Harshness is not the problem, credibility is. I don't mean that you sound like you're lying, but that it's impossible for anyone to evaluate your assertion or how it applies to their use case. Anyone who reads your comment (including me) just files it away in their head as "somebody on HN made an unsubstantiated comment that Julia sucks in production in some vague way".
If I were in the position of making or advising someone making the decision of whether or not to use Julia, an unsubstantiated, untestable assertion like that makes virtually no difference in my decision or advice. I hope it's clear this is not an insult, it's just that if I have no way to evaluate your assertion or its applicability, there's no way for me to incorporate it into my decision or advice.
By contrast, if you could point to even one concrete example, I could file it away in my head as "someone on HN pointed out that Julia has X, Y, and Z problem". If I were in a position of making or advising a decision on Julia, I would be able to evaluate whether it applies the use case in question, whether it points to some deeper design problems with Julia, or whether I even think it's a problem at all.
You're right, I see the problem. I'll do an article and post it with detailed analysis. It will take some time because repos are like Tolstoy's quote - "All good repos are alike, every broken repo is broken in its own way". There are issues ranging from startup time, upstream packages breaking, constantly changing ecosystem (given for a new language), quirky behaviors and certain problems related to HTTP package and LibPQ packages that I couldn't even debug. So, I rewrote them in Python.
Until then - I concede, please take it with a grain of salt and use your own judgement.
It's quite possible that certain area of the Julia ecosystem are not not mature enough. The situation continues to improve quickly as far as I can tell.
Rather than rewriting code in Python, we took an approach of using PyCall. It turns out that the overhead of calling Python is very small... at least not to an extent that I had to worry about. For my production system, we used this strategy to access Oracle and Apache Kafka. The code still looks very clean as the calls to the Python packages are just like normal Julia function calls.
I can't fault you for switching to something more battle-tested, but did you file issues for the LibPQ.jl issues you encountered? I wrote and maintain the package and we (and others) use it in production so bug reports are much appreciated.
If I were in the position of making or advising someone making the decision of whether or not to use Julia, an unsubstantiated, untestable assertion like that makes virtually no difference in my decision or advice. I hope it's clear this is not an insult, it's just that if I have no way to evaluate your assertion or its applicability, there's no way for me to incorporate it into my decision or advice.
By contrast, if you could point to even one concrete example, I could file it away in my head as "someone on HN pointed out that Julia has X, Y, and Z problem". If I were in a position of making or advising a decision on Julia, I would be able to evaluate whether it applies the use case in question, whether it points to some deeper design problems with Julia, or whether I even think it's a problem at all.