FairwindsOps/goldilocks
Goldilocks
Goldilocks is a suite of tools for analyzing resource usage and managing autoscaling policies in Kubernetes. It functions as a resource optimizer and capacity planner, providing a dashboard and command line interface to analyze workload utilization patterns and suggest efficient CPU and memory requests and limits for containers.
The project distinguishes itself by visualizing recommendations from the Vertical Pod Autoscaler via a web interface and providing a lifecycle manager to create and configure these autoscaler objects. It includes capabilities to aggregate resource recommendations across namespaces and filter out specific containers, such as sidecars, to refine the analysis.
The system covers broader capabilities including workload resource tuning, namespace-scoped monitoring, and the orchestration of custom resource policies to balance system stability with hardware cost efficiency.
Features
- Kubernetes Resource Optimizers - Analyzes workload utilization patterns to suggest the most efficient resource limits and requests for containers.
- Capacity Planning - Visualizes resource recommendations to balance system stability with hardware cost efficiency.
- Kubernetes Capacity Planning Dashboards - Aggregates resource usage and VPA recommendations in a dashboard to simplify scaling decisions.
- VPA Orchestration - Automates the deployment and management of Vertical Pod Autoscalers across namespaces.
- Resource Recommendation Engines - Analyzes workload utilization patterns to suggest optimal CPU and memory requests to prevent waste.
- Resource Request Optimization - Analyzes actual CPU and memory usage to determine the most efficient resource requests and limits for containers.
- VPA Lifecycle Managers - Provides a CLI to programmatically create and delete Vertical Pod Autoscaler objects within namespaces.
- VPA Object Lifecycle Management - Manages the lifecycle of Vertical Pod Autoscaler objects through the cluster API.
- VPA Visualization Dashboards - Provides a web interface to visualize VPA recommendations for optimizing CPU and memory requests.
- Resource Visualization Dashboards - Provides a web-based dashboard to visualize suggested resource limits and actual usage.
- Reactive Visualization Dashboards - Ships a web interface that renders interactive resource recommendations and usage data for capacity planning.
- Container Selection Filters - Hides resource suggestions for specific containers, such as sidecars, using labels and CLI flags.
- Recommendation Filtering - Allows excluding sidecar containers from resource recommendations using labels and configuration flags.
- VPA Policy Configurations - Allows defining update modes and resource policies to control how recommendations are applied to workloads.
- Workload Resource Tuning - Filters resource suggestions for specific containers to optimize usage and ensure stability.
- Monitoring and Visualization - Identifies optimal resource requests and limits for workloads.
Star history
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