OpenTelemetry Operator for Kubernetes

The OpenTelemetry Operator is an implementation of a Kubernetes Operator.

The operator manages:

• OpenTelemetry Collector
• auto-instrumentation of the workloads using OpenTelemetry instrumentation libraries

Documentation

• API docs

Helm Charts

You can install Opentelemetry Operator via Helm Chart from the opentelemetry-helm-charts repository. More information is available in here.

Getting started

To install the operator in an existing cluster, make sure you have cert-manager installed and run:

kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml

Once the opentelemetry-operator deployment is ready, create an OpenTelemetry Collector (otelcol) instance, like:

kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: simplest
spec:
  config: |
    receivers:
      otlp:
        protocols:
          grpc:
          http:
    processors:
      memory_limiter:
        check_interval: 1s
        limit_percentage: 75
        spike_limit_percentage: 15
      batch:
        send_batch_size: 10000
        timeout: 10s

    exporters:
      debug:

    service:
      pipelines:
        traces:
          receivers: [otlp]
          processors: []
          exporters: [debug]
EOF

WARNING: Until the OpenTelemetry Collector format is stable, changes may be required in the above example to remain compatible with the latest version of the OpenTelemetry Collector image being referenced.

This will create an OpenTelemetry Collector instance named simplest, exposing a jaeger-grpc port to consume spans from your instrumented applications and exporting those spans via debug, which writes the spans to the console (stdout) of the OpenTelemetry Collector instance that receives the span.

The config node holds the YAML that should be passed down as-is to the underlying OpenTelemetry Collector instances. Refer to the OpenTelemetry Collector documentation for a reference of the possible entries.

🚨 NOTE: At this point, the Operator does not validate the contents of the configuration file: if the configuration is invalid, the instance will still be created but the underlying OpenTelemetry Collector might crash.

The Operator does examine the configuration file to discover configured receivers and their ports. If it finds receivers with ports, it creates a pair of kubernetes services, one headless, exposing those ports within the cluster. The headless service contains a service.beta.openshift.io/serving-cert-secret-name annotation that will cause OpenShift to create a secret containing a certificate and key. This secret can be mounted as a volume and the certificate and key used in those receivers' TLS configurations.

Upgrades

As noted above, the OpenTelemetry Collector format is continuing to evolve. However, a best-effort attempt is made to upgrade all managed OpenTelemetryCollector resources.

In certain scenarios, it may be desirable to prevent the operator from upgrading certain OpenTelemetryCollector resources. For example, when a resource is configured with a custom .Spec.Image, end users may wish to manage configuration themselves as opposed to having the operator upgrade it. This can be configured on a resource by resource basis with the exposed property .Spec.UpgradeStrategy.

By configuring a resource's .Spec.UpgradeStrategy to none, the operator will skip the given instance during the upgrade routine.

The default and only other acceptable value for .Spec.UpgradeStrategy is automatic.

Deployment modes

The CustomResource for the OpenTelemetryCollector exposes a property named .Spec.Mode, which can be used to specify whether the Collector should run as a DaemonSet, Sidecar, StatefulSet or Deployment (default).

See below for examples of each deployment mode:

• Deployment
• DaemonSet
• StatefulSet
• Sidecar

Sidecar injection

A sidecar with the OpenTelemetry Collector can be injected into pod-based workloads by setting the pod annotation sidecar.opentelemetry.io/inject to either "true", or to the name of a concrete OpenTelemetryCollector, like in the following example:

kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: sidecar-for-my-app
spec:
  mode: sidecar
  config: |
    receivers:
      jaeger:
        protocols:
          thrift_compact:
    processors:

    exporters:
      debug:

    service:
      pipelines:
        traces:
          receivers: [jaeger]
          processors: []
          exporters: [debug]
EOF

kubectl apply -f - <<EOF
apiVersion: v1
kind: Pod
metadata:
  name: myapp
  annotations:
    sidecar.opentelemetry.io/inject: "true"
spec:
  containers:
  - name: myapp
    image: jaegertracing/vertx-create-span:operator-e2e-tests
    ports:
      - containerPort: 8080
        protocol: TCP
EOF

When there are multiple OpenTelemetryCollector resources with a mode set to Sidecar in the same namespace, a concrete name should be used. When there's only one Sidecar instance in the same namespace, this instance is used when the annotation is set to "true".

