ReliabilityBeginner
Trend daily service reliability
Did the newest complete days contain a broad reliability regression?
Use the larger service-event fact table to calculate daily volume, errors, and error rate while retaining the denominator beside the rate.
The dataset and query remain in this browser.
Expected result
Daily service error rate
14.72%
2026-07-22
2.5%
2026-07-23
2.5%
2026-07-24
2.22%
2026-07-25
2.5%
2026-07-26
2.22%
2026-07-27
2.78%
2026-07-28
| service_day | operations | errors | error_rate_pct |
|---|---|---|---|
| 2026-07-22 | 360 | 53 | 14.72 |
| 2026-07-23 | 360 | 9 | 2.5 |
| 2026-07-24 | 360 | 9 | 2.5 |
| 2026-07-25 | 360 | 8 | 2.22 |
| 2026-07-26 | 360 | 9 | 2.5 |
| 2026-07-27 | 360 | 8 | 2.22 |
| 2026-07-28 | 360 | 10 | 2.78 |
How to read the query
- Each row represents one UTC day. The window includes only complete days of sample data.
- Volume and error count stay beside the rate so a change in denominator remains visible.
- The July 22 spike is a discovery signal; the next query must segment by service, release, region, or incident.
How to interpret the results
- 1Choose a complete-bucket delay that matches ingestion latency.
- 2Set an objective and minimum volume before turning the rate into an alert.
- 3Separate scheduled maintenance and known test traffic with reviewed dimensions.
Related lessons
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