36 lines
1.5 KiB
Python
36 lines
1.5 KiB
Python
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"""Aurora's mirror of TMDb's own trending lists (section 17, additive).
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TMDb already computes trending using signals Aurora has no access to
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(real view/search velocity across their whole audience) — reinventing
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that from Aurora's own popularity snapshots would require re-syncing the
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entire catalog on a schedule, which isn't feasible at any real scale.
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Instead Aurora asks TMDb what's trending right now and mirrors that
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ranked list (see app/services/ingestion/trending_ingestion.py)."""
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from __future__ import annotations
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import uuid
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from sqlalchemy import Index, Integer, String
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from sqlalchemy.orm import Mapped, mapped_column
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from app.models.base import Base, TimestampMixin, UUIDPrimaryKeyMixin
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class TrendingEntry(Base, UUIDPrimaryKeyMixin, TimestampMixin):
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"""One row per (entity, time_window) currently on Aurora's trending
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list -- replaced wholesale on each refresh, same as ReleaseDate/
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WatchProviderOffering. `updated_at` doubles as "when this window was
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last refreshed"."""
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__tablename__ = "trending_entries"
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__table_args__ = (Index("ix_trending_entries_window", "entity_type", "time_window"),)
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entity_type: Mapped[str] = mapped_column(String(30), nullable=False) # "movie" | "series"
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entity_id: Mapped[uuid.UUID] = mapped_column(nullable=False, index=True)
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time_window: Mapped[str] = mapped_column(String(10), nullable=False) # "day" | "week"
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rank: Mapped[int] = mapped_column(Integer, nullable=False)
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__all__ = ["TrendingEntry"]
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