Python, Go or Haskell?

For data science, python is the strongest language right now due to massive library support for anything you want to do. Its also the easiest to understand and find developers for (from your list of languages).

Go is also nice and will bring you support for types and high er runtime performance (but slower development speed and maintenance speed).

Haskell will be hard to find developers for, but the ones you find will be very good. But getting a team of 20 to use haskell will be a great challenge, and also the library support for data science is tiny.

Haskell. You'll be in a niche but a good niche to be in.

With Python your will be more agile. Python have many tools for you teams

- Data Science: Pandas, Scikit-learn, Numpy, ... - Web (Django,..) - APIs (Flask,..)

Also many Artificial Intelligence Tools and Apis of other services have support for Python.

The size the community is also very important. When There are a big community is more easy to find developers and events/meetups where the community talks about Python and use cases.

Haskell is also a good lenguaje and easy to learn but Python and Haskell are languages with differences https://wiki.python.org/moin/PythonVsHaskell

I can't speak to Haskell because I've never used it in a work context. I have extensively used Python and Go in both small and large codebases, however.

1. Long-term supportability: I'd definitely favor Go here because of the type system. I work(ed) in large Python codebases that deteriorate because ensuring correctness requires enormous amounts of test coverage, and refactors are absolute nightmares. People tend to rewrite code instead of refactoring because it's too difficult to figure out what on earth is going on. Deployment and cross-compilation is also a breeze with Go binaries, which has been another historical painpoint with Python.

2. Bugginess of typical code generated in the language: Favor Go for the above reasons. When the code really has to work, I choose Go. If I had more Haskell experience (and if it were a nice language to deal with), I'd probably favor Haskell for its correctness, because I still run into annoying nil pointer exceptions and array out-of-bounds in Go relatively often. I use Elm (Haskell-influenced frontend language) and love it for its lack of runtime errors.

3. Availability of developers: I'm sure there are more Python developers than Go developers. I work with data scientists, and I don't know many who are comfortable in a Go ecosystem, so if you're hiring lots of scientific computing folks, I'd go with Python. In terms of software engineers, when I've hired folks, a lot have expressed particular interest in working in Go even with no prior experience, which is a good signal. Easy for Java/C-style programmers to pick up.

Note that: You can do both/all three. We extensively use Go for systems building, and Python for data science.

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  Mypy is a static type checker for Python.

Why not Kotlin?

Didn't see anything for Haskell so I'll do that. Good link in general would be https://www.linkedin.com/pulse/haskell-data-science-good-bad...

- Python is established for Data Science vs Haskell You could use Haskell for whatever models you need (although finding 10-20 people for that might be a challenge) and it would be really easy to refactor for any changes you might have but I dunno if data science is something you'd use Haskell for. Most data science applications I can think of are either scripts to inspect/clean data (Python works well) and the rest involve a lot of trial and error (Haskell errors aren't great).

- Haskell libraries are awesome You could use Haskell for whatever important libraries you use on a regular basis - it's incredibly easy to write APIs for different languages in Haskell (which is great if you switch languages for some particular use) and it's easy to refactor (which is important in a new company that's likely to change a lot). Also, it's a lot harder to write shitty core libraries in Haskell.

I'd honestly recommend sticking to whatever language you already have code in. If you already have python models built, stick to them!

Ask your data scientists, who already use Python.

If they are opposed to Haskell or Go, then what are you going to do - fire them? Are you budgeting time/money to retrain them?

Your customers don't care what you use, unless you are selling them the actual software. (But then you would know what your customers want, and wouldn't be asking this question.

If you use language X because of a perceived marketing advantage that your customers will see you as "a cutting-edge, next-generation company" then can you estimate how much that marketing is worth? And compare it to other marketing approaches?

My view, as a long-time Python user, is of course Python.

Finding good developers is a struggle, almost regardless of where in the world you've set up shop. So finding 10-20 good developers, a significant portion of whom would have to be quite senior, probably means your "3. Availability of developers" should be "0. Availability of developers".

Basically, any language / ecosystem that should meet your most basic business and technological requirements should be heavily in contention if it is among the most widespread in the community where you're situated. And similarly, if a language is significantly less common than a language that meets the requirements, you probably shouldn't consider it.

Whether that is Python, Go, Haskell or something altogether different, only you know.

What constraints do you have? How important is scalability for this project? Do you need lots of parallelism, fast single-core execution, low memory usage, etc?

