KBlueLeaf/danbooru2023-metadata-database

Metadata Database for Danbooru2023

Danbooru 2023 datasets: https://huggingface.co/datasets/nyanko7/danbooru2023

The latest entry of this database is id 7,866,491. Which is newer than nyanko7's dataset.

This dataset contains a sqlite db file which have all the tags and posts metadata in it.
The Peewee ORM config file is provided too, plz check it for more information. (Especially on how I link posts and tags together)

The original data is from the official dump of the posts info.
Check this link for more info.

Format

This dataset contains 3 format but they store same contents:

• Sqlite (.db)
  • have 2 versions: with/without index.
• Parquet
  • Parquet files' name indicate the sqlite/duckdb table name.
  • It is recommended to use post.parquet when you need to export tons of content.
• Duckdb (.duckdb)
  • have 2 versions: with/without index.

others folder will contains some pre-exported files like tags for each post.

Details

This section contains some details that you need to be aware of if you want to use other ORM system or use plain SQL query to utilize this database.

Custom Enum Fields

Some fields in Post/Tags use my custom enum field to store type/category or something like that:

• Post.rating
  • 0: general
  • 1: sensitive
  • 2: questionable
  • 3: explicit
• Tag.type
  • 0: general
  • 1: artist
  • 2: character
  • 3: copyright
  • 4: meta

Tag List

I use peewee ManyToManyField to implement the Tag List things. Which utilize a through model which have all the pair of Tag and Post
Since it is very likely we will want to use Tag to query posts, so many-to-many is better.
The con of this design is the database file will be 1.5x larger than before(we have 0.25B entries for the post-tag pairs), but the query speed become 2~3x faster, so I think it is acceptable.

After done some checking, I can ensure that all the "categorical tag list" can be done by full list + filter, and that is how I done it now. Check the db.py for more details.

Utils

if you think above details are too complicated, just use the db_utils.py and other PeeWee API to utilize this database. I also provide a write_csv.py for exporting whole dataset into csv for data analysis.

License

The database files of this repo are licensed under MiT License.
The source code files of this repo are licensed under Apache 2.0 License.

Acknowledgement

Thx for AngelBottomless for updating new entries