Building Dashboards

Building Dashboards

Philosophy

1. Decisions first. Every panel answers a question that leads to an action.
2. Overview → drilldown → evidence. Start broad, narrow on click/filter, end with raw logs.
3. Rates and percentiles over averages. Averages hide problems; p95/p99 expose them.
4. Simple beats dense. One question per panel. No chart junk.
5. Validate with data. Never guess fields—discover schema first.
6. Compute what's asked, or defer. If a panel can't be computed, replace it with a Note documenting the blocker. Never substitute a different quantity, even disclosed. See Compute or Defer.

Entry Points

Intake: What to Ask First

1. Audience & decision

   • Oncall triage? (fast refresh, error-focused)
   • Team health? (daily trends, SLO tracking)
   • Exec reporting? (weekly summaries, high-level)

2. Scope

   • Service, environment, region, cluster, endpoint?
   • Single service or cross-service view?

3. Dataset kind. Run scripts/metrics/datasets <deploy> and check kind.

   • otel:metrics:v1 → metrics dataset, follow the Metrics path.
   • anything else → events/logs dataset, follow the APL path.

   Never run getschema on a metrics dataset. It returns 0 rows without error.

   APL path: discover fields with ['dataset'] | where _time between (ago(1h) .. now()) | getschema. Continue to steps 4–5.

   Metrics path:

   • scripts/metrics/metrics-spec <deploy> <dataset> — required before any MPL query.
   • scripts/metrics/metrics-info <deploy> <dataset> metrics | tags | tags <tag> values for discovery.
   • If discovery is empty, retry with --start 7 days ago (sparse metrics).
   • find-metrics <value> searches tag values, not metric names — use it only with a known entity name.
   • Skip to the Metrics/MPL Blueprint.

4. Golden signals (APL path)

   • Traffic: requests/sec, events/min
   • Errors: error rate, 5xx count
   • Latency: p50, p95, p99 duration
   • Saturation: CPU, memory, queue depth, connections

5. Drilldown dimensions (APL path)

   • What do users filter/group by? (service, route, status, pod, customer_id)

Dashboard Blueprint

Pick the blueprint matching the dataset kind.

APL Blueprint (events/logs datasets)

1. At-a-Glance (Statistic panels)

Single numbers that answer "is it broken right now?"

• Error rate (last 5m)
• p95 latency (last 5m)
• Request rate (last 5m)
• Active alerts (if applicable)

2. Trends (TimeSeries panels)

Time-based patterns that answer "what changed?"

• Traffic over time
• Error rate over time
• Latency percentiles over time
• Stacked by status/service for comparison

3. Breakdowns (Table/Pie panels)

Top-N analysis that answers "where should I look?"

• Top 10 failing routes
• Top 10 error messages
• Worst pods by error rate
• Request distribution by status

4. Evidence (LogStream + SmartFilter)

Raw events that answer "what exactly happened?"

• LogStream filtered to errors
• SmartFilter for service/env/route
• Key fields projected for readability

Metrics/MPL Blueprint (metrics datasets)

Use align to $__interval using … for bucketing — $__interval is supplied by the dashboard runtime. Hard-coded windows over- or under-resolve. Validate every pipeline with scripts/metrics/mpl-validate-chart; both it and chart-add --mpl reject inline time ranges ([1h..]).

Exception: for sparse metrics where $__interval rounds to empty buckets, a fixed wider window (e.g. 1h) is acceptable; document why on the chart.

1. At-a-Glance (Statistic panels)

Current values — "what's the state right now?"

• Use group using avg (gauges) or group using last (counters).
• Read the metric's unit via metrics-info … metrics <m> info and pass it to chart-add --unit. Ratio metrics (0–1) need | map * 100 in MPL before --unit "%".

2. Trends (TimeSeries panels)

Trends over time — "what changed?"

• align to $__interval using avg|sum|last.
• Group by low-cardinality tags only (≤10 series per chart).
• Embed the unit in --name ("P95 Latency (ms)", "Memory (MiB)"); scale magnitudes in MPL (| map / 1048576 for bytes → MiB).

