Prometheus Monitoring: Observability, Metrics, and Best Practices

Prometheus Monitoring: Observability, Metrics, and Best Practices

Prometheus Monitoring: Observability, Metrics, and Best Practices

Most organizations use metrics to monitor their system and architecture performance. In addition, the boom in microservices has led to a demand for improved monitoring techniques. As a result, the popularity of time series databases (TSDB) and telemetry data has skyrocketed in recent years. The Prometheus monitoring platform is one of the most widely used TSDBs available today.

Prometheus is an open-source platform that provides monitoring and alerting services via metrics. In 2012, SoundCloud first developed Prometheus. It has since become a household name when it comes to Kubernetes monitoring and observability. 

Before exploring some of the best practices to follow for Prometheus monitoring, let’s learn what Prometheus is all about in more detail.

What is Prometheus?

Prometheus is a new-age monitoring system. It is capable of gathering and archiving metrics as time-series data, documenting data with a timestamp. It is the most used database for time-series metrics data. Its goal is to provide us with information about how well our systems and apps are working. Moreover, Labels are optional key-value pairs that you can keep with measurements.

Furthermore, Prometheus is an essential pillar in DevOps as well. Monitoring every stage of the DevOps and IT operations lifecycle is crucial to maintain performance. This procedure is often termed continuous monitoring. It maintains the functionality, efficiency, and dependability of your application.

Prometheus’ main attributes are as follows:

  • PromQL- An abbreviation for Prometheus Querying Language. A querying language that is adaptable and can make use of the multi-dimensional data model.
  • Alertmanager- We can make alerts based on Prometheus measurements using Alertmanager. Alertmanager receives notifications from client applications, notably in the Prometheus server.
  • Pushgateway- Used when the nodes lack an endpoint from which the Prometheus server may collect the metrics.
  • No reliance on distributed storage- There is no dependency on distributed storage, each single server node maintains autonomy.
  • Pull model- By pulling data through HTTP, Prometheus may gather time-series data.
  • Pushing time-series data– Using an intermediary gateway, it is possible to push time-series data.
  • Monitoring target discovery– Monitoring target discovery is possible via service discovery or static configuration.
  • Multidimensional data model– Time-series data are identified using a multidimensional data model, key-value pairs, and the name of the metric.
  • Visualization– Prometheus provides several graphs and dashboard types.

Now that you have a brief overview of Prometheus, you must explore the best practices to follow for incorporating Prometheus monitoring. However, if you wish to learn about it in more detail, head over to this Blogpost on our website. 

List of Prometheus Best Practices

prometheus-metrics-min

The demands placed on Prometheus have increased along with the number of users. Create and put in place a plan that incorporates the following best practices to increase visibility and reap the full benefits of Prometheus monitoring:

Identify metrics and their purpose

Knowing when to use each of Prometheus’ four metric kinds enhances accuracy and raises contextual awareness.

Counter: A counter simply counts events at the beginning of an event.

Gauge: Identifies positive and negative changes, making it especially useful when referring to time-based data like memory usage, open requests, or temperature.

Histogram: Aids in data aggregation by selecting and classifying events using a total of gathered values.

Summary: It is comparable to the histogram metric in that it uses the result of observation counts and sums of measured data to calculate over a period of time.

Choose the ideal Prometheus Exporter

For systems like Linux or HAProxy that are difficult to scrape, Prometheus employs exporters to extract metrics. Client libraries, called exporters, are installed on the target computer to export metrics and send them to Prometheus.

With numerous Prometheus exporters in the market, choosing the right one can be pretty challenging. But if you know and follow the right tips to base your decision, you’ll find the required exporter easily. Some of these are as follows:

  • Try to be relevant when selecting an exporter as most of them are similar in functionality.
  • Examine the exporters that are in the market and compare how they handle the parameters that are important to your business. 
  • Test the exporter’s caliber in light of factors like user ratings, recent upgrades, and security alerts.
  • Check out the Exporters and Integrations page on the official Prometheus website. If it’s mentioned, then that exporter is most probably a solid option.
  • Look out for exporter metrics. If your Prometheus exporter uses the OpenMetrics format, you get extra insights into the metrics. So, choose one that has clear documentation and availability for metric data.

Label Metrics with care

Context is king, therefore you must make sure that it’s clear. You must review your exporter’s documentation thoroughly. Align your metrics labels in a way that they provide context. Additionally, you need to maintain consistency in formatting protocols when creating labels. 

Moreover, make note of the number of labels you create for the metrics. Each label uses some sort of resource. Thus, too many labels may result in increased resource costs. Hence, you ought to stick around 10 labels or less for cost-effectiveness.

Set Actionable Alerts

An explicit alerting strategy can enhance your performance monitoring. Before setting a suitable threshold, you should decide which metrics are crucial to keeping an eye on. This will help you identify problems before they have a chance to impact your end customers negatively. So the ideal threshold to set is one that prevents alert fatigue. In order for the right team to receive the notifications on time, you should also make sure they are configured correctly.

Prometheus Monitoring with LOGIQ.AI

Prometheus fire torch

Today, Prometheus goes from strength to power, solidifying its position as the industry benchmark in observability. Prometheus is now viewed as an ecosystem that includes instrumentation, exporters, alerting, metrics collection, storage, and more, not just a project or a single tool.

The benefits of Prometheus are multi-faceted. Although primarily it’s used for Kubernetes monitoring, it isn’t limited to that. Prometheus allows you to scrape and query information from many infrastructure and cloud services as well.

LOGIQ’s Point of View

Metrics serve as the main representation of the overall health of your system as well as any other important data that is necessary for monitoring, alerting, and observability. Despite the Kubernetes ecosystem expanding every day, the community still relies on certain technologies to relieve operational duties. Prometheus monitoring is definitely one of them.

Our decision to incorporate Prometheus into the LOGIQ platform was influenced by its versatility. Using this integration, you can not only leverage Kubernetes environments to identify services, pods, namespaces, and metrics but also find metrics from known infrastructures and services such as NGINX Ingress Controllers and AWS services.

If you’ve got any queries about how to use Prometheus within the LOGIQ.AI platform for full-stack observability and fine-grained data pipeline control, please do not hesitate to get in touch. Our support team will be happy to help you.

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