The annotation value can come either from the namespace, or from the pod. The most specific annotation wins, in this order:

• the pod annotation is used when it's set to a concrete instance name or to "false"
• namespace annotation is used when the pod annotation is either absent or set to "true", and the namespace is set to a concrete instance or to "false"

The possible values for the annotation can be:

• "true" - inject OpenTelemetryCollector resource from the namespace.
• "sidecar-for-my-app" - name of OpenTelemetryCollector CR instance in the current namespace.
• "my-other-namespace/my-instrumentation" - name and namespace of OpenTelemetryCollector CR instance in another namespace.
• "false" - do not inject

When using a pod-based workload, such as Deployment or StatefulSet, make sure to add the annotation to the PodTemplate part. Like:

kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
  annotations:
    sidecar.opentelemetry.io/inject: "true" # WRONG
spec:
  selector:
    matchLabels:
      app: my-app
  replicas: 1
  template:
    metadata:
      labels:
        app: my-app
      annotations:
        sidecar.opentelemetry.io/inject: "true" # CORRECT
    spec:
      containers:
      - name: myapp
        image: jaegertracing/vertx-create-span:operator-e2e-tests
        ports:
          - containerPort: 8080
            protocol: TCP
EOF

When using sidecar mode the OpenTelemetry collector container will have the environment variable OTEL_RESOURCE_ATTRIBUTESset with Kubernetes resource attributes, ready to be consumed by the resourcedetection processor.

OpenTelemetry auto-instrumentation injection

The operator can inject and configure OpenTelemetry auto-instrumentation libraries. Currently Apache HTTPD, DotNet, Go, Java, Nginx, NodeJS and Python are supported.

To use auto-instrumentation, configure an Instrumentation resource with the configuration for the SDK and instrumentation.

kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: my-instrumentation
spec:
  exporter:
    endpoint: http://otel-collector:4317
  propagators:
    - tracecontext
    - baggage
    - b3
  sampler:
    type: parentbased_traceidratio
    argument: "0.25"
  python:
    env:
      # Required if endpoint is set to 4317.
      # Python autoinstrumentation uses http/proto by default
      # so data must be sent to 4318 instead of 4317.
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318
  dotnet:
    env:
      # Required if endpoint is set to 4317.
      # Dotnet autoinstrumentation uses http/proto by default
      # See https://github.com/open-telemetry/opentelemetry-dotnet-instrumentation/blob/888e2cd216c77d12e56b54ee91dafbc4e7452a52/docs/config.md#otlp
      - name: OTEL_EXPORTER_OTLP_ENDPOINT
        value: http://otel-collector:4318
EOF

The values for propagators are added to the OTEL_PROPAGATORS environment variable. Valid values for propagators are defined by the OpenTelemetry Specification for OTEL_PROPAGATORS.

The value for sampler.type is added to the OTEL_TRACES_SAMPLER envrionment variable. Valid values for sampler.type are defined by the OpenTelemetry Specification for OTEL_TRACES_SAMPLER. The value for sampler.argument is added to the OTEL_TRACES_SAMPLER_ARG environment variable. Valid values for sampler.argument will depend on the chosen sampler. See the OpenTelemetry Specification for OTEL_TRACES_SAMPLER_ARG for more details.

The above CR can be queried by kubectl get otelinst.

Then add an annotation to a pod to enable injection. The annotation can be added to a namespace, so that all pods within that namespace wil get instrumentation, or by adding the annotation to individual PodSpec objects, available as part of Deployment, Statefulset, and other resources.

