How rolling deployments work
A rolling deployment follows a repeating three-step cycle:- Scale up candidate deployment replicas by the configured percentage.
- Shift traffic proportionally to match the new replica ratio.
- Scale down the previous deployment replicas by the same percentage.
Provisioning modes
Rolling deployments support two mutually exclusive provisioning modes. You must configure exactly one:max_surge_percent: Scales up candidate replicas before scaling down previous replicas.max_unavailable_percent: Scales down previous replicas before scaling up candidate replicas.
Enabling rolling deployments
Enable rolling deployments on any environment by updating the environment’s promotion settings. Rolling deployments are disabled by default.- cURL
- Python
Configuration reference
Configure rolling deployments through therolling_deploy_config object in the environment’s promotion_settings.
Percentage of additional replicas to provision during each step. Set to
0 to use max unavailable mode instead.Range: 0–50Percentage of replicas that can be unavailable during each step. Set to
0 to use max surge mode instead.Range: 0–50Seconds to wait after each traffic shift before proceeding to the next step. Use this to monitor metrics between steps.Range: 0–3600
Percentage of additional replicas to pre-provision on the current deployment before the rolling deployment starts. Compensates for autoscaling being disabled.Range: 0–500
promotion_settings level:
Enables rolling deployments for the environment.
Deployment statuses
Thein_progress_promotion field on the environment detail endpoint tracks the current state of a rolling deployment.
| Status | Description |
|---|---|
RELEASING | Candidate deployment is building and initializing replicas. |
RAMPING_UP | Scaling up candidate replicas and shifting traffic. |
PAUSED | Rolling deployment is paused at its current traffic split. Replicas stay at their current count. |
RAMPING_DOWN | Graceful cancel in progress. Traffic is shifting back to the previous deployment. |
SUCCEEDED | Rolling deployment completed. The candidate is now the active deployment. Autoscaling resumes. |
FAILED | Rolling deployment failed. Traffic remains on the previous deployment. Autoscaling resumes. |
CANCELED | Rolling deployment was canceled. Traffic returned to the previous deployment. Autoscaling resumes. |
in_progress_promotion object also includes percent_traffic_to_new_version, which reports the current percentage of traffic routed to the candidate deployment.
Deployment control actions
Pause
Pauses the rolling deployment after the current step completes. Use this to inspect metrics or logs before proceeding.- cURL
- Python
Resume
Resumes a paused rolling deployment from where it left off.- cURL
- Python
Cancel
Gracefully cancels the rolling deployment. Traffic ramps back to the previous deployment and candidate replicas scale down.- cURL
- Python
status of CANCELED (instant cancel for non-rolling deployments) or RAMPING_DOWN (graceful rollback for rolling deployments).
Force cancel
Immediately cancels the rolling deployment and returns all traffic to the previous deployment. Use this when you need to roll back without waiting for the graceful ramp-down.- cURL
- Python
Force roll forward
Immediately completes the rolling deployment, shifting all traffic to the candidate deployment. This works even if the deployment is in the process of rolling back.- cURL
- Python
Autoscaling during rolling deployments
To compensate for autoscaling being disabled during rolling deployments:- Set
replica_overhead_percentto pre-provision the current deployment before the rolling deployment starts. For example, a value of50adds 50% more replicas to the current deployment before any traffic shifts. - Set
stabilization_time_secondsto add a wait period between steps, giving you time to monitor metrics before the next traffic shift. - Factor in expected traffic when setting your environment’s
min_replicaandmax_replicabefore starting the rolling deployment.
SUCCEEDED, FAILED, or CANCELED.
Deployment cleanup
After a rolling deployment completes, thepromotion_cleanup_strategy setting controls what happens to the previous deployment.
SCALE_TO_ZERO: Scales the previous deployment to zero replicas. It remains available for reactivation. This is the default.KEEP: Leaves the previous deployment running at its current replica count.DEACTIVATE: Deactivates the previous deployment. It stops serving traffic and releases all resources.
- cURL
- Python