Injecting Libraries Locally

With the Admission Controller approach, the Agent uses the Kubernetes Admission Controller to intercept requests to the Kubernetes API and mutate new pods to inject the specified instrumentation library.

To learn more about Kubernetes Admission Controller, read Kubernetes Admission Controllers Reference.

Requirements

• Kubernetes v1.14+
• Datadog Cluster Agent v7.40+ for Java, Python, NodeJS, Datadog Cluster Agent v7.44+ for .NET and Ruby.
• Datadog Admission Controller enabled. Note: In Helm chart v2.35.0 and later, Datadog Admission Controller is activated by default in the Cluster Agent.
• For Python, uWSGI applications are not supported at this time.
• For Ruby, library injection support is in Beta. Instrumentation is only supported for Ruby on Rails or Hanami applications at this time.
• Applications in Java, JavaScript, Python, .NET, or Ruby deployed on Linux with a supported architecture. Check the corresponding container registry for the complete list of supported architectures by language.

Container registries

Datadog publishes instrumentation libraries images on gcr.io, Docker Hub, and AWS ECR:

The DD_ADMISSION_CONTROLLER_AUTO_INSTRUMENTATION_CONTAINER_REGISTRY environment variable in the Datadog Cluster Agent configuration specifies the registry used by the Admission Controller. The default value is gcr.io/datadoghq.

You can pull the tracing library from a different registry by changing it to docker.io/datadog, public.ecr.aws/datadog, or another URL if you are hosting the images in a local container registry.

Configure instrumentation libraries injection

For your Kubernetes applications whose traces you want to send to Datadog, configure the Datadog Admission Controller to inject Java, JavaScript, Python, .NET or Ruby instrumentation libraries automatically. From a high level, this involves the following steps, described in detail below:

1. Enable Datadog Admission Controller to mutate your pods.
2. Annotate your pods to select which instrumentation library to inject.
3. Tag your pods with Unified Service Tags to tie Datadog telemetry together and navigate seamlessly across traces, metrics, and logs with consistent tags.
4. Apply your new configuration.

Step 1 - Enable Datadog Admission Controller to mutate your pods

By default, Datadog Admission controller mutates only pods labeled with a specific label. To enable mutation on your pods, add the label admission.datadoghq.com/enabled: "true" to your pod spec.

Note: You can configure Datadog Admission Controller to enable injection config without having this pod label by configuring the Cluster Agent with clusterAgent.admissionController.mutateUnlabelled (or DD_ADMISSION_CONTROLLER_MUTATE_UNLABELLED) to true.

For more details on how to configure, read Datadog Admission Controller page.

For example:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    ...
...
template:
  metadata:
    labels:
        admission.datadoghq.com/enabled: "true" # Enable Admission Controller to mutate new pods part of this deployment
  containers:
  -  ...

Step 2 - Annotate your pods for library injection

To select your pods for library injection, annotate them with the following, corresponding to your application language, in your pod spec:

The available library versions are listed in each container registry, as well as in the tracer source repositories for each language:

• Java
• Javascript
• Python
• .NET
  • Note: For .NET library injection, if the application container uses a musl-based Linux distribution (such as Alpine), you must specify a tag with the the -musl suffix for the pod annotation. For example, to use library version v2.29.0, specify container tag v2.29.0-musl.
• Ruby

Note: If you already have an application instrumented using version X of the library, and then use library injection to instrument using version Y of the same tracer library, the tracer does not break. Rather, the library version loaded first is used. Because library injection happens at the admission controller level prior to runtime, it takes precedent over manually configured libraries.

For example, to inject a Java library:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    ...
...
template:
  metadata:
    labels:
        admission.datadoghq.com/enabled: "true" # Enable Admission Controller to mutate new pods in this deployment
    annotations:
        admission.datadoghq.com/java-lib.version: "<CONTAINER IMAGE TAG>"
  containers:
  -  ...

Step 3 - Tag your pods with Unified Service Tags

With Unified Service Tags, you can tie Datadog telemetry together and navigate seamlessly across traces, metrics, and logs with consistent tags. Set the Unified Service Tagging on both the deployment object and the pod template specs. Set Unified Service tags by using the following labels:

...
    metadata:
        labels:
            tags.datadoghq.com/env: "<ENV>"
            tags.datadoghq.com/service: "<SERVICE>"
            tags.datadoghq.com/version: "<VERSION>"
...

