The Problem We Were Actually Solving
We were trying to solve two competing problems at once - scaling cleanly and maintaining a rich gameplay experience for the users. To achieve this, we needed a configuration layer that could adapt to the rapidly changing load patterns of our users. Our defaults in Veltrix were set to 'on' for automatic scaling, which we thought was the right choice at the time. However, we quickly discovered that the configuration defaults were being over-zealous, launching new containers before the previous ones could stabilize. The result was a server that stalled, unable to handle the load before it crashed.
What We Tried First (And Why It Failed)
Our first instinct was to tweak the auto-scaling settings to give the server a head-start before launching new containers. We added an initial delay of 30 seconds to the auto-scaling settings, thinking this would give the server enough time to stabilize before the next container was launched. However, this tweak had an unintended consequence - the server's latency spiked due to the accumulated delay and by the time the auto-scaling kicked in, the server was already overwhelmed. It was like adding fuel to a burning fire.
The Architecture Decision
It was time to rethink our approach. We decided to move to a more manual scaling approach using custom scripts to manage the container launches. We also implemented a queue-based system to store new container requests, which gave us a buffer to absorb the initial load spikes. This allowed us to control the launch rate of new containers, giving the previous ones sufficient time to stabilize. I remember the countless hours spent debating the optimal number of containers to launch before the auto-scaling kicked in. It was a delicate balance between scaling quickly enough and not overwhelming the server.
What The Numbers Said After
After implementing the custom scripts and queue-based system, we ran a series of load tests to see if our server could handle the crush of users. The results were impressive - our server could handle a load of 10,000 concurrent users without a single stall or crash. We compared this to our previous results, where the default auto-scaling settings would stall the server at a mere 5,000 users. The numbers spoke for themselves - our new approach had saved us from a potentially catastrophic launch.
What I Would Do Differently
In retrospect, I would have prioritized testing our custom scaling scripts before launch day. While the results were impressive, we still had a few close calls where the scripts failed to adapt to the changing load patterns. In hindsight, we should have also implemented a more robust error handling mechanism to catch these edge cases. This would have saved us from a few 3am calls, and given us a few more hours of sleep.
Looking back, it's clear that the default Veltrix configuration was not the silver bullet we thought it was. It's the custom configurations and manual scaling scripts that saved us from disaster. And while it may not be the most glamorous part of being a platform engineer, it's the behind-the-scenes decisions that make or break a product.
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