fix(metrics): time-series rows get time-ordered ids, so an insert stops costing a disk read (#5149)

Measured on dev (385a579), agent posts cut to five samples still dropped:
flush=8.8s for 990 rows, 6.6s for 680, 8.4s for 1000 — about 110 rows/s,
~9 ms a row, with rules and evaluation near zero. One random read per
row fits a random uuid4 primary key whose index no longer fits in memory:
every new row lands on a random page of it. (Inferred from the rate; the
table and index sizes have not been read.)

- steward.core.ids.time_ordered_id: a UUIDv7-layout string (48-bit ms
  timestamp, then random), same 36-char shape as the stored uuid4 ids, so
  no migration. Python's uuid7() arrives in 3.14; the image runs 3.13.
- Used for plugin_metrics and its hourly rollup, monitor_results,
  audit_events, the docker metrics/hourly/events/logs tables, and the
  traefik and unifi time series.
- MAX_SAMPLES_PER_INGEST 5 → 2 (~400 rows) for now: it fits the agent's
  10 s even at the old rate, so hosts land data whichever way the new rate
  comes out. Raise it once the new rate is measured.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

plugins/unifi/models.py

@@ -1,10 +1,10 @@
 # plugins/unifi/models.py
 from __future__ import annotations
-import uuid
 from datetime import datetime, timezone
 from sqlalchemy import Boolean, DateTime, Float, Integer, String, Text
 from sqlalchemy.orm import Mapped, mapped_column
 from steward.models.base import Base
+from steward.core.ids import time_ordered_id
 
 
 # ── Time-series (one row per scrape) ──────────────────────────────────────────
@@ -13,7 +13,7 @@ class UnifiWanStat(Base):
     """WAN interface health — one row per scrape."""
     __tablename__ = "unifi_wan_stats"
 
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=time_ordered_id)
     scraped_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc))
     is_up: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
     wan_ip: Mapped[str | None] = mapped_column(String(64), nullable=True)
@@ -27,7 +27,7 @@ class UnifiClientSnapshot(Base):
     """Aggregated client counts + top talkers — one row per scrape."""
     __tablename__ = "unifi_client_snapshots"
 
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=time_ordered_id)
     scraped_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc))
     total_clients: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
     wireless_clients: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
@@ -41,7 +41,7 @@ class UnifiWlanStat(Base):
     """Per-SSID/radio stats — one row per scrape per SSID+radio combination."""
     __tablename__ = "unifi_wlan_stats"
 
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=time_ordered_id)
     scraped_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc))
     ssid: Mapped[str] = mapped_column(String(255), nullable=False)
     radio: Mapped[str | None] = mapped_column(String(8), nullable=True)    # ng=2.4GHz, na=5GHz, 6e=6GHz
@@ -56,7 +56,7 @@ class UnifiDpiSnapshot(Base):
     """DPI application/category traffic snapshot — one row per scrape (JSON blob)."""
     __tablename__ = "unifi_dpi_snapshots"
 
-    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=time_ordered_id)
     scraped_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc))
     # JSON: [{"cat_id", "cat_name", "rx_bytes", "tx_bytes"}] aggregated across all clients
     by_category_json: Mapped[str] = mapped_column(Text, nullable=False, default="[]")