Abhay Chauhan

When systems slow down, the first instinct is usually: “Let’s add more cache.” That helps — until it doesn’t. At scale, read performance is rarely limited by a single slow component. 👉 Read the full article below. linkedin.com/pulse/read-path…

Caching is one of the first tools engineers reach for when systems slow down. Add Redis. Add Memcached. Lower latency. Problem solved. Until it isn’t. 👉 Read the full article below. linkedin.com/pulse/cache-lia…

When people hear sharding, they think: “Split data across machines → system scales.” That’s only partly true. In real systems, sharding is about preventing one machine from becoming a bottleneck, not magically handling infinite traffic. linkedin.com/pulse/sharding-…

One of the most expensive mistakes teams make is asking: “Which database should we use?” The better question is: “What problem are we solving today, and which problems will appear at 10× scale?” 👉 Read the full article below. medium.com/codeonboard/choos…

"API Design at Scale" (What breaks after your first million requests) Most APIs are designed for correctness. Very few are designed for failure, retries, evolution, and abuse. linkedin.com/pulse/api-desig…

Most engineers learn load balancing as “Put a load balancer in front. Use round-robin. Done.” That mental model works at 1k RPS. It breaks completely at 100k–1M RPS, multi-region systems, or stateful services. 👉 Read the full article below. linkedin.com/pulse/load-bala…

Capacity estimation is often treated like a warm-up exercise in system design. A few numbers. Some back-of-the-envelope math. Then straight to architecture diagrams. That approach misses the point... Click to read: linkedin.com/pulse/capacity-…

Functional vs Non-Functional Requirements in Ad Impression Tracking (Beyond the Obvious) linkedin.com/pulse/functiona… #SoftwareEngineering