get_anomaly gives each occurrence against what is usual at that hour, the
metric around the newest one with its normal, what else happened on the
resource's host around each occurrence (alert transitions, Ansible runs,
other anomalies there and a count elsewhere, plus plugins' own events
through a new mcp_events hook: the host agent's maintenance jobs and
downtime, Docker's container starts, deaths and OOM kills), and how earlier
anomalies on the same metric were judged.
get_metric_normal reads the learned slots and the level that would be
flagged. review_anomaly takes one id or several, all or nothing, with a
note. get_anomaly_settings and (admin) set_anomaly_settings read and change
the review window and each normal's floor, as Settings → Thresholds does.
metric_series.points takes an optional `until`; core.alerts gains
host_of_resource; floor_setting names its floor.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Host CPU, memory and load per core, container CPU and memory, and every
monitor's response time learn a normal per hour (milestone 513, System 182).
A reading past its fixed cutoff but within its normal reads healthy and says
what is usual at this hour; one well out of it keeps its grade and is a
metric anomaly. Disk, a monitor down and an agent offline keep their cutoffs.
- Each normal's floor, the smallest departure that counts, is a setting:
Settings -> Thresholds -> Normals (norms.floor.*). Defaults: host CPU 10
points, host memory 5, load per core 10, container CPU 10 points of one
core, container memory 5, monitor response 100 ms; Traefik's 2-point 4xx
and 50 ms p95 floors move there from constants. learn_normal takes a
floor function (floor_setting) read at each pass.
- learn_normal(parts=False): a host learns, its cores don't.
- Ingest records load_pct (load_1m / cores x 100), the figure pages grade.
- Shared grading: anomalies.against / graded, worst_reading(normals=),
hosts' host_grades, docker usage(thresholds=, norms=), monitors'
latency_grade. Applied to host vitals, the Hosts widget, MCP overview,
Docker usage, and the monitor list, widget and page.
- The monitor and container pages list their anomalies.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
#5741 "Traefik routers are graded against their own normal", milestone 513.
- A router's 4xx share and p95 learn their normal (learn_normal in the
plugin's setup). Once a router has 5 days of history, a reading over the
fixed cutoff but inside its normal is healthy, and the healthy table says
it is usual for that router. One out of its normal is a warning that says
what is usual, and a metric anomaly.
- The 5xx share is a failure: fixed cutoff always, whatever the history or
traffic (System 182's ruling).
- A 4xx share over fewer requests than Settings -> Thresholds' new "Fewest
requests to judge 4xx" (30 per 15 minutes) isn't graded on the page, and
isn't judged for anomalies.
- Core: learn_normal(gate=(metric, least)) judges a key only while its
resource's gate metric averages at least `least`; `least` may be a
function, so it follows a setting. learn_normal(since=...) learns only
from history after a moment: Traefik latency learns from #5755's
per-scrape figures only.
- The Traefik page lists the routers' metric anomalies of the last 30 days
(_anomaly_card.html). The dashboard widget keeps its why to problems.
- Thresholds and docs say when the cutoffs give way to a normal.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
#5740 "A metric that departs from its normal is recorded as an anomaly to
review, and is findable after it ends", milestone 513.
- Every minute each learned key's last 15 minutes is judged against its
hour's normal: above the usual hold x1.25 and by the metric's floor opens
an occurrence, which ends when the level comes back.
- metric_anomalies holds one row per item to review; a repeat while it
still waits joins it as an occurrence (metric_anomaly_spans).
- An ended row nobody reviews within anomalies.review_days (14, Settings ->
Thresholds) lapses; an ongoing one never does.
- Learning skips every hour an occurrence covers unless its anomaly was
marked normal: no normal is learned without review.
- Alerts -> Anomalies lists those to review first, then the reviewed and
lapsed under day dividers; marking is operator/admin and audited.
- The Overview widget and get_overview warn of open ones and count those
to review; the host page shows its own; the weekly report lists the
week's and the lapsed; MCP list_anomalies and review_anomaly.
- Reviewed and lapsed rows are pruned at data.retention_days, and kept
in backups like alert history.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
A metric registered with learn_normal() gets, for every resource reporting
it, a usual hold (the 90th percentile across past days of each hour's p95)
and a usual peak for each slot:
- an hour of the day once 5 days of history exist;
- an hour of the week once history reaches back 3 weeks, so weekly jobs are
learned too.
Each slot takes in the hours either side, so a job that runs a little late
stays normal. One SQL statement relearns everything hourly into
metric_norms, from the rollup plus the raw window not rolled up yet;
norms_at() reads the slot a moment falls in. Nothing is registered yet:
Traefik (#5741) and hosts (#5742) opt in.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
bvandeusenandClaude Opus 5.5