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app/services/bulk_import.py fetches and parses TMDb's daily ID export files (published once a day specifically for full-catalog sync, unlike /discover which caps at 10,000 results per query) -- today's file with a fallback to yesterday's, since TMDb publishes at a fixed time and "today's" may not exist yet. app/scripts/bulk_import.py is a standalone ops script (not an arq task, since enqueueing millions of jobs would blow past arq's job_timeout) that fans every id from the export into the existing per-entity ingestion pipeline -- get_or_create_ingestion_job + the same arq task each admin endpoint already uses, so a single id's behavior is identical whether it came from the admin API or this script. Bounded concurrency (50) on the enqueue phase; idempotency-keyed jobs make interrupting and re-running the script safe. Real TMDb catalog size (checked live): ~1.24M movies, 231K series, 4.9M people, 9.8K collections -- ~6.4M entities. At default worker concurrency this is realistically weeks of continuous processing, not hours, documented as such in README.md. Also added 429 (rate-limited) handling to TMDbProvider before running this at volume: previously a 429 fell into the generic >=400 branch and failed the request outright with zero retry, since only httpx.TransportError triggered the existing retry decorator. Now a 429 raises ProviderRateLimitedError carrying the response's own Retry-After hint, and tenacity's wait strategy honors that hint directly instead of guessing via exponential backoff. Co-Authored-By: Claude Sonnet 5 <[email protected]>
254 lines
10 KiB
Markdown
254 lines
10 KiB
Markdown
# Aurora Core
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Aurora Core is Aurora Dragon Studio's production-grade media metadata,
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discovery, and intelligence backend — a TMDb/IMDb-style canonical data layer
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for movies, TV, people, and everything that connects them. It is **not** a
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Plex application: Plex, Overseerr, Radarr, Sonarr, and future Aurora
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software are consumers built around Core, never dependencies of it.
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See [`docs/aurora-core-spec.md`](docs/aurora-core-spec.md) for the full
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build specification this repository implements, and
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[`docs/architecture.md`](docs/architecture.md) for how it's put together.
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## Architecture at a glance
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A modular monolith (not microservices): a stateless FastAPI API behind a
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reverse proxy, PostgreSQL as the sole source of truth, Redis for disposable
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caching/rate-limiting/queueing, and arq workers for async metadata
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ingestion. Full diagram and reasoning in
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[`docs/architecture.md`](docs/architecture.md).
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- **API**: FastAPI + Pydantic v2, stateless, horizontally scalable
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- **Database**: PostgreSQL via SQLAlchemy 2.0 (async) + Alembic migrations
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- **Aurora IDs**: UUIDv7 primary keys, never a provider's ID — see
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[`docs/database.md`](docs/database.md)
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- **Cache/queue**: Redis + arq
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- **Search**: PostgreSQL full-text + trigram, behind a swappable
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`SearchProvider` abstraction
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- **Providers**: TMDb today (`app/providers/tmdb.py`), pluggable
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## Prerequisites
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- Docker + Docker Compose (recommended path)
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- *or*, for running outside Docker: Python 3.12, a local PostgreSQL 17 with
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`pg_trgm` available, and Redis 7
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## Local setup (Docker — recommended)
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```bash
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cp .env.example .env
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# edit .env: set AURORA_SECRET_KEY, AURORA_ADMIN_API_KEYS, and (optionally)
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# TMDB_API_KEY — see https://www.themoviedb.org/settings/api
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docker compose up --build
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```
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This starts PostgreSQL, Redis, runs `alembic upgrade head` (the `migrate`
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service), then the API (`http://localhost:8000`) and a worker. API docs:
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`http://localhost:8000/docs`.
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## Local setup (without Docker)
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```bash
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python -m venv .venv
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.venv/Scripts/activate # Windows; use `source .venv/bin/activate` on macOS/Linux
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pip install -e ".[dev]"
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cp .env.example .env # point DATABASE_URL / DATABASE_URL_SYNC / REDIS_URL at your own instances
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alembic upgrade head
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uvicorn app.main:app --reload
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```
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Run a worker in a second terminal:
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```bash
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arq app.workers.worker.WorkerSettings
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```
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## Environment configuration
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All configuration is environment-based (`app/core/config.py`); see
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[`.env.example`](.env.example) for every variable and its meaning. Never
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commit a real `.env` — only `.env.example` (placeholders) belongs in Git.
