OpenTelemetry and Prometheus are classified as monitoring tools, but they also have significant differences that your company should know about. 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.
Prometheus, however, prioritizes reliability over accuracy. As a result, it’s an ideal solution for cloud-based dynamic systems, such as microservices. However, the system will only work well with a billing application that requires accuracy. The drawbacks of Prometheus may not prevent it from being an excellent tool for monitoring applications and infrastructures.
What is OpenTelemetry (OTEL)?
In addition to collecting telemetry data across applications, OpenTelemetry also 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.
How is OTEL built?
OpenTelemetry allows companies to collect telemetry data regardless of the provider. Distributed systems rely heavily on telemetry data for monitoring their state. Therefore, a global standard was required for microservices and polyglot architectures. As of now, OTEL is well-positioned to fill this void.
Your company can use programming languages, including Go, Java, and Python, to implement OpenTelemetry’s vendor-neutral open-source tools, APIs, and SDKs. Besides specifying what needs to be measured, collecting the relevant data, cleaning and organizing the information, and exporting it to a monitoring backend, these tools work together to execute the measurement.
Furthermore, OpenTelemetry’s components are loosely coupled, so you can easily pick and choose which parts of OTEL you want to integrate. The main OpenTelemetry components are:
- Application programming interfaces (APIs): With APIs, you can instrument your code and coordinate data collection across your entire system.
- Data specifications: OpenTelemetry Protocol (OTLP) and relevant semantic conventions used by applications are defined by the data specifications.
- Software development kits (SDKs): Using libraries, SDKs implement and support APIs for data gathering, processing, and exporting. The SDKs are also language-specific, similar to APIs.
- OpenTelemetry Collector: This can be used across a wide range of systems, whether they are open-source or commercial.
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 CNCF incubated Kubernetes as well.
How does OTEL work?
OpenTelemetry works with three major forms of telemetry data: tracing, metrics, and logging. With OTEL, you can track requests within the 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 cohere a multi-layered ecosystem. As a result, your company may address performance issues more efficiently.
What are the features of OTEL?
OpenTelemetry’s main features include the following:
- Support for telemetry data in consistent formats across all major programming
- OpenTelemetry exports telemetry data in multiple formats to a backend of your choice
- OpenTelemetry allows you to log, monitor metrics, and trace telemetry data
- OpenTelemetry supports multi-language and technology-agnostic instrumentation
Why is OpenTelemetry important?
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.
With OpenTelemetry, you can handle new technologies, unlike with commercial solutions, which must be integrated to make their products work together. Aside from simplifying alerting, troubleshooting, and debugging, OTEL data is also helpful for monitoring.
Collecting and analyzing telemetry data has always been used to understand system behavior. However, recent network complexity has made collecting and analyzing telemetry data more challenging. It can take hours or days to trace the cause of an individual incident in these labyrinthine systems.
Using OpenTelemetry, you can correlate traces, logs, and metrics from across applications and services, improving observability. APM (application performance monitoring) and other vital functions are also enabled via the open-source project by removing roadblocks to instrumentation. Ultimately, this results in better service reliability, reduced downtime, and efficiency in identifying and resolving incidents.
OpenTelemetry advantages and disadvantages
OTEL is quickly becoming the industry standard in telemetry data generation. Distributed systems rely heavily on observability to gauge their health. Telemetry data makes observability possible. Using OpenTelemetry, your company can generate telemetry data without depending on multiple vendors. Here are the advantages and disadvantages of Open Telemetry.
Advantages
- Better performance: OpenTelemetry manages and generates telemetry efficiently.
- Collector: OpenTelemetry allows your company to receive, process, and export telemetry data in various formats.
- Easy to use: OpenTelemetry is business-friendly, with documentation that allows you to troubleshoot and repair bugs more quickly.
- Language support: OpenTelemetry supports C++, Go, Java, Javascript, Python, etc.
- Libraries: OpenTelemetry offers auto-instrument frameworks and libraries to reduce codebase change requirements.
- Observability: Integration points for OpenTelemetry allow the collected data to be ingested by observability platforms.
- Partners and backers: Includes Google, Microsoft, and other prominent cloud vendors.
- Flexibility: OpenTelemetry is vendor-neutral, which allows your company to switch your tools.
Disadvantages
- Lack of Storage: OpenTelemetry does not provide a visualization layer or backend storage.
