Building High-Performance Systems for Betting Platforms

The betting industry has some of the most demanding technical requirements in software. During my time at Ladbrokes Coral, I helped deliver a multi-million pound next-generation betting platform. Here's what building high-performance systems in this domain taught me.

The Challenge

Betting platforms have unique constraints:

Latency matters — Odds change by the second. Slow systems mean missed bets and lost revenue.
Spikes are extreme — Major sporting events can increase traffic 100x in seconds.
Accuracy is paramount — Financial calculations must be precise. Always.
Availability is critical — Downtime during a major event is catastrophic.

The Architecture

Event-Driven Design with Kafka

We chose Apache Kafka as the backbone for asynchronous communication. Why?

Decoupling — Services publish events without knowing who consumes them
Replay capability — Can rebuild state from event history
Scalability — Handles massive throughput with proper partitioning
Durability — Events are persisted, not lost

Spring Boot Microservices

Java with Spring Boot gave us:

Battle-tested frameworks for REST APIs
Excellent tooling and debugging support
Strong typing for financial calculations
Huge talent pool for hiring

Strategic Caching with Redis

For a betting platform, cache invalidation is critical. Odds must be current. We implemented:

Short TTLs for volatile data (live odds)
Longer TTLs for stable data (historical results)
Pub/sub for cache invalidation across instances

Search with Elasticsearch

Finding bets, searching events, filtering by criteria — all needed to be fast. Elasticsearch gave us the full-text search and aggregation capabilities required.

Performance Optimization Techniques

1. Connection Pooling

Database connections are expensive. We pooled aggressively and monitored connection usage carefully. HikariCP became our friend.

2. Async Where Possible

Not everything needs to be synchronous. Bet confirmations, notifications, analytics — these could be processed asynchronously, reducing response times for the critical path.

3. JVM Tuning

High-performance Java means understanding the JVM:

G1GC for balanced latency and throughput
Heap sizing based on actual usage patterns
Avoiding object allocation in hot paths

4. Contract-First API Design

With multiple teams building services, API contracts were defined upfront using OpenAPI. This prevented integration surprises and enabled parallel development.

The CI/CD Transformation

One of my key contributions was leading the transition from EC2 to ECS (Elastic Container Service). This gave us:

Faster deployments — Container images deploy in seconds
Better resource utilization — Dynamic scaling based on demand
Simplified rollbacks — Just point to the previous image
Consistency — Same container runs in dev, staging, and prod

Testing High-Performance Systems

Standard unit tests aren't enough. We invested in:

Load testing — Simulating peak traffic before it happens
Chaos engineering — What happens when Redis dies?
Performance regression tests — Catching slowdowns before production
Contract tests — Ensuring services can talk to each other

Key Learnings

Measure everything — You can't optimize what you don't measure. Instrument from day one.
Design for failure — Services will fail. Networks will partition. Plan for it.
Understand your domain — Betting has unique patterns. Grand National day is different from a Tuesday afternoon.
Invest in tooling — Good debugging tools pay for themselves many times over.
Performance is a feature — It's not an afterthought. It's a requirement.

The Human Element

Technical excellence alone isn't enough. High-performance teams build high-performance systems. Clear communication, shared ownership, and a culture of excellence made the difference.

Working on performance-critical systems? I'd love to exchange ideas. Get in touch.