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By Eleanor Bennett


4 min read

You may have previously heard about OpenTelemetry (also known as OTel) if you have looked into improved ways of standardising different data types. In this article, we’ll delve into the key things you need to know about OpenTelemetry and how this unified standard may become the future of how logs, metrics, events and traces are all handled.

What Is OpenTelemetry?

OpenTelemetry provides a framework to generate telemetry data and is based on offering standardisation for four primary data types, traces, metrics, events and logs. These various different data types all generate a record of what has occurred within your system which can be analysed to uncover the root cause of errors and understand other issues in greater detail.

The OpenTelemetry project does not offer a back end and is entirely open-source as well as being completely vendor agnostic. By not offering a back end, OpenTelemetry offers the end user more freedom in allowing them to switch between using any service of choice without affecting the quality or consistency of their telemetry data.

OpenTelemetry is the second most active project hosted by the CNCF (Cloud Native Computing Foundation). OpenTelemetry has already welcomed contributions from a wide number of cloud providers, vendors and end-users who are all passionate about the project’s success and who work in tandem to ensure the reliability of its collectors, APIs and libraries.

As OpenTelemetry is so well developed and supported by such a large number of engineers many of the common blockers you may encounter should be addressed and answered by preexisting documentation.

What Is OpenTelemetry Used For?

OpenTelemetry assists in making robust and portable telemetry a feature of cloud-native software. By leveraging semantic conventions that define an open and universal standard, OpenTelemetry makes any telemetry data it generates more portable across both vendors and open-source tooling.

The project aims to achieve this goal by providing a set of APIs, libraries and collectors which are bundled automatically. By using these resources you can easily collect the telemetry data you need in order to observe and monitor your systems fully in an external tool of your choosing.

OpenTelemetry can help to provide context on why your application is running too slowly, is encountering errors or can help assist you in improving overall code quality.

OpenTelemetry facilitates the measurement of the performance of an application or distributed system, remotely, universally across programming languages and operating systems.

The History Of OpenTelemetry

OpenTelemetry is made up of two former projects that had many overlapping similarities, these projects being, OpenCensus (for generating metrics data) and OpenTracing (for generating trace data).

OpenTracing provided an API that helped developers instrument tracing into their codebase prior to being archived in January 2022 and Opencensus was an open-sourced project out of Google that supported both the capturing of retracing permissions and metrics.

Both projects were able to make observability easier and expedite the wider adoption of distributed tracing by the software industry. However, prior to the projects becoming merged, developers had to choose between these two different options and their respective pros and cons. To reduce unnecessary administration, the idea was brought forward to instead have a single standard for observability instead of two competing standards.

Due to these two solutions working with many of the same end goals in mind, in late 2019 Opencensus and Opentracing were merged to form OpenTelemetry.

What Is An OpenTelemetry Collector?

An important component of OpenTelemetry is its collector, which allows you to perform at scale. OpenTelemetry sends your data to the collector, and from here the collector is then responsible for handling scaling, throughput and sending the data to a backend. It's also possible to perform in-transit operations on your data such as appending or removing metadata, for example.

While you might not initially have a use case to run the OpenTelemetry collector, it is worth knowing that this is an easy to configure step within the process of using the project. There are also many examples of working collectors in the OpenTelemetry repository itself which will allow you to simply set up the collector when you do have a suitable use case.

In the event that you don't wish to maintain multiple agents (such as Jaeger & Prometheus), the main benefit of using the OpenTelemetry collector is that it can be used as a single solution which can fully support the sending of telemetry data to numerous backends.

Observability For Telemetry Data

The telemetry data that is collected and transferred using OTel still needs to be processed using an external analysis tool to provide its most valuable insights in a readily consumable format. In order to harness your traces, metrics and logs, you will need to unlock the ability to correlate and generate insights within an external backend.

When you are choosing a backend where you want to send the telemetry data you have options such as using a tool like Jaeger, using another open-source tool or using a proprietary service as well as any backend that will support the use of the OTLP (OpenTelemetry protocol) format.

By sending your OpenTelemetry data to an external service, you can easily search your information and use it to play a part in larger infrastructure reporting dashboards.

If you wish to handle your OpenTelemetry data in an external analysis tool then a platform such as for OpenTelemetry provides complete cross-compatibility to handle telemetry data for monitoring, alerting and analysis

While OpenTelemetry is not an observability platform, is, OpenTelemetry is simply a standard for creating OpenTelemetry data itself. Once you have generated this data, allows you to highlight the status of your distributed system by providing a single source of truth in which to centralise your data for reporting, monitoring and alerting.

If you found this post informative then why not check out our previous guide to Kibana vs Grafana or our previous post on what is null in Java?

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