DATA OPS
Open GitHub Issues for New Expensive BigQuery Queries
Daily, detects newly appeared high-cost BigQuery query patterns, traces each to its owner, and opens a labeled GitHub issue assigned to that owner asking for optimization.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionAggregate spend by query fingerprint and owner with 14-day baselineBigQuery
- LogicKeep newly expensive fingerprints (high yesterday, low baseline)
- ActionOpen labeled GitHub issue per offender, assigned to ownerGitHub
- OutputPost summary of opened issues to SlackSlack
What it does
It fingerprints query shapes (normalized SQL text) and flags patterns that newly crossed a cost threshold yesterday but weren't expensive before. For each new offender it identifies the owner, then files a GitHub issue assigned to them with the query, billed bytes, estimated cost, and an `expensive-query` label so optimization becomes tracked engineering work, not a one-off Slack ping that gets lost.
When to use it
Use it when expensive queries should be fixed at the source and tracked to completion. Issues create a durable backlog, tie cost regressions to the people who can fix them, and let you measure how many got resolved.
How it works
- 1A daily schedule triggers the run.
- 2BigQuery aggregates yesterday's spend by normalized query fingerprint and owner, plus the prior 14-day baseline per fingerprint.
- 3A logic step keeps only fingerprints over the cost threshold that had near-zero baseline spend (newly expensive).
- 4For each new offender, a GitHub issue is created, assigned to the mapped owner, labeled, and populated with the query and cost details.
- 5A summary of issues opened is posted to a Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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