Capacity constraints in GCP us-central1 may delay or fail some task executions

Final update

We have seen improved capacity in the GCP us-central1 region across additional machine types, and error rates for worker node and pod scale-up have dropped. If any tasks failed during this incident, you can clear and rerun them. To recover automatically from future capacity constraints, we recommend setting `retries` and `retry_delay` on your tasks or in `default_args`. If you’re still seeing issues, please contact Astronomer Support.

1. Resolved · Oct 14, 00:34 UTC
   We have seen improved capacity in the GCP us-central1 region across additional machine types, and error rates for worker node and pod scale-up have dropped. If any tasks failed during this incident, you can clear and rerun them. To recover automatically from future capacity constraints, we recommend setting `retries` and `retry_delay` on your tasks or in `default_args`. If you’re still seeing issues, please contact Astronomer Support.
2. Investigating · Oct 6, 03:33 UTC
   We are currently seeing a capacity shortage in the Google Cloud (GCP) `us-central1` region. As a result, some Deployments running in this region may experience delays when scaling up worker nodes and pods. Some tasks may remain in a queued state for longer than usual while waiting for compute capacity. In some cases, tasks that stay queued beyond their configured timeout may fail. **Recommended workaround:** Configuring automated task retries (for example, setting `retries` and a `retry_delay` on your tasks or in your DAG's `default_args`) will help tasks recover automatically once capacity becomes available. We are working to address the issue and keep this page posted as we have more updates.