Mirrors TMDb's own /trending/{movie|tv}/{day|week} ranking rather than
Aurora computing trending itself from popularity deltas -- that would
require re-syncing the entire catalog on a schedule, infeasible at any
real scale, whereas TMDb already computes this well from signals (real
view/search velocity) Aurora has no access to.
New: TrendingEntry model + migration 0003, MetadataProvider.get_trending
(concrete/optional, like aclose(), so existing test fakes don't all need
updating), refresh_trending() which actually ingests each trending item
(only ~20 per window, cheap enough to fully ingest rather than apply the
never-fabricate-a-stub rule recommendations uses), a twice-daily arq cron
job (WorkerSettings.cron_jobs, new to this codebase), a manual admin
trigger for right after a deploy, and GET /movies/trending + GET
/series/trending.
Co-Authored-By: Claude Sonnet 5 <[email protected]>
New TMDb integration end to end: append_to_response "watch/providers",
normalizer parsing grouped by country/monetization type, a WatchProvider
catalog entry (external-id keyed, same get_or_create pattern as Company/
Collection/Person) + polymorphic WatchProviderOffering table (new
migration 0002), ingestion write (including provider logos via the
existing Artwork table), and API exposure -- embedded on movie/series
detail plus a dedicated /watch-providers sub-route, shaped to mirror
TMDb's own response for easy porting.
Note: this data comes from JustWatch via TMDb and carries its own
attribution requirement separate from TMDb's general one -- flagged in
docs/api.md, must be satisfied before showing this in the frontend.
Co-Authored-By: Claude Sonnet 5 <[email protected]>
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]>
Collections were bare stubs (name only) lazily created the moment a
movie's belongs_to_collection reference was seen. Adds a real
ingest_collection pipeline: TMDb's /collection/{id} for overview and
poster/backdrop artwork, plus ordered CollectionMovie membership --
linking only movies already ingested into Aurora, the same
never-fabricate-a-stub rule movie recommendations already follow.
get_or_create_collection now refreshes name/overview on an existing
stub instead of only creating, guarded so a bare movie-stub call
(overview=None) never clobbers real data a full ingestion already
filled in.
New POST /api/v1/admin/ingest/collection endpoint and
ingest_collection_task; GET /api/v1/collections/{id} needed no changes
since its read path (collection_service.py) already assembled the full
detail shape.
Co-Authored-By: Claude Sonnet 5 <[email protected]>
