Wavefront-hpa-adapter 0.9.10

• Enables autoscaling of Kubernetes deployments based on Wavefront metrics
• Works as a Kubernetes Custom Metrics API server
• Uses Wavefront query language to fetch metrics
• Supports scaling based on average CPU or memory usage of pods
• Provides granular control over scaling parameters like target value, min/max replicas, etc.
• Supports scaling deployments across multiple namespaces in a single cluster

The Wavefront HPA Adapter can be used in various scenarios to scale Kubernetes deployments automatically based on Wavefront metrics. Here are two examples:

• Scaling a deployment based on CPU usage: If you have a deployment that receives variable traffic throughout the day, you can use the HPA Adapter to scale it up or down based on the average CPU usage of pods. This ensures that your application can handle the traffic without wasting resources.
• Scaling a deployment based on memory usage: If your deployment is memory-intensive, you can use the HPA Adapter to scale it up or down based on the average memory usage of pods. This ensures that your application has enough memory to operate efficiently without causing out-of-memory errors.

The Wavefront HPA Adapter can be set up in a few simple steps:

1. Install the adapter as a Kubernetes deployment using a YAML file.
2. Configure the adapter with your Wavefront API key and Wavefront query to fetch metrics.
3. Create a HorizontalPodAutoscaler (HPA) in Kubernetes and reference the Custom Metrics API server provided by the HPA Adapter.
4. Set the scaling target, min/max replicas, and other parameters in the HPA manifest.

• Written in Go language
• Uses Kubernetes Custom Metrics API server to expose metrics
• Supports Wavefront query language to fetch metrics
• Supports scaling based on average CPU or memory usage of pods
• Provides granular control over scaling parameters like target value, min/max replicas, etc.
• Can scale deployments across multiple namespaces in a single cluster.