How OpenTelemetry facilitates automatic and manual instrumentation of applications.

In order to make a system observable, it must be instrumented: That is, code from the system’s components must emit traces, metrics, and logs.

Without being required to modify the source code you can collect telemetry from an application using Automatic Instrumentation. If you previously used an APM agent to extract telemetry from your application, Automatic Instrumentation will give you a similar out of the box experience.

To facilitate the instrumentation of applications even more, you can manually instrument your applications by coding against the OpenTelemetry APIs.

For that you don’t need to instrument all the dependencies used in your application:

  • some of your libraries will be observable out of the box by calling the OpenTelemetry API themselves directly. Those libraries are sometimes called natively instrumented.
  • for libraries without such an integration the OpenTelemetry projects provide language specific Instrumentation Libraries

Note, that for most languages it is possible to use both manual and automatic instrumentation at the same time: Automatic Instrumentation will allow you to gain insights into your application quickly and manual instrumentation will enable you to embed granular observability into your code.

The exact installation mechanism for manual and automatic instrumentation varies based on the language you’re developing in, but there are some similarities covered in the sections below.

Automatic Instrumentation

If applicable a language specific implementation of OpenTelemetry will provide a way to instrument your application without touching your source code. While the underlying mechanism depends on the language, at a minimum this will add the OpenTelemetry API and SDK capabilities to your application. Additionally they may add a set of Instrumentation Libraries and exporter dependencies.

Configuration is available via environment variables and possibly language specific means such as system properties in Java. At a minimum, a service name must be configured to identify the service being instrumented. A variety of other configuration options are available and may include:

  • Data source specific configuration
  • Exporter configuration
  • Propagator configuration
  • Resource configuration

Manual Instrumentation

Import the OpenTelemetry API and SDK

You’ll first need to import OpenTelemetry to your service code. If you’re developing a library or some other component that is intended to be consumed by a runnable binary, then you would only take a dependency on the API. If your artifact is a standalone process or service, then you would take a dependency on the API and the SDK. For more information about the OpenTelemetry API and SDK, see the specification.

Configure the OpenTelemetry API

In order to create traces or metrics, you’ll need to first create a tracer and/or meter provider. In general, we recommend that the SDK should provide a single default provider for these objects. You’ll then get a tracer or meter instance from that provider, and give it a name and version. The name you choose here should identify what exactly is being instrumented – if you’re writing a library, for example, then you should name it after your library (for example com.legitimatebusiness.myLibrary) as this name will namespace all spans or metric events produced. It is also recommended that you supply a version string (i.e., semver:1.0.0) that corresponds to the current version of your library or service.

Configure the OpenTelemetry SDK

If you’re building a service process, you’ll also need to configure the SDK with appropriate options for exporting your telemetry data to some analysis backend. We recommend that this configuration be handled programmatically through a configuration file or some other mechanism. There are also per-language tuning options you may wish to take advantage of.

Create Telemetry Data

Once you’ve configured the API and SDK, you’ll then be free to create traces and metric events through the tracer and meter objects you obtained from the provider. Make use of Instrumentation Libraries for your dependencies – check out the registry or your language’s repository for more information on these.

Export Data

Once you’ve created telemetry data, you’ll want to send it somewhere. OpenTelemetry supports two primary methods of exporting data from your process to an analysis backend, either directly from a process or by proxying it through the OpenTelemetry Collector.

In-process export requires you to import and take a dependency on one or more exporters, libraries that translate OpenTelemetry’s in-memory span and metric objects into the appropriate format for telemetry analysis tools like Jaeger or Prometheus. In addition, OpenTelemetry supports a wire protocol known as OTLP, which is supported by all OpenTelemetry SDKs. This protocol can be used to send data to the OpenTelemetry Collector, a standalone binary process that can be run as a proxy or sidecar to your service instances or run on a separate host. The Collector can then be configured to forward and export this data to your choice of analysis tools.

In addition to open source tools such as Jaeger or Prometheus, a growing list of companies support ingesting telemetry data from OpenTelemetry. For details, see Vendors.