Getting Started With Prometheus Workshop: Introduction to Prometheus
Interested in open-source observability but lack the knowledge to just dive right in? If so, learn about a workshop that is designed for you!
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Are you looking to get away from proprietary instrumentation? Are you interested in open-source observability but lack the knowledge to just dive right in?
If so, this workshop is for you, designed to expand your knowledge and understanding of open-source observability tooling that is available to you today.
Dive right into a free, online, self-paced, hands-on workshop introducing you to Prometheus. Prometheus is an open-source systems monitoring and alerting tool kit that enables you to hit the ground running with discovering, collecting, and querying your observability today. Over the course of this workshop, you will learn what Prometheus is and is not, install it, start collecting metrics, and learn all the things you need to know to become effective at running Prometheus in your observability stack.
In this article, you'll be introduced to some basic concepts and learn what Prometheus is and is not before you start getting hands-on with it in the rest of the workshop.
Introduction to Prometheus
I'm going to get you started on your learning path with this first lab that provides a quick introduction to all things needed for metrics monitoring with Prometheus. Note this article is only a short summary, so please see the complete lab found online here to work through it in its entirety yourself:
The following is a short overview of what is in this specific lab of the workshop. Each lab starts with a goal. In this case, it is fairly simple:
This lab introduces you to the Prometheus project and provides you with an understanding of its role in the cloud-native observability community.
The start is with background on the beginnings of the Prometheus project and how it came to be part of the Cloud Native Computing Foundation (CNCF) as a graduated project. This leads to some basic outlining of what a data point is, how they are gathered, what makes them a metric and all using a high-level metaphor.
You are then walked through what Prometheus is, why we are looking at this project as an open-source solution for your cloud-native observability solution, and more importantly, what Prometheus can not do for you.
You are then presented with an overview of all the powerful features and tools you'll find in your new Prometheus toolbox:
- Dimensional data model - For multi-faceted tracking of metrics
- Query language - PromQL provides a powerful syntax to gather flexible answers across your gathered metrics data.
- Time series processing - Integration of metrics time series data processing and alerting
- Service discovery - Integrated discovery of systems and services in dynamic environments
- Simplicity and efficiency - Operational ease combined with implementation in Go language
Finally, you'll touch on the fact that Prometheus has a very simple design and functioning principle and that this has an impact on running it as a highly available (HA) component in your architecture.
This aspect is only briefly touched upon, but don't worry: we cover this in more depth later in the workshop.
At the end of each lab, including this one, you are presented with the end state (in this case we have not yet done anything), a list of references for further reading, a list of ways to contact me for questions, and a link to the next lab.
Missed Previous Labs?
This is one lab in the more extensive free online workshop. Feel free to start from the very beginning of this workshop here if you missed anything previously:
You can always proceed at your own pace and return any time you like as you work your way through this workshop. Just stop and later restart Perses to pick up where you left off.
Coming up Next
I'll be taking you through the following lab in this workshop where you'll learn how to install and set up Prometheus on your own local machine. Stay tuned for more hands-on material to help you with your cloud-native observability journey.
Published at DZone with permission of Eric D. Schabell. See the original article here.
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