A distributed AI workload scheduler built to demonstrate Clean Architecture, Domain-Driven Design, and real infrastructural decoupling.
AetherGrid takes workloads, matches them against available compute nodes based on resource requirements and constraints, and manages the full lifecycle: queued, scheduled, running, completed, failed, retried. Jobs run through workers registered against nodes, and job execution ownership is enforced through time-bound leases rather than a simple assignment flag.
| Layer | Technology |
|---|---|
| Language | Python 3.12 |
| API Framework | FastAPI |
| Database | PostgreSQL (raw psycopg, no ORM), with SQLite for select repositories in local development |
| Frontend | React + TypeScript + Vite |
| Testing | pytest, contract testing across repository implementations |
| CI/CD | GitHub Actions |
| Containerization | Docker + Docker Compose |
| Architecture | Clean Architecture, Domain-Driven Design, Repository Pattern, SOLID |
Most scheduler side-projects are a single main.py script wrapped in a while True loop polling an in-memory dictionary. They work fine, right up until you need to swap the persistence engine, add a new constraint type, or figure out why a job silently disappeared, or why two workers picked up the same job at once.
AetherGrid was built around one rule: the domain logic doesn't know or care where the data lives. Jobs, nodes, workers, and the allocation algorithm are pure Python with zero infrastructure dependencies. The database is a detail, not the foundation. This project is a concrete demonstration that these architectural patterns aren't just conference-talk vocabulary; they're guardrails that keep a codebase understandable as it grows, and as its correctness requirements get harder.
- Job lifecycle management: explicit state transitions (Queued → Scheduled → Running → Completed/Failed) with configurable retry policies and priority-aware scheduling
- Constraint-aware best-fit allocator: matches workloads to nodes based on resource requirements and labels, while skipping nodes that are draining or offline
- Worker registration and heartbeats: workers register against a node, report liveness, and are marked dead when heartbeats stop
- Lease-based execution ownership: when a worker accepts a job, it holds a renewable, expiring lease on that job, continuously renewed for the job's entire execution, so retries, reconnects, network failures, and jobs that simply run long can't result in two workers executing the same job
- Real subprocess execution with enforced timeouts: jobs with a command run as real subprocesses, with a two-stage shutdown (graceful
SIGTERM, thenSIGKILLafter a grace period) if a job overruns its execution timeout - Node liveness tracking: heartbeat-based health checks, automatic detection of offline nodes, and resource reclamation when work fails or nodes disappear
- Reconciliation with bounded retries: jobs abandoned by a dead worker or an offline node are reclaimed back to the queue within their retry budget, and fail outright once that budget is exhausted, so a single unhealthy node can't cause a job to be reassigned and abandoned indefinitely
- Domain event recording: every lifecycle transition a job goes through,
JobCreated,JobScheduled,WorkerAssigned,LeaseAcquired,LeaseReleased,JobCompleted/JobFailed, andJobReclaimed, is persisted as an immutable event at the exact point it happens, queryable per-job viaGET /jobs/{id}/history - Live dashboard: submit jobs, register nodes, and watch cluster health, resource usage, and recent job activity in real time
The system is split into four layers, with dependencies pointing inward:
Domain: Job, Node, Worker, and Lease aggregates enforce their own invariants. The scheduling algorithm and job lifecycle state machine live here as plain Python, with no imports from FastAPI or psycopg. Delete the infrastructure layer entirely and the domain tests still pass.
Application: Services such as ScheduleJobService/SchedulerService, AssignWorkerService, AcquireLeaseService, and ClusterHealthService coordinate domain objects and repositories without embedding business rules that belong one layer down. A WorkerExecutionLoop drives a worker through executing its assigned job as a real subprocess, continuously renewing its lease on a background thread for the job's entire runtime, recording the real outcome, and releasing the lease regardless of that outcome. A renewal that fails means the lease has already been reclaimed elsewhere, and the loop discards its result rather than risk persisting it against another worker's in-progress or completed work. A ReconciliationLoop catches the failure modes the happy path can't: crashed workers, expired leases, state left inconsistent by infrastructure failures.
Infrastructure: PostgreSQL implementations exist for every repository (Node, Job, Worker, Lease, Event), written with raw psycopg instead of an ORM, a deliberate choice to keep query behavior and transaction boundaries visible rather than abstracted away. Node, Job, and Event additionally have SQLite implementations for local development. Every repository is validated against a shared contract test suite run against each backend it supports, so switching between implementations, or trusting that they behave identically, is a tested guarantee rather than an assumption.
Presentation: FastAPI endpoints for jobs, nodes, and workers that validate input, call an application service, and return a response. No business logic lives here.
Every non-obvious decision, why domain owns scheduling instead of application, why raw psycopg over an ORM, how job lifecycle transitions are enforced, why leases exist instead of a simple assignment field, why renewal is a strict update rather than an upsert, why job commands are deliberately not exposed over the public API yet, is documented as an ADR in /docs/adr.
The execution engine can run real, arbitrary commands as subprocesses, with real timeout enforcement. Job.command and Job.exit_code exist on the domain model and are fully tested at the service layer. They are not exposed through the public CreateJobRequest API, and this is enforced by a test asserting the field's absence from every response, not left as a comment.
Exposing arbitrary caller-supplied commands over an endpoint with no authentication would mean shipping unauthenticated remote code execution. Building the capability correctly and proving it works, while deferring public exposure until authentication exists, was judged a more honest state to ship than either skipping the feature or exposing it prematurely. See ADR 0012.
190 tests across domain, application, infrastructure, and API layers:
- Full domain logic coverage: job lifecycle, retry policy, constraint matching, node and worker liveness, lease semantics
- Contract tests proving every repository's in-memory, SQLite (where implemented), and PostgreSQL implementations behave identically, including foreign-key-enforced aggregates such as
WorkerandLease, and specifically that lease renewal fails rather than resurrects a lease already reclaimed by reconciliation - Application service tests for every use case, including lease acquisition, renewal, release, reconciliation repair (both the requeue-with-retries-remaining path and the fail-outright-once-exhausted path), and real subprocess execution (including a test that genuinely kills a process that ignores
SIGTERM, forcingSIGKILL) - Event recording tests proving every lifecycle event fires at the correct point, in the correct order, across the full job lifecycle, scheduling, assignment, lease acquisition and release, completion, failure, and reconciliation reclaim
- API-level tests against real FastAPI endpoints
pytestgit clone https://github.com/wycliffRotich-dev/aethergrid.git
cd aethergrid
docker compose up --buildThis starts the API and a Postgres instance. Run the frontend separately:
cd frontend
npm install
npm run devCI runs the full test suite against a live Postgres service on every push. See .github/workflows.
- A live event feed on the dashboard, backed by a new
GET /eventsendpoint, now that every job lifecycle event actually fires and has somewhere real to be fed from - Visibility into workers in the dashboard; worker registration and lifecycle currently have no frontend surface at all
- Hardening the API for public deployment (rate limiting, structured logging, error tracking, authentication), which also unblocks safely exposing real job commands over the public API rather than keeping them internal-only
- Live cloud deployment with CI/CD auto-deploy on merge
This isn't trying to compete with Kubernetes or Ray at scale. It's a demonstration of how to build a system that stays understandable as it grows: layered correctly, tested honestly, and documented well enough that someone else could pick it up and know exactly why every piece is where it is, including the pieces that are deliberately half-built and marked as such.
MIT License.