Caching thread with a real-world speed win
What happened
A backend engineer shared a detailed thread on CDN and caching choices that cut page load from 3 seconds to 0.4 seconds by pushing static assets to CDN, caching APIs in Redis and keeping the database as the source of truth. The post lays out practical rules — CDN for images/CSS/JS, Redis for API hits, DB for authoritative data — and gives a concrete system-design example that’s directly applicable in interview scenarios. The writeup is a tidy case study for anyone prepping design rounds on performance and caching trade-offs. (x.com)
Why it matters
The thread was posted by backend engineer Ritesh Roushan and points to longer writeups where he walks through real incidents: one Medium post titled “N+1 Queries Almost Killed My FastAPI Production Feed” documents a five‑line code fix that reduced endpoint latency by about 93% (published Mar 7), and another post titled “Why Redis Made Our API Slower (And How We Fixed It)” calls out a caching anti‑pattern and its remediation. (devxritesh.medium.com) (x.com) Roushan condensed those incidents into a compact, interview‑friendly case study in the thread, with a schematic of the request path, before/after latency numbers and the exact code changes he used to prove the gains; the thread links back to the full step‑by‑step examples and code snippets on his Medium page. (x.com) (devxritesh.medium.com) The thread’s infrastructure recommendations rely on three pillars that engineers commonly name differently: a content delivery network, an in‑memory cache, and a durable database. A content delivery network is a geographically distributed set of servers that stores copies of static files (images, stylesheets, scripts) close to users so network travel time drops. (cloudflare.com) An in‑memory cache is a data store that keeps data in RAM so reads happen in sub‑millisecond time rather than disk‑backed database time; Redis is the widely used open‑source example referenced in the thread. (redis.io) A durable database is the canonical, on‑disk store used for writes and long‑term consistency. (devxritesh.medium.com) The thread and linked posts show concrete engineering patterns and the common failure modes that matter in interviews. Roushan demonstrates the cache‑aside pattern — read the cache first, if it’s missing then read the database and populate the cache — and he highlights the N+1 query problem (many small database calls where one joined query would suffice) as the cause of the 93% regression he fixed. (redis.io) (freecodecamp.org) He also flags cache invalidation and TTLs (time‑to‑live, the expiry a cache entry uses) as the operational knobs teams must specify when proposing a design. (devx.com) The writeups supply actionable implementation details worth memorizing for design interviews: use immutable, content‑hashed filenames so static assets can be cached "forever" at the edge and invalidated by changing the filename (a common CDN upload pattern), measure a baseline and show exact before/after latencies, and prefer short, deterministic cache lifetimes or versioned keys to avoid stale reads. (github.com) (devxritesh.medium.com)
Key numbers
- A backend engineer shared a detailed thread on CDN and caching choices that cut page load from 3 seconds to 0.4 seconds by pushing static assets to CDN, caching APIs in Redis and keeping the database as the source of truth.
Quick answers
What happened in Caching thread with a real-world speed win?
A backend engineer shared a detailed thread on CDN and caching choices that cut page load from 3 seconds to 0.4 seconds by pushing static assets to CDN, caching APIs in Redis and keeping the database as the source of truth. The post lays out practical rules — CDN for images/CSS/JS, Redis for API hits, DB for authoritative data — and gives a concrete system-design example that’s directly applicable in interview scenarios. The writeup is a tidy case study for anyone prepping design rounds on performance and caching trade-offs. (x.com)
Why does Caching thread with a real-world speed win matter?
The thread was posted by backend engineer Ritesh Roushan and points to longer writeups where he walks through real incidents: one Medium post titled “N+1 Queries Almost Killed My FastAPI Production Feed” documents a five‑line code fix that reduced endpoint latency by about 93% (published Mar 7), and another post titled “Why Redis Made Our API Slower (And How We Fixed It)” calls out a caching anti‑pattern and its remediation. (devxritesh.medium.com) (x.com) Roushan condensed those incidents into a compact, interview‑friendly case study in the thread, with a schematic of the request path, before/after latency numbers and the exact code changes he used to prove the gains; the thread links back to the full step‑by‑step examples and code snippets on his Medium page. (x.com) (devxritesh.medium.com) The thread’s infrastructure recommendations rely on three pillars that engineers commonly name differently: a content delivery network, an in‑memory cache, and a durable database. A content delivery network is a geographically distributed set of servers that stores copies of static files (images, stylesheets, scripts) close to users so network travel time drops. (cloudflare.com) An in‑memory cache is a data store that keeps data in RAM so reads happen in sub‑millisecond time rather than disk‑backed database time; Redis is the widely used open‑source example referenced in the thread. (redis.io) A durable database is the canonical, on‑disk store used for writes and long‑term consistency. (devxritesh.medium.com) The thread and linked posts show concrete engineering patterns and the common failure modes that matter in interviews. Roushan demonstrates the cache‑aside pattern — read the cache first, if it’s missing then read the database and populate the cache — and he highlights the N+1 query problem (many small database calls where one joined query would suffice) as the cause of the 93% regression he fixed. (redis.io) (freecodecamp.org) He also flags cache invalidation and TTLs (time‑to‑live, the expiry a cache entry uses) as the operational knobs teams must specify when proposing a design. (devx.com) The writeups supply actionable implementation details worth memorizing for design interviews: use immutable, content‑hashed filenames so static assets can be cached "forever" at the edge and invalidated by changing the filename (a common CDN upload pattern), measure a baseline and show exact before/after latencies, and prefer short, deterministic cache lifetimes or versioned keys to avoid stale reads. (github.com) (devxritesh.medium.com)
