@hackproduct9: Your database is on fire. 🔥 One machine, all the traffic — storage maxes out, writes back up, reads crawl. So you shard: split the data across many machines. But how does each key find its shard? Three ways 👇 1️⃣ Range — IDs fall into buckets (0–1M, 1M–2M, 2M–3M). Great for range scans and multi-tenant apps. ⚠️ Recent data piles onto one hot shard. 2️⃣ Hash — hash(id) % N scatters keys evenly across shards. The default everyone assumes. ⚠️ Adding shards reshuffles data → reach for consistent hashing. 3️⃣ Directory — a lookup table maps id → shard; you check it first, then query that shard. Move any key anytime. ⚠️ Every request pays an extra hop, and the table is a single point of failure. The #1 interview mistake: sharding before you've proven a single DB can't cope. Do the math first. Save this for your next system design round. 🔖 Which should we break down next — consistent hashing or the celebrity hot-key problem? Let’s learn! #systemdesign #AIEngineering #AIEngineer #Consistency

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