Single Step APM Instrumentation

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Overview

Single Step Instrumentation (SSI) for APM installs the Datadog Agent and instruments your applications in one step, with no additional configuration steps required.

Compatibility

To see requirements for compatible languages, operating systems, and architectures, see Single Step Instrumentation compatibility.

Enabling APM on your applications

If you install or update a Datadog Agent with the Enable APM Instrumentation option selected, the Agent is installed and configured to enable APM. This automatically instruments your application, without any additional installation or configuration steps.

The following examples show how it works for each deployment type.

If you've previously used Single Step Instrumentation with Linux hosts, update to the latest version.

For an Ubuntu host:

  1. Run the one-line installation command:

    DD_API_KEY=<YOUR_DD_API_KEY> DD_SITE="<YOUR_DD_SITE>" DD_APM_INSTRUMENTATION_ENABLED=host DD_APM_INSTRUMENTATION_LIBRARIES="java:1,python:2,js:5,dotnet:3,php:1" DD_ENV=<AGENT_ENV> bash -c "$(curl -L https://install.datadoghq.com/scripts/install_script_agent7.sh)"

    Replace <YOUR_DD_API_KEY> with your Datadog API key, <YOUR_DD_SITE> with your Datadog site, and <AGENT_ENV> with the environment your Agent is installed on (for example, staging).

    See Advanced options for more options.

  2. Start a new shell session.

  3. Restart the services on the host or VM.

For a Docker Linux container:

  1. Run the one-line installation command:
    DD_APM_INSTRUMENTATION_ENABLED=docker DD_APM_INSTRUMENTATION_LIBRARIES="java:1,python:2,js:5,dotnet:3,php:1" DD_NO_AGENT_INSTALL=true bash -c "$(curl -L https://install.datadoghq.com/scripts/install_script_agent7.sh)"
  2. Configure the Agent in Docker:
    docker run -d --name dd-agent \
  -e DD_API_KEY=<YOUR_DD_API_KEY> \
  -e DD_APM_ENABLED=true \
  -e DD_ENV=<AGENT_ENV> \
  -e DD_APM_NON_LOCAL_TRAFFIC=true \
  -e DD_DOGSTATSD_NON_LOCAL_TRAFFIC=true \
  -e DD_APM_RECEIVER_SOCKET=/var/run/datadog/apm.socket \
  -e DD_DOGSTATSD_SOCKET=/var/run/datadog/dsd.socket \
  -v /var/run/datadog:/var/run/datadog \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  gcr.io/datadoghq/agent:7
    Replace <YOUR_DD_API_KEY> with your Datadog API key and <AGENT_ENV> with the environment your Agent is installed on (for example, staging).
    See Advanced options for more options.
  3. Restart the Docker containers.
  4. Explore the performance observability of your services in Datadog.

You can enable APM by installing the Agent with either:

  • Datadog Operator
  • Datadog Helm chart
Single Step Instrumentation doesn't instrument applications in the namespace where you install the Datadog Agent. It's recommended to install the Agent in a separate namespace in your cluster where you don't run your applications.

Requirements

  • Kubernetes v1.20+
  • Helm for deploying the Datadog Operator.
  • Kubectl CLI for installing the Datadog Agent.

Follow these steps to enable Single Step Instrumentation across your entire cluster with the Datadog Operator. This automatically sends traces for all applications in the cluster that are written in supported languages.

Note: To configure Single Step Instrumentation for specific namespace or pods, see Advanced options.

To enable Single Step Instrumentation with the Datadog Operator:

  1. Install the Datadog Operator v1.5.0+ with Helm:

    helm repo add datadog https://helm.datadoghq.com
helm repo update
helm install my-datadog-operator datadog/datadog-operator

  2. Create a Kubernetes secret to store your Datadog API key:

    kubectl create secret generic datadog-secret --from-literal api-key=$DD_API_KEY

  3. Create datadog-agent.yaml with the spec of your Datadog Agent deployment configuration. The simplest configuration is as follows:

    apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
spec:
  global:
    site: <DATADOG_SITE>
    tags:
      - env:<AGENT_ENV>
    credentials:
      apiSecret:
        secretName: datadog-secret
        keyName: api-key
  features:
    apm:
      instrumentation:
        enabled: true
        targets:
          - name: "default-target"
            ddTracerVersions:
              java: "1"
              dotnet: "3"
              python: "2"
              js: "5"
              php: "1"

    Replace <DATADOG_SITE> with your Datadog site and <AGENT_ENV> with the environment your Agent is installed on (for example, env:staging).

