Demystifying Prometheus for Beginners

Demystifying Prometheus for Beginners

Metrics are the primary means of representing your system’s general health and any other valuable information for monitoring, alerting, and observability. Despite the Kubernetes ecosystem growing by the day, the community continues to rely on particular technologies to ease operation workloads. One of them is Prometheus. Prometheus bridges the gap between monitoring and alerting.

What exactly is Prometheus?

Prometheus is a major open-source metric instrumentation, collecting, and storage toolset founded in 2012 by SoundCloud. Since then, it has emerged from the Cloud Native Computing Foundation, it has established itself as the standard for Kubernetes monitoring. As a critical component of DevOps. It is the most popular database for time-series metrics data and has one of the most significant contributor groups in the open-source world. Its purpose is to inform us about the performance of our apps and systems. All metrics data is kept as a time series in Prometheus, which means that data is stored with the timestamp at which it was captured. Optional key-value combinations called labels can also be maintained alongside metrics.

Prometheus Architecture

Metrics are scraped and stored by the Prometheus server. The server’s nodes are self-contained and do not depend on shared storage. Prometheus has the following key components:

PromQL

PromQL is a functional query language that Prometheus provides. Users can use PromQL to query, select, and aggregate time-series data in real-time. Users’ results by querying metrics with PromQL can be visualized as a graph, viewed as tabular data, or routed to third-party analytics systems via an API.

Alertmanager

Allows us to create alerts based on Prometheus metrics. Client applications, particularly the Prometheus server, transmit alerts to Alertmanager. It provides powerful deduplication, grouping, and routing functions, as well as the ability to route via other systems such as PagerDuty and OpsGenie. Prometheus depends on scraping or pulling, metrics from specific endpoints, which is the key to understanding it. This requires your application to publish a metrics endpoint and advise the Prometheus server on how to scrape it.

Pushgateway

If the nodes do not contain an endpoint from which the Prometheus server may gather the metrics, Prometheus offers a push gateway. This is excellent for one-off tasks that run, collect the data, turn the data into the Prometheus data format and then send that data into the Prometheus server.

Service discovery

Prometheus was built from the bottom up to function in dynamic settings like Kubernetes and requires very minimal configuration when initially installed. As a result, it conducts an automated discovery of operating services in order to make monitoring successful.

Prometheus Metrics and Data Model

Prometheus stores all data as time series; streams of timestamped values corresponding to the same metric and designated dimensions. Prometheus creates temporary derived time series as a consequence of queries, in addition to stored time series.

Names and labels for metrics

The metric name and optional key-value combinations called labels are used to identify each time series. The metric name specifies the general aspect of a system that is measured. This may include ASCII characters, numerals, underscores, and colons, among other things.

Labels allow Prometheus’ dimensional data model: each given set of labels for the same metric name identifies a specific dimensional instance of that metric. These dimensions can be used to filter and aggregate data in the query language. A new time series will be created whenever any label value is changed, including adding or deleting labels.

ASCII characters, digits, and underscores can all be used in label names and label values can contain Unicode character

Samples

The real-time series data is made up of samples. Each sample has the following components: a millisecond-precision timestamp and a float64 value.

Notation

Time series are typically identified using specific notation when given a metric name and a collection of labels.

Prometheus Use Cases

Prometheus is regarded as one of the best application monitoring software solutions due to its versatility, ease of use, and seamless integration. It is well-known among developers as an excellent monitoring tool for performance monitoring, cloud monitoring, IoT monitoring, and other tasks. Its adaptability proposes a wide range of applications, including python web apps and Kubernetes applications. It can handle practically every kind of statistic that your app may provide and receive.

  • To gather metrics, Prometheus does not need us to install any special software or settings on our actual servers, or even in our container images.
  • Our apps do not need to consume CPU cycles to send metrics to a central collector using Prometheus.
  • We can tell when data was last collected from each individual target thanks to Prometheus’ centralized setup and administration panel.
  • Prometheus smoothly manages service loss and unavailability. When a target application goes down, Prometheus uses a time-series database, which means we don’t need millions of individual metric entries; instead, we capture snapshots of metrics at regular intervals and then compare how much has changed. Our metrics platform’s network traffic and costs are reduced as a result.
  • Gathering numeric metrics from a service that operates continuously is one of the most common use cases. You may ask Prometheus using the hostname, path, and port to have the server scrape the data you need as long as the files are published on an HTTP server. Monitoring an app’s service metrics may provide you with valuable information like memory use, CPU usage, thread count, and more.
  • When recording solely numeric time-series data, Prometheus is an excellent choice. It’s suitable for both machine-centric and extremely dynamic service-oriented monitoring. One of its strengths is its ability to query multi-dimensional data.
  • Prometheus server is a stand-alone program that assists you in swiftly diagnosing system issues. Because Prometheus does not need substantial architecture, you may depend on it for monitoring even if certain parts of the architecture are faulty.
  • Another use for Prometheus is operating system monitoring. When a server’s hard drive is full or a CPU is continually operating at 100%, you’ll want to know. Memory use, website uptime, and the number of HTTP requests for each page are some of the additional metrics that users often monitor in order to make necessary modifications.
  • This is why Prometheus has been used by businesses across a wide range of sectors, including gaming and digital payment processing, as well as data science and government organizations. Prometheus is a prominent open-source monitoring tool that can help you track data, handle numerous alarms, and more, no matter what sector you’re in.

Conclusion

We hope this article helped you understand the core concepts of Prometheus as a monitoring tool. Prometheus has numerous benefits and isn’t exclusively used to monitor Kubernetes environments. You can use Prometheus to scrape and query metrics from other cloud services and infrastructure components, too. This flexibility of Prometheus is why we chose to integrate Prometheus deeply into the LOGIQ platform. With this integration, not only can you automatically discover services, pods, namespaces, and metrics in your Kubernetes environments, but you can also use it to uncover metrics from popular services like NGINX Ingress Controllers, AWS services, and other infrastructure. In our next article, we’ll take a look at how Prometheus performs when it comes to monitoring multi-cluster environments.

In the meanwhile, if you have any questions about how you can leverage Prometheus within the LOGIQ.AI platform to achieve full-stack observability and granular data pipeline control, do reach out!

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