accessed data in the background, ensuring hot data never actually expires and causes cache misses.
This provides consistent performance for your most important data. Users never wait for cache misses on popular content because it’s always fresh. The cache handles the refresh timing automatically based on expiration windows and access frequency.
The trade-off is additional background load on your data store and more complex cache logic. You’re doing extra work to refresh data that might not be accessed again. Refresh-ahead works well for relatively stable datasets where the most popular items stay popular over time.
Like cache warming, refresh-ahead can work with either core strategy, though it’s more commonly implemented at the cache layer rather than in application code.
Write Strategies
While caching is primarily about reads, you still need to handle what happens when data changes. The main approaches are write-through (update cache and database together), write-around (skip the cache, write directly to database), and write-back (write to cache first, database later).
Write-through ensures consistency but slows down writes. Write-around prevents cache pollution from rarely-read data. Write-back maximizes write performance but risks data loss if the cache fails.
Most applications use write-around or write-through depending on whether consistency or performance matters more for their specific use case.
Picking the Right Strategy
Start with the core strategy that fits your architecture. Cache-aside gives you fine-grained control over what gets cached and when, but requires more application code. Read-through simplifies your application logic but gives the cache layer more responsibility.
Once you have a core strategy working, consider optimization techniques. Use cache warming when you have predictable access patterns and want to eliminate cold start penalties. Consider refresh-ahead when you have stable hot data that needs consistent performance.
The choice usually comes down to control versus simplicity, and how predictable your access patterns are. Cache-aside works well when you want to optimize specific queries or implement business-specific caching rules. Read-through is better when you want transparent performance optimization without cluttering your application code.
Most real systems combine strategies depending on the data type. Your user sessions might use different patterns than your product catalog data. The key is understanding the trade-offs and picking the approach that fits your specific read patterns and consistency requirements.