PLG Stack: Monitoring and Observability in Kubernetes – Pilot Post
For nearly a decade, I’ve been running a personal lab cloud environment. What began as a handful of plain Linux virtual machines has grown steadily as I explored new technologies. In the early days, I managed everything with custom Puppet modules I wrote myself (you can find more details in my deepwiki).
When containerization gained momentum, I hopped aboard the Kubernetes train. Running Kubernetes introduced new challenges for infrastructure monitoring. The ELK stack quickly became too heavy and maintenance-intensive for my needs. Traditional tools like Nagios and Zabbix weren’t well-suited for containerized environments either—though I still rely on a fully Puppet-managed Nagios setup for the underlying Linux nodes.
That’s when I discovered the PLG stack: Prometheus, Loki, and Grafana. It offered a lightweight, powerful observability solution perfectly matched to Kubernetes.
What Each Component Does
- Prometheus: A time-series database and monitoring system focused on collecting, storing, and querying metrics. It excels at tracking performance data, alerts, and trends.
- Loki: A log aggregation system designed for simplicity and efficiency. It has a much smaller footprint than Elasticsearch while still delivering fast log search and correlation with metrics.
- Grafana: The visualization layer. It brings everything together with beautiful dashboards for metrics, logs, traces, and more.
In this series, we’ll explore how to deploy and manage these components inside Kubernetes while using them to monitor both Kubernetes workloads and external infrastructure.
Documentation for these tools can sometimes feel dense or assume advanced knowledge. I’ll break down the fundamentals clearly—sharing the lessons it took me time to learn—so you can get up and running faster.
Since Prometheus is the heart of the stack, we’ll begin there in the next post.
Visualizing the PLG Stack
Here’s a high-level overview of how the components work together:
This diagram shows the flow:
- Your applications and infrastructure push metrics to Prometheus and logs to Loki.
- Grafana queries both systems to deliver rich, correlated dashboards.
This pilot sets the stage for the series. In upcoming posts, we’ll dive deep into installing Prometheus in Kubernetes, configuring service discovery, setting up Loki for efficient logging, building effective Grafana dashboards, and more.
Stay tuned! If you have specific questions or topics you’d like covered, feel free to comment below.
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