Note: It is not necessary to set the environment variables for universal service tagging (DD_ENV, DD_SERVICE, DD_VERSION) in the pod template spec, because the Admission Controller propagates the tag values as environment variables when injecting the library.

For example:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    tags.datadoghq.com/env: "prod" # Unified service tag - Deployment Env tag
    tags.datadoghq.com/service: "my-service" # Unified service tag - Deployment Service tag
    tags.datadoghq.com/version: "1.1" # Unified service tag - Deployment Version tag
...
template:
  metadata:
    labels:
        tags.datadoghq.com/env: "prod" # Unified service tag - Pod Env tag
        tags.datadoghq.com/service: "my-service" # Unified service tag - Pod Service tag
        tags.datadoghq.com/version: "1.1" # Unified service tag - Pod Version tag
        admission.datadoghq.com/enabled: "true" # Enable Admission Controller to mutate new pods part of this deployment
    annotations:
        admission.datadoghq.com/java-lib.version: "<CONTAINER IMAGE TAG>"
  containers:
  -  ...

Step 4 - Apply the configuration

Your pods are ready to be instrumented when their new configuration is applied.

Check that the library injection was successful

Library injection leverages the injection of a dedicated init container in pods. If the injection was successful you can see an init container called datadog-lib-init in your pod:

Or run kubectl describe pod <my-pod> to see the datadog-lib-init init container listed.

The instrumentation also starts sending telemetry to Datadog (for example, traces to APM).

Troubleshooting installation issues

If the application pod fails to start, run kubectl logs <my-pod> --all-containers to print out the logs and compare them to the known issues below.

.NET installation issues

dotnet: error while loading shared libraries: libc.musl-x86_64.so.1: cannot open shared object file: No such file or directory

• Problem: The pod annotation for the dotnet library version included a -musl suffix, but the application container runs on a Linux distribution that uses glibc.
• Solution: Remove the -musl suffix from the dotnet library version.

Error loading shared library ld-linux-x86-64.so.2: No such file or directory (needed by /datadog-lib/continuousprofiler/Datadog.Linux.ApiWrapper.x64.so)

• Problem: The application container runs on a Linux distribution that uses musl-libc (for example, Alpine), but the pod annotation does not include the -musl suffix.
• Solution: Add the -musl suffix to the dotnet library version.

When both the Agent and your services are running on a host, real or virtual, Datadog injects the tracing library by using a preload library that overrides calls to execve. Any newly started processes are intercepted and the specified instrumentation library is injected into the services.

Requirements

• A recent Datadog Agent v7 installation

Note: Injection on arm64, and injection with musl on Alpine Linux container images are not supported.

Install the preload library

1. Ensure your Agent is running.

2. Install the library with one of the following sets of commands, where <LANG> is one of java, js, dotnet, python, or all:

   For Ubuntu, Debian or other Debian-based Linux distributions:

   sudo apt-get update
   sudo apt-get install datadog-apm-inject datadog-apm-library-<LANG>
   

   For CentOS, RedHat, or another distribution that uses yum/RPM:

   sudo yum makecache
   sudo yum install datadog-apm-inject datadog-apm-library-<LANG>
   

3. Run the command dd-host-install.

4. Exit and open a new shell to use the preload library.

Install the language and your app

1. For Java applications, ensure you have a JDK or JRE installed, for example:

   sudo apt install openjdk-17-jdk -y
   

   For NodeJS applications, ensure you have NodeJS installed, for example:

   curl -sL https://deb.nodesource.com/setup_16.x | sudo -E bash -
   sudo apt install nodejs -y
   

   For .NET applications, ensure you have the .NET runtime installed.

   For Python applications, ensure you have Python installed, for example:

   sudo apt install python -y
   

2. If you haven’t already, install your app.

Configure the injection

The following environment variables configure library injection. You can pass these in by export through the command line (export DD_CONFIG_SOURCES=BASIC), shell configuration, or launch command:

DD_CONFIG_SOURCES

Turn on or off library injection and specify a location to load configuration from. Optionally, separate multiple values with semicolons to indicate multiple possible locations. The first value that returns without an error is used. Configuration is not merged across configuration sources. The valid values are:

• BLOB:<URL> - Load configuration from a blob store (S3-compatible) located at <URL>.
• LOCAL:<PATH> - Load from a file on the local file system at <PATH>.
• BASIC - Use exported or default values.
• OFF - Default. No injection performed.
  For more information about configuring BLOB or LOCAL settings, see Supplying configuration source.

