Short version of the situation is that I have an old site I frequent for user written stories. The site is ancient (think early 2000’s), and has terrible tools for sorting and searching the stories. Half of the time, stories disappear from author profiles. Thousands of stories and you can only sort by top, new, and 30-day top.
I’m in the process of programming a scraper tool so I can archive the stories and give myself a library to better find forgotten stories on the site. I’ll be storing tags, dates, authors, etc, as well as the full body of the text.
Concerning the data, there are a few thousand stories- ascii only, and various data points for each story with the body of many stores reaching several pages long.
Currently, I’m using Python to compile the data and would like to know what storage solution is ideal for my situation. I have a little familiarity with SQL, json, and yaml, but not enough to know what might be best. I am also open to any other solutions that work well with Python.
Definitely SQLite. Easily accessible from Python, very fast, universally supported, no complicated setup, and everything is stored in a single file.
It even has a number of good GUI frontends. There’s really no reason to look any further for a project like this.
One concern I’m seeing from other comments is that I may have more data than SQLite is ideal for. I have thousands of stories (My estimate is between 10 and 40 thousand), and many of the stories can be several pages long.
Ha no. SQLite can easily handle tens of GB of data. It’s not even going to notice a few thousand text files.
The initial import process can be sped up using transactions but as it’s a one-time thing and you have such a small dataset it probably doesn’t matter.
That’s good to know.