Scaling a Peer-to-Peer Sports Betting App: The Tech, The Challenge, The Solution
by Nathan Thompson, Managing Partner
Scaling a Peer-to-Peer Sports Betting App: The Tech, The Challenge, The Solution
Building a peer-to-peer (P2P) sports betting platform is a complex engineering challenge. Unlike traditional sportsbooks that set odds and take the opposite side of a bet, a P2P model requires matching bettors against each other, handling real-time odds changes, ensuring instant settlements, and scaling for massive spikes during major events like the Super Bowl or Champions League Final.
When I architected our iOS sports betting app, choosing the right tech stack wasn’t just important—it was mission-critical. A P2P betting platform is essentially a real-time financial exchange, where milliseconds matter, and every component must work seamlessly to maintain fairness, integrity, and scalability.
The Core Challenges
A P2P betting marketplace is not just another transactional app. It operates in a world of low-latency, high-frequency, event-driven processing, where failure at scale isn’t an option. Some of the biggest technical hurdles included:
Real-time Bet Matching – Unlike sportsbooks, where odds are controlled by the operator, P2P platforms must match bets dynamically between users with opposing positions. That means real-time processing of thousands of concurrent bet offers.
Fair & Secure Transaction Handling – Funds must be escrowed before the bet is confirmed and settled instantly once the event outcome is determined. Any delays in processing could lead to disputes or user dissatisfaction.
Handling Event-Driven Data at Scale – Every goal, touchdown, or three-pointer triggers a chain of microservices, updating bet statuses, adjusting odds, notifying users, and ensuring payments are settled in real-time. A delay in processing even milliseconds of data could create unfair advantages.
Scalability for High Traffic Spikes – Betting activity is not linear. It spikes massively minutes before a game starts and during key in-game moments. The system needs to scale dynamically, handling both steady traffic and sudden surges.
Why .NET Core, Kafka, and gRPC Were the Perfect Fit
A traditional monolithic architecture would crumble under these requirements. A distributed microservices architecture was the only viable solution. Here’s why we made these key choices:
1. .NET Core Microservices for Scalability & Performance
✔ Highly modular & scalable – We designed independent microservices for bet matching, payments, user management, and analytics.
✔ Asynchronous processing – .NET Core, with background workers and event-driven architecture, allowed us to handle high concurrency.
✔ Cross-platform & containerized – Running .NET Core microservices in Docker containers allowed for seamless scaling in Kubernetes.
2. Kafka for Real-Time, Event-Driven Communication
✔ Bet matching and event updates – Kafka acts as a real-time message broker, ensuring bet requests are processed instantly.
✔ Decoupling services for high availability – Each microservice (e.g., bet processing, payments, fraud detection) listens to Kafka topics and reacts independently.
✔ Handling sudden traffic spikes gracefully – With Kafka’s replayability and partitioning, we could buffer incoming bets and process them at scale without losing data.
3. gRPC for Low-Latency Microservice Communication
✔ 5-10x faster than REST – We needed ultra-low-latency communication between core services (e.g., bet matching, payment handling).
✔ Binary serialization (Protocol Buffers) – This significantly reduced the payload size, improving speed.
✔ Real-time bidirectional streaming – gRPC allowed us to stream bet updates instantly to users.
Scaling Considerations: Making the Platform Bulletproof
Building the architecture was just half the battle—ensuring it could scale and handle real-world betting patterns was equally critical.
Handling High Traffic Surges – We used autoscaling in Kubernetes, dynamically adding more replicas of microservices as betting demand increased. Kafka helped smooth out burst loads by queuing events for asynchronous processing.
Ensuring Data Consistency & Preventing Loss – Betting requires eventual consistency with strong guarantees. Kafka’s log retention and replayability ensured no bet request was lost even in case of a system failure.
Preventing Fraud & Ensuring Fair Play – Real-time bet matching and settlement had to detect suspicious patterns (e.g., last-second bet placement advantages). We implemented machine learning-based fraud detection, monitoring Kafka event streams in real-time for anomalies.
Real-time Analytics for Odds & Risk Management – With all bet activity flowing through Kafka, we could process real-time analytics to adjust risk exposure dynamically and ensure the marketplace remained balanced.
The Future of Peer-to-Peer Betting & Event-Driven Systems
This project was a deep dive into the power of modern microservices architecture, real-time event processing, and low-latency transactions.
What excites me the most is how event-driven systems and decentralized marketplaces are shaping the future. Whether in sports betting, fintech, or real-time gaming, the principles remain the same:
Real-time. Event-driven. Scalable. Resilient.