Instrument OpenTelemetry for Non-Kubernetes Environments in One Simple Step

Observability Courtney Gannon

Since the beginning of our involvement with the OpenTelemetry project, Splunk’s mission has been to make OpenTelemetry accessible and easy to adopt for every organization and every environment. We’re excited to share our latest donation to the project, the OpenTelemetry Injector.

The OpenTelemetry Injector makes implementation incredibly easy and expands OpenTelemetry's reach and ease of use for organizations with diverse infrastructure. It enables zero-code instrumentation for applications running on Linux hosts and significantly reduces the operational burden of implementation. With the OpenTelemetry Injector, you can capture metrics, traces, logs, and profiles from your infrastructure and applications with a single simple step. By making instrumentation easier and less intrusive, the injector encourages wider adoption of OpenTelemetry for organizations with existing applications where code changes might be difficult or impractical.

How the OpenTelemetry Injector Works

The OpenTelemetry injector is a shared library (written in Zig) that is intended to be used via the environment variable LD_PRELOAD, the /etc/ld.so.preload file, or similar mechanisms to inject environment variables into processes at startup.

It serves two main purposes:

The injector can be used to enable automatic zero-touch instrumentation of processes. For this to work, the injector binary needs to be bundled together with the OpenTelemetry auto-instrumentation agents for the target runtimes.

Official RPM and DEB packages that contain the injector as well as the auto-instrumentation agents are available, and can be downloaded from the releases page (once the first release has been published). The OpenTelemetry injector Debian/RPM packages install the OpenTelemetry auto-instrumentation agents, the libotelinject.so shared object library, and a default configuration file to automatically instrument applications and services to capture and report distributed traces and metrics to the OpenTelemetry Collector.

The opentelemetry-injector deb/rpm package installs and supports configuration of the following Auto Instrumentation agents:

Getting Started

The following methods are supported to manually activate and configure Auto Instrumentation after installation of the opentelemetry-injector deb/rpm package (requires root privileges):

Note: To prevent conflicts and duplicate traces/metrics, only one method should be activated on the target system.

Get Involved

Want to learn more or contribute?

💬 Join the conversation in the #otel-injector channel on the OpenTelemetry Slack.
🔗 Check out the opentelemetry-injector repository.

You can read more about the OpenTelemetry Injector and read about Splunk’s other contributions to the OpenTelemetry project in this blog or watch this on-demand webinar, "OTel Demystified: From Community Contributions to Splunk's Enhanced Distribution."

Related Articles

Observability Costs: Tips for More Efficient Data Management
Observability
5 Minute Read

Observability Costs: Tips for More Efficient Data Management

Splunker Joanna Zouhour explores tips for utilizing OpenTelemetry and Splunk's advanced tools to separate valuable data from noise and streamline your observability practice.
Observability That Tracks What Matters: Monitoring Critical User Journeys End to End
Observability
7 Minute Read

Observability That Tracks What Matters: Monitoring Critical User Journeys End to End

Your backend might be green, but is your user experience? This guide breaks down 7 must-have capabilities to observe Critical User Journeys end to end.
Splunk APM Expands Code Profiling Capabilities with Several New GAs
Observability
3 Minute Read

Splunk APM Expands Code Profiling Capabilities with Several New GAs

We’re excited to share new Splunk capabilities to help measure how code performance impacts your services – Splunk APM’s AlwaysOn Profiling now supports .NET and Node.js applications for CPU profiling and Java applications for memory profiling.