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## Database migrations
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```bash
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alembic upgrade head # apply all migrations
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alembic revision --autogenerate -m "add xyz" # generate the next one after changing models
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alembic downgrade -1 # roll back one revision
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```
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The application never creates tables at startup — see
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[`docs/database.md`](docs/database.md#migrations).
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## Ingesting data
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Ingestion always runs asynchronously through a worker, never inline in a
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request (see [`docs/architecture.md`](docs/architecture.md)). With
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`TMDB_API_KEY` set and a worker running:
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```bash
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curl -X POST http://localhost:8000/api/v1/admin/ingest/movie \
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-H "X-API-Key: $AURORA_ADMIN_API_KEYS" \
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-H "Content-Type: application/json" \
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-d '{"tmdb_id": "603"}'
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# -> {"job_id": "...", "arq_job_id": "...", "status": "enqueued"}
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curl http://localhost:8000/api/v1/admin/ingest/jobs/<job_id> \
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-H "X-API-Key: $AURORA_ADMIN_API_KEYS"
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# -> poll until "status": "succeeded"
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curl "http://localhost:8000/api/v1/search?q=the%20matrix"
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```
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Ingesting a series also queues its posters/backdrops/profile images for
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local download automatically (only the provider's *primary* image per
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entity, not every alternate) — see the next section.
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### Bulk-importing TMDb's entire catalog
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`app/scripts/bulk_import.py` enqueues an ingestion job for every id in
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TMDb's [daily ID export files](https://developer.themoviedb.org/docs/daily-id-exports)
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(published once a day specifically for full-catalog sync, unlike
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`/discover`, which caps at 10,000 results per query) — the same
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one-id-at-a-time pipeline the admin endpoints above use, just enqueued for
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every id TMDb has rather than one at a time by hand:
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```bash
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docker compose -f docker-compose.prod.yml exec worker \
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python -m app.scripts.bulk_import movie series person collection
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```
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As of 2026-09, TMDb's real catalog is roughly 1.24M movies, 231K TV
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series, 4.9M people, and 9.8K collections — **~6.4M entities total**. At
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this project's default worker concurrency (`WORKER_CONCURRENCY=4` × 2
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worker replicas), expect this to run for **on the order of weeks**, not
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hours — it does not rate-limit itself against TMDb; TMDb's request rate is
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governed entirely by how many workers are consuming the queue
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concurrently (raise `WORKER_CONCURRENCY` / scale up workers to go faster,
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mindful of TMDb's own rate limits — `app/providers/tmdb.py` backs off on a
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429 using TMDb's own `Retry-After` hint, but sustained high concurrency
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will still see more of them). Run it detached
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(`docker compose ... exec -d worker ...`, or under `nohup`/`tmux`/`screen`)
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so it survives a disconnect, and use `--dry-run` first to see counts
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without enqueueing anything. Re-running (e.g. after an interruption) is
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safe — every id is idempotency-keyed, so already-created jobs are reused,
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never duplicated.
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Expect substantial disk usage from artwork self-hosting cascading on
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every successful ingestion (see the next section) at this scale — budget
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accordingly before running it against the full catalog.
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## Artwork
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Images are referenced by their provider CDN URL (TMDb) from the moment
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they're ingested, and cached locally in the background afterward — API
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responses point at the provider until a local copy exists, then switch over
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automatically, with no client-visible change. Cached files live under
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`MEDIA_ROOT` (a Docker volume shared between `api` and `worker`, see
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`docker-compose.yml`) and are served at `MEDIA_URL_PREFIX` (default
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`/media`). See [`docs/architecture.md`](docs/architecture.md#artwork-self-hosting-section-10)
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and [`docs/database.md`](docs/database.md#artwork-storage-self-hosting) for
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how it works and its current single-host-deployment assumption.
