Design Instagram

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Instagram's photo/video sharing platform - feed generation, Stories, Reels, and Explore at 2 B users.
• •Design for a peak load target around 80,000 RPS (including burst headroom).
• •Monthly active users: 2,000,000,000
• •Media uploads/day: ~100,000,000
• •Total media stored: Exabytes
• •Feed load time: < 500 ms
• •Story availability: 24 hours

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.
• •Media Processing: Split ingest, transform, and delivery into independent stages with async orchestration.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Sharding: Choose shard keys around access patterns and growth hotspots, not just data size.
• •Message Queues: Move non-blocking and retry-heavy work to async consumers with explicit retry and DLQ policies.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •Store original media durably and make transforms replayable.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Support rebalancing and hotspot detection from day one.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •CDN: Long TTL improves latency/cost; short TTL improves freshness.
• •Media Processing: Pre-processing improves playback UX, but requires substantial compute/storage budget.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Sharding: Sharding improves horizontal scale but makes cross-shard queries and transactions harder.

Practical Notes

• •Separate the media storage layer (object store + CDN) from the metadata layer (Cassandra/PostgreSQL for posts, likes, follows).
• •Feed generation: pre-compute candidate posts → ML ranking service → cache top-500 posts per user in Redis.
• •Use a DAG-based media processing pipeline: upload → virus scan → resize → watermark → CDN push.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> DNS -> CDN Edge -> Load Balancer -> Core Service -> Primary NoSQL DB -> Redis Cache -> Message Queue

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Async queue/event bus isolates bursty workloads and supports retries without blocking synchronous requests.
• •Media processing is handled by background workers so user-facing latency stays low.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.