HPA is a Kubernetes feature that automatically scales the number of pods in a deployment, replication controller, or replica set based on observed CPU utilization or other metrics.

    In this documentation, we'll be providing you with detailed guidance on diagnosing potential challenges that may arise within the HPA. We’ll target typical sources of errors and offer actionable recommendations for effective resolutions.


    How Monitoring Works

    We monitor the following HPA Metrics:

    • kubernetes.horizontalpodautoscaler.min_replicas.count
    • kubernetes.horizontalpodautoscaler.max_replicas.count
    • kubernetes.horizontalpodautoscaler.current_replicas.count

    Blue Matador's monitoring system is designed to actively identify instances where the Horizontal Pod Autoscaler within a Kubernetes cluster hits its minimum or maximum replicas limit. We continuously monitor the scaling behavior of the HPA, ensuring that it remains within the predefined constraints set by the user or the system. 

    When the maximum or minimum replicas limit is reached and the monitor thresholds are met, Blue Matador promptly triggers event notifications, providing engineering teams with timely awareness of potential scalability issues. This allows teams to take proactive measures to address issues before they impact the performance or stability of an application.


    HPA Reaching Max Replicas Limit

    If your HPA continuously reaches the Max Replicas Limit, it could indicate an issue impacting the availability and performance of Kubernetes workloads. This scenario is often due to misconfigurations, resource constraints, or inaccurate metric readings. 

    Possible Solutions

    • Incorrect maxReplicas value set in the HPA configuration
      • Verify the maxReplicas value in your HPA configuration and adjust it if necessary.
    • Insufficient cluster resources to accommodate additional replicas    
      • Check cluster resource utilization to ensure sufficient capacity for scaling.
    • Metrics not reflecting actual resource usage accurately
      • Review metric sources and adjust metrics to reflect actual resource usage better.


    HPA Only Using The Minimum Number of Replicas

    When your HPA loads are consistently using only the minimum number of replicas you have, this scenario may indicate inefficiencies in workload management, potentially resulting in unnecessary resource allocation and increased infrastructure costs.

    Possible Solutions

    • Incorrect minReplicas value set in the HPA configuration.
      • Confirm the minReplicas value in the HPA configuration and adjust it if necessary.
    • Misconfiguration of metric thresholds or target utilization.
      • Adjust metric thresholds or target utilization to allow scaling when appropriate.


    Fluctuating Replicas without Stability

    If replicas are scaling frequently between min and max values without stable performance here are a few things you could try:

    Possible Solutions

    • Rapid fluctuations in metric values causing frequent scaling events
      • Smooth out metric data or implement proper smoothing mechanisms to reduce rapid fluctuations.
    • Incorrect or overly aggressive HPA configuration.
      • Review and fine-tune HPA configuration parameters such as target utilization and scaling behavior.
    • Noisy or erratic metric data leads to unstable scaling decisions.
      • Consider adjusting metric collection intervals or filtering noisy data to improve stability.

    Remember to monitor HPA events, inspect HPA configurations, and analyze metric data regularly to troubleshoot and optimize HPA behavior effectively. Additionally, consult Kubernetes documentation and community resources for further assistance with specific issues or advanced troubleshooting techniques.