    See Advanced options for more options.

  4. Run the following command:

    kubectl apply -f /path/to/your/datadog-agent.yaml

  5. After waiting a few minutes for the Datadog Cluster Agent changes to apply, restart your applications.

Follow these steps to enable Single Step Instrumentation across your entire cluster with Helm. This automatically sends traces for all applications in the cluster that are written in supported languages.

Note: To configure Single Step Instrumentation for specific namespace or pods, see Advanced options.

To enable Single Step Instrumentation with Helm:

  1. Add the Helm Datadog repo:

     helm repo add datadog https://helm.datadoghq.com
 helm repo update

  2. Create a Kubernetes secret to store your Datadog API key:

    kubectl create secret generic datadog-secret --from-literal api-key=$DD_API_KEY

  3. Create datadog-values.yaml and add the following configuration:

    datadog:
 apiKeyExistingSecret: datadog-secret
 site: <DATADOG_SITE>
 tags:
      - env:<AGENT_ENV>
 apm:
   instrumentation:
      enabled: true
      targets:
        - name: "default-target"
          ddTracerVersions:
            java: "1"
            dotnet: "3"
            python: "2"
            js: "5"
            php: "1"

    Replace <DATADOG_SITE> with your Datadog site and <AGENT_ENV> with the environment your Agent is installed on (for example, env:staging).

    See Advanced options for more options.

  4. Run the following command:

    helm install datadog-agent -f datadog-values.yaml datadog/datadog

  5. After waiting a few minutes for the Datadog Cluster Agent changes to apply, restart your applications.

After you complete these steps, you may want to enable runtime metrics or view observability data from your application in the Software Catalog.

Advanced options

When you run the one-line installation command, there are a few options to customize your experience:

DD_APM_INSTRUMENTATION_LIBRARIES - customizing APM libraries

By default, Java, Python, Ruby, Node.js, PHP and .NET Core Datadog APM libraries are installed when DD_APM_INSTRUMENTATION_ENABLED is set. DD_APM_INSTRUMENTATION_LIBRARIES is used to override which libraries are installed. The value is a comma-separated string of colon-separated library name and version pairs.

Example values for DD_APM_INSTRUMENTATION_LIBRARIES:

  • DD_APM_INSTRUMENTATION_LIBRARIES="java:1" - install only the Java Datadog APM library pinned to the major version 1 release line.
  • DD_APM_INSTRUMENTATION_LIBRARIES="java:1,python:2" - install only the Java and Python Datadog APM libraries pinned to the major versions 1 and 2 respectively.
  • DD_APM_INSTRUMENTATION_LIBRARIES="java:1.38.0,python:2.10.5" - install only the Java and Python Datadog APM libraries pinned to the specific versions 1.38.0 and 2.10.5 respectively.

Available versions are listed in source repositories for each language:

DD_APM_INSTRUMENTATION_LIBRARIES - customizing APM libraries

By default, Java, Python, Ruby, Node.js and .NET Core Datadog APM libraries are installed when DD_APM_INSTRUMENTATION_ENABLED is set. DD_APM_INSTRUMENTATION_LIBRARIES is used to override which libraries are installed. The value is a comma-separated string of colon-separated library name and version pairs.

Example values for DD_APM_INSTRUMENTATION_LIBRARIES:

  • DD_APM_INSTRUMENTATION_LIBRARIES="java:1" - install only the Java Datadog APM library pinned to the major version 1 release line.
  • DD_APM_INSTRUMENTATION_LIBRARIES="java:1,python:2" - install only the Java and Python Datadog APM libraries pinned to the major versions 1 and 2 respectively.
  • DD_APM_INSTRUMENTATION_LIBRARIES="java:1.38.0,python:2.10.5" - install only the Java and Python Datadog APM libraries pinned to the specific versions 1.38.0 and 2.10.5 respectively.

Available versions are listed in source repositories for each language:

Configuring instrumentation for namespaces and pods

By default, Single Step Instrumentation instruments all services in all namespaces in your cluster. Alternatively, you can create targeting blocks with the targets label to specify which workloads to instrument and what configurations to apply.

