acousticbrainz-server

The server components for the AcousticBrainz project.

Please report issues here: http://tickets.musicbrainz.org/browse/AB

Installation and Running

Docker

We use docker and docker-compose to run the AcousticBrainz server. Ensure that you have these tools installed.

Configuration

Copy the following two configuration files:

1. config.py.example to config.py
2. profile.conf.in.sample to profile.conf.in in the ./hl_extractor/ directory In profile.conf.in you need to set the models_essentia_git_sha value. Unless you know what you are doing, this value should be v2.1_beta1

Running docker-compose commands

For convenience, we provide a script develop.sh which does the same as running:

docker-compose -f docker/docker-compose.yml -p acousticbrainz-server <args>

Build and initial configuration

Build the docker containers needed for AcousticBrainz by running the following:

./develop.sh build

The first time you install AcousticBrainz, you will need to initialize the AcousticBrainz database:

./develop.sh run --rm  webserver python2 manage.py init_db

Running

Start the webserver and other required services with:

./develop.sh up 

You will be able to view your local AcousticBrainz server at http://localhost:8080

Development notes

Database

In order to load a psql session, use the following command:

./develop.sh run --rm db psql -U acousticbrainz -h db

Building static files

We use Gulp as our JavaScript/CSS build system.

First-time gulp setup

For development, the first time that you install acousticbrainz you must install node packages in your local directory.

./develop.sh run --rm --user `id -u`:`id -g` -e HOME=/tmp webserver npm install

This has the effect of creating a node_modules directory in your local code checkout. The --user and -e flags are needed on a Linux host to make this directory owned by your local user.

To build stylesheets and javascript bundles, run gulp:

./develop.sh run --rm webserver ./node_modules/.bin/gulp

You will need to rebuild static files after you modify JavaScript or CSS.

Login

To use the dataset tools you need to configure OAuth with MusicBrainz. Log in to your MusicBrainz account (or create one if needed) and create a new application.

Choose a name (for example, "AcousticBrainz development"), set Type to "Web Application" and set the Callback URL to http://localhost:8080/login/musicbrainz/post

Copy the OAuth Client ID and OAuth Client Secret values to config.py as MUSICBRAINZ_CLIENT_ID and MUSICBRAINZ_CLIENT_SECRET.

You should now be able to use the menu in the top corner of your AcousticBrainz server to log in.

Admin interface

Once you have logged in, you can make your user an admin, by running

./develop.sh run --rm webserver python2 manage.py add_admin <your user>

You should now be able to access the admin section at http://localhost:8080/admin

Working with data

Importing

Before you import or export data, make sure you understand how docker bind mounts work. The following commands will work if you specify paths in the current directory, but if you want to specify paths somewhere else (e.g. a Downloads or tmp directory) you must specify an additional --mount flag.

AcousticBrainz provides data dumps that you can import into your own server. Latest database dump is available at http://acousticbrainz.org/download. You need to download full database dump from this page and use it during database initialization:

./develop.sh run --rm webserver python2 manage.py init_db path_to_the_archive

you can also easily remove existing database before initialization using --force option:

./develop.sh run --rm webserver python2 manage.py init_db --force path_to_the_archive

or import archive after database is created:

./develop.sh run --rm webserver python2 manage.py import_data path_to_the_archive

You can also import dumps that you created yourself. This process is described below (see dump full_db command).

Exporting

There are several ways to export data out of AcousticBrainz server. You can create full database dump or export only low-level and high-level data in JSON format. Both ways support incremental dumping.

Examples

Full database dump:

./develop.sh run --rm webserver python2 manage.py dump full_db

JSON dump:

./develop.sh run --rm webserver python2 manage.py dump json

Creates two separate full JSON dumps with low-level and high-level data.

Incremental dumps:

./develop.sh run --rm webserver python2 manage.py dump incremental

Creates new incremental dump in three different formats: usual database dump, low-level and high-level JSON.

Previous incremental dumps:

./develop.sh run --rm webserver python2 manage.py dump incremental --id 42

Same as another one, but recreates previously created incremental dump.

Test your changes with unit tests

Unit tests are an important part of AcousticBrainz. It helps make it easier for developers to test changes and help prevent easily avoidable mistakes later on. Before commiting new code or making a pull request, run the unit tests on your code.

./test.sh

This will start a set of docker containers separate to your development environment, run the tests, and then stop and remove the containers. To run tests more rapidly without having to bring up and take down containers all the time, you can run each step individually. To bring up containers in the background:

./test.sh -u

Then run your tests when you need with:

./test.sh [optional arguments to pass to py.test]

Stop the test containers with:

./test.sh -s

This will stop but not delete the containers. You can delete the containers with:

./test.sh -d

We use the -p flag to docker-compose to start the test containers as a new project, acousticbrainztest so that containers don't conflict with already running development containers. You can access containers directly while they are running (e.g. with docker exec) with this name (e.g. acousticbrainztest_db_1)

The database has no separate volume for data, this means that any data in the test database will disappear when the containers are deleted (at the end of standalone ./test.sh, or after ./test.sh -d)

We forward the port from postgres to localhost:15431, so you can connect to it with psql on your host if you want to inspect the contents of the database.