7 tricky system‑design prompts
Akshay Shinde surfaced seven low‑level system design prompts interviewers love — think sharded Twitter with WebSockets, TinyURL with caching and rate limits, WhatsApp E2E encryption, TikTok video pipelines, Uber geospatial matching, Dropbox chunking, and Amazon‑grade transactions. These examples highlight interviewers' appetite for tradeoffs, low‑level detail, and real production constraints. (x.com)
System-design interview prep resources and FAANG coaching lists show URL shorteners, messaging, newsfeed/timeline, video pipelines, ride-matching, file-sync and distributed transactions as recurring case studies for 2026 interviews. (designgurus.io) Designing a sharded Twitter-style timeline requires a hybrid fan‑out: push precomputed timelines (Redis sorted sets) for typical users and pull/merge at read time for celebrity accounts to avoid write amplification, with precomputed inboxes often holding ~500–800 post IDs to hit sub‑millisecond p50 reads. (archdesign-io.vercel.app) A production TinyURL exercise forces explicit capacity math—examples estimate ~365B short links → ~36.5 TB of storage at 100 bytes/link—and interview answers must show caching to hit ~90% cache rates plus IP‑based rate limiting and cache‑stampede mitigations. (bytebytego.com) WhatsApp’s end‑to‑end encryption is built on the Signal Protocol (integrated in 2016) and Meta has published Key Transparency work to help verify directory integrity as E2EE scales across billions of users. (signal.org) Short‑video pipelines (TikTok scale) require ingest, multi‑profile transcoding, CDN distribution and per‑viewer bitrate selection to handle tens of millions of uploads per day (platform estimates commonly cite ~20–34M videos/day), so interview designs must address batch/streaming transcode costs, storage tiers and CDN cache policies. (sendshort.ai) Real‑time geospatial matching relies on hierarchical spatial indexes (Uber’s H3) and fast supply/demand aggregation (driver GPS often sampled every few seconds) to run sub‑100ms dispatch decisions, while file‑sync designs use content‑addressed chunking/dedup (client block hashes, chunk sizes often in MBs) and Dropbox’s Nucleus rewrite shows why a robust sync engine matters in production; at the database layer Amazon’s Dynamo principles and DynamoDB’s later ACID transaction APIs exemplify the tradeoffs between availability, eventual consistency and the need for transactional primitives at scale. (archdesign-io.vercel.app)
