model-platform/DEVELOP.md

# DEVELOP.md — Developer Guide

This guide is for engineers working on the model platform codebase. For
high-level design see `ARCHITECTURE.md`; for the current state of in-flight
refactors see `HANDOVER.md`.

## Code layout

```
common/                Pure-Python shared library
  config.py            Settings (pydantic-settings, lru_cache singleton)
  db/                  SQLAlchemy 2.0 async engine, session_scope, Base
  db/models/           26 tables in 9 domain files (zero FK, zero relationship)
  scheduler/           build_sqlalchemy_jobstore (delayed import)
  storage/             AsyncStorageBackend abstraction (s3 + local impls) + Pydantic schemas
  eventing.py          add_outbox_event / utcnow / event_time
  service_app.py       /health/ready TCP probe, /api/v1/health
  schemas.py           StrictModel base
  utils.py             get_free_port, start_process

backend/               Public FastAPI service + internal /internal/v1/* sub-app
  main.py              lifespan + route registration
  jupyter.py           /api/v1/auth/jupyter — the ONLY auth entry
  scripts.py           CRUD for scripts/notebooks (object storage via AsyncStorageBackend)
  schedules.py         DAG CRUD: schedules, nodes, edges
  schedule_runs.py     Trigger / list / get runs
  schedule_schemas.py  Pydantic request/response models
  admin.py             Admin endpoints
  resources.py         Misc data resources
  storage_api.py       /internal/v1/* (sub-app merged into main)
  storage_client.py    Stub (HTTP client removed post-migration; rewrite pending)
  schedule_client.py   Placeholder module (was the HTTP-push executor client)
  runtime_client.py    Self-contained httpx wrapper for the runtime
  jupyter.py           auth_request handler
  dependencies.py      request_context, database_session

schedule/              Schedule Executor (DAG worker)
  context.py           Constants + naive_utc
  scheduler.py         CronScheduler (APScheduler + 5s sync loop)
  orchestrator.py      DispatchOrchestrator (Outbox poll + DAG advance)
  worker.py            NodeExecutor (notebook / python execution)
  service.py           SchedulerService facade (composes the three)
  main.py              Lifespan + FastAPI app
  storage_client.py    Stub (SchedulerStorageClient rewrite pending — use AsyncStorageBackend directly)
  execution.py         execute_artifact (notebook + python paths)
  notebook_runner.py   Subprocess entry point (nbclient)

runtime/               Jupyter Runtime
  main.py              FastAPI entry: jupyter action endpoints
  process.py           Per-workspace subprocess pool + asyncio locks
  mount.py             rclone FUSE mount lifecycle

frontend/              React Router SPA (vite build → nginx)
  app/                 features/ routes/ services/ components/

migrations/            Alembic schema versions
docker-compose.yml     4 services
default.conf           Nginx template
scripts/nginx-entrypoint.sh
.env.example
```

## Configuration system

All env vars go through one place: `common/src/common/config.py`.

```python
from common.config import settings

settings.database_url          # str
settings.storage_backend       # str: "s3" (default) or "local"
settings.local_storage_base_dir  # str: root dir for storage data (default "/data"); see "Storage" below for per-mode derivation
settings.s3_endpoint            # str (full URL, e.g. "http://s3:9000"; s3 mode only)
settings.s3_access_key          # str (s3 mode only)
settings.s3_secret_key          # str (s3 mode only)
settings.s3_workspace_bucket    # str (s3 mode only)
settings.s3_version_bucket      # str (s3 mode only)
settings.s3_run_log_bucket      # str (s3 mode only)
settings.s3_trash_bucket        # str (s3 mode only)
settings.s3_trash_retention_days  # int (s3 mode only)
settings.jwt_secret             # HS256 secret for the auth_request handler
settings.backend_api_url       # schedule → backend HTTP base
settings.runtime_api_url       # backend → runtime HTTP base
settings.public_base_url       # runtime public base URL
settings.service_name          # surfaced in /health
settings.readiness_targets     # CSV host:port list for /health/ready
```

`Settings` reads from process env first, then from a `.env` file at CWD
if present. `pydantic-settings` auto-loads. `case_sensitive=False` so
`DATABASE_URL` / `database_url` both work.

### Adding a new env var

1. Add the field to `Settings`:
   ```python
   new_var: str = Field(default="x", description="...")
   ```
2. Add the line to `.env.example` with a comment.
3. Use `settings.new_var` at the call site.

