Observability tools, like OpenTelemetry and Prometheus, are critical for ensuring optimal performance and reliability in cloud-native applications. While OpenTelemetry provides comprehensive telemetry data collection, supporting traces, logs, and metrics for whole-system observability, Prometheus excels in monitoring and analyzing time-series data.
While you can use Prometheus and OpenTelemetry together, they have distinctly different capabilities that set each apart. Prometheus is ideal for dynamic systems and focused monitoring of individual components, and OpenTelementry offers a unified view across distributed systems. Both OpenTelemetry and Prometheus provide options for data collection but are significantly different from one another.
This comprehensive overview of OpenTelemetry and Prometheus covers key features and capabilities, as well as advantages and disadvantages, of both tools.
For cloud-native applications, OpenTelemetry is the future of instrumentation. It’s the first critical step that allows companies to monitor and improve application performance. OpenTelemetry also supports multiple programming languages and technologies.
In addition to collecting telemetry data across applications, OpenTelemetry provides services and supporting infrastructures for those applications. It is a vendor-neutral telemetry standard used throughout the industry. OTel combines cloud technologies, orchestration engines, and containers to facilitate faster digital innovation.
Of course, OpenTelemetry also provides enterprises with flexibility by allowing them to standardize the way they collect data with less vendor lock-in and greater interoperability. The best way for companies to move forward is by understanding their customers’ needs and how they interact with their online experiences.
OpenTelemetry’s vendor-neutral open-source tools, APIs, and SDKs support multiple programming languages, including Go, Java, and Python. These tools work together to execute the measurement by specifying what needs to be measured, collecting relevant data, cleaning and organizing information, and exporting data to a monitoring backend.
Prometheus is a metrics monitoring tool used for monitoring time-series data that changes over time. Initially developed at SoundCloud in 2012, the CloudNative Computing Foundation accepted Prometheus in 2016 as the second project slated to graduate from the foundation after the open-source system Kubernetes.
Prometheus prioritizes reliability over accuracy, making it an ideal solution for cloud-based dynamic systems, such as microservices. The system also works with billing applications that require accuracy and may provide the best solution for relative monitoring applications and infrastructures.
SoundCloud originally developed Prometheus as a toolkit for alerting and monitoring systems. It’s a free, open-source software application, so it’s easy to integrate natively. Since its launch, many organizations and companies have adopted Prometheus, and the project has an active user and developer community.
OTel is becoming the industry standard in telemetry data generation. Distributed systems rely heavily on observability to gauge their health, and telemetry data makes observability possible. Using OpenTelemetry, companies can generate telemetry data without depending on multiple vendors.
OpenTelemetry allows companies to collect telemetry data regardless of the provider. Distributed systems rely heavily on telemetry data for monitoring their states. Therefore, a global standard is required for microservices and polyglot architectures. As of now, OTel is well positioned to fill this void.
Furthermore, OpenTelemetry’s components are loosely coupled to provide integration options. The main OpenTelemetry components are:
In short, OpenTelemetry APIs, SDKs, libraries, and integrations can collect and manage telemetry data (traces, metrics, and logs). The OpenTelemetry project was created through a merger between OpenCensus and OpenTracing. The Cloud Native Computing Foundation (CNCF) also incubated Kubernetes.
Prometheus stores data locally on disk so that it can be accessed and queried quickly. It also allows metrics to be stored remotely. Prometheus servers are stand-alone and do not rely on remote services or network storage.
In Prometheus, data is collected as a time series generated by an underlying pull model. At specific intervals of time, Prometheus queries a list of data sources or exporters. For reference and querying, Prometheus data is stored in metrics.
Prometheus stores data locally on disk so it can be accessed and queried quickly. It also allows metrics to be stored remotely. In addition, the Prometheus servers are stand-alone and do not rely on remote services or network storage.
As a multidimensional data model, Prometheus supports PromQL*, a language that allows companies to query the metrics data collected. Not only can companies pull model data collection over HTTP with Prometheus, but it also supports a basic visualization layer and offers an alert manager to handle alerts.
Prometheus Query Language, or PromQL, is the query language provided by Prometheus for selecting and aggregating data. It is precisely adjusted to work in convention with a time-series database and offers time-related query functionalities.
Prometheus’s main features are:
OpenTelemetry works with three major forms of telemetry data: tracing, metrics, and logging. OTel can help track requests within a system to identify performance issues and failures. In terms of metrics, the system tracks and reports on processes through histograms, gauges, and other graphical reports that are easy to understand. Finally, the last way to analyze logging messages is to analyze those specific to a given application.
