I'm currently working on a project that requires working with gigabytes of scientific data sets. The data sets are in the form of very large arrays (30,000 elements) of integers and floating point numbers. The problem here is that they are too large too fit into memory, so I need an on disk solution for storing and working with them. To make this problem even more fun, I am restricted to using a 32-bit architecture (as this is for work) and I need to try to maximize performance for this solution.
So far, I've worked with HDF5, which worked okay, but I found it a little too complicated to work with. So, I thought the next best thing would be to try a NoSQL database, but I couldn't find a good way to store the arrays in the database short of casting them to character arrays and storing them like that, which caused a lot of bad pointer headaches.
So, I'd like to know what you guys recommend. Maybe you have a less painful way of working with HDF5 while at the same time maximizing performance. Or maybe you know of a NoSQL database that works well for storing this type of data. Or maybe I'm going in the totally wrong direction with this and you'd like to smack some sense into me.
Anyway, I'd appreciate any words of wisdom you guys can offer me :)
Assuming your data sets really are large enough to merit (e.g., instead of 30,000 elements, a 30,000x30,000 array of doubles), you might want to consider STXXL. It provides interfaces that are intended to (and largely succeed at) imitate those of the collections in the C++ standard library, but are intended to work with data too large to fit in memory.