* Dieter Vanderelst (2005-09-06 18:03 +0100) > I'm currently comparing Python versus Perl to use in a project that > involved a lot of text processing. I'm trying to determine what the most > efficient language would be for our purposes. I have to admit that, > although I'm very familiar with Python, I'm complete Perl noob (and I > hope to stay one) which is reflected in my questions. > > I know that the web offers a lot of resources on Python/Perl > differences. But I couldn't find a satisfying answer to my questions: > > 1 - How does the speed of execution of Perl compares to that of Python?
Of course Python is faster than Perl. It's the same reason why Mercedes are faster than BMWs (or was it the other way round?). > 2 - Regular Expressions are a valuable tool in text processing. I have > noticed that Regular Expressions are executed very fast in Python. Does > anybody know whether Python executes RE faster than Perl does? Again: this question doesn't make sense. It's up to you to write your Regular Expressions fast. > 3 - In my opinion Python is very well suited for text processing. Does > Perl have any advantages over Python in the field of textprocessing > (like a larger standard library maybe). > > I hope somebody can answer my questions. Of course, every remark and tip > on Python/Perl in texprocessing is most welcome. http://gnosis.cx/TPiP/ "In case regular expression operations prove to be a genuinely problematic performance bottleneck in an application, there are four steps you should take in speeding things up. Try these in order: 1. Think about whether there is a way to simplify the regular expressions involved. Most especially, is it possible to reduce the likelihood of backtracking during pattern matching? You should always test your beliefs about such simplification, however; performance characteristics rarely turn out exactly as you expect. 2. Consider whether regular expressions are -really- needed for the problem at hand. With surprising frequency, faster and simpler operations in the [string] module (or, occasionally, in other modules) do what needs to be done. Actually, this step can often come earlier than the first one. 3. Write the search or transformation in a faster and lower-level engine, especially [mx.TextTools]. Low-level modules will inevitably involve more work and considerably more intense thinking about the problem. But order-of-magnitude speed gains are often possible for the work. 4. Code the application (or the relevant parts of it) in a different programming language. If speed is the absolutely first consideration in an application, Assembly, C, or C++ are going to win. Tools like swig--while outside the scope of this book--can help you create custom extension modules to perform bottleneck operations. There is a chance also that if the problem -really must- be solved with regular expressions that Perl's engine will be faster (but not always, by any means)." -- http://mail.python.org/mailman/listinfo/python-list
