Initial Aurora Core 0.1 implementation

Production-grade media metadata backend per docs/aurora-core-spec.md:
Aurora-owned UUIDv7 identity system, normalized movie/TV/people/credits
schema, provider abstraction with a TMDb adapter, an idempotent async
ingestion pipeline (normalize -> match -> upsert -> provenance), a
Postgres FTS/trigram search layer, a stateless FastAPI v1 API with
Redis caching/rate-limiting, arq workers, dev/prod Docker Compose
topologies, unit + integration + load tests, CI, and architecture/
database/API/ERD documentation.

Co-Authored-By: Claude Sonnet 5 <[email protected]>

app/workers/tasks.py

@@ -0,0 +1,22 @@
+"""arq task functions. Thin wrappers around the ingestion runner so retry/
+concurrency/observability behavior lives in arq's worker loop, not here.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from app.core.logging import get_logger
+from app.services.ingestion.runner import run_movie_ingestion, run_person_ingestion
+
+logger = get_logger(__name__)
+
+
+async def ingest_movie_task(ctx: dict[str, Any], external_id: str) -> str:
+    movie_id = await run_movie_ingestion(external_id)
+    return str(movie_id)
+
+
+async def ingest_person_task(ctx: dict[str, Any], external_id: str) -> str:
+    person_id = await run_person_ingestion(external_id)
+    return str(person_id)

app/workers/worker.py

@@ -0,0 +1,37 @@
+"""arq worker process entrypoint.
+
+Run with: `arq app.workers.worker.WorkerSettings`
+
+Workers scale independently of the API (section 1) — run as many worker
+processes/containers as needed; arq's Redis-backed queue coordinates them so
+each job is picked up by exactly one worker.
+"""
+
+from __future__ import annotations
+
+from arq.connections import RedisSettings
+
+from app.core.config import get_settings
+from app.core.logging import configure_logging, get_logger
+from app.workers.tasks import ingest_movie_task, ingest_person_task
+
+logger = get_logger(__name__)
+
+
+async def startup(ctx: dict) -> None:
+    configure_logging()
+    logger.info("worker_started")
+
+
+async def shutdown(ctx: dict) -> None:
+    logger.info("worker_stopped")
+
+
+class WorkerSettings:
+    functions = [ingest_movie_task, ingest_person_task]
+    on_startup = startup
+    on_shutdown = shutdown
+    redis_settings = RedisSettings.from_dsn(get_settings().REDIS_URL)
+    max_jobs = get_settings().WORKER_CONCURRENCY
+    job_timeout = 300
+    max_tries = 5