Do **not** call `os.environ["NEW_VAR"]` or `os.getenv("NEW_VAR")` in
application code. The grep below should return zero hits:

```bash
grep -rnE 'os\.(environ\[?["\x27][A-Z_]+|getenv\(["\x27][A-Z_]+)' --include="*.py" \
  backend/src/ schedule/src/ runtime/src/ common/src/
```

## Conventions

### Database / SQLAlchemy

- **No foreign keys, no `relationship`** — every join is explicit.
- Every table has `is_deleted TINYINT(1) NOT NULL DEFAULT 0` and
  `deleted_at DATETIME(3) NULL`; queries must filter `is_deleted == 0`
  (or `deleted_at.is_(None)`) to avoid logical-deleted rows.
- Domain files under `common/src/common/db/models/` are split by
  bounded context: `audit` / `events` / `experiments` / `identity` /
  `runtime` / `schedules` / `scripts` / `storage` / `workspaces`.
- All models are `class X(Base)` SQLAlchemy 2.0 declarative-mapped.
- The full schema is in `migrations/versions/`. Apply with:
  ```bash
  uv run --frozen --package backend alembic upgrade head
  ```

### Storage

- All object bytes go through `common.storage.AsyncStorageBackend`,
  created by `create_storage(config)` from `common.storage.factory`.
- Two backends are registered: `local` (filesystem, local mode) and
  `s3` (S3-compatible service, s3 mode). Selection is per-deployment
  via `settings.storage_backend` (`"s3"` default, `"local"` for
  dev / single-node / air-gapped).
- The factory helper `build_storage_config(bucket_name)` returns the
  right `create_storage` kwargs for each of the 4 purpose buckets
  (`workspace`, `version`, `run_log`, `trash`). Use it in lifespan code;
  route handlers don't see the difference.
- Bucket resolution from `usage_type` is in **one place**
  (`backend/storage_api.py:resolve_bucket`); route handlers only know
  about `app.state.object_stores[bucket_name]`.
- The runtime's view of the workspace bucket on disk is exposed by
  `common.storage.workspaces_root()`:
  - `s3` mode: `${settings.local_storage_base_dir}/workspaces`
    (default `/data/workspaces`, the rclone FUSE mount target).
  - `local` mode: `${settings.local_storage_base_dir}/workspace`
    (default `/data/workspace`, a subdir of the shared local-storage
    volume).
  `settings.local_storage_base_dir` is the **only** path setting; the
  helper handles the per-mode suffix. Don't read `settings.workspaces_root`
  or any other path setting directly in runtime code — use this helper.
- The pre-2026 abstraction (`RustFSObjectStore` / `common.storage.client`
  / `StorageClient` HTTP wrapper) is gone. Don't reintroduce it.

### Auth

- Browser → `/jupyter/{workspace_id}/...` → Nginx `auth_request` →
  `GET /api/v1/auth/jupyter` (Backend).
- The handler:
  1. Parses cookie / Bearer JWT (HS256 + `settings.jwt_secret`).
  2. Verifies `WorkspaceMembers` for the workspace.
  3. Verifies `Scripts.is_locked` for the requested notebook path
     (owner / unlocked → allow; otherwise 403).
  4. Calls `RuntimeClient.get_workspace` / `start_workspace`.
  5. Returns `x-upstream-addr` + `x-jupyter-internal-token` response
     headers. **Browser never holds the runtime token.**
- Nginx captures the headers via `auth_request_set` and proxies to the
  upstream sub-process with `Authorization: token $jupyter_token`.

### Permission gates

- For write operations on a script/notebook, call
  `require_script_modify_access(script, user_id=..., is_admin=...)`
  from `backend/scripts.py`. It enforces:
  - admin or owner → allow
  - non-owner, `is_locked == 0` → allow
  - non-owner, `is_locked == 1` → 403
- Read endpoints (`list_scripts`, `get_script`) intentionally do **not**
  check `is_locked` — workspace members can see the script list.

