instrumenting-applications.md

Instrumenting applications with EDOT SDKs on Kubernetes

Elastic Distributions of OpenTelemetry (EDOT) SDKs cover multiple languages:

• EDOT Java
• EDOT .NET
• EDOT Node.js
• EDOT Python
• EDOT PHP

This section provides guidance and examples for applications instrumentation in a Kubernetes environment for all supported languages.

In Kubernetes environments with the OpenTelemetry Operator, automatic (or zero-code) instrumentation simplifies the process by injecting and configuring instrumentation libraries into the targeted Pods.

On the other hand, manual instrumentation with OpenTelemetry allows you to customize trace spans, metrics, and logging directly in your application’s code. This approach provides more granular control over what and how data is captured.

Table of contents

• Supported languages
• Prerequisites
• Auto-instrumentation basics
• Configuring auto-instrumentation
• How auto-instrumentation works
• Advanced configuration
• Manual instrumentation

Supported languages

The following table illustrates the different languages supported by OpenTelemetry (OTel) and the Elastic Stack, the type of SDK/API used for instrumentation (either zero-code or source code dependencies), and the corresponding deployment types (on-premises, ESS, or serverless) for each language.

Language	OTel SDK/API Type	Deployment Model Support
Java	EDOT Java - zero-code instrumentation	All deployment types
Node.js	EDOT Node.js - zero-code instrumentation	All deployment types
.NET	EDOT .NET - zero-code instrumentation	All deployment types
PHP	EDOT PHP - source code dependencies	All deployment types
Python	EDOT Python - zero-code instrumentation	All deployment types
Swift	EDOT Swift - source code dependencies	ESS, on-premises
Android	EDOT Android - source code dependencies	ESS, on-premises
Javascript	Vanilla OTel RUM SDK/API - source code dependencies	ESS, on-premises
Rust	Vanilla OTel Rust SDK/API - source code dependencies	All deployment types
Ruby	Vanilla OTel Ruby SDK/API - source code dependencies	All deployment types
Go	Vanilla OTel Go SDK/API - zero-code instrumentation	All deployment types
C++	Vanilla OTel C++ SDK/API - source code dependencies	All deployment types

Prerequisites

Before starting with application auto-instrumentation, ensure the following prerequisites are in place for proper setup:  

• Install the OpenTelemetry operator and EDOT collectors following the getting started guide.
• Ensure a valid kind: Instrumentation object exists in the cluster.

Auto-instrumentation basics

Zero-code instrumentation is handled by the operator through Instrumentation objects, used to automatically inject the necessary SDKs and configuration into application workloads.

If you followed the getting started guide to install the operator, there should be an Instrumentation object with name elastic-instrumentation in namespace opentelemetry-operator-system:

kubectl get instrumentation -A
NAMESPACE                       NAME                      AGE     ENDPOINT                                                                                                SAMPLER                    SAMPLER ARG
opentelemetry-operator-system   elastic-instrumentation   5d20h   http://opentelemetry-kube-stack-daemon-collector.opentelemetry-operator-system.svc.cluster.local:4318   parentbased_traceidratio   1.0

The Instrumentation object stores important parameters:

• The exporter endpoint represents the destination for the traces, in this case the HTTP receiver configured in the EDOT DaemonSet Collector. That endpoint has to be reachable by the Pods being instrumented.

  exporter:
    endpoint: http://opentelemetry-kube-stack-daemon-collector.opentelemetry-operator-system.svc.cluster.local:4318

• Language-specific images used by the operator to inject the appropriate library into each Pod.

  dotnet:
    image: docker.elastic.co/observability/elastic-otel-dotnet:edge
  java:
    image: docker.elastic.co/observability/elastic-otel-javaagent:1.0.0
  nodejs:
    image: docker.elastic.co/observability/elastic-otel-node:0.4.1
  python:
    image: docker.elastic.co/observability/elastic-otel-python:0.4.1

Configuring auto-instrumentation

To enable auto-instrumentation, add the corresponding language annotation to the Pods template (spec.template.metadata.annotations) in your Deployment or relevant workload object (StatefulSet, Job, CronJob, etc.).

