DATA OPS
Honeycomb Cardinality Cost Watcher with Derived-Column Consolidation Proposals
Scans Honeycomb datasets daily for high-cardinality columns whose event volume spiked.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionFetch column cardinality and event volume from HoneycombHoneycomb
- LogicFlag columns spiking vs 7-day baseline
- ActionDraft derived-column consolidation proposal with OpenAIOpenAI
- OutputPost ranked proposals to Slack review channelSlack
What it does
Every morning this workflow pulls per-column cardinality and recent event-volume stats from your Honeycomb datasets, flags columns whose unique-value count or ingest volume jumped beyond a threshold versus their 7-day baseline, and asks an LLM to draft a specific derived-column consolidation (for example, bucketing raw `user_id` into a `user_tier` rollup). The proposal lands in Slack so an engineer can approve or reject it.
When to use it
Use it when Honeycomb bills are creeping up and you suspect a few runaway high-cardinality fields are driving event cost. Good for platform and observability teams who want a daily nudge instead of a quarterly fire drill.
How it works
- 1A daily schedule fires the run.
- 2Query Honeycomb for column cardinality and event volume per dataset.
- 3Logic compares each column against its 7-day baseline and keeps only spiking, high-cardinality columns.
- 4OpenAI drafts a derived-column consolidation proposal with the exact column, suggested rollup, and estimated event reduction.
- 5Post the ranked proposals to a Slack review channel with approve/reject context.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect OpenAIModels, embeddings, files.
- 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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