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>
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
import enum
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import uuid
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from datetime import date, datetime
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from sqlalchemy import Date, DateTime, Enum, Float, ForeignKey, Integer, String, UniqueConstraint, func
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from sqlalchemy.dialects.postgresql import UUID
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from sqlalchemy.orm import Mapped, mapped_column
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from app.models.base import Base, TimestampMixin, UUIDPKMixin
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class Gender(str, enum.Enum):
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female = "female"
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male = "male"
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transgender_female = "transgender_female"
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transgender_male = "transgender_male"
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non_binary = "non_binary"
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intersex = "intersex"
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unknown = "unknown"
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class Performer(UUIDPKMixin, TimestampMixin, Base):
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__tablename__ = "performers"
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canonical_name: Mapped[str] = mapped_column(String(256), nullable=False)
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name_normalized: Mapped[str] = mapped_column(String(256), nullable=False, index=True)
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slug: Mapped[str] = mapped_column(String(256), nullable=False, unique=True)
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gender: Mapped[Gender | None] = mapped_column(Enum(Gender, name="performer_gender"))
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birth_date: Mapped[date | None] = mapped_column(Date)
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country: Mapped[str | None] = mapped_column(String(64))
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# Continuous search worker: kiedy ostatni per-performer search across tubes.
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# Queue: ORDER BY last_searched_at NULLS FIRST, search_run_count ASC. Po pełnym
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# sweep cykliczne refresh najstarszych.
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last_searched_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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search_run_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default="0"
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)
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# Denormalizowany licznik scen z żywym playback (refresh w tle). Patrz migracja
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# 0019 + _job_refresh_taxonomy_counts. Sortowanie "popular" + badge w favorites.
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scene_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default="0"
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)
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class PerformerAlias(Base):
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__tablename__ = "performer_aliases"
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__table_args__ = (UniqueConstraint("performer_id", "alias_normalized"),)
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id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True), primary_key=True, server_default=func.gen_random_uuid()
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)
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performer_id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True),
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ForeignKey("performers.id", ondelete="CASCADE"),
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nullable=False,
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index=True,
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)
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alias: Mapped[str] = mapped_column(String(256), nullable=False)
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alias_normalized: Mapped[str] = mapped_column(String(256), nullable=False, index=True)
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source_id: Mapped[uuid.UUID | None] = mapped_column(
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UUID(as_uuid=True), ForeignKey("sources.id", ondelete="SET NULL")
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)
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class PerformerExternalRef(Base):
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__tablename__ = "performer_external_refs"
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source_id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True), ForeignKey("sources.id", ondelete="CASCADE"), primary_key=True
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)
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external_id: Mapped[str] = mapped_column(String, primary_key=True)
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performer_id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True),
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ForeignKey("performers.id", ondelete="CASCADE"),
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nullable=False,
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index=True,
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)
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confidence: Mapped[float] = mapped_column(Float, nullable=False, default=1.0, server_default="1.0")
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first_seen: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now(), nullable=False
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)
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last_seen: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now(), nullable=False
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)
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