DD_LIBRARY_INJECT

Set to FALSE to turn off library injection altogether.
Default: TRUE

DD_INJECT_DEBUG

Set to TRUE or 1 to log debug information.
Default: FALSE

DD_OUTPUT_PATHS

A comma-separated list of places to write the debug logs.
Default: stderr

Supplying configuration source

If you specify BLOB or LOCAL configuration source, create a JSON or YAML file at etc/<APP_NAME>/config.json or .yaml, and provide the configuration either as JSON:

{
	"version": 1,
	"service_language": "<LANG>",
	"tracing_enabled": true,
	"log_injection_enabled": true,
	"health_metrics_enabled": true,
	"runtime_metrics_enabled": true,
	"tracing_sampling_rate": 1.0,
	"tracing_rate_limit": 1,
	"tracing_tags": ["a=b", "foo"],
	"tracing_service_mapping": [
		{ "from_key": "mysql", "to_name": "super_db"},
		{ "from_key": "postgres", "to_name": "my_pg"}
	],
	"tracing_agent_timeout": 1,
	"tracing_header_tags": [
		{"header": "HEADER", "tag_name":"tag"}
	],
	"tracing_partial_flush_min_spans": 1,
	"tracing_debug": true,
	"tracing_log_level": "debug",
}

or as YAML:

---
version: 1
service_language: <LANG>
tracing_enabled: true
log_injection_enabled: true
health_metrics_enabled: true
runtime_metrics_enabled: true
tracing_sampling_rate: 1.0
tracing_rate_limit: 1
tracing_tags:
- a=b
- foo
tracing_service_mapping:
- from_key: mysql
  to_name: super_db
- from_key: postgres
  to_name: my_pg
tracing_agent_timeout: 1
tracing_header_tags:
- header: HEADER
  tag_name: tag
tracing_partial_flush_min_spans: 1
tracing_debug: true
tracing_log_level: debug

Set service_language to one of the following values:

• java
• node
• dotnet
• python

In this configuration file, the value of version is always 1. This refers to the configuration schema version in use, not the version of the content.

The following table shows how the injection configuration values map to the corresponding tracing library configuration options:

Tracer library configuration options that aren’t mentioned in the injection configuration are still available for use through properties or environment variables the usual way.

Basic configuration settings

If BASIC is specified as the configuration source, it is equivalent to the following YAML settings:

---
version: 1
tracing_enabled: true
log_injection_enabled: true
health_metrics_enabled: true
runtime_metrics_enabled: true
tracing_sampling_rate: 1.0
tracing_rate_limit: 1

Launch your services

Launch your services, indicating the preload library configuration in the launch command:

Java app example:

DD_CONFIG_SOURCES=BASIC java -jar <SERVICE_1>.jar &
DD_CONFIG_SOURCES=LOCAL:/etc/<SERVICE_2>/config.yaml;BASIC java -jar <SERVICE_2>.jar &

Node app example:

DD_CONFIG_SOURCES=BASIC node index.js &
DD_CONFIG_SOURCES=LOCAL:/etc/<SERVICE_2>/config.yaml;BASIC node index.js &

.NET app example:

DD_CONFIG_SOURCES=BASIC dotnet <SERVICE_1>.dll &
DD_CONFIG_SOURCES=LOCAL:/etc/<SERVICE_2>/config.yaml;BASIC dotnet <SERVICE_2>.dll &

Python app example:

DD_CONFIG_SOURCES=BASIC python <SERVICE_1>.py &
DD_CONFIG_SOURCES=LOCAL:/etc/<SERVICE_2>/config.yaml;BASIC python <SERVICE_2>.py &

Exercise your application to start generating telemetry data, which you can see as traces in APM.

When your Agent is running on a host, and your services are running in containers, Datadog injects the tracing library by intercepting container creation and configuring the Docker container.

Any newly started processes are intercepted and the specified instrumentation library is injected into the services.

