Row grain
One terminal outcome per logical job.
Decision supported
Choose whether to inspect capacity, retry policy, job code, or a dependency.
Metric contract
Put the denominator and units beside the chart
Adapt these names to your contract while preserving the declared grain. Inspect the linked event schema before mapping fields from production producers.
- Completed jobs
- Terminal failure rate
- Retry rate
- p95 duration
failure rate %
Demonstration data, not a customer result or benchmark
Review the query before adapting the fields
This public query is a starting definition. Add a bounded time filter, complete-bucket policy, environment, and minimum volume appropriate to your production event contract.
SELECT
job_name,
COUNT(*) AS completed_jobs,
ROUND(
100.0 * SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END)
/ NULLIF(COUNT(*), 0),
2
) AS terminal_failure_rate_pct,
ROUND(AVG(attempt_count), 2) AS average_attempts,
approx_percentile_cont(duration_ms, 0.95) AS p95_duration_ms
FROM job_runs
GROUP BY job_name
ORDER BY terminal_failure_rate_pct DESC, completed_jobs DESC;Prove these before publishing the result
- A stable job identifier connects attempts without exposing job payloads.
- Attempt count includes the terminal attempt.
- Queue wait and execution duration use separate millisecond fields.
Checks that keep the dashboard trustworthy
- Use attempt-level data only when the denominator is explicitly attempts.
- Add queue wait percentiles beside execution duration.
- Use start and terminal lifecycle events to detect vanished workers.
Interpretation boundary
What this result cannot prove by itself
Count one terminal outcome per job. Attempt-level logs inflate volume and can make a recovered retry look like a failed customer workflow.
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