dtctl

Dynatrace Control with dtctl

Operate dtctl, the kubectl-style CLI for Dynatrace. Pattern: dtctl <verb> <resource> [flags].

Initialization

Run once to establish context, permissions, and the command catalog:

dtctl commands --brief -o json          # all commands, flags, resources + aliases (capabilities)
dtctl config current-context            # active context
dtctl config describe-context $(dtctl config current-context) --plain  # env URL + safety level
dtctl auth status --plain               # token type (OAuth vs API/platform) + safety level

Safety levels: readonly, readwrite-mine, readwrite-all, dangerously-unrestricted.

Don't use dtctl auth whoami to test connectivity — it needs an OAuth token with app-engine:apps:run and returns a spurious 403 for plain API or read-scoped tokens even when reads work. Confirm with a real get/query.

DQL (required reading)

Before writing, modifying, or running any DQL (dtctl query, dtctl wait query, query files), consult references/DQL-reference.md and follow it over any assumption or memory.

dtctl query "fetch logs | filter status='ERROR' | limit 100" -o json --plain
dtctl query -f query.dql --set host=h-123 --set timerange=2h -o json --plain   # Go-template vars
dtctl wait query "fetch spans | filter test_id='test-123'" --for=count=1 --timeout 5m
dtctl query "timeseries avg(dt.host.cpu.usage)" -o chart --plain

dtctl not installed/working? See references/troubleshooting.md.

Resources & verbs

Resources and aliases are discoverable via dtctl commands (run at init). They include: analyzer, anomaly-detector, app, aws/azure/gcp connection & monitoring, bucket, copilot-skill, dashboard, document, edgeconnect, extension, extension-config, function, group, intent, lookup, notebook, notification, sdk-version, segment, settings, settings-schema, slo, slo-template, trash, user, workflow, workflow-execution. Use IDs, not names — names may be ambiguous and fail.

Davis analyzers: before running one, dtctl describe analyzer <id> shows its required/optional inputs and result schema (add --doc for full docs, -o json for the raw schemas); dtctl verify analyzer <id> -f in.json validates an input without executing (exit 0 valid / 1 invalid).

Output for agents

--agent/-A is auto-detected in AI environments (implies --plain; opt out with --no-agent). It wraps output in {ok, result, context} (errors: {ok:false, error:{code,message}}, where context carries total, has_more, suggestions).

-o toon          # token-efficient structured output — prefer for agents
-o json|yaml|csv # other machine formats
-o jsonl|parquet # streaming / columnar export for large results (pipe to a file, query with DuckDB)
-o chart|sparkline|barchart   # time series
-o table|wide    # human-readable (table is the default)
--jq '.[].id'    # filter structured output (json|yaml|toon; other formats auto-promote to json)

Prefer --agent plus -o toon and --jq to cut tokens.

Query results: branch on result.kind

In agent mode dtctl query defaults to --spill=auto: large results spill to a local file and return a summary instead of dumping rows into context. Never assume result is an array — branch on result.kind:

Treat an unknown kind as opaque and fall back to context (decided, total, warnings, suggestions). Sampled results put stats in a sample_stats block (basis: "sample") — not population truth.

dtctl query "fetch logs | limit 1000000" --agent     # auto-spills if large
dtctl query "fetch logs" --spill=never               # force every row inline
dtctl query "fetch logs" --spill-to ./out.jsonl      # explicit path: jsonl|json|csv|parquet
dtctl query "fetch logs" --spill=auto --spill-threshold 100KB

Inspect a spilled file (no Grail re-query)

dtctl inspect <file> reads the rows the summary left out — bounded, streaming, agent-context-friendly — so you never re-run the Grail scan. Pick exactly one primitive per call:

dtctl inspect <path> --head 20                          # first N rows (the manifest never carried rows)
dtctl inspect <path> --tail 10                          # last N rows
dtctl inspect <path> --page --offset 1000 --limit 50    # a window deep in the result (file order)
dtctl inspect <path> --head 20 --fields timestamp,content  # project columns (composable)
dtctl inspect <path> --schema                           # re-derive columns + types + null counts
dtctl inspect <path> --stats                            # re-derive the per-column profile (or --stats=col,col)
dtctl inspect --list                                    # lost the path? enumerate spilled files in this context

It is not a query engine — no filter/SQL/GROUP BY. For aggregates, push the work back into DQL (… | summarize …); for complex local analysis, hand the file to your preferred local analytics tooling. An oversized inspect window re-spills to a new file rather than flooding context, and refuses files from another context/tenant.

Log pattern analysis (token-frugal)

For free-text log triage, don't dump raw content — extract the taxonomy server-side, then drill:

1. dtctl exec analyzer dt.statistics.clustering.LogPatternExtractor --input '{"logQuery":"<DQL>","numberOfExamples":2}' → DPL templates + match counts. logQuery is a plain DQL string (not an object) yielding timestamp+content. Projects well with --jq to {patternExpression, numberOfMatches}.
2. Lift a patternExpression verbatim into parse content, "..." (rename captures f_1→meaningful), then summarize … by:{field} to extract/count at row scale. Unmatched lines yield null captures.
3. Need raw rows? Drill with fetch … --agent and let it spill (above), then read them with dtctl inspect <path> --head/--page (above).

Apply & templates

dtctl apply is idempotent: POST when new, PUT when the file has an id. YAML/DQL files support Go templates filled via --set:

title: "{{.environment}} Deployment"
cron: "{{.schedule | default "0 0 * * *"}}"

dtctl apply -f file.yaml --set environment=prod --set schedule="0 6 * * *"

Dashboards

Create/update: dtctl apply -f dashboard.yaml. Export for reference: dtctl get dashboard <id> -o yaml --plain. Full schema + visualizationSettings: references/resources/dashboards.md.

name: "Dashboard Name"
type: dashboard
content:
  settings:
    defaultTimeframe: { enabled: true, value: { from: now()-2h, to: now() } }
  layouts:
    "1": { x: 0, "y": 0, w: 12, h: 6 }    # 24-col grid (full=24); quote "y" (YAML bool)
  tiles:
    "1":
      title: "Tile"
      type: data                          # data | markdown
      query: "fetch logs | limit 10"
      visualization: lineChart            # singleValue|lineChart|areaChart|barChart|pieChart|table|honeycomb|scatterplot
      davis: { enabled: false, davisVisualization: { isAvailable: true } }

Gotchas: set davis.enabled: false on data tiles; makeTimeseries for log/span series, timeseries for metrics; id present → update, absent → create; the version warning on create is benign.

Permissions & safety

• Verify before mutating: dtctl auth can-i <verb> <resource>. Scopes: TOKEN_SCOPES.md.
• Destructive ops may be blocked by safety level — switch with dtctl config use-context <name>, or raise the level when creating the context.
• Prefer get/describe first; --mine scopes to resources you own; --plain for all machine consumption.

More

troubleshooting · multi-tenant config · DQL · notebooks · extensions · dtctl --help, dtctl <command> --help