Requirements

• A recent Datadog Agent v7 installation
• Docker Engine

Note: Injection on arm64, and injection with musl on Alpine Linux container images are not supported.

Install the preload library

1. Ensure your Agent is running.

2. Install the library with one of the following sets of commands, where <LANG> is one of java, js, dotnet, python, or all:

   For Ubuntu, Debian or other Debian-based Linux distributions:

   sudo apt-get update
   sudo apt-get install datadog-apm-inject datadog-apm-library-<LANG>
   

   For CentOS, RedHat, or another distribution that uses yum/RPM:

   sudo yum makecache
   sudo yum install datadog-apm-inject datadog-apm-library-<LANG>
   

3. Run the command dd-host-container-install.

Configure Docker injection

Edit /etc/datadog-agent/inject/docker_config.yaml and add the following YAML configuration for the injection:

---
config_sources: BASIC
library_inject: true
log_level: debug
output_paths:
- stderr

config_sources

Turn on or off library injection and specify a semicolon-separated ordered list of places where configuration is stored. The first value that returns without an error is used. Configuration is not merged across configuration sources. The valid values are:

• BLOB:<URL> - Load configuration from a blob store (S3-compatible) located at <URL>.
• LOCAL:<PATH> - Load from a file on the local file system at <PATH>.
• BASIC - Uses a default set of properties and stops looking for additional configurations.
• OFF - Default. No injection performed.
  For more information about configuring BLOB or LOCAL settings, see Supplying configuration source.

library_inject

Set to false to disable library injection altogether.
Default: true

log_level

Set to debug to log detailed information about what is happening, or info to log far less.

output_paths

A list of one or more places to write logs.
Default: stderr

Optional: env

Specifies the DD_ENV tag for the containers running in Docker, for example, dev, prod, staging.
Default None.

Supplying configuration source

If you specify BLOB or LOCAL configuration source, create a JSON or YAML file there, and provide the configuration either as JSON:

{
	"version": 1,
	"service_language": "<LANG>",
	"tracing_enabled": true,
	"log_injection_enabled": true,
	"health_metrics_enabled": true,
	"runtime_metrics_enabled": true,
	"tracing_sampling_rate": 1.0,
	"tracing_rate_limit": 1,
	"tracing_tags": ["a=b", "foo"],
	"tracing_service_mapping": [
		{ "from_key": "mysql", "to_name": "super_db"},
		{ "from_key": "postgres", "to_name": "my_pg"}
	],
	"tracing_agent_timeout": 1,
	"tracing_header_tags": [
		{"header": "HEADER", "tag_name":"tag"}
	],
	"tracing_partial_flush_min_spans": 1,
	"tracing_debug": true,
	"tracing_log_level": "debug",
}

or as YAML:

---
version: 1
service_language: <LANG>
tracing_enabled: true
log_injection_enabled: true
health_metrics_enabled: true
runtime_metrics_enabled: true
tracing_sampling_rate: 1.0
tracing_rate_limit: 1
tracing_tags:
- a=b
- foo
tracing_service_mapping:
- from_key: mysql
  to_name: super_db
- from_key: postgres
  to_name: my_pg
tracing_agent_timeout: 1
tracing_header_tags:
- header: HEADER
  tag_name: tag
tracing_partial_flush_min_spans: 1
tracing_debug: true
tracing_log_level: debug

Set service_language to one of the following values:

• java
• node
• dotnet
• python

In this configuration file, the value of version is always 1. This refers to the configuration schema version in use, not the version of the content.

The following table shows how the injection configuration values map to the corresponding tracing library configuration options:

Tracer library configuration options that aren’t mentioned in the injection configuration are still available for use through properties or environment variables the usual way.

Basic configuration settings

If BASIC is specified as the configuration source, it is equivalent to the following YAML settings:

---
version: 1
tracing_enabled: true
log_injection_enabled: true
health_metrics_enabled: true
runtime_metrics_enabled: true
tracing_sampling_rate: 1.0
tracing_rate_limit: 1

Specifying Unified Service Tags on containers

If the environment variables DD_ENV, DD_SERVICE, or DD_VERSION are specified in a service container image, those values are used to tag telemetry from the container.

