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Kubernetes #2 monitoring

Kubenetes monitoring basic
Heapster, influxdb, prometheus, cAdvior
Kubenetes dashboard

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Kubernetes #2 monitoring

  1. 1. Kubernetes #2. Monitoring 조대협 (http://bcho.tistory.com)
  2. 2. Agenda ● Kubernetes Monitoring Concept ● Kubernetes Monitoring Architecture ● Kubernetes Dashboard ● Google Stackdriver ● Datadog
  3. 3. Kubernetes Monitoring Concept
  4. 4. Kubernetes monitoring stack 1. Host-level metrics and event (CPU,Memory,Disk etc) 2. Container(Docker/Pod) level metrics (CPU,Memory,Disk) 3. Application metric (APM etc) 4. Kubernetes level - events, by label Image source : https://www.datadoghq.com/blog/how-to-collect-and-graph- kubernetes-metrics/
  5. 5. K8u monitoring solutions market (2017) Source : https://thenewstack.io/5-tools-monitoring-kubernetes-scale-production/
  6. 6. Kubernetes Monitoring Architecture
  7. 7. Kubernetes resource monitoring ● cAdvisor collect metrics from nodes and pods and send it into Kubelet ● Kublet sends all of pods in nodes information to Heapster. ● Heapster runs as a Pod in cluster ● Heapster stores all of info into storage backend (Google Stack driver, influx DB) ● It can be visualized by using Grafana Source : https://www.datadoghq.com/blog/how-to-collect-and-graph- kubernetes-metrics/
  8. 8. Components cAdvisor ● Open source container resource usage and performance analysis agent ● Auto discovers all containers in the machine ● Collect CPU,memory,file system and network usage statics for containers ● Overall machine usage (node) by analyzing the ‘root’ container on the machine InfluxDB ● Open source time series DB Grafana ● Dashboard visualization tools
  9. 9. Components cAdvisor application metric (Alpha) ● https://github.com/google/cadvisor/blob/mast er/docs/application_metrics.md ● cAdvisor can also be configured to collect application metrics. A container can expose application metrics through multiple ways ○ on a status page, ○ through structured info like prometheus, ○ or have a separate API for fetching stats. ● Endpoint (Location to collect metrics from) ● Name of metric ● Type (Counter, Gauge, ...) ● Data Type (int, float) ● Units (kbps, seconds, count) ● Polling Frequency ● Regexps (Regular expressions to specify which metrics to collect and how to parse them) Step 2. Passing configuration to cAdvisor Step 1. Create configuration
  10. 10. Kubernetes Logging ● It supports logging with ELK or Google stack driver ● ELK ○ The fluentd-elasticsearch pods gather logs from each node and send them to the elasticsearch- logging pods ○ These Elasticsearch pods store the logs and expose them via a REST API. ○ The kibana-logging pod provides a web UI for reading the logs stored in Elasticsearch, and is part of a service named kibana-logging Image source : https://kubernetes.io/docs/tasks/debug-application-cluster/logging-elasticsearch-kibana/
  11. 11. Kubernetes dashboard
  12. 12. Dashboard Dashboard is Web-UI based kubernetes interface ● Overview of application running on cluster ● Monitoring resources ● Create or modifying Kubernetes resources (such as deployments, jobs, daemonset etc) For example, you can scale a Deployment, initiate a rolling update, restart a pod or deploy new applications using a deploy wizard. Dashboard is not deployed in default. To deploy dashboard run script % kubectl create -f https://raw.githubusercontent.com/kubernetes/dashboard/master/src/deploy/recommended/kubernetes-dashboard.yaml
  13. 13. Connect to dashboard https://github.com/kubernetes/dashboard/wiki/Accessing-Dashboard---1.7.X-and-above ● For dev ○ kubectl proxy ○ http://localhost:8001/api/v1/namespaces/kube-system/services/https:kubernetes- dashboard:/proxy/ ● In production
  14. 14. Create sample user ● Guide : https://github.com/kubernetes/dashboard/wiki/Creating- sample-user
  15. 15. Logs
  16. 16. Shell It can also run bash shell thru dashboard
  17. 17. Datadog for Kubernetes
  18. 18. Reference ● GKE monitoring with datadog https://www.datadoghq.com/blog/monitor-google-kubernetes-engine/

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