Java:

instrumentation.opentelemetry.io/inject-java: "true"

NodeJS:

instrumentation.opentelemetry.io/inject-nodejs: "true"

Python:

instrumentation.opentelemetry.io/inject-python: "true"

.NET: .NET auto-instrumentation also honors an annotation that will be used to set the .NET Runtime Identifiers(RIDs). Currently, only two RIDs are supported: linux-x64 and linux-musl-x64. By default linux-x64 is used.

instrumentation.opentelemetry.io/inject-dotnet: "true"
instrumentation.opentelemetry.io/otel-dotnet-auto-runtime: "linux-x64" # for Linux glibc based images, this is default value and can be omitted
instrumentation.opentelemetry.io/otel-dotnet-auto-runtime: "linux-musl-x64"  # for Linux musl based images

Go:

Go auto-instrumentation also honors an annotation that will be used to set the OTEL_GO_AUTO_TARGET_EXE env var. This env var can also be set via the Instrumentation resource, with the annotation taking precedence. Since Go auto-instrumentation requires OTEL_GO_AUTO_TARGET_EXE to be set, you must supply a valid executable path via the annotation or the Instrumentation resource. Failure to set this value causes instrumentation injection to abort, leaving the original pod unchanged.

instrumentation.opentelemetry.io/inject-go: "true"
instrumentation.opentelemetry.io/otel-go-auto-target-exe: "/path/to/container/executable"

Go auto-instrumentation also requires elevated permissions. The below permissions are set automatically and are required.

securityContext:
    capabilities:
     add:
     - SYS_PTRACE
    privileged: true
    runAsUser: 0

Apache HTTPD:

instrumentation.opentelemetry.io/inject-apache-httpd: "true"

Nginx:

instrumentation.opentelemetry.io/inject-nginx: "true"

OpenTelemetry SDK environment variables only:

instrumentation.opentelemetry.io/inject-sdk: "true"

The possible values for the annotation can be

• "true" - inject and Instrumentation resource from the namespace.
• "my-instrumentation" - name of Instrumentation CR instance in the current namespace.
• "my-other-namespace/my-instrumentation" - name and namespace of Instrumentation CR instance in another namespace.
• "false" - do not inject

Multi-container pods with single instrumentation

If nothing else is specified, instrumentation is performed on the first container available in the pod spec. In some cases (for example in the case of the injection of an Istio sidecar) it becomes necessary to specify on which container(s) this injection must be performed.

For this, it is possible to fine-tune the pod(s) on which the injection will be carried out.

For this, we will use the instrumentation.opentelemetry.io/container-names annotation for which we will indicate one or more pod names (.spec.containers.name) on which the injection must be made:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment-with-multiple-containers
spec:
  selector:
    matchLabels:
      app: my-pod-with-multiple-containers
  replicas: 1
  template:
    metadata:
      labels:
        app: my-pod-with-multiple-containers
      annotations:
        instrumentation.opentelemetry.io/inject-java: "true"
        instrumentation.opentelemetry.io/container-names: "myapp,myapp2"
    spec:
      containers:
      - name: myapp
        image: myImage1
      - name: myapp2
        image: myImage2
      - name: myapp3
        image: myImage3

In the above case, myapp and myapp2 containers will be instrumented, myapp3 will not.

🚨 NOTE: Go auto-instrumentation does not support multicontainer pods. When injecting Go auto-instrumentation the first pod should be the only pod you want instrumented.

Multi-container pods with multiple instrumentations

Works only when operator.autoinstrumentation.multi-instrumentation feature is enabled.

