OpenTelemetry is an open source project managed by the Cloud Native Computing Foundation (CNCF). It provides a set of APIs, libraries, agents, and instrumentation for capturing distributed traces and metrics from your application. The project aims to make telemetry data a built-in feature of cloud-native software.
The main innovation of OpenTelemetry is that it provides a single set of APIs to capture both traces and metrics. This is particularly useful for developers, as it simplifies the process of instrumenting their applications. It also allows for the collection of data that is consistent across multiple programming languages and environments.
OpenTelemetry’s design takes into account the diverse landscape of microservices, offering a unified approach to understanding the performance and behavior of systems, regardless of their underlying technology or programming language.
Prometheus is another open-source project hosted by the CNCF, designed to handle time-series data (metrics) generated by distributed applications. It’s widely recognized for its robust data model, powerful query language, and its ability to generate precise, real-time metrics.
Prometheus’s architecture is based on a pull model, where it fetches data from your services at regular intervals. This model offers advantages such as simplicity of configuration and the ability to monitor services behind a firewall. It also supports a wide range of service discovery mechanisms, making it easy to monitor dynamic environments.
One of the standout features of Prometheus is its multidimensional data model and flexible query language, called PromQL. It allows users to select and aggregate time-series data in real-time, providing valuable insights into the system’s behavior.
In this article
While both OpenTelemetry and Prometheus are tools for observability, they serve different purposes and offer different capabilities. OpenTelemetry is a toolkit for collecting telemetry data — traces and metrics — from your applications, while Prometheus is a monitoring system and time-series database.
The primary difference between OpenTelemetry and Prometheus lies in the type of data they handle. OpenTelemetry is designed to capture both traces and metrics, providing a holistic view of your system’s performance. On the other hand, Prometheus focuses on the collection and storage of time-series data, which is primarily metrics.
Another key difference is the way they collect data. OpenTelemetry uses a combination of push and pull models, allowing it to collect data in real-time and over long periods. Prometheus, however, uses a pull model, fetching data from your services at regular intervals.
Learn more in our detailed guide to OpenTelemetry architecture
Combining OpenTelemetry with Prometheus gives you the best of both worlds — the comprehensive tracing and metrics collection capabilities of OpenTelemetry, coupled with the powerful monitoring and alerting features of Prometheus.
OpenTelemetry’s unified APIs for traces and metrics provide comprehensive visibility into the performance and behavior of your applications. When paired with Prometheus’s monitoring capabilities, you get real-time insights into your systems’ operational status, allowing you to quickly identify and address issues.
By integrating OpenTelemetry with Prometheus, you can collect, query, and analyze traces and metrics in one place. This simplifies observability by providing a consistent and intuitive way to access, visualize, and work with telemetry data, making it easier to gain insights and troubleshoot issues in complex distributed systems.
OpenTelemetry’s efficient distributed architecture allows it to handle the collection of traces and metrics across large, distributed systems without adding significant overhead. Similarly, Prometheus’s scalable design ensures that it can handle large volumes of time-series data with high performance and reliability. By working together, OpenTelemetry and Prometheus can support monitoring and observability for large-scale systems without affecting performance.
Another significant benefit of integrating OpenTelemetry and Prometheus is the versatility they offer in visualizing data. OpenTelemetry’s flexible data model allows it to collect a wide variety of data types, from simple counters to complex histograms. This data can then be queried and visualized using Prometheus’s powerful query language, PromQL, which offers a rich set of functions to analyze your data.
Moreover, Prometheus integrates seamlessly with Grafana, a popular open-source visualization tool. With Grafana, you can create intuitive and interactive dashboards that provide flexible views of your application’s performance.
To get the most out of OpenTelemetry and Prometheus, it’s important to follow some best practices. These practices will help you ensure that your data is consistent, meaningful, and easy to analyze.
Consistent naming makes it easier to search for and aggregate metrics, especially when you have hundreds or even thousands of them. It also makes your metrics self-descriptive, allowing anyone in your team to understand what they represent without needing extensive documentation.
A good naming convention includes the type of data being collected (e.g., request, error), the service it’s coming from, and a description of the metric itself. For instance, http_request_duration_seconds clearly indicates that this metric represents the duration of HTTP requests in seconds.
Histograms and summaries are powerful data types in Prometheus that allow you to analyze the distribution of values. They are particularly useful for tracking latencies, response sizes, or any other metrics that follow a distribution.
Histograms and summaries can give you a lot more insight into your data than simple counters or gauges. They allow you to observe trends over time, identify outliers, and understand the behavior of your system under different conditions. Therefore, it’s highly recommended to use these data types when using Prometheus with OpenTelemetry.
High metric cardinality (a large number of dimensions for each metric) can negatively impact the performance of your Prometheus server. Therefore, it’s essential to limit the cardinality of your metrics.
By using histograms and summaries, you can significantly reduce metric cardinality. Instead of recording every single value, these data types allow you to record the distribution of values, significantly reducing the number of unique values you need to store. You can also limit cardinality by reducing the number of labels you use, as explained below.
Labels in Prometheus allow you to add dimensions to your metrics, making them more descriptive and easier to query. However, it’s essential to use labels judiciously. Adding too many labels to a metric is another cause of high cardinality, which can degrade Prometheus performance.
A good rule of thumb is to only add labels that add meaningful information to your metrics. These could include the environment (e.g., production, staging), the service name, the endpoint, etc. Avoid adding labels that have a high number of unique values, such as user IDs or timestamps, as they can quickly explode your metric cardinality.
The scrape interval determines how often Prometheus pulls metrics from your services. Choosing the right scrape interval depends on your specific needs. If you need real-time data, you might want to set a low scrape interval. However, keep in mind that scraping too frequently can put a lot of load on your services and your Prometheus server.
On the other hand, if your data doesn’t change frequently or if you’re okay with slightly delayed data, you can set a higher scrape interval to reduce the load.
Microservices Monitoring with Lumigo
OpenTelemetry offers a pluggable architecture that enables you to add technology protocols and formats easily. Using OpenTelemtry, Lumigo provides containerized applications with end-to-end observability through automated distributed tracing.