Overview
K8s Cleaner Overview
Image overview of the features of K8s Cleaner
As Kubernetes deployments grow in size and complexity, maintaining a clean, efficient cluster becomes essential for ensuring performance and resource optimization. K8s Cleaner is a robust Kubernetes controller that automates the identification and removal of unused, stale, or unhealthy resources in your cluster, empowering teams to streamline maintenance while minimizing resource waste.
Comprehensive Automated Resource Cleanup
K8s Cleaner is designed to handle any resource type, including standard Kubernetes resources and custom resource definitions (CRDs). Users can create custom rules to define criteria for identifying unused or unhealthy resources, or leverage a comprehensive library of pre-defined rules for common use cases. These pre-built rules cover stale jobs, outdated deployments, unused secrets, cluster roles, config maps, ingress, persistent volumes, pods, service accounts, and stateful sets.
Additionally, K8s Cleaner can identify expired resources based on time-to-live (TTL), explicit expiration dates, or resource age, allowing for versatile and effective resource lifecycle management.
Flexible Scheduling and Dry Run Options
K8s Cleaner supports flexible scheduling using Cron syntax, enabling users to define how frequently resource scans and cleanup tasks are executed. Whether you need daily maintenance or weekly sweeps, the scheduler can be tailored to suit your needs.
To avoid unintended changes, K8s Cleaner includes a Dry Run mode, which provides a preview of resources that would be affected during a cleanup process. This mode helps teams understand the impact of their rules before making changes, ensuring safe and informed cluster management.
Advanced Filtering and Notifications
K8s Cleaner offers sophisticated filtering capabilities to target specific resources. Filters can be configured using user-defined labels, allowing teams to specify label keys, operations (e.g., equal, not equal), and values. This fine-grained control ensures precision in selecting outdated or irrelevant resources for cleanup.
To keep teams informed, K8s Cleaner integrates with popular communication channels for notifications. Users can receive updates via Slack, Webex, Discord, Email, or Microsoft Teams. These notifications provide clear records of cleaned-up resources, whether removed or optimized, helping teams stay on top of resource management activities.
Seamless Integration and Custom Logic
K8s Cleaner integrates seamlessly into existing Kubernetes frameworks, making onboarding straightforward. Users can deploy it as a DaemonSet or a CronJob, depending on their operational needs. The controller runs periodically, executing cleanup tasks without disrupting other cluster operations.
For advanced use cases, K8s Cleaner supports custom logic written in the Lua scripting language. This feature enables power users to define complex selection criteria or resource handling workflows, allowing unparalleled customization for specific scenarios. With Lua scripting, users can refine how K8s Cleaner identifies and handles stale resources, enabling tailored resource management strategies that meet unique organizational needs.
Why Choose K8s Cleaner?
K8s Cleaner simplifies Kubernetes cluster maintenance with features designed for efficiency, flexibility, and reliability: Automation: Automatically identifies and removes resource clutter, ensuring optimal resource utilization. Adaptability: Offers customizable rules and advanced scripting capabilities for diverse use cases. Transparency: Provides a Dry Run mode and detailed notifications for informed decision-making. Integration: Works seamlessly within Kubernetes environments, reducing setup complexity.
Use Cases Development Environments: Clean up test resources or ephemeral workloads that accumulate during active development cycles. Production Clusters: Remove stale jobs, outdated deployments, or unused secrets to enhance performance and reduce costs. Cluster Hygiene: Maintain a well-organized and efficient cluster by addressing unhealthy or expired resources promptly. Compliance and Governance: Ensure resources adhere to organizational policies regarding expiration and lifecycle management.
Conclusion
As Kubernetes environments scale, managing resources effectively is vital for maintaining cluster health and operational efficiency. K8s Cleaner offers an automated, customizable solution that simplifies resource management, allowing teams to focus on delivering value without worrying about resource sprawl. From flexible scheduling to advanced filtering and notifications, K8s Cleaner is designed to adapt to your unique needs, ensuring a clean, optimized Kubernetes cluster.
Highlights
- K8s Cleaner handles any resource type, including standard Kubernetes resources and CRDs
- K8s Cleaner allows users to specify the frequency of scans using Cron syntax
- K8s Cleaner sends notifications via popular channels like Slack, Webex, Discord, Email, and Microsoft Teams
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Helm install
- Amazon EKS
- Amazon EKS Anywhere
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
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Usage instructions
Go to the EKS Console and Create a Kubernetes Cluster: Click Add cluster Create. Enter cluster name and select Kubernetes version. Choose or create IAM roles. Configure networking (VPC, subnets, and security groups). Click Create. Add Worker Nodes: Navigate to the Compute section of your cluster. Create a node group, specifying instance type, scaling configuration, and IAM roles.
Once the cluster is created, configure kubectl to interact with it: aws eks update-kubeconfig --region us-east-1 --name my-cluster
To install the k8s-cleaner with a Helm chart, follow the below command.
helm install k8s-cleaner 709825985650.dkr.ecr.us-east-1.amazonaws.com/sveltos/k8s-cleaner:1.5.0
--version 1.5.0
--namespace k8s-cleaner
--create-namespace #
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