How We Think About Scalable Messaging Architecture

An overview of the technical considerations for building reliable messaging systems.

Core Requirements

High availability - Messages must be deliverable 24/7Low latency - Users expect near-instant deliveryMessage ordering - Messages should appear in the right sequenceDelivery guarantees - No message should be lost

Architecture Patterns

We use an event-driven architecture where: - Every message is an immutable event - Events are stored in sequence - Services react to events asynchronously

Event-Driven Design

We use an event-driven architecture where: - Every message is an immutable event - Events are stored in sequence - Services react to events asynchronously

Message Queues

Message queues help handle load spikes: - Buffer messages during high traffic - Enable retry logic for failures - Decouple sending from delivery

Real-Time Connections

For instant message delivery: - WebSocket connections for active sessions - Efficient connection management at scale - Graceful reconnection handling

Handling Scale

As load increases: - Add more application servers - Partition data by user or conversation - Distribute load across regions

Horizontal Scaling

As load increases: - Add more application servers - Partition data by user or conversation - Distribute load across regions

Caching

Reduce database load: - Cache recent messages in memory - Cache user sessions - Invalidate cache on updates

Database Choices

Different data has different needs: - Fast storage for recent messages - Efficient storage for message history - Full-text search for finding content

Reliability Strategies

No single point of failure: - Multiple server instances - Database replication - Multi-region deployment

Redundancy

No single point of failure: - Multiple server instances - Database replication - Multi-region deployment

Monitoring

Know when things go wrong: - Track message delivery rates - Monitor latency percentiles - Alert on anomalies

Graceful Degradation

When systems are stressed: - Prioritize message delivery - Defer non-critical operations - Provide feedback to users

Lessons Learned

Start simple - Optimize when you have dataMeasure everything - You can't improve what you don't measurePlan for failure - Every component will fail eventuallyTest at scale - Load testing catches issues before users do

Kanvo Team

Writes here about what Kanvo is building and why — the send path, the evidence it leaves, and the decisions behind both.

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