DEVOPS
Deploy Markers in Honeycomb with Change-Failure Tracking
On each production deploy, drops a marker into Honeycomb, then watches for elevated error rates in the deploy window to classify the release as a change failure and record…
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub production deployment-status webhook (success)GitHub
- ActionCreate dated deploy marker in HoneycombHoneycomb
- ActionQuery post-deploy error rate in deploy windowHoneycomb
- LogicClassify change failure and measure time-to-restore
- OutputWrite deploy outcome and MTTR to PostgresPostgres
What it does
This workflow enriches your observability with deployment context and captures the two harder DORA metrics: change-failure rate and time-to-restore. On each production deploy it places a Honeycomb marker so traces align to releases, then evaluates the post-deploy error rate. If errors breach the threshold it records the deploy as a change failure; when error rates recover it logs the restore duration.
When to use it
Use it when you run Honeycomb for tracing and want deploys correlated to reliability automatically, plus a data-backed change-failure and MTTR signal instead of guesswork during incident reviews.
How it works
- 1A GitHub production deployment-status webhook fires on `success`.
- 2The flow creates a dated deploy marker in Honeycomb tagged with the release SHA and service.
- 3It queries Honeycomb for the service error rate across the deploy window.
- 4A logic step compares the error rate to the baseline; if it breaches, the release is tagged a change failure and a restore timer starts.
- 5The final step writes the deploy outcome, change-failure flag, and time-to-restore to a Postgres metrics table.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect HoneycombDistributed traces and queries.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 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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