"""Aurora's mirror of TMDb's own trending lists (section 17, additive).

TMDb already computes trending using signals Aurora has no access to
(real view/search velocity across their whole audience) — reinventing
that from Aurora's own popularity snapshots would require re-syncing the
entire catalog on a schedule, which isn't feasible at any real scale.
Instead Aurora asks TMDb what's trending right now and mirrors that
ranked list (see app/services/ingestion/trending_ingestion.py)."""

from __future__ import annotations

import uuid

from sqlalchemy import Index, Integer, String
from sqlalchemy.orm import Mapped, mapped_column

from app.models.base import Base, TimestampMixin, UUIDPrimaryKeyMixin


class TrendingEntry(Base, UUIDPrimaryKeyMixin, TimestampMixin):
    """One row per (entity, time_window) currently on Aurora's trending
    list -- replaced wholesale on each refresh, same as ReleaseDate/
    WatchProviderOffering. `updated_at` doubles as "when this window was
    last refreshed"."""

    __tablename__ = "trending_entries"
    __table_args__ = (Index("ix_trending_entries_window", "entity_type", "time_window"),)

    entity_type: Mapped[str] = mapped_column(String(30), nullable=False)  # "movie" | "series"
    entity_id: Mapped[uuid.UUID] = mapped_column(nullable=False, index=True)
    time_window: Mapped[str] = mapped_column(String(10), nullable=False)  # "day" | "week"
    rank: Mapped[int] = mapped_column(Integer, nullable=False)


__all__ = ["TrendingEntry"]