How can I use Python for large scale development?

Question

I would be interested to learn about large scale development in Python and especially in how do you maintain a large code base?

• When you make incompatibility ch

Answer 1

I had some experience with modifying "Frets On Fire", an open source python "Guitar Hero" clone.

as I see it, python is not really suitable for a really large scale project.

I found myself spending a large part of the development time debugging issues related to assignment of incompatible types, things that static typed laguages will reveal effortlessly at compile-time. also, since types are determined on run-time, trying to understand existing code becomes harder, because you have no idea what's the type of that parameter you are currently looking at.

in addition to that, calling functions using their name string with the __getattr__ built in function is generally more common in Python than in other programming languages, thus getting the call graph to a certain function somewhat hard (although you can call functions with their name in some statically typed languages as well).

I think that Python really shines in small scale software, rapid prototype development, and gluing existing programs together, but I would not use it for large scale software projects, since in those types of programs maintainability becomes the real issue, and in my opinion python is relatively weak there.

Answer 2

Incompatible changes to the signature of a method. This doesn't happen as much in Python as it does in Java and C++.

Python has optional arguments, default values, and far more flexibility in defining method signatures. Also, duck typing means that -- for example -- you don't have to switch from some class to an interface as part of a significant software change. Things just aren't as complex.

How do you find all the places where that method is being called? grep works for dynamic languages. If you need to know every place a method is used, grep (or equivalent IDE-supported search) works great.

How do you find out what operations an instance provides, since you don't have a static type to lookup?

a. Look at the source. You don't have the Java/C++ problem of object libraries and jar files to contend with. You don't need all the elaborate aids and tools that those languages require.

b. An IDE can provide signature information under many common circumstances. You can, easily, defeat your IDE's reasoning powers. When that happens, you should probably review what you're doing to be sure it makes sense. If your IDE can't reason out your type information, perhaps it's too dynamic.

c. In Python, you often work through the interactive interpreter. Unlike Java and C++, you can explore your instances directly and interactively. You don't need a sophisticated IDE.

Example:

  >>> x= SomeClass()
  >>> dir(x)

How do you handle/prevent typing errors? Same as static languages: you don't prevent them. You find and correct them. Java can only find a certain class of typos. If you have two similar class or variable names, you can wind up in deep trouble, even with static type checking.

Example:

class MyClass { }
class MyClassx extends MyClass { }

A typo with these two class names can cause havoc. ["But I wouldn't put myself in that position with Java," folks say. Agreed. I wouldn't put myself in that position with Python, either; you make classes that are profoundly different, and will fail early if they're misused.]

Are UnitTest's used as a substitute for static type checking? Here's the other Point of view: static type checking is a substitute for clear, simple design.

I've worked with programmers who weren't sure why an application worked. They couldn't figure out why things didn't compile; the didn't know the difference between abstract superclass and interface, and the couldn't figure out why a change in place makes a bunch of other modules in a separate JAR file crash. The static type checking gave them false confidence in a flawed design.

Dynamic languages allow programs to be simple. Simplicity is a substitute for static type checking. Clarity is a substitute for static type checking.

Answer 3

My general rule of thumb is to use dynamic languages for small non-mission-critical projects and statically-typed languages for big projects. I find that code written in a dynamic language such as python gets "tangled" more quickly. Partly that is because it is much quicker to write code in a dynamic language and that leads to shortcuts and worse design, at least in my case. Partly it's because I have IntelliJ for quick and easy refactoring when I use Java, which I don't have for python.

Answer 4

The usual answer to that is testing testing testing. You're supposed to have an extensive unit test suite and run it often, particularly before a new version goes online.

Proponents of dynamically typed languages make the case that you have to test anyway because even in a statically typed language conformance to the crude rules of the type system covers only a small part of what can potentially go wrong.

Answer 5

Since nobody pointed out pychecker, pylint and similar tools, I will: pychecker and pylint are tools that can help you find incorrect assumptions (about function signatures, object attributes, etc.) They won't find everything that a compiler might find in a statically typed language -- but they can find problems that such compilers for such languages can't find, too.

Python (and any dynamically typed language) is fundamentally different in terms of the errors you're likely to cause and how you would detect and fix them. It has definite downsides as well as upsides, but many (including me) would argue that in Python's case, the ease of writing code (and the ease of making it structurally sound) and of modifying code without breaking API compatibility (adding new optional arguments, providing different objects that have the same set of methods and attributes) make it suitable just fine for large codebases.

Answer 6

Don't use a screw driver as a hammer

Python is not a statically typed language, so don't try to use it that way.

When you use a specific tool, you use it for what it has been built. For Python, it means:

• Duck typing : no type checking. Only behavior matters. Therefore your code must be designed to use this feature. A good design means generic signatures, no dependences between components, high abstraction levels.. So if you change anything, you won't have to change the rest of the code. Python will not complain either, that what it has been built for. Types are not an issue.

• Huge standard library. You do not need to change all your calls in the program if you use standard features you haven't coded yourself. And Python come with batteries included. I keep discovering them everyday. I had no idea of the number of modules I could use when I started and tried to rewrite existing stuff like everybody. It's OK, you can't get it all right from the beginning.

You don't write Java, C++, Python, PHP, Erlang, whatever, the same way. They are good reasons why there is room for each of so many different languages, they do not do the same things.

Unit tests are not a substitute

Unit tests must be performed with any language. The most famous unit test library (JUnit) is from the Java world!

This has nothing to do with types. You check behaviors, again. You avoid trouble with regression. You ensure your customer you are on tracks.

Python for large scale projects

Languages, libraries and frameworks don't scale. Architectures do.

If you design a solid architecture, if you are able to make it evolves quickly, then it will scale. Unit tests help, automatic code check as well. But they are just safety nets. And small ones.

Python is especially suitable for large projects because it enforces some good practices and has a lot of usual design patterns built-in. But again, do not use it for what it is not designed. E.g : Python is not a technology for CPU intensive tasks.

In a huge project, you will most likely use several different technologies anyway. As a SGBD (French for DBMS) and a templating language, or else. Python is no exception.

You will probably want to use C/C++ for the part of your code you need to be fast. Or Java to fit in a Tomcat environment. Don't know, don't care. Python can play well with these.

As a conclusion

My answer may feel a bit rude, but don't get me wrong: this is a very good question.

A lot of people come to Python with old habits. I screwed myself trying to code Java like Python. You can, but will never get the best of it.

If you have played / want to play with Python, it's great! It's a wonderful tool. But just a tool, really.