1. Static typing increasing confidence and velocity
2. Better developer-facing tooling increasing velocity
3. More employees knowledgeable about Go than Python
4. More enthusiasm (and therefore faster velocity) around Go development.
The blog post was about the engineering challenges they faced and how they solved them and I think it was a great write-up in that regard. The post wasn't about why they switched this service from Python to Go.
I'm the kind of hacker who if a service runs out of memory every 2 hours, writes a crontab to restart it every hour after X random minutes so they don't all restart at the same time. It gets a lot of eye rolls from the other engineers searching for perfection, but it tends to produce services quickly that are highly reliable.
And look now the engineers who like chaos monkey don't even have to set that up. It's built in.
It looks like most of the savings were in switching from pillow to opencv, something that thumbor already does. https://github.com/thumbor/opencv-engine
1. Add profiling and telemetry to their Python code. Refactor the codebase based on insights from this.
2. Write a C<->Python interop for their image libraries.
I can't see the cost of #2 being any different than the cost they paid on writing it in Go. As for #1, depending on how the code is structured, a rewrite may have been less time than profiling spaghetti code. At that point, it depends on how much Go experience the team has.