3. Breakdowns (TimeSeries or Table panels)

Per-entity detail — "where should I look?"

• Metrics broken down by entity (host, pod, service).
• Filter to keep series count manageable.
• One dimension per panel; don't overload a single chart.

4. Entity State (TimeSeries or Table panels)

Boolean/state metrics — answer "what is on/off/active?"

• Use align to $__interval using last.
• Sparse state metrics may need a fixed wider interval (1h+).

Required Chart Structure

Each chart needs a unique kebab-case id (error-rate, p95-latency); every layout i must match one. Pass the same id to chart-add --id and layout-pack <id>:…. dashboard-assemble cross-checks before emit.

Chart Unit Configuration

Pass a friendly unit string to chart-add --unit ("%", "s", "ms", "B", "req/s"). The script picks unit enum + customUnits suffix per chart type. customUnits is a label, not a formatter — scale magnitudes in MPL (| map / 1048576 for bytes → MiB, | map / 1000000 for bytes → MB, | map * 100 for 0–1 ratio → percent). For metrics charts, read the source unit from metrics-info … metrics <m> info and pass it through. Internals (advanced options the agent may merge with jq): reference/chart-config.md.

Compute or Defer

Each panel either computes the requested quantity, or it's replaced by a Note documenting the blocker. Substituting a different quantity is never acceptable — disclaimers don't reach whoever acts on the number.

Defer template (use chart-add --type Note):

**Deferred — blocked by:** <one-line reason>.

**Original spec:** <what the panel should compute, dimensions, unit>.

**To unblock:** <pointer to the fix>.

Common blockers: MPL parser limits, missing tag with no reverse-tag equivalent, missing metric with no OTel rename match. Full rationale: reference/design-playbook.md § Substituting a Different Quantity.

Chart Types

Per-type APL recipes: reference/chart-cookbook.md.

Chart Configuration

chart-add covers the common path (type, id, name, query, dataset, unit, sparkline). For options it doesn't expose — aggChartOpts variants on TimeSeries, tableSettings.columns on Table/LogStream, hideHeader, etc. — start from a chart-add output and merge the extra fields with jq. See reference/chart-config.md for the full option set, and the rejected-field list before merging anything bespoke.

APL Patterns

Time Filtering

Dashboard chart queries inherit time from the picker — omit _time filters. Ad-hoc queries (Axiom Query tab, axiom-sre) need an explicit where _time between (ago(1h) .. now()).

Bin Size Selection

Use bin_auto(_time) — it adjusts to the dashboard time window. Manual bin(_time, …) is only justified for non-standard cases (e.g. matching an upstream batch interval); document why.

Cardinality Guardrails

Bound summarize … by … with top N or a filter. Unbounded grouping on high-cardinality fields (user_id, trace_id) blows up.

| summarize count() by route | top 10 by count_   // bounded
| summarize count() by user_id                    // unbounded — avoid

Field Escaping

Fields with dots need bracket notation:

| where ['kubernetes.pod.name'] == "frontend"

Fields with dots IN the name (not hierarchy) need escaping:

| where ['kubernetes.labels.app\\.kubernetes\\.io/name'] == "frontend"

Recipes

Traffic, error-rate, latency-percentile, and other golden-signal APL recipes: reference/chart-cookbook.md.

Layout Composition

layout-pack packs charts row-major into the 12-column grid using per-type defaults (Statistic 3×3, TimeSeries 6×4, Table 6×5, LogStream 12×6, Note 12×2). Override with id:WxH when needed. Section blueprints: reference/layout-recipes.md. Naming and panel-ordering conventions: reference/design-playbook.md.

Dashboard Settings

Refresh Rate

dashboard-assemble --refresh oncall|team|exec (60/300/900s) or pass an explicit integer (≥60). Short refresh + long time range = expensive queries; pick the longer end for exec/weekly boards.

