Expand your Kubernetes knowledge with techniques to make your clusters dynamic and resilient.

DevOps for Developers

The world of DevOps has exploded with the help of technologies like Kubernetes and Docker.
In this course, you will learn about more advanced Kubernetes features such as monitoring, alerting, logging, and scaling - crucial concepts for any DevOps professional. You will learn how to monitor clusters, how to send alerts when potential issues arise, and how to query the whole system’s metrics and logs.
By the end of this course, you will have the required knowledge to make your clusters and applications truly dynamic and resilient, so that they require minimal manual involvement and are self-adaptive.

Kubernetes Monitoring, Logging and Auto-Scaling



***In critical moments, men sometimes see exactly what they wish to see.***

***- Spock***

## Introduction to logs
So far, our primary focus was on metrics. We used them in different forms and for different purposes. In some cases, we used metrics to scale Pods and nodes. In others, metrics were used to create alerts that would notify us when there is an issue that cannot be fixed automatically. We also created a few dashboards.

However, metrics are often not enough. That is especially true when dealing with issues that require manual interventions. When metrics alone are insufficient, we usually need to consult logs hoping that they will reveal the cause of the problem.

Logging is often misunderstood or, to be more precise, mixed with metrics. For many, the line between logs and metrics is blurred. Some are extracting metrics from logs. Others are treating metrics and logs as the same source of information. Both approaches are wrong. Metrics and logs are separate entities, they serve different purposes, and there is a clear distinction between them. We store them separately, and we use them to solve different types of issues. We'll park that and a few other discussions. Instead of going into details based on theory, we'll explore them through hands-on examples. 

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In the next lesson, we will create a cluster.

# Introduction to logs
So far, our primary focus was on metrics. We used them in different forms and for different purposes. In some cases, we used metrics to scale Pods and nodes. In others, metrics were used to create alerts that would notify us when there is an issue that cannot be fixed automatically. We also created a few dashboards.

However, metrics are often not enough. That is especially true when dealing with issues that require manual interventions. When metrics alone are insufficient, we usually need to consult logs hoping that they will reveal the cause of the problem.

Logging is often misunderstood or, to be more precise, mixed with metrics. For many, the line between logs and metrics is blurred. Some are extracting metrics from logs. Others are treating metrics and logs as the same source of information. Both approaches are wrong. Metrics and logs are separate entities, they serve different purposes, and there is a clear distinction between them. We store them separately, and we use them to solve different types of issues. We'll park that and a few other discussions. Instead of going into details based on theory, we'll explore them through hands-on examples. 

---
In the next lesson, we will create a cluster.

Before We Begin

Before Getting Started

Autoscaling Deployments and StatefulSets

Auto-Scaling Nodes Of A Kubernetes Cluster

Collecting and Querying Metrics and Sending Alerts

Debugging Issues Discovered Through Metrics and Alerts

Extending HorizontalPodAutoscaler With Custom Metrics

Visualizing Metrics And Alerts

Collecting And Querying Logs

Conclusion

Introduction

Introduction to logs