bvandeusenandClaude Opus 5.5
622addc09e
Each source in an anomaly's `around` was cut to its oldest 40 with nothing in the reply to say so; on a busy host the events nearest the occurrence were the ones dropped. Each source now keeps the 40 nearest the occurrence's start, and `around_left_out` counts the rest. Sources return everything their bounded query read (Docker and the host agent no longer cut at 40 and 20 themselves), so the count is honest. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
405 lines
20 KiB
Python
405 lines
20 KiB
Python
"""Metric anomaly tools (#5740): metrics that left what is normal for them at
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that hour, and marking each normal or real."""
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from __future__ import annotations
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from datetime import timedelta
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from sqlalchemy import select
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from steward.core import anomalies as anom
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from steward.core import metric_series
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from steward.core.norms import LEARNED, norms_at, slot_of
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from steward.core.settings import (DEFAULTS, NORM_FLOORS, floor_of, get_all_settings, set_setting,
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to_thresholds_cfg)
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from steward.mcp._context import current_caller, mcp_app
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from steward.models.norms import MetricAnomaly, MetricAnomalySpan, MetricNorm
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from steward.models.users import UserRole
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from . import _events as events
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from ._audit import audit_mcp, mcp_username
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from ._shape import bounded, clamp_limit, db_session, iso, now_utc, rnd
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LIST_MAX = 200
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REVIEW_MAX = 100 # anomalies one review_anomaly call may mark
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def _row(v: anom.AnomalyView) -> dict:
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a, last = v.anomaly, v.last_span
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return {
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"anomaly_id": a.id,
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"source_module": a.source_module,
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"resource": a.resource_name,
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"metric": a.metric_name,
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"review": a.review,
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"ongoing": v.ongoing,
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"started_at": iso(a.started_at),
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"ended_at": iso(a.ended_at),
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"occurrences": a.occurrences,
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"last_occurrence": {"started_at": iso(last.started_at), "ended_at": iso(last.ended_at),
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"peak_level": rnd(last.peak_level, 3)} if last else None,
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"peak_level": rnd(a.peak_level, 3),
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"peak_at": iso(a.peak_at),
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"usual_hold": rnd(a.usual_hold, 3),
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"usual_peak": rnd(a.usual_peak, 3),
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"reviewed_by": a.reviewed_by,
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"reviewed_at": iso(a.reviewed_at),
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"note": a.note,
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"page": v.href,
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}
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async def list_anomalies(review: str = "to_review", limit: int = 50) -> dict:
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"""Metric anomalies: a learned metric whose 15-minute level went well
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above what is normal for it at that hour (its usual hold ×1.25, and by
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more than the metric's floor).
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review: "to_review" (default) — waiting for someone to mark them; "normal"
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— accepted, their hours are learned; "real" — a real problem, not learned;
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"lapsed" — ended and nobody reviewed them within the review window
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(Settings → Thresholds), so they are not learned; "" for all of them.
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One row covers every repeat on a resource and metric while it waits for
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review: `occurrences` counts them and `last_occurrence` is the latest.
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`peak_level` is the highest 15-minute level any occurrence held, against
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`usual_hold` (the level that hour usually holds) and `usual_peak` (how high
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it usually goes), all in the metric's own unit. `ongoing` is true while an
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occurrence is still going. To-review rows come first, newest occurrence
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first. review_anomaly marks one.
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"""
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if review and review not in anom.REVIEWS:
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raise ValueError(f"review must be one of {', '.join(anom.REVIEWS)}, or empty for all")
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limit = clamp_limit(limit, 50, LIST_MAX)
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async with db_session() as db:
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rows = await anom.list_anomalies(db, (review,) if review else None, limit=LIST_MAX)
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views = await anom.views(db, rows)
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to_review = await anom.to_review_count(db)
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out = bounded([_row(v) for v in views], limit,
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key="anomalies", more="Pass a larger `limit`, or a `review` state.")
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out["to_review"] = to_review
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return out
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async def review_anomaly(anomaly_ids: list[str] | str, review: str, note: str = "") -> dict:
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"""Mark metric anomalies "normal" or "real": one id, or several judged
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the same way (list_anomalies gives ids; get_anomaly shows the evidence).
