Design Twitter

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design the architecture behind Twitter - tweets, timelines, trends, and search at 500 M+ users.
• •Design for a peak load target around 34,722 RPS (including burst headroom).
• •Monthly active users: 500,000,000
• •Daily tweets: ~500,000,000
• •Max followers per user: 50,000,000+
• •Timeline load time: < 300 ms
• •Search latency: < 300 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

• •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.
• •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.
• •Search: Use primary store for writes and async index updates for search relevance + scale.
• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Support rebalancing and hotspot detection from day one.
• •Guarantee idempotent consumers and trace every message with correlation IDs.
• •Track indexing lag and support reindex from source of truth.

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.
• •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.
• •Message Queues: Async pipelines absorb spikes well, but increase eventual-consistency complexity.
• •Search: Search index gives rich querying but introduces eventual consistency and index ops overhead.

Practical Notes

• •Hybrid fan-out: pre-compute timelines for users with < 10 k followers (write path), fetch on-the-fly for celebrities (read path).
• •Tweet storage: shard by user_id; timeline cache in Redis (sorted set by timestamp, trim to last 800 tweets).
• •Elasticsearch cluster for full-text tweet search, partitioned by time (recent tweets in hot shards).

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.

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.