Annotations defining which language instrumentation will be injected are required. When feature is enabled, specific for Instrumentation language containers annotations are used:

Java:

instrumentation.opentelemetry.io/java-container-names: "java1,java2"

NodeJS:

instrumentation.opentelemetry.io/nodejs-container-names: "nodejs1,nodejs2"

Python:

instrumentation.opentelemetry.io/python-container-names: "python1,python3"

DotNet:

instrumentation.opentelemetry.io/dotnet-container-names: "dotnet1,dotnet2"

Go:

instrumentation.opentelemetry.io/go-container-names: "go1"

ApacheHttpD:

instrumentation.opentelemetry.io/apache-httpd-container-names: "apache1,apache2"

SDK:

instrumentation.opentelemetry.io/sdk-container-names: "app1,app2"

If language instrumentation specific container names are not specified, instrumentation is performed on the first container available in the pod spec (only if single instrumentation injection is configured).

In some cases containers in the pod are using different technologies. It becomes necessary to specify language instrumentation for container(s) on which this injection must be performed.

For this, we will use language instrumentation specific container names annotation for which we will indicate one or more container names (.spec.containers.name) on which the injection must be made:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment-with-multi-containers-multi-instrumentations
spec:
  selector:
    matchLabels:
      app: my-pod-with-multi-containers-multi-instrumentations
  replicas: 1
  template:
    metadata:
      labels:
        app: my-pod-with-multi-containers-multi-instrumentations
      annotations:
        instrumentation.opentelemetry.io/inject-java: "true"
        instrumentation.opentelemetry.io/java-container-names: "myapp,myapp2"
        instrumentation.opentelemetry.io/inject-python: "true"
        instrumentation.opentelemetry.io/python-container-names: "myapp3"
    spec:
      containers:
      - name: myapp
        image: myImage1
      - name: myapp2
        image: myImage2
      - name: myapp3
        image: myImage3

In the above case, myapp and myapp2 containers will be instrumented using Java and myapp3 using Python instrumentation.

NOTE: Go auto-instrumentation does not support multicontainer pods. When injecting Go auto-instrumentation the first container should be the only you want to instrument.

NOTE: This type of instrumentation does not allow to instrument a container with multiple language instrumentations.

NOTE: instrumentation.opentelemetry.io/container-names annotation is not used for this feature.

Use customized or vendor instrumentation

By default, the operator uses upstream auto-instrumentation libraries. Custom auto-instrumentation can be configured by overriding the image fields in a CR.

apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: my-instrumentation
spec:
  java:
    image: your-customized-auto-instrumentation-image:java
  nodejs:
    image: your-customized-auto-instrumentation-image:nodejs
  python:
    image: your-customized-auto-instrumentation-image:python
  dotnet:
    image: your-customized-auto-instrumentation-image:dotnet
  go:
    image: your-customized-auto-instrumentation-image:go
  apacheHttpd:
    image: your-customized-auto-instrumentation-image:apache-httpd
  nginx:
    image: your-customized-auto-instrumentation-image:nginx

The Dockerfiles for auto-instrumentation can be found in autoinstrumentation directory. Follow the instructions in the Dockerfiles on how to build a custom container image.

Using Apache HTTPD autoinstrumentation

For Apache HTTPD autoinstrumentation, by default, instrumentation assumes httpd version 2.4 and httpd configuration directory /usr/local/apache2/conf as it is in the official Apache HTTPD image (f.e. docker.io/httpd:latest). If you need to use version 2.2, or your HTTPD configuration directory is different, and or you need to adjust agent attributes, customize the instrumentation specification per following example:

apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: my-instrumentation
  apache:
    image: your-customized-auto-instrumentation-image:apache-httpd
    version: 2.2
    configPath: /your-custom-config-path
    attrs:
    - name: ApacheModuleOtelMaxQueueSize
      value: "4096"
    - name: ...
      value: ...

