What We Tried First (And Why It Failed)
At first, we thought that our system was designed to auto-scale. After all, our documentation said so. We'd deployed a combination of Kubernetes, Apache Cassandra, and Apache Kafka. The idea was to have a highly available system that could automatically scale to meet demand. But the truth was, our system was not designed for auto-scaling. It was designed for high availability, which is a different beast altogether.
When we first started to see the problem, we thought that the solution lay in configuring our Kubernetes cluster to auto-scale. We increased the number of replicas, tuned the horizontal pod autoscaler, and even deployed a custom-made cluster autoscaler. But the problem persisted. What we didn't realize at the time was that our system was not designed to handle the communication overhead that came with auto-scaling. Every time we added a new server, the existing servers would have to re-establish connections to the new server, leading to a significant increase in latency.
The Architecture Decision
After months of debugging and analyzing our system, we finally realized that the problem lay in our choice of database. We were using Apache Cassandra, which is a great choice for high availability but not so great for auto-scaling. The reason was simple: Cassandra is designed to handle large amounts of data but is not optimized for high network traffic. When we added a new server, the existing servers would have to re-establish connections to the new server, leading to a significant increase in latency.
We decided to switch to Google Cloud Bigtable, which is designed to handle high network traffic and is optimized for auto-scaling. We also modified our system to use a leader-follower architecture, which eliminated the need for each server to re-establish connections to the new server. The result was a significant decrease in latency and a huge reduction in operator workload.
What The Numbers Said After
After deploying the new architecture, we saw a significant improvement in performance. Our latency decreased by an average of 30%, and our operator workload decreased by 50%. We were also able to reduce our server utilization from 70% to 40%, which allowed us to take advantage of our cloud provider's reserved instance pricing.
Here are some specific metrics that we tracked:
- Average latency: 500ms → 350ms
- Server utilization: 70% → 40%
- Operator workload: 12 hours/day → 6 hours/day
What I Would Do Differently
If I had to do it all over again, I would have chosen a different database from the start. I would have gone with a database that is specifically designed for auto-scaling, such as Google Cloud Bigtable or Amazon Aurora. I would also have designed the system to use a leader-follower architecture from the start, which would have eliminated the need for each server to re-establish connections to the new server.
In the end, our experience with the treasure hunt engine was a valuable learning experience. It taught us the importance of choosing the right databases and architectures for our system, and it showed us that "auto-scaling" is not just about adding more servers, but about designing a system that can communicate efficiently with those servers.
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