k8s-resource-monitor 0.1.0

A CLI tool for monitoring Kubernetes resources and autoscaling

Kubernetes Resource Monitor CLI

A CLI tool for monitoring and managing Kubernetes resources. This tool provides real-time monitoring, auto-scaling recommendations, resource trend visualization, and alerting functionality.

Features

• Monitor real-time resource usage (CPU and Memory) of pods in a Kubernetes namespace.
• Provide auto-scaling recommendations based on custom policies.
• Visualize resource usage trends for a specific pod.
• Configure Horizontal Pod Autoscalers (HPA) based on resource usage.
• Set up alerts for high resource usage via email and Slack.

Prerequisites

• Python 3.7+
• Kubernetes cluster and kubectl configured
• Kubernetes Metrics Server installed on your cluster
• Basic knowledge of Kubernetes pods and deployments

Setup Instructions

2. Create a Virtual Environment

python3 -m venv .venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Set Up Kubernetes Configuration

Ensure your Kubernetes cluster is configured and accessible. You should be able to run kubectl get pods from your terminal.

5. Initialize the Database

The tool uses SQLite for storing historical resource usage data. You can initialize the database by running the monitoring command (explained below).

Commands and Usage

1. Monitor Resource Usage

Monitor real-time CPU and memory usage for pods in a specific namespace.

python3 -m k8s_monitor.cli monitor --namespace <namespace-name>

Options:

--namespace: Kubernetes namespace to monitor (default: default). --use-mock: Use mock data instead of real Kubernetes cluster data. Example:

python3 -m k8s_monitor.cli monitor --namespace default

2. Auto-Scale Pods

Provide auto-scaling recommendations based on custom policies for a Kubernetes namespace.

python3 -m k8s_monitor.cli auto-scale --namespace <namespace-name>

Options:

--namespace: Kubernetes namespace to monitor (default: default). --use-mock: Use mock data instead of real Kubernetes cluster data. Example:

python3 -m k8s_monitor.cli auto-scale --namespace default

3. Visualize Resource Trends

Visualize CPU and memory usage trends for a specific pod over a specified time period.

python3 -m k8s_monitor.cli visualize-trends --namespace <namespace-name> --pod-name <pod-name> --duration <time-in-minutes>

Options:

--namespace: Kubernetes namespace to monitor (default: default). --pod-name: The name of the pod to visualize trends for. --duration: Time duration (in minutes) for historical data (default: 60 minutes). Example:

python3 -m k8s_monitor.cli visualize-trends --namespace default --pod-name nginx-pod --duration 60

4. Configure Alerts

Send email alerts independently of monitoring. You must configure email settings before running this command.

python3 -m k8s_monitor.cli email-alert --email-host <host> --email-port <port> --sender-email <sender-email> --sender-password <password> --recipient-email <recipient-email> --subject <subject> --message <message>

Options:

--email-host: SMTP host for sending alerts. --email-port: SMTP port for sending alerts. --sender-email: Sender email address. --sender-password: Sender email password. --recipient-email: Recipient email address. --subject: Subject of the email alert. --message: Message content for the email alert.

Example:

python3 -m k8s_monitor.cli email-alert --email-host smtp.example.com --email-port 587 --sender-email [e-mail] --sender-password password --recipient-email [e-mail] --subject "Alert: High CPU Usage" --message "The CPU usage for pod nginx-pod is above the threshold."

5. Set Configuration

Configure the monitoring tool to send alerts via Slack or email.

python3 -m k8s_monitor.cli set-config --slack-webhook-url <url> --email-host <host> --email-port <port> --sender-email <email> --sender-password <password> --recipient-email <email>

Options:

--slack-webhook-url: Slack Webhook URL for sending alerts. --email-host: SMTP host for sending email alerts. --email-port: SMTP port for sending email alerts. --sender-email: Sender email address. --sender-password: Sender email password. --recipient-email: Recipient email address.

Example:

python3 -m k8s_monitor.cli set-config --slack-webhook-url https://hooks.slack.com/services/ABC123 --email-host smtp.example.com --email-port 587 --sender-email [e-mail] --sender-password password --recipient-email [e-mail]

6. View and Reset Configuration

You can view or reset the current configuration using the following commands:

View current configuration:

python3 -m k8s_monitor.cli view-config

Reset the configuration:

python3 -m k8s_monitor.cli reset-config

7. Set Auto-Scaling Policy

Set or update the auto-scaling policy for your Kubernetes cluster. This will be used to make scaling recommendations.

python3 -m k8s_monitor.cli set-autoscaling-policy --cpu-threshold <cpu-percentage> --memory-threshold <memory-percentage> --max-replicas-change <replicas> --scaling-strategy <static|dynamic>

Options:

--cpu-threshold: CPU usage threshold for auto-scaling (in percentage). --memory-threshold: Memory usage threshold for auto-scaling (in percentage). --max-replicas-change: Maximum number of replicas to scale up or down. --scaling-strategy: Scaling strategy (static or dynamic).

Example:

python3 -m k8s_monitor.cli set-autoscaling-policy --cpu-threshold 80 --memory-threshold 75 --max-replicas-change 5 --scaling-strategy dynamic

8. Set Namespaces

Set or update the namespaces that should be monitored.

python3 -m k8s_monitor.cli set-namespaces --namespaces <namespace1> <namespace2>

Options:

--namespaces: List of namespaces to monitor.

Example:

python3 -m k8s_monitor.cli set-namespaces --namespaces default production

9. View and Reset Namespaces

You can view or reset the monitored namespaces:

View current namespaces:

python3 -m k8s_monitor.cli view-namespaces

Reset namespaces to default:

python3 -m k8s_monitor.cli reset-namespaces

Contribution

Feel free to submit issues and pull requests to enhance the tool further.

License

This project is licensed under the MIT License - see the LICENSE file for details.