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Comment by skohan | Hacker News Reader

Yeah I would agree the use cases you mentioned are probably fine for Julia as well. I could be more precise and say that Julia is not relevant for performance-relevant applications.

The problem with ignoring performance is, many applications can become a dependency of a performance relevant application even if that was not the original intent.

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We recently used Julia to solve (correctly) a super complex scheduling problem for a major airline. Our budget was 5min, Python would get not even close to a solution in that time. The tool is being actively used in production.

Julia is not not only fine, but my go to language for performance problems.

Ok but comparing with Python is like a worst-case scenario, and long running tasks would amortize Julia’s performance issues.

There are a great many use-cases/language comparisons you could find where Julia would be the worse performing language.

You seem to confuse performance and latency. High performance is Julia's main selling point. Latency is its Achilles heel.

It's being very actively worked on however.

Latency is a type of performance. For instance if you have even a few milliseconds latency it's relevant in terms of UI performance.

Then you should specify. You made a blanket statement about performance, while using the least commonly considered type. If you just say 'performance', most people will assume you mean throughput.

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