ENGINEERING
Datadog BigQuery anomaly to GitLab tuning issue with captured plan
Triggered by a Datadog anomaly monitor on BigQuery slot or runtime metrics, identifies the specific query behind the anomaly window.
How it runs
The automated pipeline, trigger to output.
- TriggerDatadog anomaly monitor firesDatadog
- ActionFind heaviest jobs in anomaly windowBigQuery
- LogicConfirm regression and map dataset to team
- ActionCapture query plan and cost deltaBigQuery
- OutputOpen assigned GitLab tuning issueGitLab
What it does
Instead of reading job history on a fixed cadence, this lets Datadog's anomaly detection decide when something is off, then does the forensics: it correlates the anomalous time window to the offending query, pulls its plan and cost delta, and files a GitLab tuning issue routed to the team that owns the dataset.
When to use it
Use when you already run BigQuery metrics through Datadog and would rather act on statistical anomalies than static thresholds. Good for catching unusual regressions that a fixed cost threshold would miss while still avoiding alert noise.
How it works
- 1A Datadog anomaly monitor on slot-ms or runtime fires a webhook with the anomaly window.
- 2BigQuery job history is queried for the heaviest jobs inside that window to identify the offender.
- 3A logic step confirms the regression and maps the dataset to its owning team.
- 4The query plan and cost delta are captured into a tuning report.
- 5A GitLab issue is opened, labeled and assigned to the owning team as the output.
Set it up
What you configure once, before turning it on.
- 1Connect DatadogMetrics, traces, log search.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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