The Problem We Were Actually Solving
In hindsight, we were trying to solve a complex problem with a naive solution. We aimed to create a system that could handle tens of thousands of concurrent users, each interacting with a dynamic set of events and resources. Sounds simple enough, but the twist was that these events and resources were constantly changing, requiring near instantaneous updates to the system.
What We Tried First (And Why It Failed)
We began by designing a caching layer to store metadata about events and resources. We chose Redis as our caching solution, thinking it would provide the necessary performance boost. However, we soon realized that our initial implementation was flawed. We had incorrectly configured the caching layer to store too much data, resulting in an exponential growth in cache size.
As the system scaled, the cache size ballooned, causing the system to slow down and eventually freeze. This was the "System Wide Cache Flush" moment – the point at which the system became unresponsive due to its own caching mechanism. It was a moment of pure chaos.
The Architecture Decision
After the first launch debacle, we decided to revisit the caching implementation. We recognized that we needed a more fine-grained approach to caching, one that would store only the necessary metadata and implement an eviction policy to prevent the cache from growing too large.
We opted for a hierarchical caching solution, using a combination of Redis and an in-memory cache. This allowed us to store frequently accessed data in memory and lesser-used data in Redis. We also implemented a least recently used (LRU) eviction policy to ensure that the cache didn't grow indefinitely.
What The Numbers Said After
After the changes, we observed a significant reduction in cache size and a much smoother system performance. The average cache size reduced from 500 GB to 50 GB, and the system was able to handle the increased load without freezing. The number of errors related to the cache flush also decreased dramatically.
Here are some metrics that highlight the improvement:
- Average cache size: 50 GB (down from 500 GB)
- System response time: 200 ms (down from 500 ms)
- Error rate: 0.1% (down from 5%)
What I Would Do Differently
In hindsight, I would have approached the caching problem from a different angle. I would have started with a more minimalist caching strategy, gradually introducing more features as needed. This would have allowed us to identify the root cause of the problem earlier and avoid the "System Wide Cache Flush" moment.
Additionally, I would have implemented more robust monitoring and alerting systems to detect potential issues before they became critical. Having a better understanding of the system's performance and behavior would have allowed us to take proactive measures to prevent the meltdown.
The on-call rotation got quieter when we removed the payment platform dependency. Here is what replaced it: https://payhip.com/ref/dev4
Top comments (0)