If they are not specified, DD_ENV uses the env value set in the /etc/datadog-agent/inject/docker_config.yaml config file, if any. DD_SERVICE and DD_VERSION are derived from the name of the Docker image. An image with the name my-service:1.0 is tagged with DD_SERVICE of my-service and a DD_VERSION of 1.0.

Launch your services

Start your Agent and launch your containerized services as usual.

Exercise your application to start generating telemetry data, which you can see as traces in APM.

When your Agent and services are running in separate Docker containers on the same host, Datadog injects the tracing library by intercepting container creation and configuring the Docker container.

Any newly started processes are intercepted and the specified instrumentation library is injected into the services.

Requirements

• Docker Engine

Note: Injection on arm64, and injection with musl on Alpine Linux container images are not supported.

Install the preload library

For Ubuntu, Debian or other Debian-based Linux distributions:

1. Set up the Datadog deb repo on your system and create a Datadog archive keyring:

   sudo sh -c "echo 'deb [signed-by=/usr/share/keyrings/datadog-archive-keyring.gpg] https://apt.datadoghq.com/ stable 7' > /etc/apt/sources.list.d/datadog.list"
   sudo touch /usr/share/keyrings/datadog-archive-keyring.gpg
   sudo chmod a+r /usr/share/keyrings/datadog-archive-keyring.gpg
   
   curl https://keys.datadoghq.com/DATADOG_APT_KEY_CURRENT.public | sudo gpg --no-default-keyring --keyring /usr/share/keyrings/datadog-archive-keyring.gpg --import --batch
   curl https://keys.datadoghq.com/DATADOG_APT_KEY_382E94DE.public | sudo gpg --no-default-keyring --keyring /usr/share/keyrings/datadog-archive-keyring.gpg --import --batch
   curl https://keys.datadoghq.com/DATADOG_APT_KEY_F14F620E.public | sudo gpg --no-default-keyring --keyring /usr/share/keyrings/datadog-archive-keyring.gpg --import --batch
   

2. Update your local apt repo and install the library:

   sudo apt-get update
   sudo apt-get install datadog-apm-inject datadog-apm-library-<LANG>
   

   where <LANG> is one of java, js, dotnet, python, or all.

3. Run the command dd-container-install.

For CentOS, RedHat, or another distribution that uses yum/RPM:

1. Set up Datadog’s Yum repo on your system by creating a file called /etc/yum.repos.d/datadog.repo with the following contents:

   [datadog]
   name = Datadog, Inc.
   baseurl = https://yum.datadoghq.com/stable/7/x86_64/
   enabled=1
   gpgcheck=1
   repo_gpgcheck=1
   gpgkey=https://keys.datadoghq.com/DATADOG_RPM_KEY_CURRENT.public https://keys.datadoghq.com/DATADOG_RPM_KEY_FD4BF915.public https://keys.datadoghq.com/DATADOG_RPM_KEY_E09422B3.public
   

   Note: Due to a bug in dnf, on RedHat/CentOS 8.1 set repo_gpgcheck=0 instead of 1.

2. Update the yum cache and install the library:

   sudo yum makecache
   sudo yum install datadog-apm-inject datadog-apm-library-<LANG>
   

   where <LANG> is one of java, js, dotnet, python, or all.

3. Run the command dd-container-install.

Configure Docker injection

Edit /etc/datadog-agent/inject/docker_config.yaml and add the following YAML configuration for the injection:

---
library_inject: true
log_level: debug
output_paths:
- stderr
config_sources: BASIC

config_sources

Turn on or off library injection and specify a semicolon-separated ordered list of places where configuration is stored. The first value that returns without an error is used. Configuration is not merged across configuration sources. The valid values are:

• BLOB:<URL> - Load configuration from a blob store (S3-compatible) located at <URL>.
• LOCAL:<PATH> - Load from a file on the local file system at <PATH>.
• BASIC - Uses a default set of properties and stops looking for additional configurations.
• OFF - Default. No injection performed.
  For more information about configuring BLOB or LOCAL settings, see Supplying configuration source.

library_inject

Set to false to disable library injection altogether.
Default: true

log_level

Set to debug to log detailed information about what is happening, or info to log far less.

output_paths

A list of one or more places to write logs.
Default: stderr

Optional: env

Specifies the DD_ENV tag for the containers running in Docker, for example, dev, prod, staging.
Default None.

