You're reading from Docker for Developers Develop and run your application with Docker containers using DevOps tools for continuous delivery

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781789536058

Length 468 pages

Edition 1st Edition

Tools

Docker

Concepts

Containerization

Authors (3):

Richard Bullington-McGuire

Michael Schwartz

Andrew K. Dennis

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Table of Contents (21) Chapters

Preface

1. Section 1: An Introduction to Docker – Containers and Local Development

2. Chapter 1: Introduction to Docker FREE CHAPTER

3. Chapter 2: Using VirtualBox and Docker Containers for Development

4. Chapter 3: Sharing Containers Using Docker Hub

5. Chapter 4: Composing Systems Using Containers

6. Section 2: Running Docker in Production

7. Chapter 5: Alternatives for Deploying and Running Containers in Production

8. Chapter 6: Deploying Applications with Docker Compose

9. Chapter 7: Continuous Deployment with Jenkins

10. Chapter 8: Deploying Docker Apps to Kubernetes

11. Chapter 9: Cloud-Native Continuous Deployment Using Spinnaker

12. Chapter 10: Monitoring Docker Using Prometheus, Grafana, and Jaeger

13. Chapter 11: Scaling and Load Testing Docker Applications

14. Section 3: Docker Security – Securing Your Containers

15. Chapter 12: Introduction to Container Security

16. Chapter 13: Docker Security Fundamentals and Best Practices

17. Chapter 14: Advanced Docker Security – Secrets, Secret Commands, Tagging, and Labels

18. Chapter 15: Scanning, Monitoring, and Using Third-Party Tools

19. Chapter 16: Conclusion – End of the Road, but not the Journey

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Prometheus is the dominant Kubernetes-based system for collecting metrics on cluster operations. Prometheus sports a wide range of features related to handling time-series data, visualizing data, querying it, and sending alerts based on metrics data.

This metrics data might include a variety of time-series data for CPU usage, both for nodes and for pods; storage utilization; application health, as defined by readiness probes; and other application-specific metrics. Prometheus uses a pull model where it polls endpoints for numeric data. Pods, DaemonSets, and other Kubernetes resources supporting Prometheus use annotations to advertise that Kubernetes should scrape them for metrics data via HTTP, usually via a /metrics endpoint. This can include data from Nodes, surfaced through a DaemonSet called node_exporter that runs on each Node.

It stores the metrics data it receives by associating this data with a metric name and a set...