The DevOps 2.5 Toolkit
Monitoring, Logging, and Auto-Scaling Kubernetes: Making Resilient, Self-Adaptive, And Autonomous Kubernetes Clusters
BIRMINGHAM - MUMBAI
- Title Page
- Copyright and Credits
- About Packt
- Dedication
- Contributors
- Preface
- Autoscaling Deployments and StatefulSets Based on Resource Usage
- Auto-scaling Nodes of a Kubernetes Cluster
- Collecting and Querying Metrics and Sending Alerts
- Creating a cluster
- Choosing the tools for storing and querying metrics and alerting
- A quick introduction to Prometheus and Alertmanager
- Which metric types should we use?
- Alerting on latency-related issues
- Alerting on traffic-related issues
- Alerting on error-related issues
- Alerting on saturation-related issues
- Alerting on unschedulable or failed pods
- Upgrading old Pods
- Measuring containers memory and CPU usage
- Comparing actual resource usage with defined requests
- Comparing actual resource usage with defined limits
- What now?
- Debugging Issues Discovered Through Metrics and Alerts
- Extending HorizontalPodAutoscaler with Custom Metrics
- Visualizing Metrics and Alerts
- Creating a cluster
- Which tools should we use for dashboards?
- Installing and setting up Grafana
- Importing and customizing pre-made dashboards
- Creating custom dashboards
- Creating semaphore dashboards
- A better dashboard for big screens
- Prometheus alerts vs. Grafana notifications vs. semaphores vs. graph alerts
- What now?
- Collecting and Querying Logs
- Creating a cluster
- Exploring logs through kubectl
- Choosing a centralized logging solution
- Exploring logs collection and shipping
- Exploring centralized logging through Papertrail
- Combining GCP Stackdriver with a GKE cluster
- Combining AWS CloudWatch with an EKS cluster
- Combining Azure Log Analytics with an AKS cluster
- Exploring centralized logging through Elasticsearch, Fluentd, and Kibana
- Switching to Elasticsearch for storing metrics
- What should we expect from centralized logging?
- What now?
- What Did We Do?
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