AGA Online Ratings protocol and implementation
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Online Ratings

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AGA Online Ratings Server and implementation

The goal of the AGA Online Ratings Server is to provide Go Servers with a standard way to report results between AGA members that happen on their servers to us for computing a cross-server rating.

Other goals of the project can be found on the implementation plan here

Using the API (Go Server implementers)

All api endpoints accept and return JSON. Available endpoints:

  • POST /api/v1/games Report a game result.
  • GET /api/v1/games/<game_id> Get a game result by ID
  • GET /api/v1/games/<game_id>/sgf Get a game's SGF file by ID
  • GET /api/v1/players/<player_id> Get a player by ID
  • GET /api/v1/players?token=<token> Get a player by their secret token.

Here's an example HTTP request to create a game:

POST /api/v1/games HTTP/1.1
Content-Type: application/json
X-Auth-Server-Token: secret_kgs
X-Auth-Black-Player-Token: player_1_token
X-Auth-White-Player-Token: player_2_token

  "black_id": 1,
  "white_id": 2,
  "server_id": 1,
  "result": "W+R",
  "date_played": "2015-02-26T10:30:00",
  "game_record": "(;FF[4]GM[1]SZ[19]CA[UTF-8]BC[ja]WC[ja]EV[54th Japanese Judan]PB[Kono Takashi]BR[8p]PW[O Meien]WR[9p]KM[6.5]DT[2015-02-26]RE[W+R];B[qd];W[dp];B[pq];W[od])"

You can also submit a game_url in lieu of the game_record field. server_token is the game server's secret token, and black_token, white_token are the player's secret tokens. Your server_id can be discovered through the UI for online-ratings.

Getting Started (Online Ratings backend developers)


Before you get started working on Online Ratings, you'll need to do some setup:

  • Choose your package manager
  • Install Python3 and the relevant dependencies
  • Install the Docker command line tools.
  • Get set up with a VM to use with Docker
  • Build and run the app on the VM with Docker
  • log in using the fake login credentials found in web/scripts/

Package Managers

This dev guide assumes a POSIX tool chain. Most developers on this project use OSX.

  • OSX: Install homebrew
  • Linux/Ubuntu: You should already apt-get installed

Python and Dependencies

To get things working (without docker), you'll need

Assuming you're running on OSX:

  1. Install Python3
    • OSX: brew install python3
    • Linux: You probably already have Python3 installed. If not: sudo apt-get install python3
  2. Install virtualenv for
    • OSX: brew install pyenv-virtualenv
    • Linux: ? TODO(someone with a linux box): fill this in.
  3. Create a virtual environment for online-ratings.
    • virtualenv -p $(which python3) ~/py3-or
    • source ~/py3-or/bin/activate
  4. Install pip
    • curl | python3
  5. Install postgres
    • OSX: brew install postgresql
    • Linux: See here
  6. Install the python dependencies with pip.
    • cd to online-ratings/web directory and run: pip install -r requirements.txt
  7. Run the tests!
    • cd to online-ratings/web directory and run: python3 -m unittest discover

Getting set up with Docker


You'll want to install docker, docker-machine, and docker-compose. This is easily done with Docker for Mac.


Install docker and docker-compose


Then the following commands should start the app running and start tailing the logs.

cp .env_example .env
docker-compose build
docker-compose up

The build step will create docker containers for each part of the app (nginx, flask, etc.). The up -d step will coordinate the running of all the containers as specified in the docker-compose yaml file.

Important Note! If this is the first time you've set up the database, you'll need to create the initial tables with:

docker-compose run --rm web python /usr/src/app/ db upgrade

and then create some fake data to populate the db

docker-compose run --rm web python /usr/src/app/ create_all_data

The dockerfile configuration will then serve the app at localhost:80.

You can remap the ports that the app listens on by editing docker-compose.yml and changing the nginx ports mapping to something like "8080:80"


You might find it useful to have a python shell in Docker. This lets you interactively play with database queries and such.

docker-compose run --rm web python /usr/src/app/ shell
>>> from app.models import Player
>>> print(Player.query.filter(
Player FooPlayerKGS, id 1

You might also find it useful to have a postgres shell.

docker-compose run --rm psql

Making database changes

We use Alembic / Flask-Migrate to run database schema updates.

To make a database schema change, first make your changes to, and rebuild the docker containers with docker-compose build.

Then, run the following command to autogenerate an Alembic migration file.

docker-compose run --rm db_migrate python db migrate -m "description of schema changes"

The db_migrate container is specifically build to mount your local filesystem's alembic migrations directory to the container's migration directory. That way, the alembic-generated files are mirrored in your own filesystem, and you'll be able to commit them to source control.

See Alembic documentation for limitations on what schema changes the autogenerate can/cannot detect.

To actually execute the migration, run

docker-compose build
docker-compose run --rm web python db upgrade

Running Locally

Generally, we prefer running with Docker. However, if you wish to run the web server locally (perhaps for a faster iteration cycle) you can do so with the following:

cd online-ratings
sed 's/^\([^#]\)/export \1/g' .env_example > .env_local 
source .env_local
cd web
pip install -r requirements.txt
python3 runserver

You should see:

* Running on (Press CTRL+C to quit)

Note: At this point, you still need to run your local Online Ratings instance against a database instance. You can either create a local postgres instance and create some data. Or, you can point your local server at the running Docker images. For that, all you need to do is run through the Docker startup instructions above and then change DB_SERVICE in your .env_local to

Running the Tests

The standard unittest module has a discovery feature that will automatically find and run tests. The directions given below will search for tests in any file named test_*.py.

source bin/activate
cd web
python -m unittest discover

To see other options for running tests, you may:

cd <repo root directory>
python -m unittest --help


Production is the same as local. Yay Docker!

We build images automatically as part of the .travis unittesting. If you want to know how it works, check out Travis: encrypting secret data and Travis: docker. Essentially, this does the following:

  • When changes are pushed to the release branch,
  • Check to ensure that the tests pass.
  • If so, push images to the USGO Docker hub org
    • There should be two new images, one tagged with :latest and one with the abridged commit hash.

Note that due the limitations of secrets, the commit cannot come from a fork: it must be created/pushed from the base repo (usgo/online-ratings).

Creating a Release.

In the Github UI for usgo/online-ratings, select the master branch and select 'Create Pull Request'. Then, target the release branch and describe the release. Note that as soon as the Release Pull Request is created and the Travis tests pass, a docker image will be built and pushed to the USGO Docker Hub. Importantly, this means that the push of the Docker image depends on the tests pasting, not whether the PR is merged (but it should still be merged for clarity).


On the server, we have yet to integrate the above image process: (Work in progress):

vim .env (change passwords, secret_key to production values)
docker-compose build
docker-compose up -d

API Documentation

Running locally

  1. Ensure mkdocs is installed.
  2. Run mkdocs serve from within the root of online-ratings.
  3. Load it in a browser and profit!

Making non-API Pages

Create or edit the .md files within docs/.

Refer to mkdocs for more details.

Generating API Documentation

Source files to be edited can be found in docs/schemata. The files are in YAML for improved readability.

  1. Install prmd per their instructions.
  2. From root of online-ratings, run prmd combine --meta docs/meta.yml docs/schemata | prmd verify | prmd doc > docs/

JSON Schema is the general format used for types and JSON Hyper-Schema is used for endpoint definitions.

Deploying To gh-pages

  1. Run mkdocs gh-deploy --clean.
  2. That's it!


The developer mail list can be found here:!forum/usgo-online-ratings