Design Spotify

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Spotify's music streaming platform - audio delivery, personalized playlists, offline mode, and social features for 600 M users.
• •Design for a peak load target around 80,000 RPS (including burst headroom).
• •Monthly active users: 615,000,000
• •Catalog size: 100,000,000 tracks
• •Concurrent streams (peak): ~30,000,000
• •Audio start time: < 200 ms
• •Search / autocomplete: < 50 ms

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.
• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Microservices: Split services by business boundary, not by technical layer, and enforce ownership per domain.
• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.
• •Analytics: Maintain separate OLTP and analytics paths; stream events into a warehouse/time-series layer.

4) Reliability and Failure Strategy

• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Add service-level timeout/retry budgets and contract tests.
• •Enforce lifecycle policies, retention tiers, and checksum validation.

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.
• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Microservices: Independent deployability improves scale but increases operational/debug complexity.
• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.

Practical Notes

• •Audio files are relatively small (~3-10 MB each) - CDN edge caching is extremely effective since popular tracks follow a power-law distribution.
• •Pre-buffer strategy: when a user is 80% through a song, start fetching the next track in the queue from CDN.
• •Personalization: train models offline (Spark/Hadoop), serve recommendations from a pre-computed cache (Redis) refreshed daily.

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 -> API Gateway -> Core Service -> Primary SQL DB -> Redis Cache

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Analytics pipeline is separated from OLTP path to avoid reporting workloads impacting transactions.

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.