Each target block has the following keys:

KeyDescription
nameThe name of the target block. This has no effect on monitoring state and is used only as metadata.
namespaceSelectorThe namespace(s) to instrument. Specify using one or more of:
- matchNames: A list of one or more namespace name(s).
- matchLabels: A list of one or more label(s) defined in {key,value} pairs.
- matchExpressions: A list of namespace selector requirements.

Namespaces must meet all criteria to match. For more details, see the Kubernetes selector documentation.
podSelectorThe pod(s) to instrument. Specify using one or more of:
- matchLabels: A list of one or more label(s) defined in {key,value} pairs.
- matchExpressions: A list of pod selector requirements.

Pods must meet all criteria to match. For more details, see the Kubernetes selector documentation.
ddTraceVersionsThe Datadog APM SDK version to use for each language.
ddTraceConfigsAPM SDK configs that allow setting Unified Service Tags, enabling Datadog products beyond tracing, and customizing other APM settings. See full list of options.

The file you need to configure depends on how you enabled Single Step Instrumentation:

  • If you enabled SSI with Datadog Operator, edit datadog-agent.yaml.
  • If you enabled SSI with Helm, edit datadog-values.yaml.

Example configurations

Review the following examples demonstrating how to select specific services:

This configuration:

  • enables APM for all namespaces except the jenkins namespace.
  • instructs Datadog to instrument the Java applications with the default Java APM SDK and Python applications with v.3.1.0 of the Python APM SDK.
   apm:  
     instrumentation:  
       enabled: true  
       disabledNamespaces:  
         - "jenkins"  
       targets:  
         - name: "all-remaining-services"  
           ddTraceVersions:  
             java: "default"  
             python: "3.1.0"

This configuration creates two targets blocks:

  • The first block (named login-service_namespace):
    • enables APM for services in the namespace login-service.
    • instructs Datadog to instrument services in this namespace with the default version of the Java APM SDK.
    • sets environment variables – DD_SERVICE, DD_ENV, and DD_PROFILING_ENABLED – for this target group.
  • The second block (named billing-service_apps)
    • enables APM for services in the namespace(s) with label app:billing-service.
    • instructs Datadog to instrument this set of services with v3.1.0 of the Python APM SDK.
    • sets environment variables – DD_SERVICE and DD_ENV – for this target group.
  apm:
    instrumentation:
      enabled: true
      targets:
        - name: "login-service_namespace"
          namespaceSelector:
            matchNames:
              - "login-service"
          ddTraceVersions:
            java: "default"
          ddTraceConfigs:
            - name: "DD_SERVICE"
              value: "login-service"
            - name: "DD_ENV"
              value: "prod"
            - name: "DD_PROFILING_ENABLED"  ## profiling is enabled for all services in this namespace
              value: "auto"
        - name: "billing-service_apps"
          namespaceSelector:
            matchLabels:
              app: "billing-service"
          ddTraceVersions:
            python: "3.1.0"
          ddTraceConfigs:
            - name: "DD_SERVICE"
              value: "billing-service"
            - name: "DD_ENV"
              value: "prod

This configuration does the following:

  • enables APM for pods with the following labels:
    • app:db-user, which marks pods running the db-user application.
    • webserver:routing, which marks pods running the request-router application.
  • instructs Datadog to use the default versions of the Datadog Tracer SDKs.
  • sets several Datadog environment variables to apply to each target group.
   apm:
     instrumentation:
       enabled: true
       targets:
         - name: "db-user"
           podSelector:
             matchLabels:
               app: "db-user"
           ddTraceVersions:
             java: "default"
           ddTraceConfigs:   ## trace configs set for services in matching pods
             - name: "DD_SERVICE"
               value: "db-user"
             - name: "DD_ENV"
               value: "prod"
             - name: "DD_DSM_ENABLED"  
               value: "true"
         - name: "user-request-router"
           podSelector:
             matchLabels:
               webserver: "user"
           ddTraceVersions:
             php: "default"
           ddTraceConfigs:
             - name: "DD_SERVICE"
               value: "user-request-router"
             - name: "DD_ENV"
               value: "prod

This configuration:

