Three services -- Postgres, API, UI -- with the API on Railway's private
network only, so the UI's /api proxy is the single public entry point and
there is no CORS.
The pipeline cannot run there. Nextflow shells out to `docker run` for VEP
and bcftools, and Railway gives you a container, not a Docker daemon. Rather
than leave a button that always fails, cases are annotated locally and copied
up by scripts/seed-remote.sh, and PUBLIC_PIPELINE_ENABLED=false hides the
analyse/score actions and the create-case form.
DATABASE_IDLE_CONNECTIONS=false is what makes idling work. Railway decides a
service is idle from its *outbound* traffic and sleeps it after ~5-10 minutes;
a pooled database connection is outbound traffic, so SQLAlchemy's default pool
would have kept the API awake and billable for ever. Setting it false switches
to NullPool, which costs a connection per request -- nothing at demo traffic,
the wrong trade under real load, hence the flag rather than a rewrite.
BASIC_AUTH_USER / BASIC_AUTH_PASSWORD put one shared credential in front of
the site. Nothing deployed is patient data, so this stops the URL being
wandered into rather than protecting anyone's privacy; unset, the site is
open, which is what local development wants. Compared in constant time, and
both halves of the credential are checked even when the first fails.
An end-to-end audit found the repo could not build, test or run as shipped. This
fixes every finding, then adds a Cloud Run track so the demo costs about £1/month
idle instead of ~£150.
CI (red on its first run)
- api: setuptools could not build the package (flat layout with app/ and alembic/)
- web: missing @types/node; `vitest run` exited 1 with no test files
- pipeline: the stub run needed a gitignored VCF, and no process had a stub block
- ruff pinned, mypy configured, DB tests on real Postgres (pgserver locally, service in CI)
ML serving (scores were meaningless)
- the registered model now carries its own feature engineering and returns predict_proba,
so serving sends raw columns and cannot drift from training
- resolve by registry alias (stages are deprecated in MLflow 3) and record the real
version; re-scoring upserts instead of failing on the unique constraint
- ClinVar labels parsed from VEP's lowercase terms
Pipeline
- exact ref/alt recovered from a CHROM_POS_REF_ALT VCF ID; loading is idempotent
- job status reaches running/failed/succeeded, so the UI stops polling dead jobs
- DATABASE_URL travels in the environment or a Nextflow secret, never on a command line
- VEP cache and plugins staged as inputs; the gcp profile runs tasks on Google Batch
Deployment
- the API serves /api (matching the ingress); the web app reads its API URL at runtime
- migrations run in an init container under a Postgres advisory lock
- terraform: custom VPC shared with Batch, private Cloud SQL, API enablement, Workload
Identity bindings, Secret Manager, deletion protection
- serverless track, now the default: Cloud Run services scaling to zero, a Cloud Run job
for the Nextflow driver, and Neon or Cloud SQL behind one DATABASE_URL secret. GKE and
Argo remain, behind -var deploy_kubernetes=true. See docs/cloud.md.
Correctness and security
- 409 on duplicate sample names, 422 on bad paging, natural chromosome ordering, wider
VEP text columns, enum dropped on downgrade, the sample's assembly actually used
- vcf_uri restricted to gs:// objects or files under the data root, blocking option injection
- CORS restricted to configured origins; `make down` no longer deletes volumes
Data
- docs/data.md records the peer-reviewed, openly licensed sources (GIAB HG002, ClinVar,
gnomAD) with citations and an honest evaluation plan; `make data` fetches a chr22 slice
Verified: api 50 tests, ml 18, loader 16, web 12; ruff, mypy, svelte-check, terraform
validate and both kustomize overlays clean.
Kemal Yaylali