### Outbox events

- The platform's only async-messaging fabric is the MySQL
  `OutboxEvents` table. Producers (Backend) write rows in the same
  transaction as the business state. Consumers (Schedule Executor)
  poll every 250 ms and update `event_status` to `published` or
  `failed` (with retry).
- Use `common.eventing.add_outbox_event` to write.
- `event_type` values currently in use:
  - `schedule.run.requested` — produced by `schedule_runs.py` / cron post-back
  - `job.node.execute`       — produced by orchestrator when a node is ready
  - `job.node.finished`      — produced by worker after node execution
- `consumer_inbox` provides exactly-once delivery per
  `(consumer_name, event_id)` (with `process_status: processing →
  succeeded` lifecycle).

### Async / sync signatures

- `SchedulerService.start()` and `SchedulerService.close()` are
  `async def` (so the FastAPI lifespan can `await` them).
- `CronScheduler.start()`, `DispatchOrchestrator.start()` are
  **`def` (sync)** — they only `create_task(...)` and return. Don't
  `await` them.
- `cron.start()`, `orchestrator.start()`, `worker.handle_node_execute`
  are wired together in `SchedulerService.__init__`; their lifetime
  is owned by the facade.

## Local development

### One-time setup

```bash
# Python workspace (monorepo via uv workspaces)
uv sync --all-packages

# Frontend deps
cd frontend && pnpm install && cd ..
```

### Per-service dev

```bash
# Backend (terminal 1)
export DATABASE_URL="mysql+asyncmy://model_platform:model_platform@127.0.0.1:3306/model_platform?charset=utf8mb4"
export STORAGE_BACKEND=s3
export S3_ACCESS_KEY=modelplatform
export S3_SECRET_KEY=modelplatformsecret
export S3_ENDPOINT=http://127.0.0.1:9000
# Or for local mode:
# export STORAGE_BACKEND=local
# export LOCAL_STORAGE_BASE_DIR=/data
uv run --frozen --package backend uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload

# Schedule Executor (terminal 2)
uv run --frozen --package schedule uvicorn schedule.main:app --host 0.0.0.0 --port 8001 --reload

# Runtime (terminal 3 — needs SYS_ADMIN, FUSE, devmode)
uv run --frozen --package runtime python -m runtime.main
```

### Frontend dev

```bash
cd frontend
pnpm dev          # http://localhost:5173, proxies /api to backend
pnpm typecheck
pnpm build
```

### Static checks

```bash
# Python compile
uv run --frozen --package backend python -m compileall -q backend/src common/src
uv run --frozen --package schedule python -m compileall -q schedule/src
uv run --frozen --package runtime python -m compileall -q runtime/src

# Type check (frontend)
cd frontend && pnpm typecheck && cd ..

# Docker compose config
docker compose config --quiet
```

### Smoke test

```bash
# Run the full ORM import + Settings smoke test
PYTHONPATH="backend/src:common/src" uv run --frozen --package backend python -c "
from backend.main import app
from common.config import settings
print('backend:', len(app.routes), 'routes')
print('settings ok:', settings.s3_endpoint)
"
```

## Common tasks

### Add a new DAG endpoint

1. Add the route handler in `backend/schedules.py` (DAG template) or
   `backend/schedule_runs.py` (run lifecycle).
2. Validate request via `backend/schedule_schemas.py`.
3. For mutations on nodes/edges/versions: route through
   `create_script_record` / `get_script_row` and apply
   `require_script_modify_access` if it touches a script.
4. If it produces an outbox event, use
   `add_outbox_event(session, event_type="...", producer="...", ...)`.

### Add a new env var

See "Adding a new env var" above.

### Add a new MySQL table

1. Add a model class in `common/src/common/db/models/<domain>.py`.
   Include `is_deleted TINYINT(1) NOT NULL DEFAULT 0` and
   `deleted_at DATETIME(3) NULL`.
2. Export it from `common/src/common/db/models/__init__.py`.
3. Generate the migration:
   ```bash
   uv run --frozen --package backend alembic revision --autogenerate -m "add <feature>"
   ```
4. Review the generated `migrations/versions/*.py` — Alembic may
   miss comments / server defaults. Manually fix the migration.
5. Apply locally:
   ```bash
   uv run --frozen --package backend alembic upgrade head
   ```

### Wire a new storage bucket

The current 4 buckets are wired in `backend/storage_api.py:resolve_bucket`:

```python
BUCKET_FOR_USAGE: dict[str, str] = {
    "working_copy":     settings.s3_workspace_bucket,
    "public_script":    settings.s3_workspace_bucket,
    "data_resource":    settings.s3_workspace_bucket,
    "snapshot":         settings.s3_workspace_bucket,
    "version_artifact": settings.s3_version_bucket,
    "run_log":          settings.s3_run_log_bucket,
    "run_result":       settings.s3_run_log_bucket,
}
```

