MusicBrainz SQLAlchemy Models

pip install mbdata==27.1.0


MusicBrainz Database Tools

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MusicBrainz Database Mirror

This repository now contains a collection of scripts for managing a replica of the MusicBrainz database. These used to be called "mbslave", but have been moved to this repository.

The main motivation for these scripts is to be able to customize your database. If you don't need such customizations, it might be easier to use the replication tools provided by MusicBrainz itself.


You need to have Python 3.x installed on your system. You can use pipx to install this package:

sudo apt install python3 pipx
pipx install 'mbdata[replication]'

There are two ways to configure the application.

  1. You can use a config file:

    curl -o mbslave.conf
    vim mbslave.conf

    By default, the mbslave script will look for the config file in the current directory. If you want it to find it from anywhere, either save it to /etc/mbslave.conf or set the MBSLAVE_CONFIG environment variable. For example::

    export MBSLAVE_CONFIG=/usr/local/etc/mbslave.conf
  2. Alternativelly, you can use using environment variables:

    export MBSLAVE_DB_HOST=
    export MBSLAVE_DB_PORT=5432
    export MBSLAVE_DB_NAME=musicbrainz
    export MBSLAVE_DB_USER=musicbrainz
    export MBSLAVE_DB_ADMIN_USER=postgres

Database Setup

If you are starting from scratch and want a full copy of the MusicBrainz database, you can use the mbslave init command. This will create a new database and populate it with the latest data from the MusicBrainz database:

mbslave init --create-user --create-database

The other option is to create the database manually and use the mbslave psql to apply the scripts from MusicBrainz. In this case you are expected to know what you are doing.

Database Replication

You can also keep the database up-to-date by applying incrementa changes.

You need get an API token from the MetaBrainz website and you need to either add it to mbslave.conf or set the MBSLAVE_MUSICBRAINZ_TOKEN environment variable.

After that, you can use the mbslave sync command to download the latest updates:

mbslave sync

Schema Upgrade

When the MusicBrainz database schema changes, the replication will stop working. This is usually announced on the MusicBrainz blog. When it happens, you need to upgrade the database.

Release 2022-05-16 (27)

Run the upgrade scripts:

mbslave psql -f updates/schema-change/27.mirror.sql
echo 'UPDATE replication_control SET current_schema_sequence = 27;' | mbslave psql

Release 2021-05-17 (26)

Run the upgrade scripts:

mbslave psql -f updates/schema-change/26.slave.sql
echo 'UPDATE replication_control SET current_schema_sequence = 26;' | mbslave psql
2020-05-18 Upgrade to PostgreSQL 12

These steps are recommended even if you were already running on Postgres 12 before MusicBrainz moved to make PostgreSQL 12 the minimal supported version.

Run the pre-upgrade script:

mbslave psql -f updates/20200518-pg12-before-upgrade.sql

If not already on PostgreSQL 12, upgrade your cluster now (depending on your OS, using pg_upgradecluster or pg_upgrade)

After upgrading, or if already on PostgreSQL 12, run:

mbslave psql -f updates/20200518-pg12-after-upgrade.sql

Release 2019-05-14 (25)

Run the upgrade scripts:

mbslave psql -f updates/schema-change/25.slave.sql
echo 'UPDATE replication_control SET current_schema_sequence = 25;' | mbslave psql

Release 2017-05-25 (24)

Run the upgrade scripts:

mbslave psql -f updates/schema-change/24.slave.sql
echo 'UPDATE replication_control SET current_schema_sequence = 24;' | mbslave psql

Tips and Tricks

Single Database Schema

MusicBrainz uses a number of schemas by default. If you are embedding the MusicBrainz database into an existing database for your application, it's convenient to merge them all into a single schema. That can be done by changing your config like this:


After this, you only need to create the "musicbrainz" schema and import all the tables there.

Full Import Schema Upgrade

You can use the schema mapping feature to do zero-downtime upgrade of the database with full data import. You can temporarily map all schemas to e.g. "musicbrainz_NEW", import your new database there and then rename it:

echo 'BEGIN; ALTER SCHEMA musicbrainz RENAME TO musicbrainz_OLD; ALTER SCHEMA musicbrainz_NEW RENAME TO musicbrainz; COMMIT;' | mbslave psql -S

SQLAlchemy Models

If you are developing a Python application that needs access to the MusicBrainz data, you can use the mbdata.models module to get SQLAlchemy models mapped to the MusicBrainz database tables.

All tables from the MusicBrainz database are mapped, all foreign keys have one-way relationships set up and some models, where it's essential to access their related models, have two-way relationships (collections) set up.

In order to work with the relationships efficiently, you should use the appropriate kind of eager loading.

Example usage of the models:

>>> from sqlalchemy import create_engine
>>> from sqlalchemy.orm import sessionmaker
>>> from mbdata.models import Artist
>>> engine = create_engine('postgresql://musicbrainz:musicbrainz@', echo=True)
>>> Session = sessionmaker(bind=engine)
>>> session = Session()
>>> artist = session.query(Artist).filter_by(gid='8970d868-0723-483b-a75b-51088913d3d4').first()
>>> print

If you use the models in your own application and want to define foreign keys from your own models to the MusicBrainz schema, you will need to let mbdata know which metadata object to add the MusicBrainz tables to:

from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()

# this should be the first place where you import anything from mbdata
import mbdata.config

# now you can import and use the mbdata models
import mbdata.models

You can also use mbdata.config to re-map the MusicBrainz schema names, if your database doesn't follow the original structure:

import mbdata.config

If you need sample MusicBrainz data for your tests, you can use mbdata.sample_data:

from mbdata.sample_data import create_sample_data


Normally you should work against a regular PostgreSQL database with MusicBrainz data, but for testing purposes, you can use a SQLite database with small data sub-set used in unit tests. You can create the database using:

./bin/ sample.db

Then you can change your configuration:

DATABASE_URI = 'sqlite:///sample.db'

Running tests:

pytest -v

If you want to see the SQL queries from a failed test, you can use the following:


Jenkins task that automatically runs the tests after each commit is here.