[hamandcheese]

Sometimes it is the language. Or at least the ecosystem and libraries available.

My go-to example is graphql-ruby, which really chokes serializing complex object graphs (or did, it's been a while now since I've had to use it). It is pretty easy to consume 100s of ms purely on compute to serialize a complex graphql response.

[Lio]

I have mixed feelings about this. It's saying that python is too slow for data science ignoring that python can outsource that work to Pandas or NumPy.

For GraphQL on Rails you can avoid graphql-ruby and use Agoo[1] instead so that that work is outsourced to C. So in practice it's not a problem.

1. https://github.com/ohler55/agoo

[datadeft]

> python can outsource that work to Pandas or NumPy.

Exactly. So C/C++/Fortrant is better in this regard than Python.

[ainiriand]

I would make a case that that's not the language's fault. You need to assess how critical is speed in your requirements and adapt your solutions.