The constant `PURPOSE_BUCKETS = ("workspace", "version", "run_log", "trash")`
in `common.storage.factory` enumerates the four backends built in the
backend lifespan. To add a fifth bucket:

1. Add the env var to `Settings` (s3 mode only):
   ```python
   s3_<feature>_bucket: str = Field(default="<feature>", description="...")
   ```
2. Add to `.env.example` with a one-line comment.
3. Append `"<feature>"` to the `PURPOSE_BUCKETS` tuple in
   `common/storage/factory.py`. `build_storage_config("<feature>")`
   will then automatically read `settings.s3_<feature>_bucket` (s3
   mode) or use `<local_storage_base_dir>/<feature>` (local mode).
4. Extend the `Literal` in `common/storage/schemas.py` (in
   `CreateUploadRequest.usage_type`, `ServerObjectRequest.usage_type`)
   to include the new value.
5. Add an entry in `BUCKET_FOR_USAGE` mapping the new `usage_type` to
   the new bucket env var.
6. Pre-create the bucket (s3 mode) or subdirectory (local mode) in the
   deployment. The backend no longer auto-creates buckets.

A workspace's `artifact_bucket` column (when non-null) overrides the
default for that workspace, regardless of `usage_type`.

### Add a new schedule node type

`schedule/execution.py` dispatches on `script_type` in
`execute_artifact`. Add a new branch + a new `_<type>` function.
`worker.py` does not need to change — the dispatch happens inside
`execute_artifact`.

## Tests

There is **no formal test suite yet** (see HANDOVER §Pending Tasks
P1). A reasonable first test surface:

- `require_script_modify_access` (admin / owner / non-owner-unlock /
  non-owner-lock): pure-function unit test, no DB.
- `validate_dag` (cycle detection + orphan detection) in
  `backend/schedules.py`.
- `execute_artifact` end-to-end with mocked `content_hash` and a
  real `tempfile.TemporaryDirectory`.

Test convention: pytest with `pytest-asyncio` for `async def`
handlers. Use SQLite in-memory (or a MySQL test container) for DB
integration. Use moto for S3.

## Troubleshooting

### "the greenlet library is required"

SQLAlchemy 2.0 needs `greenlet` for `engine.dispose()` in async
contexts. Add `greenlet>=3.0.0` to `common/pyproject.toml` and
`uv sync --all-packages`. (Already present in this repo.)

### "Can't connect to MySQL server"

Either MySQL isn't running, or the network namespace doesn't allow
`mysql:3306` resolution. Inside the Docker network, services reach
each other by service name (`mysql`, `backend`, `runtime`,
`schedule`, `s3`).

### Jupyter routing 401s

Inspect `docker compose logs backend` — `jupyter.py:check_notebook_is_locked`
or `load_active_membership` will return an explicit reason. Then
check the JWT (use `JWT_SECRET` from `.env`).

### Schedule run never advances

`schedule_runs.run_status` is stuck at `queued`. Two likely causes:
- `outbox_events` is empty (Backend's `add_outbox_event` failed —
  check `add_outbox_event` in `schedule_runs.py`).
- The orchestrator's polling loop is dead. Check
  `docker compose logs schedule` and look for "database event loop
  failed" exceptions.

### "AttributeError: 'SchedulerService' object has no attribute 'worker'"

`worker` must be constructed before `orchestrator` in
`SchedulerService.__init__`, because orchestrator's dispatch table
captures `self.worker.handle_node_execute` at construction time.
See `service.py` — the order is load-bearing.

## Style

- Type hints everywhere (this repo uses `from __future__ import
  annotations`).
- 4-space indent, double quotes, no trailing whitespace.
- Comments are technical (explain *why*, not *what*).
- Module docstrings document non-obvious invariants. Don't add
  docstrings to functions whose behavior is self-evident from the
  name.
- 4 levels of indentation = "this function is doing too much; split
  it". (Project convention; see e.g. `execute_artifact`.)

## See also

- `ARCHITECTURE.md` — design diagrams
- `HANDOVER.md` — current refactor state and pending work
- `CLAUDE.md` — agent-facing conventions for the repo
- `models / __init__.py` — exhaustive list of all 26 tables
- `common/config.py` — all env vars in one place