The Kids First Data Service provides a REST API to the Kids First data.
If you're developing an application against the dataservice api and the data it contains, the fastest way to get a development service of your own running is with docker compose:
git clone [email protected]:kids-first/kf-api-dataservice.git
cd kf-api-dataservice
docker-compose up -d
This will start the dataservice api on port 5000 with a backing postgres
database initialized with the current datamodel.
To add mock data to the dataservice:
docker-compose exec dataservice flask populate_db
If you're developing features of the api and the data model behind the dataservice, you may want finer control over the environment. The following are the basics to get you started with a local development environment of your own:
# Get source from github
git clone [email protected]:kids-first/kf-api-dataservice.git
cd kf-api-dataservice
# Setup python environment and install dependencies
virtualenv venv && source venv/bin/activate
pip install -r dev-requirements.txt
pip install -r requirements.txt
pip install -e .
# Configure the flask application
export FLASK_APP=manage
# Setup the database (using a dockerized postgres)
docker run --name dataservice-pg -p 5432:5432 -d postgres:9.5
docker exec dataservice-pg psql -U postgres -c "CREATE DATABASE dev;"
flask db migrate
flask db upgrade
# Run the flask web application
flask run
Running postgres inside of a container and binding back to the host should
be sufficent for most development needs. If you want to access psql
directly, you can always connect using the following
(assuming the cointainer is named dataservice-pg and the database is dev):
docker exec dataservice-pg psql -U postgres dev
If you'd like to use system install of postgres, or a database running remotely, the dataservice can be configured with the following environment variables:
PG_HOST- the host postgres is running onPG_PORT- the port postgres is listening onPG_NAME- the name of the database in postgresPG_USER- the postgres user to connect withPG_PASS- the password of the user
Gen3/Indexd is used for tracking most of the file information in the data
model. It requires some environment variables to be set for the full
functionality, however, this requires a deploment of Indexd which is currently
difficult to do for development. The INDEXD_URL can be set to None so
that files may still be registered in the data model, though many of the fields
will not be persisted.
INDEXD_URL- the url of the indexd apiINDEXD_USER- the username of a user in the indexd apiINDEXD_PASS- the password of the user in the indexd api
Alternativly, an INDEXD_SECRET may be used in place of the INDEXD_USER
and INDEXD_PASS to load the secrets from vault.
The swagger docs are located at the root localhost:5000/.
An ERD (entity relation diagram) may be found in the docs/ directory, or may
be produced for changes to the data schema. To do so requires that the latest
development version of
eralchemy be installed:
pip install -e [email protected]:Alexis-benoist/eralchemy.git#egg=eralchemy
This also requires GraphViz be installed as well as PyGraphViz. PyGraphViz may have trouble finding GraphViz, in which case, see this article.
Once dependencies are installed, run:
flask erd
A new diagram will be created at docs/erd.png.
to populate database run:
flask populate_db
to clear the database run:
flask clear_db
Unit tests and pep8 linting is run via flask test
# Install test dependencies
pip install -r dev-requirements.txt
# Setup test database
docker run --name dataservice-pg -p 5432:5432 -d postgres
docker exec dataservice-pg psql -U postgres -c "CREATE DATABASE test;"
# Run tests
flask test
Any commit to any non-master branch that passes tests and contains a
Jenkinsfile in the root will be built and deployed to the dev
environment.
Merges to master will be built and deployed to the QA environment once tests have passed.