# Incident with Actions

- Provider: [GitHub Actions](https://down.guide/github-actions)
- Level: confirmed
- Impact: Degraded
- lasted 1h 1m
- started 2026-10-22 14:18 UTC
- Actions
- id: DWN-30a59b6749
- Official page: https://stspg.io/44w94j036yc8

## Updates from GitHub Actions
### 2026-10-22 14:18 UTC: investigating
We are investigating reports of degraded performance for Actions

### 2026-10-22 14:19 UTC: investigating
We’re investigating delays in processing GitHub Actions workflow and step results, along with some repository updates.

### 2026-10-22 14:35 UTC: investigating
Repository update processing has recovered. We’re continuing to work on delays affecting GitHub Actions workflow and step results. We’ll share another update as we make progress.

### 2026-10-22 15:06 UTC: investigating
We’re seeing recovery, and repository update processing has returned to normal. Delays affecting GitHub Actions workflow and step results are continuing to clear as we scale processing capacity; the remaining backlog is expected to take about 20 minutes to drain.

### 2026-10-22 15:17 UTC: monitoring
The degradation affecting Actions has been mitigated. We are monitoring to ensure stability.

### 2026-10-22 15:18 UTC: monitoring
GitHub Actions workflow and step result processing has recovered. Repository update processing has also returned to normal.

### 2026-10-22 15:18 UTC: resolved
On October 22, 2026, from 13:05 to 15:20 UTC, our event streaming platform was degraded resulting in elevated job processing latency spikes of 19-35 seconds and elevated job processing times of 4m – this resulted in delayed Actions workflow and step results (up to 13m), notifications (20m) and repository push events (12m). Recovery for repositories began at 14:32, with Actions recovery beginning at 14:59. The issue reoccurred at 16:26 again impacting Actions runs, repo push events, and notifications. Recovery began at 17:16 and completed at 2:07. 
The incident was caused by a significant increase in load coupled with client connection storms during a deployment overloading our Kafka event streaming brokers. 
To prevent future recurrence, we have expanded the affected cluster, shifted traffic to less loaded clusters, and implemented tunable client reconnect backoff.

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