You mention long-term support. I'd have to give Haskell high marks on that one. You can use Stack to get highly repeatable builds with LTS releases of Haskell and the packages you rely on. More so than any other language I'm familiar with, Haskell does a great job with long term support and builds that should be 100% reproducible in 5 years.

As far as bugginess, Haskell obviously wins here again. If you're getting runtime exceptions in Haskell then you're doing something wrong. If rock-solid stability is important to you (and you list it as #2 on your list, so I assume it is), then Haskell is hard to beat.

Availability of developers is a tough one. Python clearly has the advantage here, despite being markedly worse on the first two sections. I don't know many Haskell developers, but the Haskellers I've met have all been exceptional software developers in general.

I find it unlikely that you're going to be able to hire 20 people who already know Haskell, unless you're willing to hire remotely. However, if you're willing to teach, I think it could work out. For example, the Elm language would be a fantastic gateway to Haskell. Build your front end with Elm and make new developers work on the front end for a few weeks. Once they're comfortable with it, switch them to the back end.

A very high abstraction language for the glue (python, R, lua, ...) and a close to the iron compiled languange for the heavy lifting (C, C++, Fortran, Go, Rust, ...)

or you give julia a try.

I apologize for going off topic here by saying go for language number 4. but hear me out.

I have been struggling with my business vs pleasure languages for some time now. I work 95% in C# but whenever I find willpower to do some hobby coding I go for Haskell.

Every time I go for a new hobby project after not coding in Haskell for a while I have to go through the hurdles of remembering how to set things up, downgrading from Visual Studio to Visual Studio Code with some broken Haskell language extension and last time before I started coding my new hobby project I asked myself: What would it take for me to make C# as lovable as Haskell.

I came up with these things which annoy me most (I know everyone has their own list): 1. Lack of higher order types 2. Lack of syntactic sugar for partial function application et al. 3. Lack of GADTs 4. Still waiting for those nullable reference types.

On the other hand I would get: 1. Great language server with support for refactorings, formatting etc. 2. Test tooling and frameworks and performance analysis tooling and frameworks. 3. Way bigger ecosystem of open source libraries.

While I see a reason to go for either C# or Haskell, I really don't think it's worth going for Python or Go instead in most cases. I just need a better type system than what Go has to offer (and Python doesn't do any type checks without 3rd party support). Hell, I'd go for TypeScript or PureScript before these 2.

C# has grown a lot in the past few years. With C# 7 you have all those fancy things like local functions, pattern matching, deconstruction, async improvements etc. You have a stable language and ecosystem, open source compiler and language server (Rosyln), free editor support if you want to save (Visual Studio Code) or if you want enterprise tooling you can go up to Visual Studio Enterprise. With .Net Core going into 2.0 the coverage of .Net is really broad, and Entity Framework and Asp.Net are being rewritten from scratch for all platforms. It's easy to do foreign language interfacing if you want to use some highly performant lib like LAPAC or something and someone usually already wrapped around it.

Btw, if not Haskell, check out PureScript

Go for the largest community and biggest number of mature libraries, go for Python 3 (make sure it's 3!). It's the most mainstream in your list and for data scientists it's your only choice I would also say (when looking at Tensorflow, Numpy, Scipy, Pandas etc.)

One of the chief advantages Haskell (and, to a much lesser extent, Go) has over vanilla Python is that static typing vastly improves upon #1 and #2.

If you do end up going the Python route, I suggest you enforce using Python 3 type annotations, and use an external validator (such as mypy) to try and capture as much of that value as possible.

You should at least mention what your business is actually aiming to achieve. Are you a web app, a data science startup, a service provider with a software stack, etc.

If you're a pure data science startup, most of your talent pool will be most proficient in Python, and the highest quality APIs will be in Python.

If you're a web app, going for Go makes more sense, as the language was built from the ground up with these things in mind.

If you're business tooling or service oriented.. I'd go for C#.. it has a huge talent pool, super high quality libraries and ecosystem, etc

Python isn't strongly typed so when your codebase grows it tends to be less maintainable than strongly typed code, unless you have a lot of test coverage. But still "doesn't compile" is a very strong indicator your code isn't right.

I would expose the Python code using Flask microservices and do the rest in either Go or Haskell. But I have no idea what needs to be built outside of the data science part. If you have lots of small moving parts you can get away with using Python everywhere since you'll not end up with big balls of code.