- Poor support: There’s room for improvement with OpenTelemetry’s support and documentation.
Now that we’ve considered OpenTelemetry, let’s look at Prometheus.
What is Prometheus?
Your company can use Prometheus as a metrics monitoring tool. 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 Kubernetes. Prometheus is a good tool for monitoring time-series data that changes over time.
How is Prometheus built?
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. Many organizations and companies have adopted Prometheus since its launch, and the project has an active user and developer community.
How does Prometheus work?
In Prometheus, data is collected as time series. An underlying pull model generates the time series. During 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 quickly and queried quickly, but 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.
What are the features of Prometheus?
Prometheus’s main features are:
- Dedicated server nodes are autonomous and do not rely on distributed storage.
- Prometheus offers a data model that identifies time series data by metric name and key/values.
- Prometheus supports your company’s ability to display graphs and dashboards in multiple ways.
- The discovery of targets can be accomplished through static configuration or service discovery.
- The time series collection takes place over HTTP using a pull model.
- Through an intermediary gateway, time series can be pushed.
- With PromQL, companies can leverage dimensionality through flexible querying.
What is PromQL?
Prometheus provides its query language, PromQL (Prometheus Query Language). You can use PromQL to select and aggregate data. PromQL is precisely adjusted to work in convention with a time-series database, providing time-related query functionalities.
Why is Prometheus important?
In addition to collecting metrics about your applications and infrastructure, Prometheus can monitor its performance. A metric is a concise description of an event, such as the date, time, and descriptive value. The Prometheus software gathers only a little bit of data about many things rather than collecting a lot of data about one thing.
This allows you to better understand the trajectory and state of your system. Prometheus is popular mainly due to its powerful features for monitoring metrics, providing alerts, and automating responses to changing conditions with orchestration systems.
Prometheus advantages and disadvantages
As a multi-dimensional 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. Prometheus also offers an alert manager to handle alerts.
Advantages
- Pull-built: Prometheus actively scrapes targets to retrieve metrics from them. The Prometheus server initiates an HTTP call to fetch system/app metrics from endpoints to the exporter.
- Control: A pull-based system enables rate control, and you still have the flexibility of multiple scrap configurations. You get multiple rates for different targets, which allows you to have better control from your Prometheus server.
Disadvantages
- Limited Use Cases: Prometheus is a great tool for metrics monitoring, but it doesn’t go further than that.
- Full-stack: It is not a full-stack application monitoring tool.
- Logs: Prometheus only tracks metrics, so users need to rely on other tools for logging.
- Static: It’s only designed for a single machine, so it lacks flexibility or the ability to scale horizontally.
- Time: It requires effort and time to effectively set up exporters for a great visualization.
- Traces: Prometheus does not support traces, so it does not support root cause analysis.
Most significant differences between OTEL and Prometheus
OpenTelemetry helps companies generate telemetry data by instrumenting their code. Prometheus, on the other hand, monitors metrics. There are client libraries for both Prometheus and OpenTelemetry, but OTEL provides an all-in-one solution for logging, metrics, and tracing your code. Prometheus only generates metrics.
Visualization Layer
The visualization layer in Prometheus is basic. With OTEL, no visualization layer is provided, and any backend analysis tool can be used to analyze the data collected with OpenTelemetry.
Observability Practices
In comparing OpenTelemetry and Prometheus, the OTEL framework provides a fundamental layer for building observability practices. Those practices are critical for microservices architecture. Using Prometheus in your observability stack requires other tools for traces and logs.
Storage Solution
OpenTelemetry provides no storage solution, but it does provide exporters. Companies can configure exporters to send data to backend analysis tools. With Prometheus, you can surface data added within a given period via time-based range or duration selections.
Summing it up
Prometheus is usually integrated with other solutions to power graphical dashboards as an effective system for storing and querying metrics. Prometheus supports rich queries, metrics, and interoperability as part of the cloud-native ecosystem.
By using OpenTelemetry, organizations can better manage and collect telemetric data. In addition to being scalable and flexible, OTEL can adapt to the growth of organizations over time. With OpenTelemetry, you can collect metrics, traces, metadata, and logs. Then, you can send it to backend platforms like Prometheus for processing.
Organizations with higher levels of observability are more reliable and have fewer downtimes. As a result, organizations have a much easier time tracking down and resolving issues.