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  ...
  template:
    metadata:
      annotations:
        instrumentation.opentelemetry.io/inject-<LANGUAGE>: "opentelemetry-operator-system/elastic-instrumentation"
      ...
    spec:
      containers:
      - image: myapplication-image
        name: app
      ...

where <LANGUAGE> is one of: go , java, nodejs, python, dotnet

Note

Ensure you add the annotations at Pod level and not directly at the workload spec level (Deployment, Job, etc.). Ensure the annotation value points to an existing Instrumentation object.

Alternatively, you can enable auto-instrumentation by adding the annotation at namespace level. This approach automatically applies instrumentation to all Pods within the specified namespace.

apiVersion: v1
kind: Namespace
metadata:
  name: mynamespace
  annotations:
    instrumentation.opentelemetry.io/inject-<LANGUAGE>: "opentelemetry-operator-system/elastic-instrumentation"

After adding annotations to Pods or Namespaces, the applications must be restarted for the instrumentation injection to take effect:

kubectl rollout restart deployment/my-deployment

In case you have multiple Instrumentation objects with different settings or images, ensure you point your Pods to the desired Instrumentation objects in the annotations.

The possible values for the annotation are detailed in the Operator documentation. For reference purposes, the values are:

• "true": to inject Instrumentation instance with default name from the current namespace.
• "my-instrumentation": to inject Instrumentation instance with name "my-instrumentation" in the current namespace.
• "my-other-namespace/my-instrumentation": to inject Instrumentation instance with name "my-instrumentation" from another namespace "my-other-namespace".
• "false": do not inject.

For details on instrumenting specific languages, refer to:

• Instrumenting Java
• Instrumenting Python
• Instrumenting Node.js

Namespace based annotations example

The following example creates a namespace with an annotation to instrument all Pods of the namespace with java libraries.

kubectl create namespace java-apps

# Annotate app namespace
kubectl annotate namespace java-apps instrumentation.opentelemetry.io/inject-java="opentelemetry-operator-system/elastic-instrumentation"

# Run a java example application in the namespace
kubectl run otel-test -n java-apps --env OTEL_INSTRUMENTATION_METHODS_INCLUDE="test.Testing[methodB]" --image docker.elastic.co/demos/apm/k8s-webhook-test

Verify auto-instrumentation

After adding the annotation and restarting the Pods, run kubectl describe on your application Pod to verify the SDK has been properly attached.

Ensure that the init container, volume, and environment variables described in how auto-instrumentation works have been successfully injected into the Pod.

How auto-instrumentation works

The OpenTelemetry Operator automates the process of instrumenting applications by injecting the necessary libraries and configuration into the application Pods. The process may vary slightly depending on the language, but it generally involves the following steps:

• Creating a shared volume:

  The operator declares an emptyDir shared volume within the Pod, and mounts it the app container and a new init container. This volume serves as the medium for sharing the instrumentation library between the new init container and the application container.

• Adding an init container:

  The operator adds an init container into the Pod. This container is responsible for copying the OpenTelemetry instrumentation library to the shared volume.

• Configuring the main container:

  The operator injects environment variables into the main application container to configure OpenTelemetry settings (for example, OTEL_EXPORTER_OTLP_ENDPOINT or OTEL_TRACES_SAMPLER). Additionally, it links the instrumentation library to the application using mechanisms specific to the language runtime, such as:

  • For Java: The library is linked through the javaagent option using the JAVA_TOOL_OPTIONS environment variable.
  • For Node.js: The library is linked through the NODE_OPTIONS environment variable.
  • For Python: The operator uses the PYTHONPATH environment variable to load the library sitecustomize module.

Advanced configuration

You can apply OTEL specific configuration to your applications at two different levels:

• At Pod/container level, by using OTEL related environment variables.
• At Instrumentation object level, for example configuring different settings per language.

Use cases:

• Change the library to be injected.
• Change the exporter endpoint.
• Apply certain logging level settings (OTEL_LOG_LEVEL).

Adding extra Instrumentation objects

Consider also the creation of different Instrumentation objects for different purposes, such as:

• Different configuration options for certain languages.
• Trying out different versions of the SDKs.

(TBD: add instructions and references about Instrumentation objects)

Manual instrumentation

(TBD, in-progress)

The manual instrumentation... Configuration requirements (does every language has its own requirements)? Exporter destination? HTTP vs OTLP? does each EDOT SDK support different protocols?