  • enables APM for pods labeled app:password-resolver inside the login-service namespace.
  • instructs Datadog to use the default version of the Datadog Java Tracer SDK.
  • sets several Datadog environment variables to apply to this target.
   apm:
     instrumentation:
       enabled: true
       targets:
         - name: "login-service-namespace"
           namespaceSelector:
             matchNames:
               - "login-service"
           podSelector:
             matchLabels:
               app: "password-resolver"
           ddTraceVersions:
             java: "default"
           ddTraceConfigs:   
             - name: "DD_SERVICE"
               value: "password-resolver"
             - name: "DD_ENV"
               value: "prod"
             - name: "DD_PROFILING_ENABLED"  
               value: "auto"

This configuration enables APM for all pods except those that have either of the labels app=app1 or app=app2.

   apm:
     instrumentation:
       enabled: true
       targets:
         - name: "default-target"
           matchExpressions:
             - key: app
               operator: NotIn
               values:
               - app1
               - app2

Enabling or disabling instrumentation for namespaces

You can choose to enable or disable instrumentation for applications in specific namespaces. You can only set enabledNamespaces or disabledNamespaces, not both.

The file you need to configure depends on if you enabled Single Step Instrumentation with Datadog Operator or Helm:

To enable instrumentation for specific namespaces, add enabledNamespaces configuration to datadog-agent.yaml:

   features:
     apm:
       instrumentation:
         enabled: true
         enabledNamespaces: # Add namespaces to instrument
           - default
           - applications

To disable instrumentation for specific namespaces, add disabledNamespaces configuration to datadog-agent.yaml:

   features:
     apm:
       instrumentation:
         enabled: true
         disabledNamespaces: # Add namespaces to not instrument
           - default
           - applications

To enable instrumentation for specific namespaces, add enabledNamespaces configuration to datadog-values.yaml:

   datadog:
      apm:
        instrumentation:
          enabled: true
          enabledNamespaces: # Add namespaces to instrument
             - namespace_1
             - namespace_2

To disable instrumentation for specific namespaces, add disabledNamespaces configuration to datadog-values.yaml:

   datadog:
      apm:
        instrumentation:
          enabled: true
          disabledNamespaces: # Add namespaces to not instrument
            - namespace_1
            - namespace_2

Specifying tracing library versions

Starting with Datadog Cluster Agent v7.52.0+, you can automatically instrument a subset of your applications, based on the tracing libraries you specify.

Specify Datadog tracing libraries and their versions to automatically instrument applications written in those languages. You can configure this in two ways, which are applied in the following order of precedence:

  1. Specify at the service level, or
  2. Specify at the cluster level.

Default: If you don’t specify any library versions and apm.instrumentation.enabled=true, applications written in supported languages are automatically instrumented using the latest tracing library versions.

Specifying at the service level

To automatically instrument applications in specific pods, add the appropriate language annotation and library version for your application in your pod spec:

LanguagePod annotation
Javaadmission.datadoghq.com/java-lib.version: "<CONTAINER IMAGE TAG>"
Node.jsadmission.datadoghq.com/js-lib.version: "<CONTAINER IMAGE TAG>"
Pythonadmission.datadoghq.com/python-lib.version: "<CONTAINER IMAGE TAG>"
.NETadmission.datadoghq.com/dotnet-lib.version: "<CONTAINER IMAGE TAG>"
Rubyadmission.datadoghq.com/ruby-lib.version: "<CONTAINER IMAGE TAG>"
PHPadmission.datadoghq.com/php-lib.version: "<CONTAINER IMAGE TAG>"

Replace <CONTAINER IMAGE TAG> with the desired library version. Available versions are listed in the Datadog container registries and tracer source repositories for each language:

Exercise caution when using the latest tag, as major library releases may introduce breaking changes.

For example, to automatically instrument Java applications:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    # ...
spec:
  template:
    metadata:
      annotations:
        admission.datadoghq.com/java-lib.version: "<CONTAINER IMAGE TAG>"
    spec:
      containers:
        - # ...

Specifying at the cluster level

If you don’t enable automatic instrumentation for specific pods using annotations, you can specify which languages to instrument across the entire cluster using the Single Step Instrumentation configuration. When apm.instrumentation.libVersions is set, only applications written in the specified languages will be instrumented, using the specified library versions.

