Design Slack

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Slack's team communication platform - channels, threads, search, file sharing, integrations, and enterprise compliance at 30 M+ DAU.
• •Design for a peak load target around 1,736 RPS (including burst headroom).
• •Daily active users: 30,000,000+
• •Organizations: 750,000+
• •Peak messages/second: ~200,000
• •Message delivery latency: < 500 ms
• •Search latency: < 1 second

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

• •WebSockets: Use persistent connection gateways and decouple fanout via pub/sub or queues.
• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Search: Use primary store for writes and async index updates for search relevance + scale.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Auth: Centralize identity verification and keep authorization checks close to domain resources.
• •Microservices: Split services by business boundary, not by technical layer, and enforce ownership per domain.

4) Reliability and Failure Strategy

• •Track connection churn, backpressure, and session resumption behavior.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Track indexing lag and support reindex from source of truth.
• •Apply strict input validation and backward-compatible versioning.
• •Use short-lived tokens and secure key rotation workflows.

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

• •WebSockets: WebSockets reduce interaction latency but complicate scaling and state management.
• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Search: Search index gives rich querying but introduces eventual consistency and index ops overhead.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Auth: Central auth simplifies policy, but makes auth service availability/security critical.

Practical Notes

• •WebSocket gateway fleet: each user connects to a gateway. A pub/sub layer (Redis pub/sub or NATS) distributes messages to all gateways with subscribers.
• •Partition data by workspace_id - tenant isolation is natural and enables per-tenant compliance policies.
• •Search: Elasticsearch index per workspace. Index messages asynchronously via a message queue to avoid slowing the write path.

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 -> Load Balancer -> API Gateway -> Core Service -> Auth Service -> Primary NoSQL DB -> Realtime Bus -> Search Index

Design strengths

• •Security controls are enforced at ingress to protect downstream capacity.

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.