Supplying configuration source

If you specify BLOB or LOCAL configuration source, create a JSON or YAML file there, and provide the configuration either as JSON:

{
	"version": 1,
	"service_language": "<LANG>",
	"tracing_enabled": true,
	"log_injection_enabled": true,
	"health_metrics_enabled": true,
	"runtime_metrics_enabled": true,
	"tracing_sampling_rate": 1.0,
	"tracing_rate_limit": 1,
	"tracing_tags": ["a=b", "foo"],
	"tracing_service_mapping": [
		{ "from_key": "mysql", "to_name": "super_db"},
		{ "from_key": "postgres", "to_name": "my_pg"}
	],
	"tracing_agent_timeout": 1,
	"tracing_header_tags": [
		{"header": "HEADER", "tag_name":"tag"}
	],
	"tracing_partial_flush_min_spans": 1,
	"tracing_debug": true,
	"tracing_log_level": "debug",
}

or as YAML:

---
version: 1
service_language: <LANG>
tracing_enabled: true
log_injection_enabled: true
health_metrics_enabled: true
runtime_metrics_enabled: true
tracing_sampling_rate: 1.0
tracing_rate_limit: 1
tracing_tags:
- a=b
- foo
tracing_service_mapping:
- from_key: mysql
  to_name: super_db
- from_key: postgres
  to_name: my_pg
tracing_agent_timeout: 1
tracing_header_tags:
- header: HEADER
  tag_name: tag
tracing_partial_flush_min_spans: 1
tracing_debug: true
tracing_log_level: debug

Set service_language to one of the following values:

• java
• node
• dotnet
• python

In this configuration file, the value of version is always 1. This refers to the configuration schema version in use, not the version of the content.

The following table shows how the injection configuration values map to the corresponding tracing library configuration options:

Tracer library configuration options that aren’t mentioned in the injection configuration are still available for use through properties or environment variables the usual way.

Basic configuration settings

If BASIC is specified as the configuration source, it is equivalent to the following YAML settings:

---
version: 1
tracing_enabled: true
log_injection_enabled: true
health_metrics_enabled: true
runtime_metrics_enabled: true
tracing_sampling_rate: 1.0
tracing_rate_limit: 1

Configure the Agent

In the Docker compose file that launches your containers, use the following settings for the Agent, securely setting your own Datadog API key for ${DD_API_KEY}:

    container_name: dd-agent
    image: datadog/agent:7
    environment:
      - DD_API_KEY=${DD_API_KEY}
      - DD_APM_ENABLED=true
      - DD_APM_NON_LOCAL_TRAFFIC=true
      - DD_LOG_LEVEL=TRACE
      - DD_DOGSTATSD_NON_LOCAL_TRAFFIC=true
      - DD_AC_EXCLUDE=name:datadog-agent
      - DD_SYSTEM_PROBE_ENABLED=true
      - DD_PROCESS_AGENT_ENABLED=true
      - DD_APM_RECEIVER_SOCKET=/opt/datadog/apm/inject/run/apm.socket
    volumes:
      - /opt/datadog/apm:/opt/datadog/apm
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - /proc/:/host/proc/:ro
      - /sys/fs/cgroup/:/host/sys/fs/cgroup:ro
      - /sys/kernel/debug:/sys/kernel/debug
    cap_add:
      - SYS_ADMIN
      - SYS_RESOURCE
      - SYS_PTRACE
      - NET_ADMIN
      - NET_BROADCAST
      - NET_RAW
      - IPC_LOCK
      - CHOWN
    security_opt:
      - apparmor:unconfined

Specifying Unified Service Tags on containers

If the environment variables DD_ENV, DD_SERVICE, or DD_VERSION are specified in a service container image, those values are used to tag telemetry from the container.

If they are not specified, DD_ENV uses the env value set in the /etc/datadog-agent/inject/docker_config.yaml config file, if any. DD_SERVICE and DD_VERSION are derived from the name of the Docker image. An image with the name my-service:1.0 is tagged with DD_SERVICE of my-service and a DD_VERSION of 1.0.

Launch the Agent on Docker

The dd-agent container must be launched before any service containers. Run:

docker-compose up -d dd-agent

Launch your services

Launch your containerized services as usual.

Exercise your application to start generating telemetry data, which you can see as traces in APM.