query-metrics

作成者: axiomhq

スクリプトを介してAxiom MetricsDBに対してメトリクスクエリを実行します。利用可能なメトリクス、タグ、タグ値を検出します。メトリクスのクエリや探索を求められた場合に使用します。

npx skills add https://github.com/axiomhq/skills --skill query-metrics

Querying Axiom Metrics

All script paths are relative to this skill's folder; invoke as scripts/<name>. The target dataset must be of kind otel:metrics:v1.

Setup, prerequisites, and ~/.axiom.toml configuration: see README.md. Edge-deployment routing is automatic — the scripts read each dataset's edgeDeployment and route to the right regional endpoint without configuration.

Workflow

  1. scripts/datasets <deploy> --kind otel:metrics:v1 — list metrics datasets.
  2. scripts/metrics-specrequired before composing any query. MPL evolves; the spec is the source of truth. Also use it to answer general MPL/metrics questions.
  3. scripts/metrics-info <deploy> <dataset> metrics — list metrics with {type, temporality, unit} metadata. Read this before writing the query (see Choosing a Query Shape).
  4. scripts/metrics-info <deploy> <dataset> tags [<tag> values] — explore filter dimensions.
  5. scripts/metrics-query <deploy> '<MPL>' <start> <end> — execute. Iterate.

If the user names a specific entity (service, host, …), scripts/metrics-info <deploy> <dataset> find-metrics "<value>" finds the metrics carrying it. find-metrics searches tag values, not metric names — don't use it for general discovery.

Choosing a Query Shape

The metrics-info listing returns each metric's {type, temporality, unit}. Read these before composing — never assume a metric is a simple scalar.

FieldValuesDrives
typeGauge, CounterMonotonic, CounterNonMonotonic, HistogramRequired pre-aggregation operators.
temporalityCumulative, Delta, nullWhether counter values are running totals or per-interval deltas. null is normal for Gauges.
unitUCUM string (Cel, kW.h, s, %, [ppm], …) or nullDisplay unit; preserve when reporting results.

Rules per type (consult metrics-spec for exact operator names — they evolve):

  • Gauge — instantaneous value. Align directly with avg/min/max/sum. Don't apply a rate; you'd be averaging meaningless deltas of an instantaneous value.
  • CounterMonotonic + Cumulative — running total (resets aside). The raw values are rarely what you want. Convert to a per-second rate first, then align/aggregate.
  • CounterMonotonic + Delta — already per-interval. Sum/align without a rate step.
  • CounterNonMonotonic — can go up or down (queue depth, balance). Intent is ambiguous: rate, delta, or current value all make sense for different questions. Ask the user before picking one.
  • Histogram — not a scalar. align using avg produces nonsense. Use bucket … using with the histogram functions from metrics-spec; quantiles are float specs to those functions, and temporality selects the variant (Cumulative vs Delta interpolation). Consult metrics-spec for the exact signatures.
  • temporality: null — "not applicable for this instrument type" (the norm for Gauges), not "missing data".

When surfacing numbers, attach the unit (treat null as unitless). If you combine metrics with mismatched units in arithmetic, warn rather than silently producing a meaningless number.

Query Metrics

scripts/metrics-query [-w pixels] [--pixel-per-point n] <deploy> '<MPL>' <start> <end>
ParameterNotes
deployName from ~/.axiom.toml (e.g. prod).
MPLPipeline string. Dataset is parsed from the MPL itself.
start / endRFC3339 (2025-01-01T00:00:00Z) or relative (now-1h, now).
-w / --chart-width <px>Optional. Target chart width in pixels; lets the server resolve $__interval.
--pixel-per-point <n>Optional. Pixels per point (server default 10); with -w sets the bucket count.

Always single-quote the MPL string in the shell. MPL is full of backticks; inside double quotes the shell executes them as command substitution, silently mangling the query (or running whatever the identifier names).

Bound the output before grouping. group by <tag> returns one series per tag value with no cap — on a high-cardinality tag this floods the output. Check cardinality first (describe, or tags <tag> values) and prefer plain group using <agg> while exploring.

Examples:

scripts/metrics-query prod -w 1200 \
  '`my-dataset`:`http.server.duration` | align to $__interval using avg' \
  now-1h now

scripts/metrics-query prod -w 1200 \
  '`my-dataset`:`http.server.duration`
   | where `service.name` == "frontend" and method == "GET"
   | align to $__interval using avg
   | group by status_code using sum' \
  now-1d now

Adaptive resolution ($__interval)

Hardcoding a step (align to 5m) makes charts look wrong at other zoom levels — too sparse zoomed in, too dense zoomed out. Prefer the system parameter $__interval wherever a Duration is expected, and pass the chart width so the server picks the step:

scripts/metrics-query prod -w 1200 \
  '`my-dataset`:`http.server.duration` | align to $__interval using avg' \
  now-7d now

The metrics service computes $__interval from the query's time range and the target chart width, then snaps it up to a nice resolution from the ladder 1s, 5s, 10s, 15s, 30s, 1m, 5m, 10m, 15m, 30m, 1h, 12h, 1d, 1w, 1M, 1Y. It never drops below a metric's stored resolution.

  • No declaration needed — the server auto-registers $__interval; do not add param $__interval: Duration; (the edge forwards the query verbatim and the metrics service injects the parameter).
  • Bucket countchart-width / pixel-per-point (pixel-per-point default 10). Omit -w and the server targets ~500 buckets.
  • Works anywhere a Duration is valid, e.g. bucket to $__interval using histogram(0.5, 0.95).
  • Set -w to your render width (e.g. the metrics-chart skill's plot width) so one bucket ≈ one pixel column. The value is forwarded under the request body's queryOptions (chart-width, pixel-per-point).

