IDG Contributor Network: 3 phases of Prometheus adoption


Having assisted hundreds of enterprises in developing a new visibility strategy as they move to Kubernetes, I’ve learned a few things about how organizations learn, evolve and adopt a new method of application observability. Open source is usually essential to developing this understanding.

In the cloud-native monitoring world, Prometheus is widely considered the place to start. Just like Kubernetes is the leading open source container orchestrator in the cloud-native world, Prometheus is the leading software choice for open source cloud-native monitoring. If you’re looking for more detail on what Prometheus is and how it works, read this . While most organizations end up not using unsupported open source in production, many start here. Where you end up depends on your own business’s requirements. Let me briefly cover the three phases I typically see enterprises go through on their way to a production-ready strategy.

1. Experiment

Much like the adoption of containers or Kubernetes doesn’t happen overnight, neither will an accompanying visibility strategy. The good news is that with Prometheus, your developers can explore without constraints of time or budget.

At this phase you’re looking for:

. The Prometheus community has dozens of exporters designed to simplify scraping metrics from common software components that already expose metrics through an endpoint. They can easily be deployed through Kubernetes in a systematic way. These exporters use a push model, which may be problematic security-wise depending on the complexity of your production environment.

  • . Prometheus specifies a container and microservices friendly format that allows you to emit custom metrics directly from your applications. This is essentially to enable you to deeply observe your own code. Note that if your development team already uses a metrics format like StatsD or JMX, you may be able to use that already, but it will likely require greater operational effort and have reduced functionality. More on that in the next section.
  • OK, so now your team is gaining confidence and you’ve got the actual metrics you want, we’re done right? Not quite. Time to get into production.

    3. Operationalize

    This might come as a shocker, but your experiments will likely look nothing like your own real-world production environment 12 months down the road. Let me run through a few of the critical questions for you to consider before you operationalize your new monitoring strategy so you’re not blindsided later: