In the first part we looked at some of the consequences of Python being a dynamically-typed language. Let’s have a look at another implications such as the concept of class, how things work under the hood as well as performance.
Month: August 2014
Dynamic typing (part 1)
Python is a dynamically-typed language, just like PHP, JavaScript or Ruby, and contrary to statically-typed languages such as C/C++, Java or Scala.
In a dynamically-typed language, the code can never know in advance what will be the type of any variable. You can define a function to perform a particular operation on, say, a collection, but unless you explicitly filter out an argument that is not a collection, the code is never certain that it is indeed one – and the bytecode compiler sure cannot be certain either.
Using dynamic types has consequences for the language – both from a conceptual standpoint and from an implementation standpoint.
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Everything’s an object
In Python, pretty much everything is an object, whether it is a number, a function… or even None.
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The Garbage Collector
Python, like most modern languages, has its own garbage collector (GC). But how does the CPython GC work?
First of all, does it really matter? After, a GC is a GC, right? Well, not exactly. The GC on Java has evolved quite a bit from the days of Java 1.0 (you can find a great deal information about the various algorithms used here). In a interview, three Twitter developers explained the move from Ruby to Scala (a language that runs on top of the Java VM). And one of the reasons was that “because Ruby’s garbage collector is not quite as good as Java’s, each process uses up a lot of memory“. So yes, the GC can matter.
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Yet Another Python Internals Blog 