2163fee245
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>
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""Refresh denormalizowanych `scene_count` na tags / performers / studios.
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Liczniki są utrzymywane w tle (zamiast liczone per-request) bo agregacja po 6.3M
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scene_tags / 3M scene_performers z EXISTS do 1.15M playback_sources zajmuje ~4.3s —
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nie do zaakceptowania w hot-path UI (/tags, /performers, /studios, /favorites).
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Definicja (identyczna z dotychczasowym has_live_playback filtrem w taxonomies.py):
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scene_count = liczba scen z danym tagiem/performerem/studiem mających ≥1
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playback_source z dead_at IS NULL.
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Każdy UPDATE robi pełny LEFT JOIN (tag/performer/studio) ⨝ agregat → ustawia 0 dla
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sierot. `IS DISTINCT FROM` pomija przepisywanie niezmienionych wierszy (mniej WAL/bloat).
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Całość ~5-10s, leci co kilka godzin — counts do tego stale, co dla sortu "popular" i
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badge "(N)" jest bez znaczenia.
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"""
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from __future__ import annotations
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import logging
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from sqlalchemy import text
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from app.db import session_scope
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log = logging.getLogger(__name__)
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# Wspólny predykat: scena ma ≥1 żywy playback_source.
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_LIVE = (
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"EXISTS (SELECT 1 FROM playback_sources ps "
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"WHERE ps.scene_id = {scene_col} AND ps.dead_at IS NULL)"
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)
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_TAGS_SQL = text(
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f"""
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UPDATE tags t SET scene_count = COALESCE(a.c, 0)
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FROM tags base
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LEFT JOIN (
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SELECT st.tag_id, count(*) AS c
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FROM scene_tags st
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WHERE {_LIVE.format(scene_col="st.scene_id")}
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GROUP BY st.tag_id
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) a ON a.tag_id = base.id
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WHERE t.id = base.id AND t.scene_count IS DISTINCT FROM COALESCE(a.c, 0)
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"""
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)
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_PERFORMERS_SQL = text(
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f"""
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UPDATE performers p SET scene_count = COALESCE(a.c, 0)
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FROM performers base
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LEFT JOIN (
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SELECT sp.performer_id, count(*) AS c
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FROM scene_performers sp
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WHERE {_LIVE.format(scene_col="sp.scene_id")}
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GROUP BY sp.performer_id
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) a ON a.performer_id = base.id
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WHERE p.id = base.id AND p.scene_count IS DISTINCT FROM COALESCE(a.c, 0)
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"""
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)
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_STUDIOS_SQL = text(
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f"""
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UPDATE studios s SET scene_count = COALESCE(a.c, 0)
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FROM studios base
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LEFT JOIN (
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SELECT sc.studio_id, count(*) AS c
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FROM scenes sc
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WHERE sc.studio_id IS NOT NULL AND {_LIVE.format(scene_col="sc.id")}
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GROUP BY sc.studio_id
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) a ON a.studio_id = base.id
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WHERE s.id = base.id AND s.scene_count IS DISTINCT FROM COALESCE(a.c, 0)
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"""
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)
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def refresh_taxonomy_counts() -> dict[str, int]:
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"""Przelicza scene_count dla tags/performers/studios. Zwraca rowcount per tabela
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(ile wierszy faktycznie się zmieniło)."""
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out: dict[str, int] = {}
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with session_scope() as session:
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for name, stmt in (
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("tags", _TAGS_SQL),
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("performers", _PERFORMERS_SQL),
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("studios", _STUDIOS_SQL),
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):
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res = session.execute(stmt)
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out[name] = res.rowcount or 0
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# Commit per-tabela — długie transakcje trzymałyby locki na hot tables.
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session.commit()
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return out
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