javinpaul shares caching strategies

Engineer javinpaul shared a guide on caching strategies on May 20, laying out the core decisions engineers make when they add a cache to a production system. The post grouped caching by both location and write pattern, covering in-memory caches, CDN layers and common policies such as write-through and write-back. It also focused on the operational problems that usually follow a cache rollout, including stale reads, invalidation and cache misses. The thread arrived as caching remains a standard part of low-latency system design across web applications, APIs and distributed services. ### Which caching choices does the guide put at the center? The guide centers on a familiar set of cache layers: application or in-memory caches, distributed caches and CDN caching. In-memory caches keep hot data close to the application and are typically used for frequently requested objects, session data or computed results, while CDN caches push static and semi-static content closer to end users at the network edge. (medium.com) Redis and Memcached are commonly used examples of the in-memory layer described in the post, while CDN caching is generally used for assets such as images, HTML and API responses that can tolerate some staleness. The same sources describe caching as a way to reduce repeated database or origin-server work and cut request latency. (medium.com) ### How do the write patterns differ in practice? Cache-aside, also known as lazy loading, is the pattern most engineers encounter first. In that setup, the application checks the cache, falls back to the database on a miss, and then stores the result in cache for the next request. That keeps the cache smaller and focused on requested data, but it leaves the first miss slow and can return stale data if invalidation is weak. (medium.com) Write-through sends data to the cache and the database as part of the same write path. That usually keeps cached values fresh, but it adds write latency because both systems must be updated together. Write-back, sometimes called write-behind, updates the cache first and flushes to the database later, which improves write speed but introduces durability risk if the cache fails before persistence completes. (designgurus.io) Read-through is the related pattern in which the cache itself fetches data from the backing store on a miss, rather than requiring the application to populate the cache directly. That can simplify application logic, though it pushes more responsibility into the cache layer. ### Why does invalidation get so much attention? TTL, or time-to-live, is one of the simplest invalidation tools covered in mainstream caching guides, because it lets data expire automatically after a fixed period. (thecodeforge.io) TTL is easy to implement, but it can serve stale data until expiry and can also trigger bursts of misses when many keys expire together. Eviction policy is the second part of the same problem. (designgurus.io) LRU, LFU and FIFO are standard approaches for deciding which entries leave the cache when memory fills up, and each one behaves differently depending on whether access patterns are bursty, repetitive or unevenly distributed. ### Where do engineers usually get into trouble? Staleness is the most common failure mode in cache design. (thecodeforge.io) A cache that is fast but not synchronized with the source of truth can return outdated values after writes, partial outages or delayed invalidation events. That is why many caching guides treat consistency policy as a design choice rather than an implementation detail. (designgurus.io) Cache stampedes are another recurring problem. When a hot key expires and many requests miss at once, the backing database can be flooded with identical work, a risk that engineers typically manage with request coalescing, staggered TTLs or prewarming. ### What is the practical use of a guide like this? System design references like the one javinpaul posted are usually most useful when a team is deciding which data should be cached, where the cache should sit and how much inconsistency the application can tolerate. (medium.com) Product catalogs, timelines, media assets and read-heavy APIs often use different cache layers and write policies because their freshness requirements differ. (codelit.io) The original thread on X is the next place to look for the diagrams and examples javinpaul used. Engineers comparing cache-aside, write-through, write-back and CDN placement can review the post alongside implementation details from Redis- and interview-focused caching references. (medium.com)