# plugins/docker/models.py
from __future__ import annotations
import uuid
from datetime import datetime, timezone
from sqlalchemy import DateTime, Float, ForeignKey, Index, Integer, String, Text
from sqlalchemy.orm import Mapped, mapped_column
from steward.models.base import Base


class DockerContainer(Base):
    """Latest known state per container, scoped to the host that reported it.

    Collection is per-host via the host agent, so container names are only
    unique within a host — the natural key is (host_id, name). host_id is NOT
    NULL: every container arrives through a host_agent ingest that resolves a
    Host first. Deleting a host cascades its containers away.
    """
    __tablename__ = "docker_containers"

    host_id: Mapped[str] = mapped_column(
        String(36), ForeignKey("hosts.id", ondelete="CASCADE"), primary_key=True
    )
    name: Mapped[str] = mapped_column(String(255), primary_key=True)
    container_id: Mapped[str] = mapped_column(String(64), nullable=False, default="")
    image: Mapped[str] = mapped_column(String(512), nullable=False, default="")
    status: Mapped[str] = mapped_column(String(32), nullable=False, default="unknown")
    # running | stopped | paused | exited | dead
    cpu_pct: Mapped[float | None] = mapped_column(Float, nullable=True)
    mem_usage_bytes: Mapped[int | None] = mapped_column(Integer, nullable=True)
    mem_limit_bytes: Mapped[int | None] = mapped_column(Integer, nullable=True)
    mem_pct: Mapped[float | None] = mapped_column(Float, nullable=True)
    restart_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
    ports_json: Mapped[str] = mapped_column(Text, nullable=False, default="[]")
    # JSON: [{"host_port": 8080, "container_port": 80, "protocol": "tcp"}]
    started_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
    scraped_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True), nullable=False,
        default=lambda: datetime.now(timezone.utc),
    )


class DockerMetric(Base):
    """Time-series CPU/memory per container — one row per sample per running
    container, scoped to the reporting host."""
    __tablename__ = "docker_metrics"

    id: Mapped[str] = mapped_column(
        String(36), primary_key=True, default=lambda: str(uuid.uuid4())
    )
    host_id: Mapped[str] = mapped_column(
        String(36), ForeignKey("hosts.id", ondelete="CASCADE"), nullable=False, index=True
    )
    container_name: Mapped[str] = mapped_column(String(255), nullable=False, index=True)
    scraped_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True), nullable=False,
        default=lambda: datetime.now(timezone.utc),
        index=True,
    )
    cpu_pct: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
    mem_pct: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
    mem_usage_bytes: Mapped[int] = mapped_column(Integer, nullable=False, default=0)

    # Per-container history lookups filter on (host_id, container_name) then sort
    # by time — a composite index keeps the rows() sparkline queries cheap.
    __table_args__ = (
        Index("ix_docker_metrics_host_container_time",
              "host_id", "container_name", "scraped_at"),
    )