chore: Add scale to service and webapi so #1321
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📝 WalkthroughWalkthroughThe pull request introduces updates to two Bicep files for Azure resource deployment. It adds a new import statement for the Changes
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MagnusSandgren
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (5)
.azure/applications/web-api-so/main.bicep (2)
48-74: LGTM: Scale parameter definition with a minor suggestionThe
scaleparameter is well-defined with appropriate default values for minimum and maximum replicas, as well as scaling rules based on CPU and memory utilization. This configuration allows for dynamic scaling of the container app.Consider adding a brief comment explaining the significance of the 70% threshold for CPU and memory utilization. This would help future maintainers understand the reasoning behind these values.
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1-185: Summary: Scaling configuration successfully added to web API deploymentThe changes in this file effectively introduce scaling capabilities to the web API deployment:
- The
Scaletype is imported from the containerApp module.- A new
scaleparameter is defined with sensible default values.- The
scaleparameter is correctly passed to the containerApp module.These modifications enhance the deployment configuration by allowing dynamic scaling based on CPU and memory utilization. The implementation is clean and focused, maintaining the overall structure of the existing code.
Consider documenting the scaling strategy in a separate document or README file. This would help team members understand the rationale behind the chosen scaling configuration and make it easier to adjust in the future if needed.
.azure/applications/service/main.bicep (3)
45-71: LGTM: Scale parameter with default configuration.The
scaleparameter is well-defined with a reasonable default configuration. The scaling rules for CPU and memory utilization are set to trigger at 70%, which is a good starting point.Consider adding a comment explaining the chosen values for
minReplicasandmaxReplicas, and why 70% was selected as the threshold for both CPU and memory scaling rules. This would help future maintainers understand the reasoning behind these choices.
199-202: LGTM: Scale parameter usage and TODO comment.The
scaleparameter is correctly passed to thecontainerAppmodule.The TODO comment about user-assigned identities seems unrelated to the current changes and might be outdated. Consider addressing this comment or removing it if it's no longer relevant.
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3-202: Summary: Scaling configuration added to the service container app.The changes introduce a scaling configuration for the container app, which is a valuable addition to the deployment. The implementation is clean and follows best practices. The new
scaleparameter allows for flexible configuration of minimum and maximum replicas, as well as CPU and memory-based scaling rules.This change enhances the scalability of the service, which should improve its ability to handle varying loads. Monitor the application's performance after deployment to ensure the scaling rules are appropriate for your workload. You may need to adjust the thresholds or add more sophisticated rules based on observed behavior in production.
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📒 Files selected for processing (2)
- .azure/applications/service/main.bicep (3 hunks)
- .azure/applications/web-api-so/main.bicep (3 hunks)
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🔇 Additional comments (3)
.azure/applications/web-api-so/main.bicep (2)
3-3: LGTM: Import statement for Scale typeThe import statement for the
Scaletype from the containerApp module is correctly added. This import is necessary for the newscaleparameter introduced in this PR.
162-162: LGTM: containerApp module call updated with scale parameterThe
scaleparameter is correctly added to the containerApp module call. This change properly integrates the new scaling configuration with the existing deployment setup..azure/applications/service/main.bicep (1)
3-4: LGTM: Import statement for Scale module.The import statement for the
Scalemodule is correctly added and necessary for the newscaleparameter.
Summary by CodeRabbit
These updates improve resource management and application performance during varying load conditions.