Horizontal Pod Autoscaling (HPA) governs the spinning up and deletion of additional pods when the existing resources (CPU and Memory) of the microservice are exhausted. In Adeptia Connect, Autoscaling is by default enabled. You can enable HPA in Adeptia Connect by setting the required parameters in the global values.yaml file.
To enable HPA, you need to set the parameters as described below for each of the microservices individually. You can find these parameters in the respective section of each microservice in the global values.yaml file.
| Parameter | Description | Sample value |
|---|---|---|
| autoscaling: | ||
| enabled: | Parameter to enable HPA by setting its value to true. | TRUE |
| minReplicas: | Minimum number of pods for a microservice. | 1 |
| maxReplicas: | The maximum number of pods a microservice can scale up to. | 1 |
| targetCPUUtilizationPercentage: | The percentage value of CPU utilization at which the autoscaler spins up a new pod. | 400 |
| targetMemoryUtilizationPercentage: | The percentage value of memory utilization at which the autoscaler spins up a new pod. | 400 |
| behavior: | ||
| scaleUp: | ||
| stabilizationWindowSeconds: | The duration (in seconds) for which the application keeps a watch on the spikes in the resource utilization by the currently running pods. This helps in determining whether scaling up is required or not. | 300 |
| maxPodToScaleUp: | The maximum number of pods a microservice can scale up to at a time. | 2 |
| periodSeconds: | The time duration (in seconds) that sets the frequency of tracking the spikes in the resource utilization by the currently running pods. | 60 |
| scaleDown: | ||
| stabilizationWindowSeconds: | The duration (in seconds) for which the application keeps a watch for drop in resource utilization by the currently running pods. This helps in determining whether scaling down is required or not. | 300 |
| maxPodToScaleDown: | The maximum number of pods a microservice can scale down to at a time. | 1 |
| periodSeconds: | The time duration (in seconds) that sets the frequency of tracking the drop in the resource utilization by the currently running pods. | 60 |
Configuring HPA for runtime microservice
The parameters for configuring the runtime microservice for autoscaling slightly differ from those for the rest of the microservices.
The following table describes the autoscaling parameters for runtime microservice. You can find these parameters in the runtimeImage: section in the global values.yaml file.
| Parameter | Description | Sample value |
|---|---|---|
| RUNTIME_AUTOSCALING_ENABLED: | Parameter to enable HPA by setting its value to true. | true |
| RUNTIME_MIN_POD: | Minimum number of pods. | 1 |
| RUNTIME_MAX_POD: | The maximum number of pods the runtime microservice can scale up to. | 1 |
| RUNTIME_AUTOSCALING_TARGETCPUUTILIZATIONPERCENTAGE: | The value of CPU utilization (in percentage) at which the autoscaler spins up a new pod. | 400 |
| RUNTIME_AUTOSCALING_TARGETMEMORYUTILIZATIONPERCENTAGE: | The value of memory utilization (in percentage) at which the autoscaler spins up a new pod. | 400 |
| RUNTIME_SCALE_UP_STABILIZATION_WINDOW_SECONDS: | The duration (in seconds) for which the application keeps a watch on the spikes in the resource utilization by the currently running pods. This helps in determining whether scaling up is required or not. | 300 |
| RUNTIME_MAX_POD_TO_SCALE_UP: | The maximum number of pods the runtime microservice can scale up to at a time. | 1 |
| RUNTIME_SCALE_UP_PERIOD_SECONDS: | The time duration (in seconds) that sets the frequency of tracking the spikes in the resource utilization by the currently running pods. | 60 |
| RUNTIME_SCALE_DOWN_STABILIZATION_WINDOW_SECONDS: | The duration (in seconds) for which the application keeps a watch for drop in resource utilization by the currently running pods. This helps in determining whether scaling down is required or not. | 300 |
| RUNTIME_MAX_POD_TO_SCALE_DOWN: | The maximum number of pods the runtime microservice can scale down to at a time. | 1 |
| RUNTIME_SCALE_DOWN_PERIOD_SECONDS: | The time duration (in seconds) that sets the frequency of tracking the drop in the resource utilization by the currently running pods. | 60 |