How Slack Supports Billions of Daily Messages

Executive Summary

Slack's architecture handles billions of daily messages by leveraging a distributed microservices approach, optimized data storage, and real-time synchronization. Key components include WebSockets for persistent connections, a hybrid database strategy (PostgreSQL + Vitess), and intelligent message routing. The system prioritizes reliability, low latency, and scalability through sharding, caching (Redis/Memcached), and edge computing.

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Core Technical Concepts/Technologies

• Microservices Architecture
• WebSockets (for real-time communication)
• Hybrid Database: PostgreSQL (metadata) + Vitess (sharding)
• Caching: Redis/Memcached
• Message Queues: Kafka/RabbitMQ
• Edge Computing (reducing latency)
• Erlang/Elixir (for concurrency)

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Main Points

• Real-Time Messaging:

  • Uses WebSockets for persistent client-server connections, reducing HTTP overhead.
  • Fallback to long polling for unstable networks.

• Database Scaling:

  • PostgreSQL for critical metadata (users, channels).
  • Vitess (MySQL sharding) for horizontal scaling of message data.
  • Read replicas to distribute query load.

• Caching & Performance:

  • Redis/Memcached for frequent access patterns (e.g., unread message counts).
  • Multi-level caching (local + global) to minimize database hits.

• Message Routing:

  • Kafka queues decouple producers/consumers for reliability.
  • Edge servers route messages geographically to reduce latency.

• Fault Tolerance:

  • Stateless services enable easy failover.
  • Automated retries and dead-letter queues handle message failures.

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Technical Specifications/Implementation

• WebSocket Protocol: Custom framing for efficient binary payloads.
• Database Sharding: Messages partitioned by workspace ID (Vitess).
• Code Example: Erlang’s OTP framework ensures lightweight processes for concurrent connections.

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Key Takeaways

1. Hybrid Databases: Combine SQL (PostgreSQL) and sharded NoSQL (Vitess) for scalability + consistency.
2. Edge Optimization: Locally cached data reduces global latency.
3. Decoupled Services: Kafka ensures message durability despite service failures.
4. Graceful Degradation: Fallback mechanisms (long polling) maintain usability.

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Limitations/Caveats

• WebSocket Overhead: Requires stateful connections, complicating load balancing.
• Sharding Complexity: Cross-workspace queries may need special handling.
• Further Exploration: AI-driven auto-scaling for dynamic load shifts.