DATA OPS

BigQuery Scheduled-Query Duration Regression Watcher

After each run, it pushes scheduled-query execution time and bytes-scanned as metrics to Datadog and raises an alert when a query's runtime regresses sharply versus its baseline.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEvery 15 minutes
  • ActionRead run durations and bytesGoogle BigQueryBigQuery
  • ActionSubmit metrics to DatadogDatadogDatadog
  • LogicFlag duration regressions vs baseline
  • OutputNotify owning team in SlackSlack

What it does

It instruments scheduled queries as Datadog metrics and watches for performance regressions. A query that quietly creeps from two minutes to twenty isn't failing yet, but it's heading toward blowing its SLA window. This catches that drift early and feeds dashboards and monitors you already operate.

When to use it

Use it when you want scheduled-query performance to live in the same observability stack as the rest of your services, and when slow-burn degradation matters as much as hard failures. Best for teams standardized on Datadog for monitoring and on-call.

How it works

  1. 1A schedule fires every 15 minutes to sweep recently completed runs.
  2. 2A BigQuery action reads job durations and bytes scanned for scheduled queries from `INFORMATION_SCHEMA.JOBS`.
  3. 3A Datadog action submits per-query duration and bytes as tagged custom metrics.
  4. 4A logic step compares each run's duration to its recent baseline and flags significant regressions.
  5. 5A Datadog action posts an event annotation for flagged regressions; a Slack output notifies the owning team with the trend.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect DatadogMetrics, traces, log search.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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