A GraphQL trial for Ensembl to reduce the need for RESTful web services.
This application is implemented with Ariadne, a schema-first graphql framework for Python
GraphQL requires a schema (in /common) and implementation of resolver functions that know how to interpret specific parts of a GraphQL query. Resolvers are found in /resolver, and may also make use of "data loaders" to overcome inherent deficiencies in GraphQL implementations.
https://www.ebi.ac.uk/seqdb/confluence/display/EA/Thoas+Docs
Requires Python 3.10+.
To install dependencies, run:
pip install -r requirements.txt for just the API. Use this when deploying the service.
pip install -r requirements-dev.txt installs everything including dev dependencies like pytest, mypy etc.
Rename example_connections.conf to connections.conf and update the config values accordingly.
This command will start the server:
uvicorn --workers 1 --host=0.0.0.0 graphql_service.server:APP
To run a Uvicorn server with automatic reload for development purposes, you can use the --reload flag. This flag will make Uvicorn watch your code for changes and automatically restart the server when it detects any changes.
uvicorn --workers 1 --host 0.0.0.0 --reload graphql_service.server:APP
Also, if you're developing in PyCharm, you will probably find it useful to create a run configuration so that you can use the debugger. Create a run configuration that looks like this:
cd ensembl-thoas
pytest .
From the root of the repository:
cd ensembl-thoas
pylint $(git ls-files '*.py') --fail-under=9.5
cd ensembl-thoas
mypy graphql_service
black . --check --diff previews the formatting.
black . applies the formatting in-place.
Build the image using ./Dockerfile:
docker build -t $NAME:$VERSION .
Run a container with the image (--publish below is exposing the container's ports to the host network):
docker container run --publish 0.0.0.0:80:80/tcp --publish 0.0.0.0:8000:8000/tcp -ti $NAME:$VERSION
The connection configuration is assumed to exist in the repo as the file ./connections.conf and gets built into the Docker
image. On Kubernetes cluster, these configs are passed through k8s objects called a escrets. If we want to emulate this
in Docker then we could look into using Docker bind mounts.