Building Real-Time Systems at Scale

When your system needs to process 10 million events per second without breaking a sweat, textbook solutions won't cut it. Here's what we learned building real-time infrastructure that actually scales.

The Problem with "Best Practices"

Most scaling advice assumes you're dealing with thousands of users, not millions. When you cross into true high-volume territory, everything changes. Network latency becomes your enemy. Database locks become bottlenecks. Even logging can bring your system to its knees.

We learned this the hard way while building a live analytics platform that needed to ingest, process, and query data in real-time for enterprise clients.

Architecture Principles That Actually Work

Event-Driven from Day One

Don't bolt on event streaming later. Build your entire system around events from the start. Every state change is an event. Every user action is an event. This gives you audit trails, replay capability, and natural scalability for free.

Embrace Eventual Consistency

Strong consistency is expensive at scale. Most of your use cases don't actually need it. Learn to embrace eventual consistency and your system will thank you with 10x better performance.

The Data Gravity Problem

Data has gravity—the more you have, the harder it is to move. Place your compute close to your data, not the other way around. We reduced our processing latency by 80% by moving computation to where data already lives.

The Tech Stack That Scales

After testing dozens of configurations, here's what we landed on:

Apache Kafka for event streaming. Nothing else comes close for this volume. We run a 50-node cluster handling 10M+ messages per second with sub-10ms latency.

ClickHouse for analytics. When you need to query billions of rows in milliseconds, ClickHouse delivers. Column-oriented storage makes aggregations screaming fast.

Redis for caching and session management. Keep hot data in memory. We cache aggressively and invalidate surgically.

Kubernetes for orchestration. Auto-scaling that actually works. When traffic spikes 10x on Black Friday, the system scales automatically.

Lessons from Production

Monitor Everything, Alert on What Matters

We collect 2TB of metrics daily but only alert on business-critical thresholds. Too many alerts and you'll ignore them all. Focus on what actually indicates system health.

Backpressure is Your Friend

Don't let producers overwhelm consumers. Implement backpressure at every level. It's better to slow down gracefully than crash spectacularly.

Test at Scale or Don't Test

Load testing with 100 users tells you nothing about behavior at 10 million. We built a shadow traffic system that replays production load in our test environment. It caught issues no synthetic test ever could.

The Human Element

Technology is only half the battle. The other half is organizational:

Build teams around services, not layers. Each team owns their domain end-to-end—from database to UI. This eliminates handoffs and accelerates shipping.

Automate everything. Deploy dozens of times per day. Our deployment pipeline runs 300+ automated checks before code hits production.

Practice chaos engineering. We randomly kill services in production to ensure the system can handle failure. If it can't fail gracefully, it's not production-ready.

What We'd Do Differently

If we started over today, we'd invest more in observability from day one. Understanding system behavior at scale is harder than building it.

We'd also standardize earlier on protocols and data formats. Technical debt at scale is expensive to fix.

The Bottom Line

Building systems that scale isn't about picking the right database or framework. It's about architectural choices that compound over time. Start with solid foundations, measure everything, and be willing to throw away solutions that don't scale.

The real test isn't whether your system works at 1,000 requests per second. It's whether it still works at 100,000—and whether you can sleep through the night when it does.