Social Feed 3 - Global Platform with Media

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Scale to 500 M users, add video/image uploads, content moderation, and multi-region deployment.
• •Design for a peak load target around 34,722 RPS (including burst headroom).
• •Registered users: 500,000,000
• •Daily active users: 150,000,000
• •Media uploads/day: ~2,000,000
• •Video length: ≤ 60 seconds
• •Moderation latency: < 30 seconds

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

• •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.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.
• •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.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.

4) Reliability and Failure Strategy

• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •Store original media durably and make transforms replayable.
• •Design region failover and data residency controls as first-class requirements.
• •Support rebalancing and hotspot detection from day one.
• •Guarantee idempotent consumers and trace every message with correlation IDs.

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

• •CDN: Long TTL improves latency/cost; short TTL improves freshness.
• •Media Processing: Pre-processing improves playback UX, but requires substantial compute/storage budget.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.
• •Sharding: Sharding improves horizontal scale but makes cross-shard queries and transactions harder.
• •Message Queues: Async pipelines absorb spikes well, but increase eventual-consistency complexity.

Practical Notes

• •An object store (S3) + CDN for media delivery; a transcoding pipeline (FFmpeg workers) behind a queue.
• •Shard posts by user_id to keep a user's data co-located; use a shard map (consistent hashing).
• •ML-based content moderation can run as an async step in the media processing pipeline.

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 -> Replica SQL DB -> Message Queue

Design strengths

• •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.
• •Monitoring and logs are wired in from day one for rapid incident triage.

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.