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How to Scale a System from 0 to 10M+ Users

https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users
45•olayiwoladekoya•1h ago

Comments

olayiwoladekoya•1h ago
I really enjoyed reading this. Much like Instagram, which had thousands of users sign up on the first day, if you aren't able to scale because of your skill level, wouldn't that affect usage and lead to comments like: 'The app/site is so slow'?
lesuorac•1h ago
Aren't comments like "the site is too slow" similar to "the city is too crowded"?

Twitter famously had a "fail whale" but it didn't stop the company from growing. If you have market demand (and I guess advertising) then you can get away with a sub-optimal product for a long time.

paulnpace•1h ago
Reddit is still around.
daitangio•33m ago
I agree. Go fast with a suboptimal architecture. If success arise, throw away version 1 and rebuild from scratch. Often is more effettive.
alexfoo•29m ago
> Twitter famously had a "fail whale" but it didn't stop the company from growing. If you have market demand (and I guess advertising) then you can get away with a sub-optimal product for a long time.

Agreed, but there's still an element of survivorship bias there. Plenty of companies failed as they couldn't keep up with their scaling requirements and pushed the "getting away with a sub-optimal product" for too long a time.

jstanley•20m ago
Do you have some good examples?
smallnix•12m ago
This touches the toupet fallacy: "I never saw a large company fail to grow large because of deferred scaling"

Friendster might fit though: https://highscalability.com/friendster-lost-lead-because-of-...

arter45•33m ago
It depends on the adoption model.

If it’s just “sign up any time you want and go”, yes, it can go that way.

If it’s “join that waiting list” or “book a call” (for KYC purposes or whatever), you have a buffer.

If user count is more or less constant (most internal websites, for example), it’s probably not an issue.

And so on.

littlestymaar•58m ago
Not criticizing the core idea, which is sound (don't waste ressource overengineering at the beginning, evolve your architecture to match your actual scale as you grow), but the “number of users” figures in this post are completely nonsensical. You ought to multiply them by 100 (if you're being conservative) or even 1000 (depending on the consumption pattern for the user).

Modern hardware is fast, if you cannot fit more than 100 users (not even 100 concurrent users) on a single $50/month server, you're doing something very very wrong.

Even repurposed 10 years old fairphone[1] can handle more than that.

[1]: https://far.computer

don_neufeld•55m ago
Agreed, the numbers were shockingly low.
louismerlin•50m ago
Amazing to see my little phone pop up randomly on hacker news :D

Thank you stranger.

jbrooks84•30m ago
Nice read
Nextgrid•11m ago
Good post in general but some caveats:

1) His user numbers are off by an order of magnitude at least, as other comments have mentioned. Even a VM/VPS should handle more, and a modern bare-metal server will do way more than the quoted numbers.

2) Autoscaling is a solution to the self-inflicted problem of insanely-high cloud prices, which cloud providers love because implementing it requires more reliance on proprietary vendor-specific APIs. The actual solution is a handful of modern bare-metal servers at strategic locations which allow you to cover your worst-case expected load while being cheaper than the lowest expected load on a cloud. Upside: lower prices & complexity. Downside: say goodbye to your AWS ReInvent invite.

3) Microservices. Apparently redeploying stateless appservers is a problem (despite the autoscaling part doing exactly this in response to load spikes which he's fine with), and his solution is to introduce 100x the management overhead and points of failure? The argument about scaling separate features differently doesn't make sense either - unless your code is literally so big it can't all fit in one server, there is no problem having every server be able to serve all types of requests, and as a bonus you no longer have to predict the expected load on a per-feature basis. A monolith's individual features can still talk to separate databases just fine.

withinboredom•2m ago
And to add to this: virtually every programming language allows you to define multiple entry points. So you can have your workers in the exact same codebase as your api and even multiple api services. They can share code and data structures or whatever you need. So, if you do need this kind of complexity with multiple services, you don’t need separate repos and elaborate build systems and dependency hell.

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https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users
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