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normal: this level is usual for this resource at that time; its hours are
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learned into the metric's normal, so it stops being flagged once learned.
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real: it was a real problem; its hours stay out of what is learned.
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note: say why (what it was, what you checked); kept with each review, so
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the next look at this resource and metric sees the precedent.
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A lapsed anomaly may still be marked. All or nothing: an unknown id marks
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none. Audited as "<user> via MCP", one row per anomaly.
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"""
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ids = [anomaly_ids] if isinstance(anomaly_ids, str) else list(anomaly_ids or [])
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ids = list(dict.fromkeys(i.strip() for i in ids if i and i.strip()))
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if not ids:
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raise ValueError("Name at least one anomaly id; list_anomalies shows them.")
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if len(ids) > REVIEW_MAX:
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raise ValueError(f"At most {REVIEW_MAX} anomalies at once.")
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caller = current_caller()
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marked = []
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async with db_session() as db, db.begin():
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for anomaly_id in ids:
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try:
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a = await anom.mark(db, anomaly_id, review, mcp_username(caller.username), note)
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except LookupError:
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raise ValueError(f"No anomaly {anomaly_id!r}; list_anomalies shows them. "
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"Nothing was marked.") from None
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marked.append(({"source": a.source_module, "resource": a.resource_name,
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"metric": a.metric_name, "note": a.note},
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{"anomaly_id": a.id, "resource": a.resource_name, "metric": a.metric_name,
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"review": a.review}))
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reviewed_by, reviewed_at, kept = a.reviewed_by, a.reviewed_at, a.note
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for (detail, row) in marked:
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await audit_mcp(f"metric_anomaly.marked_{review}", "metric_anomaly", row["anomaly_id"], detail)
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return {"marked": [row for _d, row in marked], "review": review, "reviewed_by": reviewed_by,
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"reviewed_at": iso(reviewed_at), "note": kept}
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# ── One anomaly, understood ─────────────────────────────────────────────────
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AROUND = timedelta(minutes=30) # what else was going on: this long either side of an occurrence
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SPANS_MAX = 20
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async def _norm_map(db, key: tuple[str, str, str]) -> tuple[str | None, dict[int, MetricNorm]]:
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"""The key's learned slots, {slot: row}, of the kind it uses."""
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rows = list((await db.execute(select(MetricNorm).where(
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MetricNorm.source_module == key[0], MetricNorm.resource_name == key[1],
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MetricNorm.metric_name == key[2]))).scalars())
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kind = "week" if any(r.kind == "week" for r in rows) else "day" if rows else None
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return kind, {r.slot: r for r in rows if r.kind == kind}
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def _usual_at(kind, slots: dict, at) -> MetricNorm | None:
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return slots.get(slot_of(at, kind)) if kind else None
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def series_pad(started_at, ended_at, now) -> timedelta:
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"""How far either side of an occurrence its series reaches: as long as
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the occurrence, at least an hour and at most six."""
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held = (ended_at or now) - started_at
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return min(max(held, timedelta(hours=1)), timedelta(hours=6))
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async def get_anomaly(anomaly_id: str, occurrences: int = 5) -> dict:
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"""One metric anomaly with what is needed to judge it: each occurrence
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against what is usual at that hour, the metric around it, what else was
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going on, and how earlier ones on the same metric were judged.
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occurrences: how many of its occurrences to show, newest first (default
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5, max 20).
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- `anomaly`: the list_anomalies row, including `usual_hold` and
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`usual_peak` as they stood when it was first flagged.
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- `place`: the host the resource lives on (`host`, `host_id`), when it
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has one: an agent series, a container, a monitor pinned to a host.
