Event schema
Fields the query expects
| Field | Type | Why it exists |
|---|---|---|
| timestamp_utc | Timestamp | Lifecycle event time in UTC. |
| incident_id | Utf8 | Stable incident identifier. |
| service | Utf8 | Primary affected service. |
| event_name | Utf8 | impact_started, detected, or resolved. |
| severity | Utf8 | Reviewed incident severity. |
| environment | Utf8 | Deployment environment. |
Copy the query
WITH incident_times AS (
SELECT
incident_id,
service,
MIN(CASE WHEN event_name = 'impact_started' THEN timestamp_utc END)
AS impact_started_at,
MIN(CASE WHEN event_name = 'detected' THEN timestamp_utc END)
AS detected_at,
MAX(CASE WHEN event_name = 'resolved' THEN timestamp_utc END)
AS resolved_at
FROM incident_lifecycle_events
WHERE timestamp_utc >= now() - INTERVAL '90 days'
AND environment = 'production'
GROUP BY incident_id, service
)
SELECT
service,
COUNT(*) AS incidents,
AVG(date_part('epoch', detected_at - impact_started_at) / 60.0)
AS average_detection_minutes,
AVG(date_part('epoch', resolved_at - detected_at) / 60.0)
AS average_recovery_minutes
FROM incident_times
WHERE impact_started_at IS NOT NULL
AND detected_at IS NOT NULL
AND resolved_at IS NOT NULL
GROUP BY service
ORDER BY average_recovery_minutes DESC, service;This read-only query is planned and executed against an empty typed table with Apache DataFusion 45.2.0. We review the synthetic sample output separately. Check field types, thresholds, and counting rules against your own data. Read the testing methodology.
Query result
Incident detection and recovery time
Billing-worker has the longer observed detection and recovery intervals.
| service | incidents | average_detection_minutes | average_recovery_minutes |
|---|---|---|---|
| billing-worker | 1 | 15 | 60 |
| checkout-api | 2 | 7.5 | 32.5 |
Synthetic example output. Run the query against your own event schema and thresholds before using it for operational decisions.
Reproduce the example
Download the sample data
The JSON bundle includes the event schema with field types, reproducible input rows, exact SQL, expected output, review notes, and engine version. The CSV contains the displayed result.
How the SQL works
- 1Lifecycle events are pivoted to one row per incident before duration calculations.
- 2Detection measures impact start to detection; recovery measures detection to resolution.
- 3Incomplete incidents are excluded from the completed-duration average and should be reported separately.
Edge cases to check
- Define whether recovery ends at mitigation, full restoration, or incident closure and use one rule consistently.
- Reopened incidents may need multiple impact intervals rather than one maximum resolved time.
- Averages are illustrative; report medians and percentiles when incident volume is sufficient.
Recommended dashboard
- Bars: average detection and recovery minutes by service
- Trend: completed incident durations by month
- Table: open incidents missing a resolved event
Alert guidance
Use operational alerts for live impact; use this result as a response-quality review and trend metric.
Read alert setupSet up the events this query needs
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