The file you need to configure depends on if you enabled Single Step Instrumentation with Datadog Operator or Helm:

For example, to instrument .NET, Python, and Node.js applications, add the following configuration to your datadog-agent.yaml file:

   features:
     apm:
       instrumentation:
         enabled: true
         libVersions: # Add any libraries and versions you want to set
            dotnet: "3.2.0"
            python: "1.20.6"
            js: "4.17.0"

For example, to instrument .NET, Python, and Node.js applications, add the following configuration to your datadog-values.yaml file:

   datadog:
     apm:
       instrumentation:
         enabled: true
         libVersions: # Add any libraries and versions you want to set
            dotnet: "3.2.0"
            python: "1.20.6"
            js: "4.17.0"

Container registries

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

Languagegcr.iohub.docker.comgallery.ecr.aws
Javagcr.io/datadoghq/dd-lib-java-inithub.docker.com/r/datadog/dd-lib-java-initgallery.ecr.aws/datadog/dd-lib-java-init
Node.jsgcr.io/datadoghq/dd-lib-js-inithub.docker.com/r/datadog/dd-lib-js-initgallery.ecr.aws/datadog/dd-lib-js-init
Pythongcr.io/datadoghq/dd-lib-python-inithub.docker.com/r/datadog/dd-lib-python-initgallery.ecr.aws/datadog/dd-lib-python-init
.NETgcr.io/datadoghq/dd-lib-dotnet-inithub.docker.com/r/datadog/dd-lib-dotnet-initgallery.ecr.aws/datadog/dd-lib-dotnet-init
Rubygcr.io/datadoghq/dd-lib-ruby-inithub.docker.com/r/datadog/dd-lib-ruby-initgallery.ecr.aws/datadog/dd-lib-ruby-init
PHPgcr.io/datadoghq/dd-lib-php-inithub.docker.com/r/datadog/dd-lib-php-initgallery.ecr.aws/datadog/dd-lib-php-init

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.

For instructions on changing your container registry, see Changing Your Container Registry.

Removing Single Step APM instrumentation from your Agent

If you don’t want to collect trace data for a particular service, host, VM, or container, complete the following steps:

Removing instrumentation for specific services

To remove APM instrumentation and stop sending traces from a specific service, follow these steps:

  1. Add the DD_INSTRUMENT_SERVICE_WITH_APM environment variable to the service startup command:

    DD_INSTRUMENT_SERVICE_WITH_APM=false <service_start_command>

  2. Restart the service.

  1. Add the DD_INSTRUMENT_SERVICE_WITH_APM environment variable to the service startup command:
    docker run -e DD_INSTRUMENT_SERVICE_WITH_APM=false <service_start_command>
  2. Restart the service.

With workload selection, you can enable and disable tracing for specific applications. See configuration details here.

Using the Datadog Admission Controller

As an alternative, or for a version of the agent that does not support workload selection, you can also disable pod mutation by adding a label to your pod.

In addition to disabling SSI, the following steps disable other mutating webhooks. Use with caution.
  1. Set the admission.datadoghq.com/enabled: label to "false" for the pod spec:
    spec:
  template:
    metadata:
      labels:
        admission.datadoghq.com/enabled: "false"
  2. Apply the configuration:
    kubectl apply -f /path/to/your/deployment.yaml
  3. Restart the services you want to remove instrumentation for.

Removing APM for all services on the infrastructure

To stop producing traces, uninstall APM and restart the infrastructure:

  1. Run:
    dd-host-install --uninstall
  2. Restart the services on the host or VM.
  1. Run:
    dd-container-install --uninstall
  2. Restart Docker:
    systemctl restart docker
    Or use the equivalent for your environment.

The file you need to configure depends on if you enabled Single Step Instrumentation with Datadog Operator or Helm:

  1. Set instrumentation.enabled=false in datadog-agent.yaml:

    features:
  apm:
    instrumentation:
      enabled: false

  2. Deploy the Datadog Agent with the updated configuration file:

    kubectl apply -f /path/to/your/datadog-agent.yaml

  1. Set instrumentation.enabled=false in datadog-values.yaml:

    datadog:
  apm:
    instrumentation:
      enabled: false

  2. Run the following command:

    helm upgrade datadog-agent -f datadog-values.yaml datadog/datadog

Troubleshooting

Single Step Instrumentation is not taking effect

Single Step Instrumentation automatically disables when it detects custom instrumentation in your application. If you want to use SSI, you’ll need to:

  1. Remove any existing custom instrumentation code.
  2. Restart your application.

Further reading

Documentation, liens et articles supplémentaires utiles:

Enable Runtime MetricsDOCUMENTATION more more
PREVIEWING: aliciascott/DOCS-10258