feat(docker): retention + hourly rollup for metrics/events with Settings windows
Bounds Docker time-series growth (the main scaling concern). New docker_metrics_hourly table + docker_006 migration; a plugin retention module (docker.run_retention capability) rolls raw docker_metrics older than the raw window into hourly averages (idempotent upsert), deletes the rolled raw rows, then prunes stale rollups + lifecycle events. Core cleanup.py drives it each hourly run via the capability (no plugin-model import), reading the three retention windows fresh from settings so changes apply without restart (rule 25). Settings → "Thresholds & Retention" gains a Docker retention card (raw / rolled-up / events windows, working defaults 7/90/30 days). Unit tests cover the hour-aligned cutoff/bucketing helpers; integration test exercises the real rollup-average + prune across both windows. Milestone 77 task #941. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016Jg27rgypiW2efULXJDtMC
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# plugins/docker/retention.py
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"""Bound Docker time-series growth: roll up old metrics, prune old rows.
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Published as the "docker.run_retention" capability (see __init__.setup) so the
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core cleanup task can drive it WITHOUT importing the docker models (same
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opportunistic-coupling pattern as docker.persist_host_samples). Runs inside the
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caller's open transaction; never opens or commits its own.
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The scaling concern is docker_metrics: ~2880 rows/container/day at a 30s sample.
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We keep raw samples for a short window, then aggregate everything older into
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hourly averages (docker_metrics_hourly) and delete the raw rows — so multi-day
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history stays cheap to store and query. docker_events is light but unbounded
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without a cutoff, so it gets a (longer) window too.
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"""
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from __future__ import annotations
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from datetime import datetime, timedelta
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def _hour_floor(dt: datetime) -> datetime:
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"""Truncate a datetime down to the start of its hour (drops min/sec/µs)."""
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return dt.replace(minute=0, second=0, microsecond=0)
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def _rollup_cutoff(now: datetime, raw_days: int) -> datetime:
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"""Hour-aligned boundary below which raw metrics get rolled up + deleted.
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Aligning to the hour means we only ever roll up *whole* elapsed hours — a
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bucket is never split across the keep/roll boundary, so re-running can't
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produce a partial-then-complete duplicate for the same hour.
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"""
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return _hour_floor(now - timedelta(days=raw_days))
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async def run_docker_retention(
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session,
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*,
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events_days: int,
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metrics_raw_days: int,
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metrics_rollup_days: int,
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now: datetime | None = None,
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) -> dict:
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"""Roll up + prune Docker time-series. Returns a counts dict for logging.
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1. Aggregate docker_metrics older than the (hour-aligned) raw window into
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docker_metrics_hourly (avg cpu/mem per container per hour), upserting so a
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re-run is idempotent, then delete those raw rows.
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2. Prune rolled-up rows older than the rollup window.
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3. Prune docker_events older than the events window.
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"""
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from datetime import timezone
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from sqlalchemy import delete, func, select
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from sqlalchemy.dialects.postgresql import insert as pg_insert
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from .models import DockerEvent, DockerMetric, DockerMetricHourly
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if now is None:
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now = datetime.now(timezone.utc)
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rolled = rolled_rows = events_pruned = rollup_pruned = 0
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# ── 1. Roll up raw metrics older than the raw window into hourly buckets ──
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raw_cutoff = _rollup_cutoff(now, metrics_raw_days)
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hour = func.date_trunc("hour", DockerMetric.scraped_at)
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agg = (
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select(
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DockerMetric.host_id,
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DockerMetric.container_name,
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hour.label("bucket"),
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func.avg(DockerMetric.cpu_pct).label("cpu_pct"),
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func.avg(DockerMetric.mem_pct).label("mem_pct"),
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func.avg(DockerMetric.mem_usage_bytes).label("mem_usage_bytes"),
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func.count().label("sample_count"),
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)
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.where(DockerMetric.scraped_at < raw_cutoff)
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.group_by(DockerMetric.host_id, DockerMetric.container_name, hour)
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)
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for r in (await session.execute(agg)).all():
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stmt = (
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pg_insert(DockerMetricHourly)
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.values(
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host_id=r.host_id,
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container_name=r.container_name,
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bucket=r.bucket,
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cpu_pct=float(r.cpu_pct or 0.0),
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mem_pct=float(r.mem_pct or 0.0),
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mem_usage_bytes=int(r.mem_usage_bytes or 0),
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sample_count=int(r.sample_count or 0),
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)
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.on_conflict_do_update(
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constraint="uq_docker_metrics_hourly_bucket",
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set_={
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"cpu_pct": float(r.cpu_pct or 0.0),
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"mem_pct": float(r.mem_pct or 0.0),
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"mem_usage_bytes": int(r.mem_usage_bytes or 0),
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"sample_count": int(r.sample_count or 0),
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},
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)
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)
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await session.execute(stmt)
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rolled += 1
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rolled_rows += int(r.sample_count or 0)
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if rolled:
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await session.execute(
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delete(DockerMetric).where(DockerMetric.scraped_at < raw_cutoff)
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)
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# ── 2. Prune rolled-up rows beyond the rollup window ──
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rollup_cutoff = now - timedelta(days=metrics_rollup_days)
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res = await session.execute(
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delete(DockerMetricHourly).where(DockerMetricHourly.bucket < rollup_cutoff)
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)
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rollup_pruned = res.rowcount or 0
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# ── 3. Prune lifecycle events beyond the events window ──
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events_cutoff = now - timedelta(days=events_days)
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res = await session.execute(
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delete(DockerEvent).where(DockerEvent.at < events_cutoff)
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)
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events_pruned = res.rowcount or 0
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return {
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"buckets_rolled": rolled,
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"raw_rows_rolled": rolled_rows,
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"rollup_pruned": rollup_pruned,
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"events_pruned": events_pruned,
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}
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