I'm working on a script to read a list of MS DevStudio .dsw files and generate a list of projects to compile. The list has to be ordered for dependancies which means that I need to read all the data before writing my list, and that I need to get several things for each project: dsw filename(and path), project name, dsp filename (and path), and dependancies. I wrote most of this with some success, using hashes to store my data and a recursive algorithm to resolve dependancies. But I didn't want to rebuild projects, so I had code in their to try and weed out repeats. The repeat weeding is also important when resolving dependancies. (Otherwise it will take a century to converge). Alas, some of the repeats have different ideas on what they are dependant on, meaning I need to get the dependancy list for every repeat when I'm reading the files. This script bombed for a variety of reasons, most of which are related to a poor data model for storing all that data.
Does anyone have a good way to store lots of data like that? (short of taking an object oriented approach... I'm trying to keep this simple and straight forward) Once I figure out a better way to represent everything I am confident that I can generate a better script to do this then the one I have now.
In reply to Data Structs
by Adam