keda-dispatcher 0.1.3

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keda-dispatcher

Local dev setup with FastAPI/Redis/S3-compatible storage.

Built-in routes

create_app always includes the built-in /proc routes (see keda_dispatcher/api/proc.py). Passing extra_routers just adds more routers on top; it does not remove the defaults.

Configuration (env vars)

These map directly to keda_dispatcher.settings.Settings:

Env	Default	Notes
APP_TITLE	ProcGate	API title
APP_VERSION	package __version__	API version
ENABLE_DOCS	false	Set to true to show Swagger/Redoc
ROOT_PATH	(empty)	Prefix for reverse proxies (e.g. /api)
HOST	0.0.0.0	Uvicorn host
PORT	8080	Uvicorn port
WORKERS	1	Uvicorn workers
LOG_LEVEL	info	Uvicorn log level
RELOAD	false	Uvicorn reload flag
REDIS_URL	redis://localhost:6379/0	Required
QUEUE_KEY	queue:jobs	Redis list key for jobs
R2_ENDPOINT_URL	(empty)	Cloudflare R2 endpoint
R2_ACCESS_KEY_ID	(empty)	R2 access key
R2_SECRET_ACCESS_KEY	(empty)	R2 secret key
R2_BUCKET	proc-data	R2 bucket
EXTRA_API_MODULES	(empty)	Comma-separated pkg.module:router_or_factory list

CLI flags (e.g. --host, --port, --workers, --log-level, --reload, --extra-router) override env values at startup.

Adding external APIs (APIRouter)

Pass routers via CLI (no env needed):

poetry run keda-dispatcher \
  --extra-router myapp.extra:router \
  --extra-router myapp.health:get_router \
  --host 0.0.0.0 --port 8080

• router_or_factory can be an APIRouter instance or a zero-arg factory returning one.
• --extra-router is repeatable; values are passed to create_app as extra_routers.

Example: start from an external script __main__

Minimal __main__ that injects extra routers and runs uvicorn:

# myservice/__main__.py
import uvicorn
from keda_dispatcher.settings import Settings
from keda_dispatcher.app_factory import create_app
from myapp.api import router as custom_router
from myapp.health import get_router

def main():
    settings = Settings.from_env()
    extra = [custom_router, get_router()]
    app = create_app(settings, extra_routers=extra)

    uvicorn.run(app, host=settings.host, port=settings.port, reload=settings.reload)

if __name__ == "__main__":
    main()

Run:

python -m myservice

Quick demo

Run:

bash run_demo.sh

Details and code live in tutorials/external_api.md, tutorials/custom_api.py, tutorials/health.py, and run_demo.sh.

Data handling and lifecycle

• POST /proc/ — issue a process_id and store metadata in Redis (status=created).
• GET /proc/ — list processes; optional status filter.
• PUT /proc/{id}/data or /data/json — upload bytes/JSON to R2 at proc/{id}/input; metadata becomes uploaded.
• POST /proc/{id}/run — enqueue a job into Redis list QUEUE_KEY (default queue:jobs); metadata becomes queued.
• DELETE /proc/{id}/queue — remove a queued job for this process_id from Redis and reset status (uploaded if data exists, otherwise created).
• GET /proc/{id}/status — returns Redis metadata (status, timestamps, r2_key, etc.).
• DELETE /proc/{id}/data — delete R2 object proc/{id}/input and reset metadata (status=deleted, r2_key/bucket cleared). Process ID remains.
• DELETE /proc/{id}/kill — mark metadata as killed (does not remove queue entries; workers should honor status).
• DELETE /proc/{id} — remove R2 object (if present) and delete metadata from Redis. Fails if status is queued or running.
• GET /proc/healthz — health check (Redis ping; R2 connectivity if configured).

Note: both binary and JSON uploads share the same R2 key proc/{id}/input, so /data deletion removes either.

Architecture (Mermaid)

flowchart LR
    client((Client))
    api["FastAPI /proc (keda-dispatcher)"]
    redis["Redis meta + queue"]
    r2["Cloudflare R2 proc/{id}/input"]
    worker["Worker (your impl)"]
    keda["KEDA ScaledObject (Redis scaler)"]

    client --> api
    api -->|metadata| redis
    api -->|enqueue| redis
    api -->|upload| r2

    redis <-. monitor .-> keda
    keda -->|scale| worker
    worker -->|dequeue/process| redis
    worker -->|read input| r2
    worker -->|update status| redis

KEDA ScaledObject example

See k8s/keda-scaledobject.yaml for a Redis scaler sample. Key points:

• listName must match QUEUE_KEY (default queue:jobs).
• address should point to your Redis service (e.g., redis.default.svc.cluster.local:6379).
• If Redis auth is enabled, set usernameFromEnv / passwordFromEnv env vars on the worker Deployment and uncomment in the ScaledObject.
• scaleTargetRef.name should be the Deployment that dequeues jobs.

CI/CD

• Tests: .github/workflows/test.yml (runs on main/dev and PRs, matrix on Python 3.10–3.12, executes poetry run pytest)
• Publish: .github/workflows/publish.yml (runs on GitHub Releases published event; poetry publish --build to PyPI)
• Publishing needs a repo secret PYPI_API_TOKEN (a PyPI token like pypi-AgENd...). If the token is missing, publish is skipped with a log message.