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- `occurrences[]`: `started_at`, `ended_at`, `held_s`, `peak_level`,
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and `usual` for that hour as Steward has learned it now (`usual_hold`,
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`usual_peak`, `flag_above`), plus `around`: alert transitions, Ansible
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runs, other anomalies on the host, what plugins saw (container
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starts, deaths and OOM kills; maintenance jobs; reboots and agent
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stops; for a Traefik router, its swarm service's containers on any
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host), and kind "steward": a stretch Steward itself wasn't recording
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(a restart or deploy), around which any reading can be wrong. From 30
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minutes before to 30 minutes after. Each kind keeps the 40 events
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nearest the occurrence's start; `around_left_out` counts the rest
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(0 when nothing was cut). And
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`anomalies_elsewhere`, how many anomalies on other hosts overlapped it.
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Many at once points at something shared (network, storage, Steward).
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- `series`: the newest shown occurrence with time either side (as long
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as it lasted, 1 to 6 hours), as [time, average, usual_hold at that
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hour] points.
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- `precedent`: earlier anomalies on this resource and metric, how they
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were judged and the note left.
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Judge it with review_anomaly: "normal" when it is how this resource
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behaves at that time (its hours are then learned), "real" when it was a
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problem. Say why in the note.
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"""
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limit = clamp_limit(occurrences, 5, SPANS_MAX)
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now = now_utc()
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async with db_session() as db:
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a = await db.get(MetricAnomaly, (anomaly_id or "").strip())
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if a is None:
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raise ValueError(f"No anomaly {anomaly_id!r}; list_anomalies shows them.")
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[view] = await anom.views(db, [a])
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key = (a.source_module, a.resource_name, a.metric_name)
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spans = list((await db.execute(
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select(MetricAnomalySpan).where(MetricAnomalySpan.anomaly_id == a.id)
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.order_by(MetricAnomalySpan.started_at.desc()))).scalars())
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kind, slots = await _norm_map(db, key)
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learned = LEARNED.get((a.source_module, a.metric_name))
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floor = learned.least_departure if learned else None
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place = await events.place_of(db, a.source_module, a.resource_name)
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shown = []
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for span in spans[:limit]:
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usual = _usual_at(kind, slots, span.started_at)
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near, elsewhere, left_out = await events.around(db, place, a.id, span.started_at - AROUND,
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(span.ended_at or now) + AROUND, span.started_at)
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shown.append({
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"started_at": iso(span.started_at), "ended_at": iso(span.ended_at),
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"ongoing": span.ended_at is None,
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"held_s": int(((span.ended_at or now) - span.started_at).total_seconds()),
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"peak_level": rnd(span.peak_level, 3),
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"usual": ({"slot": slot_label(kind, usual.slot), "usual_hold": rnd(usual.usual_hold, 3),
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"usual_peak": rnd(usual.usual_peak, 3),
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"flag_above": rnd(flag_above(usual.usual_hold, floor), 3) if floor is not None else None}
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if usual else None),
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"around": near,
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"around_left_out": left_out,
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"anomalies_elsewhere": elsewhere,
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})
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series = None
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if spans:
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newest = spans[0]
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pad = series_pad(newest.started_at, newest.ended_at, now)
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frm, to = newest.started_at - pad, min((newest.ended_at or now) + pad, now)
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pts = await metric_series.points(db, *key, frm, target=60, until=to)
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series = {"from": iso(frm), "to": iso(to), "columns": ["at", "value", "usual_hold"],
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"points": [[iso(t), rnd(v, 3),
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rnd(u.usual_hold, 3) if (u := _usual_at(kind, slots, t)) else None]
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for t, v in pts]}
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earlier = list((await db.execute(
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select(MetricAnomaly).where(
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MetricAnomaly.source_module == a.source_module, MetricAnomaly.resource_name == a.resource_name,
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MetricAnomaly.metric_name == a.metric_name, MetricAnomaly.id != a.id)
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.order_by(MetricAnomaly.last_started_at.desc()).limit(10))).scalars())
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return {
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"anomaly": _row(view),
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"place": {"host": place.host_name, "host_id": place.host_id},
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"floor": rnd(floor, 3) if floor is not None else None,
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"depart_factor": anom.DEPART,
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"occurrences": shown,
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"occurrences_total": len(spans),
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"truncated": len(spans) > limit,
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"series": series,
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"precedent": [{"anomaly_id": e.id, "review": e.review, "note": e.note, "reviewed_by": e.reviewed_by,
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"started_at": iso(e.started_at), "ended_at": iso(e.ended_at),
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"occurrences": e.occurrences, "peak_level": rnd(e.peak_level, 3),
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"usual_hold": rnd(e.usual_hold, 3)} for e in earlier],
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"next": (f'get_metric_normal(source_module="{a.source_module}", resource="{a.resource_name}", '
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f'metric="{a.metric_name}")'),
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}
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# ── The learned normal ──────────────────────────────────────────────────────
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_DAYS = ("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
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def slot_label(kind: str, slot: int) -> str:
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"""A slot as a reader names it: "03:00 UTC", or "Sun 03:00 UTC"."""
