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.
If scraping is reliable, I’d use the classic python pickle or JSON.dump
For a few thousand I would just use a sqlite dB…
3 tables:
- Story with fields: Id, title, text
- Meta with fields: Id, story-id, subject, contents
- Tags with fields Id, story-id, tag
Use SQL joins for sorting etc.
Sqlite is easily converted to other formats if you decide to use more complex solutions.
Python sqlite3 module for the metadata and it has some features now for full text search that can probably handle a few thousand stories. For a bigger collection like ao3, try solr.apache.org or elastic search etc.
I would separate concerns. For the scraping, I would dump data as json onto disk. I would consider the folder structure I put them into, whether as individual files, or a JSON document per line in bigger files for grouping. If the website has good URL structure, the path could be useful for speaking author and or id identifiers in folders or files.
Storing json as text is simple. Depending on the amount, storing plain text is wasteful, and simple text compression could significantly reduce storage size. For text-only stories it’s unlikely to become significant though, and not compressing makes the scraping process, and potentially validating completeness of scraped data simpler.
I would then keep this data separate from any modifications or prototyping I would do regarding modification or extension of data and presentation/interfacing.
After reading some of the other comments, I’m definitely going to separate the systems. I’ll use something like json or yaml as the output for the raw scraped data, and some sort of database for the final program.