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## Tests
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```bash
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pytest tests/unit -v # no external dependencies
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# Integration/API tests need a real Postgres + Redis:
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docker compose up -d postgres redis
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createdb -h localhost -U aurora aurora_core_test # or: docker exec -it aurora-core-postgres-1 createdb -U aurora aurora_core_test
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export TEST_DATABASE_URL=postgresql+asyncpg://aurora:aurora@localhost:5432/aurora_core_test
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export TEST_REDIS_URL=redis://localhost:6379/15
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pytest tests/integration -v
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```
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Lint and type-check:
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```bash
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ruff check app migrations tests
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mypy app
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```
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Load testing (targets: 500 concurrent users, search p95 < 500ms, other
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endpoints p95 < 250ms, 5xx rate < 0.1% — see
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[`docs/aurora-core-spec.md`](docs/aurora-core-spec.md) section 2):
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```bash
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pip install -e ".[load]"
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locust -f tests/load/locustfile.py --host http://localhost:8000
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# open http://localhost:8089, seed some data first (see tests/load/locustfile.py)
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```
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## API documentation
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- Interactive (OpenAPI/Swagger): `/docs`, controlled by
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`AURORA_API_DOCS_ENABLED` (set `false` in production if you don't want it
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publicly exposed)
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- Reference: [`docs/api.md`](docs/api.md)
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- Schema/ERD: [`docs/database.md`](docs/database.md),
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[`docs/erd.md`](docs/erd.md)
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## Worker operation
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Workers (arq, `app/workers/worker.py`) scale independently of the API —
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`docker-compose.prod.yml` runs 2 worker replicas and 3 API replicas as an
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example. Concurrency per worker process is controlled by
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`WORKER_CONCURRENCY`. Jobs are idempotent (keyed by
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`tmdb:<entity_type>:<external_id>`), retryable (`max_tries` in
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`app/workers/worker.py`), and observable via
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`GET /api/v1/admin/ingest/jobs/{job_id}`.
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## Production deployment
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The production host runs cPanel, which already owns ports 80/443 for other
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sites on the box — there's no Traefik here. `docker-compose.prod.yml` binds
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the API container to `127.0.0.1:8000` only; the host's own webserver
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(Apache or LiteSpeed, via cPanel) reverse-proxies `api.amdbs.net` to it and
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terminates TLS itself (cPanel's AutoSSL can issue the Let's Encrypt cert
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once the subdomain exists and DNS resolves).
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1. **DNS** (Cloudflare, where `amdbs.net` is managed): add an A record for
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`api.amdbs.net` pointing at the server's IP.
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2. **cPanel**: create the `api.amdbs.net` subdomain so AutoSSL picks it up
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and issues a cert; then reverse-proxy that vhost to `127.0.0.1:8000` —
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either cPanel/WHM's Application Manager if available, or a custom
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Apache/LiteSpeed vhost include (`ProxyPass`/`ProxyPassReverse` for
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Apache; LiteSpeed's External App + rewrite rule for LiteSpeed).
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3. **`.env`** on the server (never committed) — fill in every value in
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`.env.example`, including the "Production only" section at the bottom
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(`POSTGRES_USER`/`PASSWORD`/`DB`), plus:
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```bash
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CORS_ORIGINS=https://www.amdbs.net # must include the frontend's real origin
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```
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4. **Deploy**:
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```bash
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docker compose -f docker-compose.prod.yml up --build -d
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```
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PgBouncer sits in front of PostgreSQL (transaction pooling —
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`DATABASE_BEHIND_TRANSACTION_POOLER=true` disables asyncpg's server-side
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prepared statements, which don't survive transaction-mode pooling). See
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[`docs/architecture.md`](docs/architecture.md).
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Aurora Core's production domain split is `api.amdbs.net` (this backend) /
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`www.amdbs.net` (frontend, a separate project).
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## Known limitations
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See [`docs/database.md#known-limitations`](docs/database.md#known-limitations)
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and [`docs/api.md#known-limitations`](docs/api.md#known-limitations) for the
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current, tracked list (no curated language/country seed data, no fuzzy
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entity matching, no field-level provenance, list-shaped responses beyond
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search and browse/discover return everything with no pagination). None of
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these block the Core 0.1 milestone (section 34): search → movie/series/person
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detail works end-to-end against real TMDb data, verified on a real
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PostgreSQL + Redis +
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Docker deployment (not just this repo's own dev machine).
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