Master DevOps Monitoring with Prometheus

Set up a testing workspace to learn Prometheus hands-on with VirtualBox, Visual Studio Code, Ubuntu Server, and the Docker Engine, using SSH and a built-in terminal.

Explore core monitoring principles, how Prometheus works, and its metrics, logs, push and pull methods, data model, and server architecture in the DevOps lifecycle.

Explore the DevOps lifecycle and the five stages of monitoring, from planning and coding through continuous integration, deployment, and continuous monitoring, with data collection, storage, alerting, visualization, analytics and reporting.

Discover how the Prometheus server architecture connects data collection via pull-based scraping, exporters, push gateway, and service discovery, stores time series data locally, and visualizes with PromQL, Grafana, and Alertmanager.

Explore the Prometheus server package components, including console libraries, the time series database, and configuration checks, and learn to adjust retention, paths, and command line flags while verifying yaml.

Explore the four metric types in Prometheus—counter, gauge, summary, and histogram—and learn how each tracks events, current state, latency, and quantiles.

Add Prometheus instrumentation to a dockerized Flask app using WSGI and the Python client, exposing port 5000 and scraping metrics at /metrics.

Learn to scrape container metrics with cAdvisor, Google's container advisor, a service discovery tool for docker containers; deploy via Docker Compose and view CPU, memory, network, and filesystem metrics.

Configure the text file collector in node exporter to read .prom files from a directory, bind the temp folder in Docker, and push metrics to Prometheus via the push gateway.

Master labeling for Prometheus monitoring by targeting source labels, replacing and external labels, and configuring black box exporter, while adding instrumentation and metrics to your web app.

Apply metrics relabelling in Prometheus to drop high cardinality metrics by regexing the name, while they remain scraped by the Jira plugin exporter and hidden in the expression browser.

Learn how to use sum and count aggregators in Prometheus to compute total file system sizes, count Linux and Windows machines, and determine CPUs per instance by filtering labels.

Explore min and max operators to find group-level extremes, and use top K, bottom K, and count value operators with real-world examples like cpu idle time and gc duration.

Explore rate, irate, avg, and avg_over_time in PromQL to analyze counters and aggregates. Learn to use 5-minute range vector, handle scrape variability, and improve alerts with average over time.