Parameters

MPL can declare parameters (param $svc: string;). Pass values with repeated -p name=value. The script applies the API's param__ prefix; values are forwarded verbatim as MPL literals (string literals include their quotes).

scripts/metrics-query \
  -p svc='"frontend"' \
  -p window='5m' \
  prod \
  'param $svc: string; param $window: Duration;
   `otel-metrics`:`http.server.duration` | where `service.name` == $svc | align to $window using avg' \
  now-1h now

Required parameters must be supplied; optional ones may be omitted. Resulting request body shape:

{
  "apl": "param $svc: string; …",
  "startTime": "now-1h",
  "endTime": "now",
  "params": { "param__svc": "\"frontend\"", "param__window": "5m" }
}

Literal syntax per type lives in metrics-spec.

Discovery (metrics-info)

Time range defaults to the last 24h; override with --start / --end. Both accept RFC3339 (offsets allowed) or relative now / now-<N><unit> with <unit> in s m h d w, resolved to RFC3339 UTC client-side. This is narrower than metrics-query, which forwards times to the server unparsed and so also accepts forms like now-1y; in metrics-info anything outside now / now-<N>[smhdw] must already be RFC3339 or the request 400s.

CommandReturns
metrics-info <d> <ds> metricsAll metrics, keyed by name, with {type, temporality, unit}.
metrics-info <d> <ds> metrics --by-typeSame listing grouped by type (client-side reshape).
metrics-info <d> <ds> metrics --type Gauge --type HistogramFiltered listing (repeatable, OR semantics; composes with --by-type).
metrics-info <d> <ds> metrics <metric> infoSingle metric's {type, temporality, unit}. Non-zero exit if absent.
metrics-info <d> <ds> metrics <metric> describeBundle: metadata + all tags + tag values in one call (replaces 1+1+N round trips). Flags: --no-values (tag names only), --values-limit N (cap per-tag values; default 50, 0 = unlimited).
metrics-info <d> <ds> metrics <metric> tagsTags carried by a specific metric.
metrics-info <d> <ds> metrics <metric> tags <tag> valuesTag values for that metric.
metrics-info <d> <ds> metrics <metric> tags <tag> typeProbe whether the tag is int/float/string/bool. Returns {type, present_types}; mixed if multiple types coexist, absent if not present.
metrics-info <d> <ds> tagsAll tags in the dataset.
metrics-info <d> <ds> tags <tag> valuesAll values for a tag (across metrics).
metrics-info <d> <ds> find-metrics "<value>"Metrics that carry the given tag value (not metric name).

Error Handling

HTTP errors return JSON with code and message; some include a detail object:

{"code": 400, "message": "MPL syntax error: …"}

Syntax errors (400) include an annotated source pointer listing the valid operators at the failure position — read it, it usually names the fix.

CodeCause
400Invalid query syntax or bad dataset name
401Missing/invalid auth
403No permission
404Dataset not found
429Rate limited — back off and retry; don't tight-loop
500Internal error

Requests time out client-side after 120s (AXIOM_MAX_TIME to override; AXIOM_CONNECT_TIMEOUT for the 10s connect timeout).

On 500, re-run with curl -v to capture the traceparent / x-axiom-trace-id header and report it — the trace ID is what the backend team needs to debug.

Scripts

ScriptUsage
scripts/setupCheck requirements and config.
scripts/datasets <deploy> [--kind <kind>]List datasets with edge deployment.
scripts/metrics-specFetch the MPL query spec.
scripts/metrics-query [-w px] [--pixel-per-point n] <deploy> <mpl> <start> <end>Execute a query; use $__interval + -w for adaptive resolution.
scripts/metrics-info <deploy> <dataset> ...Discover metrics, tags, values.
scripts/axiom-api <deploy> <method> <path> [body]Low-level API calls.
scripts/resolve-url <deploy> <dataset>Resolve to the edge deployment URL.

Run any script without arguments for full usage.

axiomhqのその他のスキル

metrics-chart
axiomhq
Axiomメトリクスクエリ結果(application/vnd.metrics.v3+json)を折れ線グラフとしてレンダリングします。デフォルトでは依存関係のないUnicode/ASCIIを使用し、インラインPNG/SVG/sixelにアップグレード可能…
official
spl-to-apl
axiomhq
Splunk SPLクエリをAxiom APLに変換します。コマンドマッピング、関数の同等機能、構文変換を提供します。Splunkからの移行時に使用します…
official
writing-evals
axiomhq
Axiom AI SDK向けの評価スイートをスキャフォールディングします。自然言語の説明から評価ファイル、スコアラー、フラグスキーマ、設定を生成します。作成時に使用…
official
axiom-apl
axiomhq
APLクエリ言語のAxiom向けリファレンス。演算子、関数、パターン、CLIの使用法を提供。専門化されたAxiomスキルによって、記述時などに自動呼び出しされる。
official
detect-anomalies
axiomhq
Axiomデータセット内の異常を統計分析で検出します。異常なパターン、ボリュームスパイク、外れ値、新しいエラータイプなどを探す際に使用します。
official
explore-dataset
axiomhq
Axiomデータセットを探索し、そのスキーマ、フィールド、ボリューム、パターンを理解します。新しいデータセットを発見する際、データ構造を調査する際、または…
official
find-traces
axiomhq
AxiomからOpenTelemetryの分散トレースを分析します。トレースIDの調査、条件(エラー、レイテンシ、サービス)によるトレースの検索、デバッグなどに使用します。
official
gilfoyle
axiomhq
SREエージェント。あなたにできないことをやる。可観測性スタックをクエリし、根本原因を見つける。パニックしない。推測しない。あなたの感情には興味がない。使用…
official