Tracing, metrics, and logging have always been crucial to observing a system across its entire lifecycle. However, the complexity of modern applications and resource layers makes it difficult to implement tracing across all services. For example, a single incident can be tracked for hours when paired with vague log data because the information needs to be siloed consistently.
OpenTelemetry (and other similar products) seeks to correct this problem through its consolidated system for metrics, tracing, and logging. GitHub provides a platform for those interested in participating in a community to influence and improve OpenTelemetry as an open-source project. Analyzing telemetry data can help create a multi-layered ecosystem. As a result, your company may be able to address performance issues more efficiently.
OpenTelemetry’s main features include the following:
By following these practices, you can help ensure a smooth and effective OTel integration.
By following these practices, you can effectively integrate Prometheus into your existing systems.
This step-by-step guide provides an example of OpenTelemetry setup using Go.
For more details on setting up OTel, visit the OpenTelemetry Go guide.
Getting started with Prometheus is fairly simple and straightforward. These steps will help you get your Prometheus setup underway.
The Prometheus getting started guide offers more in-depth details for setting up Prometheus.
To collect and transmit telemetry data to backend platforms, OpenTelemetry is essential. The common instrumentation format overcomes visibility gaps across all services. In addition, engineers can install a new proprietary agent whenever a backend platform is changed.
OpenTelemetry also handles new technologies, unlike commercial solutions, which must be integrated to make products work together. Aside from simplifying alerting, troubleshooting, and debugging, OTel data is helpful for monitoring, too.
Collecting and analyzing telemetry data has always been used to understand system behavior. However, recent network complexity has made this more challenging. Tracing the cause of an individual incident in these labyrinthine systems can take hours or days.
OpenTelemetry can help correlate traces, logs, and metrics from across applications and services, improving observability. The open-source project also enables APM (application performance monitoring) and other vital functions by removing roadblocks to instrumentation. Ultimately, this results in better service reliability, reduced downtime, and efficiency in identifying and resolving incidents.
Organizations widely use OpenTelemetry to trace requests across complex microservice architectures. This helps developers identify performance bottlenecks and track the flow of requests. Shopify uses OTel for tracing in its distributed systems.
OpenTelemetry provides unified observability across cloud-native environments, enabling effective monitoring of applications running on many kinds of modern infrastructures. Google integrates OpenTelemetry into its cloud-native services to give customers superior cloud monitoring capabilities.
By collecting and correlating metrics, traces, and logs, OpenTelemetry gives teams a comprehensive view of their systems and provides root cause analysis. Microsoft employs OpenTelemetry in Azure services to standardize observability across its cloud platform.
Prometheus is popular due to its powerful features for monitoring metrics, providing alerts, and automating responses to changing conditions with orchestration systems. In addition to collecting metrics—or concise descriptions of events, such as dates, times, and descriptive values—about applications and infrastructure, Prometheus can monitor performance. The Prometheus software gathers only a little bit of data about many things rather than collecting a lot of data about one thing.
Users rely on Prometheus to monitor servers, containers, and networking equipment and offer real-time data on system health and performance. Reddit relies on Prometheus to monitor its large-scale infrastructure, monitor the environment, and quickly identify issues.
Prometheus collects metrics from applications to track performance and ensure reliable operation across connected environments. GitLab employs Prometheus to monitor their CI/CD pipeline performance and monitor application services to help maintain high availability and optimize application performance.
Prometheus can integrate with alerting tools like Alertmanager to notify teams of issues based on custom thresholds, ensuring rapid response to incidents. SoundCloud, the pioneer of Prometheus, takes advantage of its alert functions to quickly notify them of issues in its streaming service infrastructure.
OpenTelemetry expects to focus on enhancing its capabilities across the board, particularly focusing on improving support for logs, metrics, and traces. OTel will likely emphasize better integration with various backends and increased automation capabilities. Users can expect advancements in community collaboration, a broader toolset for simplifying implementation, and OpenTelemetry’s use in complex cloud ecosystems. OTel also expects to provide enhanced observability in mobile environments and bring deeper integration with mobile platforms.
Prometheus has prepared a roadmap that outlines developer ambitions for the platform. Plans include improving scalability and enhanced availability features, as well as smoother integration with other cloud-native systems. Prometheus will likely continue developing long-term storage solutions, strive for better support for service discovery, and seek to boost query performance. The Prometheus community is also focused on refining the user experience, including the development of more robust visualization and alerting functions.
Organizations with higher levels of observability have a much easier time tracking down and resolving issues, resulting in more reliable operations and fewer downtimes. Tools like OpenTelemetry and Prometheus can aid organizations in meeting observability goals in different ways. While Prometheus supports rich queries, metrics, and interoperability as part of the cloud-native ecosystem, OpenTelemetry collects metrics, traces, metadata, and logs and scales with organizational growth.
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