Book Image

Datadog Cloud Monitoring Quick Start Guide

By : Thomas Kurian Theakanath
Book Image

Datadog Cloud Monitoring Quick Start Guide

By: Thomas Kurian Theakanath

Overview of this book

Datadog is an essential cloud monitoring and operational analytics tool which enables the monitoring of servers, virtual machines, containers, databases, third-party tools, and application services. IT and DevOps teams can easily leverage Datadog to monitor infrastructure and cloud services, and this book will show you how. The book starts by describing basic monitoring concepts and types of monitoring that are rolled out in a large-scale IT production engineering environment. Moving on, the book covers how standard monitoring features are implemented on the Datadog platform and how they can be rolled out in a real-world production environment. As you advance, you'll discover how Datadog is integrated with popular software components that are used to build cloud platforms. The book also provides details on how to use monitoring standards such as Java Management Extensions (JMX) and StatsD to extend the Datadog platform. Finally, you'll get to grips with monitoring fundamentals, learn how monitoring can be rolled out using Datadog proactively, and find out how to extend and customize the Datadog platform. By the end of this Datadog book, you will have gained the skills needed to monitor your cloud infrastructure and the software applications running on it using Datadog.
Table of Contents (19 chapters)
Section 1: Getting Started with Datadog
Section 2: Extending Datadog
Section 3: Advanced Monitoring

Implementing custom checks

Custom checks can be used to monitor a platform component if the available integration features are not adequate or an integration doesn't exist at all for that component. The Datadog API could be used as well in reporting custom-generated metrics to Datadog. We will explore this option with an example.

The process involved in implementing a check that publishes custom metrics is simple in Datadog and we can learn about that from the following example.

Continuing with the example of NGINX from the previous sections in this chapter, we will try to extend that integration by adding a custom metric to Datadog. This custom metric, kurian.nginx.error_log.size, tracks the size of the NGINX error log file. It's better to begin the metric name with a namespace specific to your company or department, as the metric is labeled in this example, to filter custom metrics easily.

Manually, the file size information could be gathered by running the command...