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How to design applications for hyper-growth

In recent weeks a new hype called ChatGPT became extremely popular. Millions of users were using this new AI-powered brain for writing essays, improving code, research, and many more use cases.


Hyper-growth comes with a price


Frequent outages made it evident that chat.openai.com is not ready to serve its growing user community. I don't blame openai.com, but it brings up an important question:


"How to design, validate and optimize applications for hyper-growth"?

One of the biggest problems such high-in-demand services face is that they often do not have the required design and system architecture. If their systems are overloaded due to high request volumes, it's almost impossible to provide a quick fix.


Scaling systems such as ChatGPT means changing applications to handle increasing users, transactions, or data. There are several ways to scale an application, including:

  • Vertical

  • Horizontal

  • Caching

  • CDN

  • Cloud-based Infrastructure

  • Microservice design





It is always true that scaling must be built into the foundation of every application. However, if you decide to make an application scalable after running into performance issues, it can result in massive efforts or an entire redesign.


Design systems for speed & high throughput

  • Ensure you have performance requirements

  • Run a workload modeling and calculate expected and hyper-growth user and transaction volumes

  • Integrate vertical and horizontal scaling capabilities

  • Design appropriate load and performance tests

  • Design system monitoring and alerting

  • Consider auto-scaling

  • Review the architecture with scalability experts


Test systems for speed & high throughput

  • Create a meaningful testing environment that reflects your production

  • Implement realistic load-testing scenarios

  • Continuous performance benchmarking

  • Validate your performance requirements

  • Combine load tests with chaos engineering practices

  • Validate horizontal and vertical scaling approaches

  • Optimize the system and re-run your load tests


Operate systems for speed & high throughput

  • Observability for the entire stack

  • Proactive problem detection and escalation

  • Automatic problem remediation

  • Automatic scaling

  • Continuous performance feedback


The more complex and unpredictable your business processes might be, the more critical performance engineering becomes for the overall user experience. Customers hate slow and unreliable business services, and chances are that they never return after they face application outages.

By all means, Performance Engineering practices must become a fundamental part of your value stream.


Keep up the great work! Happy Performance Engineering!













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