How We Handle Thousands of Real-Time Events in Our Sportsbook

Picture this: You’re watching a high-stakes football match. A goal is scored in the 89th minute. Within a split second, your sportsbook app updates - odds shift, markets lock or open, and everything feels seamless.

But behind that smooth front-end experience - pure complexity.

Delasport's system has to handle thousands of these updates at once - goals, fouls, injuries, substitutions, overtime - all in real time, across hundreds of matches. The scale is staggering, but it’s also where the fun begins (at least for those of us who enjoy a good concurrency challenge).

Here’s how we make it work using Java, multithreading, Kafka, and a healthy respect for immutability, as told by Delasport's Java Team Lead, Georgi Stoyanov.

The Challenge: Real-Time, High-Volume Event Processing

In a live betting environment, speed isn’t just important - it’s non-negotiable.

Every single change in the game can affect:

• Which markets are open or suspended

• Live odds

• Cash-out availability

• What the front-end needs to show, immediately

Multiply that across hundreds or even thousands of simultaneous matches. This isn’t just a stream of updates - it’s a flood. And the system must react in near real time, without dropping a beat.

Our Approach: Java Multithreading + Kafka Partitioning 

We’ve built our system around a simple principle: divide and conquer.

Kafka: The Event Traffic Controller 

Kafka acts as our real-time data backbone. Each match has its own stream of updates, funneled into a Kafka topic and partitioned - often by match ID. 

Why partitions? Because they allow us to scale horizontally and process data in parallel. Kafka also guarantees message order within a partition, which is critical when timing matters - like determining whether a goal came before a red card. 

Java Threads: The Processing Engine 

Each Kafka partition is handled by a dedicated Java thread (or thread pool, when necessary). That thread is responsible for: 

• Consuming and deserializing the event 

• Recalculating odds based on the new state 

• Updating the state of relevant markets 

• Generating fresh data for the front end 

The outcome is massively parallel processing - ordered, efficient, and fast. Each match is handled independently, which allows us to scale our processing across CPU cores and servers with minimal contention. 

Immutability: Our Best Friend in a Concurrent World 

Concurrency is tricky. Bugs caused by shared mutable state are some of the most subtle and frustrating in software engineering. 

That’s why we’ve built our system around immutable data structures. Each event message, once deserialized, is wrapped into an immutable object. Any transformation or update creates a new object. 

The benefits: 

• Thread safety without locks 

• No risk of race conditions 

• Easier debugging and reasoning about system behavior 

This design choice has dramatically improved the reliability of our processing pipeline - and simplified how our developers work with data. 

A Real-World Example 

Let’s walk through what happens when a goal is scored in Match 5678: 

1. Kafka ingests the event into the topic match-5678-events.

2. The message is routed to the appropriate partition.

3. A Java thread assigned to that partition picks up the message.

4. It processes the event: updates internal state, recalculates odds, and generates a new view of the market.

5. The updated data is pushed to the front end, usually within milliseconds.

All of this happens with minimal locking, no shared mutable state, and no interference from events in other matches. 

Lessons Learned 

After building and refining this system in production, a few lessons stand out: 

• Thread pools need tuning. Too many threads introduce overhead and can lead to excessive context switching; too few, and you create bottlenecks under load. 

• Kafka’s partitioning model is a superpower, especially when used to enforce logical boundaries like match or market IDs. It allows us to scale cleanly and ensures ordered, isolated processing. 

• Immutability pays off. It removes an entire class of concurrency bugs, improves reliability, and simplifies the mental model for developers working in a multi-threaded environment. 

• Monitoring is critical. With so many moving parts, we rely heavily on observability - metrics around latency, thread activity, queue depth, and partition lag help us maintain performance and spot issues before they escalate. 

• We're exploring virtual threads. With the introduction of Project Loom in modern Java, virtual threads offer a promising path to simplify concurrency even further - allowing us to handle many more lightweight tasks with less tuning and overhead. We’re currently evaluating how they might fit into our architecture, particularly in areas with high I/O wait times or blocking operations. 

Building a scalable, real-time sportsbook engine in Java has been one of the most rewarding engineering challenges we've tackled. It forced us to think deeply about concurrency, architecture, and fault tolerance - while still delivering a seamless experience to users who expect everything to just work. 

If you're working on anything in the real-time or high-throughput space - trading systems, analytics platforms, even multiplayer games - many of the same principles apply. 

And if you're deep in the trenches of Java concurrency and event-driven architecture, I'd love to hear how you’re approaching it.

Sounds interesting? Delasport will be at the upcoming jPrime 2025 where you can learn more about how our Java teams operate and what the opportunities for you are!