I'd choose Go. Has long-term support from Google. Generally easy to learn. Many less bugs in my experience than Python. Don't know enough about Haskell to comment.

It really depends on what you are trying to do and whether this is well-supported by the language.

Data science stuff? Python of course. Haskell has things like repa but still very immature compared to Python. You will have to reinvent the wheel a lot if you choose Haskell.

But if your developers are going to do something very different from data science, say building a typical web app, Haskell can be a good choice. As usual refer to Gabriel Gonzalez’s State of the Haskell Ecosystem: https://github.com/Gabriel439/post-rfc/blob/master/sotu.md

Haskell especially excels in (2): bugginess of typical code is pretty low even without extensive unit testing; Go probably fares pretty well but Python is terrible for a large code base. But on the other hand (3) could also potentially be an issue for Haskell, depending on the geographical location, whereas Python would likely not.

Having worked (and still working) in a Haskell startup, though I believe ultimately technology doesn’t really matter that much. The business is more important than the technology.

If you do choose Haskell though, my advice is that don’t use too many fancy type system features. A boring business app doesn’t usually need them. Stick to basic language features and perhaps just a dozen most popular language extensions; use the rest very sparingly.

Who/what company is using Haskell at work? I'm really interested how/why it would be used those days.

I guess there are domains where it's adequate, but what are those?

No need to settle on one language. There is in fact room for 2 main languages (prolly some will simple come along like JS and Bash).

Python you already have, I'd not ditch it, it can live. Main thing it lacks, imho, is a type system which relates to 1 & 2.

I'd personally not care for 3 too much. You find less Haskellers, but also less competition, and according to many they are better.

I'd say all 3 langs you named allow point 1 "well enough". Point 2, I'd say: Haskell > Go > Python, with significant margins in between.

I've been in a similar situation (choice between Scala / Python / Go), the first person I hired was a Go guy so that became the default. We ended up using Go for APIs / web stuff and Python for data science and modelling.

It depends a lot on what you are building. I'd recommend Python in general as it has more support but don't limit yourself to it as there might be some parts of your project were Go or Haskell can be more powerful.

As others have mentioned: It depends on what you are going to build, so pick the tools which fits the job best. In the end, you can build good products with all three of those languages, but not every product can be built well with any of them.

Long-term supportability: As long as you are not choosing a proprietary tech like Microsoft Access, you should be just fine. There are a lot more code bases which suffer from bad quality than from abandoned base technologies. So its much more important that you know exactly how you want to use a language and let your team create rules (bottom up) on what they want to allow themselves to do and what they don't want to (like a semantic style guide), than to pick the golden language in the first place.

My advise would be: Start by building a team. The technologies will be the result of what your team is able and likes to use. One condition: Only allow technologies they have positive personal experience with (no "I always wanted to try XYZ").

Use the right tools for the job. If you are running your Python models in containers anyway, a suitable API will work with any backend language. Go and even the JVM (Java / Scala / Kotlin) ecosystem are great candidates. Our stack uses Python (models deployed as dockerized WSGI applications) for machine learning and web services written in Java for the backend. It is relatively easy to get developers who are comfortable in the JVM ecosystem and the tooling available makes it easier to build robust systems.

We've been using Go at work for 2.5 years now and it has become my primary language of use at this point in time. I hate it. I hate it like hell. It's like they decided to take all the things that make other languages good - such as safety, brevity, intuitiveness, versioning and proper project structuring - and threw them out the window. As far as I'm concerned the good things about Go (large community, fairly efficient generated executables) do not outweigh the bad.

We've spent so much time fixing obscure bugs and behaviors that betray everything we've learned in the languages we used previously in our careers that it was really not worth it. And we still do. Finding developers is also an issue, as most developers do not really want to learn a new language, and Go is still not that common in enterprises (I used to be like that myself, but I've learned long ago that this shouldn't be such a big factor when thinking about a new job).

You can make the case that we're simply not good enough developers or Go is just not a good fit for the type of developers we are. Maybe. But the choices made in designing this language, and its popularity, absolutely baffle me.

Python for all of the above.

To be more specific: Python 3.6, with type annotations and unit tests, and good libraries (pandas and others for the data, Tornado for Web, SQLAlchemy for databases etc).

But it's much more important to get your architecture right, to know exactly what is the data flow and what is supposed to happen when and where. No single language will give you answers to that, while the architecture might decide the choice of language(s).