Sharing

API tokens create shared dashboards only (owner: "X-AXIOM-EVERYONE"); private dashboards aren't supported. Per-user data visibility is still enforced by dataset permissions.

URL Time Range Parameters

?t_qr=24h (quick range), ?t_ts=...&t_te=... (custom), ?t_against=-1d (comparison)

Setup

Tools, prerequisites, and ~/.axiom.toml configuration: see README.md. Verify with scripts/setup.

Deployment

Scripts

The two axiom-api scripts are not interchangeable. scripts/axiom-api is for the dashboard app API; scripts/metrics/axiom-api is for data/metrics endpoints and edge routing. Wrong one → 404.

Targeted Chart Updates

Use scripts/dashboard-chart-patch when changing one existing chart and the dashboard layout, metadata, and other charts should remain untouched. It calls PATCH /v2/dashboards/uid/{uid}/charts/{chartId} with a JSON Merge Patch under the chart request field.

Patch files contain only the chart fields to change:

{
  "name": "Error Rate (5m)",
  "query": { "apl": "['logs'] | summarize errors=countif(status >= 500)" },
  "config": { "stale": null }
}

null removes an existing field. Nested objects merge recursively. If id is present in the patch, it must match the <chart-id> path argument. The server validates the resulting full dashboard before saving.

Use --version <version> for optimistic concurrency after fetching the dashboard with dashboard-get. Use --overwrite only when last-write-wins behavior is intended. Continue using dashboard-update for layout changes, multi-chart edits, dashboard metadata, owner, refresh interval, or time window updates.

Workflow

chart-add, layout-pack, and dashboard-assemble own the JSON shape. Each chart lives in its own temp file; nothing chart-shaped re-enters the agent's context.

1. Discover schema (axiom-sre / getschema for events; metrics-spec + metrics-info for metrics).
2. Write each panel query. Validate APL via axiom-sre with an explicit time filter; validate MPL via scripts/metrics/mpl-validate-chart.
3. chart-add --type … --apl '<APL>' or chart-add --type … --mpl '<MPL>' --dataset <name> per chart, redirected to its own file.
4. layout-pack <id>:<Type|WxH> … for the layout (ids in display order).
5. dashboard-assemble --name … --datasets … --layout LAYOUT CHART_FILES… to compose.
6. dashboard-validate then dashboard-create (or dashboard-update).
7. dashboard-link for the URL — never hand-construct.

Sibling Skill Integration

• spl-to-apl — Splunk SPL → APL (timechart → TimeSeries, stats → Statistic/Table). See reference/splunk-migration.md.
• axiom-sre — schema discovery via getschema, baseline exploration.
• query-metrics — metrics dataset/tag/value discovery; same scripts vendored under scripts/metrics/.

Templates

Compose with chart-add + layout-pack + dashboard-assemble. Pre-built templates remain under reference/templates/ (blank.json, service-overview.json, service-overview-with-filters.json, api-health.json) for legacy use; dashboard-from-template instantiates them but assumes specific field names (service, status, route, duration_ms) and needs sed-fixing. Prefer composition for new work.

Common Pitfalls

Reference

• reference/chart-config.md — All chart configuration options (JSON)
• reference/metrics-mpl.md — Metrics/MPL chart contract and discovery scripts
• reference/smartfilter.md — SmartFilter/FilterBar full configuration
• reference/chart-cookbook.md — APL patterns per chart type
• reference/layout-recipes.md — Grid layouts and section blueprints
• reference/splunk-migration.md — Splunk panel → Axiom mapping
• reference/grafana-migration.md — Grafana panel → Axiom mapping (canonical-spec projection, PromQL→MPL pointers, OTel rename rules)
• reference/promql-to-mpl.md — PromQL → MPL translation rules (selectors, groupings, rate, histograms, ratios, reverse-tag discovery)
• reference/design-playbook.md — Decision-first design principles
• reference/templates/ — Ready-to-use dashboard JSON files

For APL syntax: https://axiom.co/docs/apl/introduction