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Hands-On Infrastructure Monitoring with Prometheus

Hands-On Infrastructure Monitoring with Prometheus

By : Joel Bastos, Pedro Araújo
3.1 (7)
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Hands-On Infrastructure Monitoring with Prometheus

Hands-On Infrastructure Monitoring with Prometheus

3.1 (7)
By: Joel Bastos, Pedro Araújo

Overview of this book

Prometheus is an open source monitoring system. It provides a modern time series database, a robust query language, several metric visualization possibilities, and a reliable alerting solution for traditional and cloud-native infrastructure. This book covers the fundamental concepts of monitoring and explores Prometheus architecture, its data model, and how metric aggregation works. Multiple test environments are included to help explore different configuration scenarios, such as the use of various exporters and integrations. You’ll delve into PromQL, supported by several examples, and then apply that knowledge to alerting and recording rules, as well as how to test them. After that, alert routing with Alertmanager and creating visualizations with Grafana is thoroughly covered. In addition, this book covers several service discovery mechanisms and even provides an example of how to create your own. Finally, you’ll learn about Prometheus federation, cross-sharding aggregation, and also long-term storage with the help of Thanos. By the end of this book, you’ll be able to implement and scale Prometheus as a full monitoring system on-premises, in cloud environments, in standalone instances, or using container orchestration with Kubernetes.
Table of Contents (21 chapters)
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1
Section 1: Introduction
5
Section 2: Getting Started with Prometheus
11
Section 3: Dashboards and Alerts
15
Section 4: Scalability, Resilience, and Maintainability

Pushing metrics

Despite the intense debate regarding push versus pull and the deliberate decision of using pull in the Prometheus server design, there are some legitimate situations where push is more appropriate.

One of those situations is batch jobs, though, for this statement to truly make sense, we need to clearly define what is considered a batch job. In this scope, a service-level batch job is a processing workload not tied to a particular instance, executed infrequently or on a schedule, and as such is not always running. This kind of job makes it very hard to generate successful scrapes if instrumented, which, as discussed previously in Chapter 5, Running a Prometheus Server, results in metric staleness, even if running for long enough to be scraped occasionally.

There are alternatives to relying on pushing metrics; for example, by using the textfile collector from node_exporter...

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