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if kind == "week":
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return f"{_DAYS[slot // 24]} {slot % 24:02d}:00 UTC"
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return f"{slot:02d}:00 UTC"
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def flag_above(norm_hold: float, floor: float) -> float:
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"""The 15-minute level that would open an anomaly in a slot whose usual
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hold is `norm_hold` (anomalies.departs)."""
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return max(norm_hold * anom.DEPART, norm_hold + floor)
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def _learned_or_refuse(source: str, metric: str):
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learned = LEARNED.get((source, metric))
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if learned is None:
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known = ", ".join(sorted(f"{s}/{m}" for s, m in LEARNED))
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raise ValueError(f"{source}/{metric} doesn't learn a normal. These do: {known}.")
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return learned
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async def get_metric_normal(source_module: str, resource: str, metric: str) -> dict:
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"""What Steward has learned is normal for one resource's metric, slot by
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slot: the yardstick an anomaly is judged against.
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source_module, resource, metric: as list_anomalies or query_metrics name
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them (e.g. "host_agent", "VDNT-Docker03", "cpu_pct").
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`kind` is "day" (24 UTC hours, once 5 days of history exist) or "week"
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(168 hours from Monday 00:00 UTC, once 21 days do). Each slot gives
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`usual_hold` (the level its hours usually held: each hour's p95, at the
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90th percentile across past days), `usual_peak` (how high they usually
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went), `days_seen`, and `flag_above`: the 15-minute average that would
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open an anomaly there (usual hold ×1.25, and at least `floor` above it).
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`floor` is the smallest departure worth calling an anomaly, and
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`floor_setting` the Settings → Thresholds floor it comes from. Hours an
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anomaly covers are learned only once someone marks it normal.
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`learned: false` with no slots means too little history yet; the fixed
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cutoffs judge it meanwhile.
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"""
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learned = _learned_or_refuse(source_module, metric)
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async with db_session() as db:
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rows = list((await db.execute(
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select(MetricNorm)
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.where(MetricNorm.source_module == source_module, MetricNorm.resource_name == resource,
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MetricNorm.metric_name == metric)
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.order_by(MetricNorm.kind, MetricNorm.slot))).scalars())
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now = now_utc()
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current = (await norms_at(db, [(source_module, resource, metric)], now)).get(
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(source_module, resource, metric))
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floor = learned.least_departure
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kinds = {r.kind for r in rows}
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kind = "week" if "week" in kinds else "day" if rows else None
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slots = [{"slot": r.slot, "label": slot_label(r.kind, r.slot), "usual_hold": rnd(r.usual_hold, 3),
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"usual_peak": rnd(r.usual_peak, 3), "days_seen": r.days_seen,
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"flag_above": rnd(flag_above(r.usual_hold, floor), 3)}
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for r in rows if r.kind == kind]
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out = {
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"source_module": source_module, "resource": resource, "metric": metric,
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"learned": bool(slots), "kind": kind,
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"floor": rnd(floor, 3), "floor_setting": learned.floor_name,
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"only_while": ({"metric": learned.gate[0],
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"at_least": rnd(learned.gate[1]() if callable(learned.gate[1]) else learned.gate[1], 3)}
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if learned.gate else None),
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"now": ({"slot": current.slot, "label": slot_label(current.kind, current.slot),
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"usual_hold": rnd(current.usual_hold, 3), "usual_peak": rnd(current.usual_peak, 3),
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"flag_above": rnd(flag_above(current.usual_hold, floor), 3)} if current else None),
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"slots": slots,
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"updated_at": iso(max((r.updated_at for r in rows), default=None)),
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}
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return out
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# ── Sensitivity (Settings → Thresholds) ─────────────────────────────────────
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def _floors_view(thresholds: dict) -> list[dict]:
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uses: dict[str, list[str]] = {}
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for (source, metric), learned in sorted(LEARNED.items()):
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if learned.floor_name:
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uses.setdefault(learned.floor_name, []).append(f"{source}/{metric}")
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return [{"name": name, "value": floor_of(thresholds, name),
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"default": float(DEFAULTS[f"norms.floor.{name}"]), "metrics": uses.get(name, [])}
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for name in NORM_FLOORS]
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async def get_anomaly_settings() -> dict:
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"""How sensitive anomaly detection is, as Settings → Thresholds sets it.
