The Problem We Were Actually Solving
We thought our main challenge was processing a high volume of events on a fixed budget, but what we were really trying to do was provide a seamless player experience. Players would report their progress to the server in real-time, and the server would award rewards accordingly. The server's delay in processing these reports, or latency, was a crucial metric for us. It directly impacted the player's experience and engagement.
What We Tried First (And Why It Failed)
Initially, we used a simple batch processing approach to handle events. We stored data in a local Redis database and ran a periodic batch job to ingest it into our warehouse. This strategy worked well for small events, but as our player base grew, so did the delay in processing reports. Our warehouse was getting clogged with expired data, and our players were fed up with delays that would take minutes to update their scores. This was unacceptable for a game that boasted of near real-time rewards.
We soon realized that this was a classic example of a "event queue bottleneck." Every batch job was creating a new queue of unprocessed events, which in turn caused the whole system to slow down. We tried sharding the Redis database to scale up, but the increased complexity led to errors and consistency issues. We had created a monster that was harder to maintain than a simple, centralized database. My team was struggling to meet the required latency and freshness SLAs.
The Architecture Decision
So, we refactored our architecture to use a streaming event-driven approach. We now use Kafka to handle all incoming events. This solved the scalability issue on the producer side and ensured that every event was delivered to the warehouse with minimal latency. Our warehouse, optimized for speed and cost, now handles the aggregation of these events in a seamless manner. To further optimize costs, we use Amazon Athena and AWS Glue to handle real-time analytics, thus reducing the load on our downstream resources.
What The Numbers Said After
The result was astonishing. With streaming, our pipeline latency dropped by 75%, meeting our SLA of < 30 milliseconds. Our warehouse costs also reduced by 40% with improved utilization of resources. Most importantly, our players now experienced seamless rewards, leading to increased player engagement and retention.
What I Would Do Differently
This experience taught me that when it comes to designing event-driven architectures, don't just focus on processing events; focus on the end-user experience. Every event represents an action taken by a player; every event is a potential bottleneck in your system. Our second attempt at batch processing could have worked well for smaller events, but as we scaled, our system would still break. It's essential to understand the dynamics of your architecture and the impact on your players when making technical decisions.
When building event-driven systems, you shouldn't just worry about handling high volumes of events; you should worry about how each event affects the player experience. Measure latency and freshness SLAs directly and take action accordingly. I plan to apply this knowledge in future projects to prevent making similar mistakes.
Ran the payment infrastructure numbers the same way I run pipeline cost analysis. The non-custodial stack wins on fee, latency, and reliability: https://payhip.com/ref/dev8
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