Importance Of Caching In Scalable Web Applications

I was asked in an interview “"Where can we cache data apart from the DB layer?” Caching helps store frequently accessed or computationally expensive data closer to where it's needed — reducing response time and improving scalability. It is not just about saving DB hits, but about optimizing latency and load throughout the entire stack. While it's common to place a cache near the database (e.g., Redis/Memcached), here are other layers where caching can be just as powerful: - Client devices – Cache API responses, UI state, and static assets in LocalStorage on client side - CDN – Cache static files (images, JS, CSS) and public GET API responses at edge locations - API Gateway – Cache GET endpoint responses or auth metadata to offload traffic from services - Load Balancers – Cache routing metadata or session affinity information for efficient request distribution - Web application servers – Cache user profiles, computed business logic, or results from third-party APIs in memory or a distributed cache Caching decisions vary by use case, but knowing where and what to cache can make a significant difference in system performance at scale. #SystemDesign #SoftwareEngineering #Caching #Scalability #DistributedSystems

No Caching = Performance Bottleneck One of the most overlooked cloud performance antipatterns is not caching data at all. You’d be surprised how many systems fetch the same data repeatedly—despite it rarely changing. Here’s what happens when you fall into the No Caching Antipattern: 🔁 Repeated DB queries for identical data 🐌 Slow response times under load 🔥 Increased I/O, latency, and cloud costs ⛔️ Risk of service throttling or failure ✅ The Fix? Cache-Aside Pattern 1. Try to get from cache 2. If not found, fetch from DB + store in cache 3. Invalidate or update on write How to Detect the No Caching Antipattern 🔍 Review app design: Is any cache layer used? Which data changes slowly? 📊 Instrument the system: How often are the same requests made? 🧪 Profile the app: Check I/O, CPU, and memory usage in a test environment 🚦 Load test: Simulate realistic workloads to measure impact under stress 📈 Analyze DB/query stats: Which queries are repeated the most? Tip: Even if data is volatile or short-lived, smart caching strategies (with TTL, invalidation, and fallbacks) can massively improve resilience and scalability. Cache wisely. Profile constantly. Monitor cache hit rates. Because not caching is costing you more than you think. Have you encountered this in the wild? Drop your experience below 👇

Most web apps you use are already inconsistent. Not by accident; by design. In distributed systems (especially over HTTP), you can’t guarantee everyone sees the latest state. Statelessness, caching, and decentralization make eventual consistency the default. So instead of fighting it, you should work with it. Here are the 3 consistency strategies you should know: 1. Expiration The server tells clients how long a cached resource is valid (e.g., 10 minutes). Clients serve the cached copy until that TTL expires—no contact with the server. Common in static content (images, CSS, etc.) or predictable APIs. ✅ Fast: No network call when fresh ❌ Stale risk: Data can change before TTL ends Best when updates are infrequent and latency matters 2. Validation Clients use ETag or Last-Modified headers to ask: "Has this changed since I last saw it?" Server returns 304 Not Modified if nothing changed, saving bandwidth. Used in APIs where data changes but you still want to avoid full fetches. ✅ Fresh: Always synced with origin ✅ Efficient: Returns headers only if unchanged ❌ Slower than cache hit: Requires server round-trip Best when consistency is critical, but you still want caching 3. Invalidation When a resource changes, the system tries to notify or purge all cached copies. This could be driven by POST, PUT, DELETE, or custom signals. In theory, it guarantees consumers don't act on old data. ✅ Strongest consistency ❌ Hard to scale: Server must track who has the resource ❌ Web-unfriendly: HTTP is stateless; invalidation off-path is unreliable Best for: internal systems, real-time apps, or websocket-based setups My default approach? Expiration + Validation Use the cache while it’s fresh. Revalidate when it’s not. It’s the best balance of performance and correctness at scale. What’s your go-to caching strategy?