MetaVault 0.4

A simple database for storing metadata associated with (media) files.

MetaVault

MetaVault is a simple database for storing metadata associated with (media) files. This is a simple wrapper around sqlite3 which mimics the behavior of a Python dictionary. This is designed to provide an easy-to-use interface for managing metadata.

If you are trying to write alot of data iteratively to the database, make sure to set manual_commit=True in the database initialization and db.commit() at the end of your loop. This will reduce the number of commits and increase the speed of writing data to the database.

Installation

Install using pip:

pip install metavault

Usage

from metavault import MetaVaultDatabase

# connect to database

database = MetaVaultDatabase('test.vault')

# create dataset (or version of dataset)

database.create_dataset('test', attributes=['artist', 'title']) # preload attributes
database['test'] = {} # different way to create dataset with no attributes

# get dataset

dataset = database['test']

# add data to dataset

dataset["riddim.mp3"] = {"artist": "Bounty Killer", "title": "Riddim Killa"}
dataset["ambient.mp3"] = {"artist": "Dog The Bounty Hunter", "title": "Trashcore"}

# iterate

for item in dataset:
    print(f"- {item}") # - {'riddim.mp3': {'artist': 'Bounty Killer', 'title': 'Riddim Killa'}}

# acts like a dictionary

print(f"\n{dataset['riddim.mp3']}") # {'artist': 'Bounty Killer', 'title': 'Riddim Killa'}

# remove data

del dataset['riddim.mp3']
print(dataset.keys()) # ['ambient.mp3']

# remove attribute

dataset.remove_attribute('artist')
print(dataset['ambient.mp3']) # {'title': 'Trashcore'}

# add attribute

dataset.add_attribute('artist')
dataset['ambient.mp3']['artist'] = "Dog The Bounty Hunter"
print(dataset['ambient.mp3']) # {'title': 'Trashcore', 'artist': 'Dog The Bounty Hunter'}

database.close()

# or with context manager

with MetaVaultDatabase('test.vault') as database:
    dataset = database['test']
    # export as various formats
    dataset.export_data('test.csv')
    dataset.export_data('test.json')
    dataset.export_data('test.jsonl')
    # or import
    dataset.import_data('test.csv')
    dataset.import_data('test.json')
    dataset.import_data('test.jsonl')

# write alot of data with manual commit to improve performance

with MetaVaultDatabase('test.vault', manual_commit=True) as database:
    database.begin_transaction() # optional, makes a 'restore point' for rollback
    try:

        dataset = database['test']
        dataset.replace_in_attribute('artist', 'Bounty Killer', 'Bounty Killer 2024') # operations on entire set
        for data in datas:
            dataset[data['filename']] = data
        database.commit()

    except Exception as e:
        database.rollback() # rollback to last 'restore point' if available
        print(e)

# make subsets

with MetaVaultDatabase('test.vault') as database:
    subset = database['test'].search(artist='Bounty Killer')
    subset_2 = database['test'].get_subset_by_key(['ambient.mp3', 'riddim.mp3'])
    subset_3 = database['test'].get_subset_by_amount(25, start=5, reverse=True)
    subset_4 = database['test'].get_subset_by_random(25)
    subset_4.export_data('subset_4.csv') # export just a subset
    combined = subset + subset_2 + subset_3 + subset_4
    database['test_subset'] = combined