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`review_days`: an ended anomaly nobody reviewed within this many days
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lapses (leaves the to-review list, kept as not reviewed, never learned).
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`floors`: per kind of metric, the smallest departure from its normal
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worth calling an anomaly, in the metric's own unit (% points, ms), with
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the metrics each one applies to. A level must also be ×`depart_factor`
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its slot's usual hold, averaged over `window_minutes`. Raising a floor
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flags less; lowering it flags more. set_anomaly_settings (admin) changes
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the review window and floors.
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"""
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async with db_session() as db:
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settings = await get_all_settings(db)
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return {"review_days": int(settings.get("anomalies.review_days") or 14),
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"depart_factor": anom.DEPART,
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"window_minutes": int(anom.WINDOW.total_seconds() // 60),
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"floors": _floors_view(to_thresholds_cfg(settings))}
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async def set_anomaly_settings(review_days: int = 0, floors: dict[str, float] | None = None) -> dict:
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"""Change how sensitive anomaly detection is, as Settings → Thresholds
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does. Takes effect on the next minute's pass; audited.
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review_days: days an ended anomaly waits for review before it lapses
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(at least 1); 0 leaves it as it is.
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floors: {name: value} for any of get_anomaly_settings' floor names, in
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that floor's unit, 0 or more. A floor not named is left as it is.
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Returns the settings as they now stand.
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"""
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floors = dict(floors or {})
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unknown = sorted(set(floors) - set(NORM_FLOORS))
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if unknown:
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raise ValueError(f"No floor named {', '.join(unknown)}; they are {', '.join(NORM_FLOORS)}.")
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changes: dict = {}
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for name, value in floors.items():
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try:
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value = float(value)
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except (TypeError, ValueError):
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raise ValueError(f"Floor {name} must be a number.") from None
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if value < 0 or value != value:
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raise ValueError(f"Floor {name} must be 0 or more.")
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changes[f"norms.floor.{name}"] = value
|
||
if review_days:
|
||
if int(review_days) < 1:
|
||
raise ValueError("review_days is at least 1.")
|
||
changes["anomalies.review_days"] = int(review_days)
|
||
if not changes:
|
||
raise ValueError("Nothing to change: pass review_days or floors.")
|
||
app = mcp_app()
|
||
async with db_session() as db:
|
||
async with db.begin():
|
||
before = await get_all_settings(db)
|
||
for key, value in changes.items():
|
||
await set_setting(db, key, value)
|
||
fresh = await get_all_settings(db)
|
||
app.config["THRESHOLDS"] = to_thresholds_cfg(fresh)
|
||
await audit_mcp("settings.saved", "settings", "anomalies",
|
||
{"section": "anomalies",
|
||
"changes": {k: {"from": before.get(k), "to": v} for k, v in changes.items()}})
|
||
return await get_anomaly_settings()
|
||
|
||
|
||
def register(register_tool) -> None:
|
||
for fn in (list_anomalies, get_anomaly, get_metric_normal, get_anomaly_settings):
|
||
register_tool(fn, role=UserRole.viewer, read_only=True)
|
||
register_tool(review_anomaly, role=UserRole.operator, read_only=False)
|
||
register_tool(set_anomaly_settings, role=UserRole.admin, read_only=False)
|