query-metrics（查询指标）

CRITICAL: ALL script paths are relative to this skill's folder. Run them with full path (e.g., scripts/metrics-query).

Querying Axiom Metrics

Query OpenTelemetry metrics stored in Axiom's MetricsDB.

Setup

Run scripts/setup to check requirements (curl, jq, ~/.axiom.toml).

Config in ~/.axiom.toml (shared with axiom-sre):

[deployments.prod]
url = "https://api.axiom.co"
token = "xaat-your-token"
org_id = "your-org-id"

The target dataset must be of kind otel:metrics:v1.

Discovering Datasets

List all datasets in a deployment:

scripts/datasets <deployment>

Filter to only metrics datasets:

scripts/datasets <deployment> --kind otel:metrics:v1

This returns each dataset's name, edgeDeployment, and kind. Use the dataset name in subsequent metrics-info and metrics-query calls.

Edge Deployment Resolution

Datasets can live in different edge deployments (e.g., us-east-1 vs eu-central-1). The scripts automatically resolve the correct regional edge URL before querying. No manual configuration is needed — metrics-info and metrics-query detect the dataset's edge deployment and route requests to the right endpoint.

If resolution fails or the edge deployment is unknown, requests fall back to the deployment URL in ~/.axiom.toml.

Learning the Metrics Query Syntax

CRITICAL: You MUST run metrics-spec before composing your first query in a session. NEVER guess MPL syntax — it changes over time and the spec is the only source of truth.

scripts/metrics-spec <deployment> <dataset>

Re-consult the spec when using an unfamiliar operator, when a query returns a syntax error, or when constructing histogram/multi-metric queries.

Workflow

1. List datasets: Run scripts/datasets <deployment> to see available datasets and their edge deployments
2. Fetch the spec: Run scripts/metrics-spec <deployment> <dataset> — this step is mandatory before writing any query
3. Discover metrics: List available metrics via scripts/metrics-info <deployment> <dataset> metrics
4. Explore tags: List tags and tag values to understand filtering options. If metrics listing fails, use tags and tag values to identify relevant entities, then use those to list metrics for specific tags.
5. Write and execute query: Compose a metrics query and run it via scripts/metrics-query
6. Iterate: Refine filters, aggregations, and groupings based on results

If the user provides a specific service, host, or entity name to search for, use find-metrics to locate matching metrics:

scripts/metrics-info <deployment> <dataset> find-metrics "frontend"

Do NOT use find-metrics as a general discovery step — it requires a known search value.

Query Metrics

Execute a metrics query against a dataset:

scripts/metrics-query <deployment> '<mpl>' '<startTime>' '<endTime>'

Examples:

# Simple query
scripts/metrics-query prod \
  '`my-dataset`:`http.server.duration` | align to 5m using avg' \
  '2025-06-01T00:00:00Z' \
  '2025-06-02T00:00:00Z'

# Query with filtering (note backticks on dotted tag names)
scripts/metrics-query prod \
  '`my-dataset`:`http.server.duration` | where `service.name` == "frontend" and method == "GET" | align to 5m using avg | group by status_code using sum' \
  'now-1d' \
  'now'

Discovery (Info Endpoints)

Use scripts/metrics-info to explore what metrics, tags, and values exist in a dataset before writing queries. Time range defaults to the last 24 hours; override with --start and --end.

List metrics in a dataset

scripts/metrics-info <deployment> <dataset> metrics

List tags in a dataset

scripts/metrics-info <deployment> <dataset> tags

List values for a specific tag

scripts/metrics-info <deployment> <dataset> tags <tag> values

List tags for a specific metric

scripts/metrics-info <deployment> <dataset> metrics <metric> tags

List tag values for a specific metric and tag

scripts/metrics-info <deployment> <dataset> metrics <metric> tags <tag> values

Find metrics matching a tag value

scripts/metrics-info <deployment> <dataset> find-metrics "<search-value>"

Custom time range

All info commands accept --start and --end for custom time ranges:

scripts/metrics-info prod my-dataset metrics \
  --start 2025-06-01T00:00:00Z \
  --end 2025-06-02T00:00:00Z

Error Handling

HTTP errors return JSON with message, code, and optional detail fields:

{"message": "description", "code": 400, "detail": {"errorType": 1, "message": "raw error"}}

Common status codes:

• 400 — Invalid query syntax or bad dataset name
• 401 — Missing or invalid authentication
• 403 — No permission to query/ingest this dataset
• 404 — Dataset not found
• 429 — Rate limited
• 500 — Internal server error

On a 500 error, re-run the failing script call with curl -v flags to capture response headers, then report the traceparent or x-axiom-trace-id header value to the user. This trace ID is essential for debugging the failure with the backend team.

Scripts

Run any script without arguments to see full usage.