I'm not going to argue with commenters, but it's my experience that points 2, 6 and 8 are laughable (I am a heavy Python user and fan). Particularly egregious is #8. CPython's concurrency's is a lightweight compared to Java, and to date I have not met anyone working in a Jython shop. Well ... we currently utilize Apache Pig and it's Python support via Jython, but that doesn't count.
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- You poor soul (for having to use Apache Pig).Dec 12, 2014
- Agreed. #8 is a straw-man argument. "Myth: Python has poor support for doing the specific subset of multi-processing that I'm going to argue is easy to solve in Python" Not everyone has the luxury of needing to solve embarrassingly-parallel request processing.Dec 12, 2014
- Not many people know that YouTube uses Python for a majority of its backendDec 12, 2014
- I only really have an issue with #2 and #6
8 isn't stated well - I think they point they're trying to make is "you can build concurrent systems which make full use of your resources in python" - but yeah, python is not the correct language if you want to do more "traditional" concurrency etc - you do need very major architectural changes to the system to make use of those resources (e.g. re-architect to use message passing etc).
Honestly: I've never come across a situation where python's concurrency support was what let me down though ..
- if you're doing concurrent processing to avoid resource contention then it's support is fine.
- If you're doing parallel work for performance in a non trivial problem then you're only conceptually looking at ~ a 5x performance improvement available from concurrency anyway (on a 2x 8-core machine, with the extra overhead for increased fetches over NUMA, reduced cache efficiency etc). You can relatively trivially get a 25x performance boost for most of these kinds of problems (if they're data structure or numerical kinds of problems) by fairly trivial C extensions, and if you need more than that then I think you should really be re-architecting your solution..Dec 13, 2014