Public instance has no accounts, so all user state was GLOBAL in DB — new users
saw/overwrote each other's (and Jan's) favorites, watched badges and blacklists
(bug 2026-06-10). Add device_id (VARCHAR 64) to 9 state tables with composite PK
(device_id, entity_id); app sends X-Device-Id header (get_device_id dep). All
favorites/scene-favorites/blacklist/watch + scene&movie list/detail (is_favorite,
watched, blacklist-hide) now filter by device. Existing rows backfilled to
'legacy-shared'; POST /me/adopt-legacy reassigns them to the caller once. Old
clients (no header) map to legacy-shared so they keep working until OTA updates.
Migration 0022: add col, backfill, composite PK. Verified on prod: 967 progress
rows preserved, device isolation holds (new device sees none of legacy state).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Counts for /tags, /performers, /studios and /favorites were computed live
per-request by aggregating scene_tags / scene_performers with an EXISTS to
playback_sources. As the catalog grew to ~1.7M scenes (6.3M scene_tags) this
ran ~4.3s for /tags?order=popular (x2 incl. the total count) and ~950ms for
the default /scenes count, making those screens load in several seconds.
- migration 0019: add scene_count (+ DESC index) to tags/performers/studios
- background job _job_refresh_taxonomy_counts (every 3h) recomputes the counts
in one UPDATE..FROM each (IS DISTINCT FROM to skip unchanged rows)
- /tags, /performers, /studios scenes path now read the column + ORDER BY the
indexed scene_count; for_movies paths keep live aggregation (small tables)
- favorites read denormalized scene_count instead of a grouped EXISTS aggregate
- /scenes default count: 10-min in-process TTL cache (header is approximate)
Measured: /tags?order=popular&per_page=500 ~8s -> 66ms incl. serialization.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Goon — self-hosted aggregator for adult-content scene metadata.
Indexes scenes from TPDB, StashDB, and 30+ public adult tube sites.
Cross-source deduplication via perceptual hash + Levenshtein distance.
FastAPI backend + APScheduler worker + React Native (Expo) mobile client.
FOSS, ad-free, donation-funded. See README for details.
