A blog post about Black Friday’s production scalability experience in payments provider company named iyzico. By Yalçın Yenigün.
First, they predicted the hourly request count of Black Friday 2019 and the maximum transaction per second (TPS) of their APIs because they needed to optimize throughput by finding and fixing bottlenecks. Thier team made predictions by using Long Short-Term Memory (LSTM) architecture. Unlike standard feedforward neural networks, LSTM has feedback connections. For more detail: https://pathmind.com/wiki/lstm.
After prediction, we knew the maximum request count per second of APIs and the predicted time frames of related peaks. In production, the engineering team simulated the same request counts via load testing by using JMeter (http://jmeter.apache.org/) and found the bottlenecks. The major improvements that we implemented:
- Feature toggling
- Health checks
- Splitting Monoliths: Split some of monolith databases.
- Cleaned blocking commands
- Improved existing monitoring tools
- Circuit breakers
- Failover tests
… and more. And you will get links to further reading and resources. Good read!
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