DEVOPS
Nightly Cloudflare Cold-Start Trend Report by Deploy Tag
Runs nightly to pull each Cloudflare Worker's cold-start p50/p95 grouped by the deploy tag that was live, writes the time series to BigQuery.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule
- ActionPull cold-start p50/p95 by deploy tagCloudflare
- ActionAppend tagged series to BigQueryBigQuery
- LogicFlag tags worsened day over day
- OutputPost Slack trend digestSlack
What it does
Once a night, this workflow collects cold-start latency percentiles for your Workers, attributes each measurement window to the deploy tag that was active during it, appends the rows to a BigQuery table, and produces a digest that highlights which tags introduced the largest cold-start increases over the trailing period.
When to use it
Use it when you want a durable, queryable history of cold-start performance per release rather than only real-time alerting. It is the reporting companion to live regression gates: useful for retros, capacity planning, and proving whether a refactor actually helped.
How it works
- 1A nightly schedule triggers the run.
- 2Pull cold-start p50/p95 per Worker, segmented by deploy tag, from Cloudflare analytics.
- 3Append the tagged time-series rows to a BigQuery table for long-term history.
- 4Branch: identify tags whose cold-start grew beyond the day-over-day threshold.
- 5Post a Slack digest ranking the worst regressing tags with their deltas and links.
Set it up
What you configure once, before turning it on.
- 1Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
- 2Connect BigQueryDatasets, queries, schemas.
- 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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