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Track API endpoint

Backend service for serving track data to Beta Ensembl.

REST API endpoints

  • track_categories/:genome_id: returns all tracks for a genome
  • track/:track_id: returns single track payload See the OpenAPI spec in ensembl-track-api-openapi.yaml for more details.

Quick Start

  1. Clone the repo:

    • $ git clone https://gitlab.ebi.ac.uk/ensembl-web/ensembl-track-api.git
    • $ cd ensembl-track-api

  2. Build the database:

    • $ docker-compose run web python manage.py makemigrations
    • $ docker-compose run web python manage.py migrate
    • $ docker-compose run web python ./utils/import_data.py

  3. Start the service:

    • $ docker-compose up #add '-d' to run in background

  4. Usage:

    • http://localhost:8000/track_categories/:genome_id # e.g. track_categories/a7335667-93e7-11ec-a39d-005056b38ce3
    • https://beta.ensembl.org/api/tracks/track_categories/:genome_id #when deployed to production

  5. Stop the endpoint:

    • $ docker-compose down #or Crtl+C if running in foreground

CI/CD and Kubernetes deployment

Configuration

The GitLab CI/CD configuration file and the Kubernetes manifests in k8s dir define the deployment accross 2 clusters and 4 namespaces. For each of the 8 environments, 5 manifest files are applied to deploy the app:

  • Applied manually: configmap.yaml, service.yaml, db_service.yaml, ingress.yaml
  • Applied by CI/CD jobs: deployment.yaml; for review apps, also: review/service.yaml, review/ingress.yaml

Deployment stages

  1. Push updates to a feature branch or update branch => review app is deployed. Open PR to dev branch.
    • Use feature branch for changes in code (uses external prod database) and update branch for changes in both code and track data (uses internal dev database).
  2. Review app PR approved => merge to dev branch => deployed to staging
  3. Merge dev branch to main => deployed to internal
  4. Click run button for Live CI/CD job in GitLab => deployed to live

Data updates

  • Track data is served from an external postgres database in both datacenters (defined in db_service.yaml). Since the database user for k8s cluster is read-only, the database build and data import commands are run from a local computer with a read/write database user (specified in settings.py), followed by granting data access for the r/o user (utils/grant_access.sh).
  • Review apps from update branch use local database in k8s. Run python utils/import_data.py in the review app pod to update the data (data source: data/track_categories.yaml)
  • Update relevant environment variables (see settings.py) before running data update scripts (e.g from a file: export $(cat .env | xargs))
  • Use ./manage.py sqlflush and ./manage.py dbshell to debug any database-related Django errors.
    • Flush database: run the database command from sqlflush (with system tables removed) in dbshell terminal