How to Estimate Server and Database Needs for Scaling Services?

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Asked By TechieDude42 On

I'm trying to figure out how to estimate server and database requirements when designing a service, particularly how to go from needing to handle a certain number of requests per second to determining the necessary server count and associated costs. I know it can vary based on architectural choices, but I'm curious about practicing the calculations. For example, how would you calculate the request capacity of a Node.js server running on an m8g.2xlarge EC2 instance? Is there a way to do some quick estimates, or do you just build a test server to gather performance data? Could someone explain how to approach this, for instance, if I wanted to create a URL shortener that needs to manage a thousand requests per second?

4 Answers

Answered By DataDrivenDee On

You really won't know for sure until you get hard data. There’s so much variance in design and usage that any estimation could be off. You could do some synthetic benchmarking, but that comes with a lot of assumptions. It’s key to understand your scaling factors and design for flexibility to scale up or down as needed.

Answered By CloudNinja88 On

It's tough to guess capacity accurately. AWS highlights that you should stop making wild guesses about your needs. Instead, create your service, set reasonable minimums, and configure auto-scaling. Also, consider running multiple smaller instances rather than fewer large ones for better scaling and resilience.

Answered By IntuitionGuru On

A mix of experience, intuition, and thorough testing is vital. Even with all that, mistakes will happen, and you'll have to adapt. Testing gives you the closest estimates, and being prepared to revise your approach based on real-world data is crucial.

Answered By ServerWhisperer99 On

Honestly, the most reliable way is to benchmark and load test your setup. You can plan for scale, but predicting exact performance can be tricky. Experience helps give you an idea of hardware capabilities, but actual performance can vary widely based on your implementation. Sometimes even a small code change can drastically impact throughput.

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