List of all available attributes can be found at otel-webserver-module

Using Nginx autoinstrumentation

For Nginx autoinstrumentation, Nginx versions 1.22.0, 1.23.0, and 1.23.1 are supported at this time. The Nginx configuration file is expected to be /etc/nginx/nginx.conf by default, if it's different, see following example on how to change it. Instrumentation at this time also expects, that conf.d directory is present in the directory, where configuration file resides and that there is a include <config-file-dir-path>/conf.d/*.conf; directive in the http { ... } section of Nginx configuration file (like it is in the default configuration file of Nginx). You can also adjust OpenTelemetry SDK attributes. Example:

apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: my-instrumentation
  nginx:
    image: your-customized-auto-instrumentation-image:nginx # if custom instrumentation image is needed
    configFile: /my/custom-dir/custom-nginx.conf
    attrs:
    - name: NginxModuleOtelMaxQueueSize
      value: "4096"
    - name: ...
      value: ...

List of all available attributes can be found at otel-webserver-module

Inject OpenTelemetry SDK environment variables only

You can configure the OpenTelemetry SDK for applications which can't currently be autoinstrumented by using inject-sdk in place of inject-python or inject-java, for example. This will inject environment variables like OTEL_RESOURCE_ATTRIBUTES, OTEL_TRACES_SAMPLER, and OTEL_EXPORTER_OTLP_ENDPOINT, that you can configure in the Instrumentation, but will not actually provide the SDK.

instrumentation.opentelemetry.io/inject-sdk: "true"

Controlling Instrumentation Capabilities

The operator allows specifying, via the feature gates, which languages the Instrumentation resource may instrument. These feature gates must be passed to the operator via the --feature-gates flag. The flag allows for a comma-delimited list of feature gate identifiers. Prefix a gate with '-' to disable support for the corresponding language or multi instrumentation feature. Prefixing a gate with '+' or no prefix will enable support for the corresponding language or multi instrumentation feature. If a language is enabled by default its gate only needs to be supplied when disabling the gate.

Language	Gate	Default Value
Java	operator.autoinstrumentation.java	enabled
NodeJS	operator.autoinstrumentation.nodejs	enabled
Python	operator.autoinstrumentation.python	enabled
DotNet	operator.autoinstrumentation.dotnet	enabled
ApacheHttpD	operator.autoinstrumentation.apache-httpd	enabled
Go	operator.autoinstrumentation.go	disabled
Nginx	operator.autoinstrumentation.nginx	disabled

Language not specified in the table are always supported and cannot be disabled.

OpenTelemetry Operator allows to instrument multiple containers using multiple language specific instrumentations. These features can be enabled using operator.autoinstrumentation.multi-instrumentation flag when installing the Operator via Helm. By default flag is disabled. For example:

helm install opentelemetry-operator open-telemetry/opentelemetry-operator --set manager.featureGates=operator.autoinstrumentation.multi-instrumentation=enabled

For more information about multi-instrumentation feature capabilities please see Multi-container pods with multiple instrumentations.

Target Allocator

The OpenTelemetry Operator comes with an optional component, the Target Allocator (TA). When creating an OpenTelemetryCollector Custom Resource (CR) and setting the TA as enabled, the Operator will create a new deployment and service to serve specific http_sd_config directives for each Collector pod as part of that CR. It will also rewrite the Prometheus receiver configuration in the CR, so that it uses the deployed target allocator. The following example shows how to get started with the Target Allocator:

apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: collector-with-ta
spec:
  mode: statefulset
  targetAllocator:
    enabled: true
  config: |
    receivers:
      prometheus:
        config:
          scrape_configs:
          - job_name: 'otel-collector'
            scrape_interval: 10s
            static_configs:
            - targets: [ '0.0.0.0:8888' ]
            metric_relabel_configs:
            - action: labeldrop
              regex: (id|name)
              replacement: $$1
            - action: labelmap
              regex: label_(.+)
              replacement: $$1

    exporters:
      debug:

    service:
      pipelines:
        metrics:
          receivers: [prometheus]
          processors: []
          exporters: [debug]

The usage of $$ in the replacement keys in the example above is based on the information provided in the Prometheus receiver README documentation, which states: Note: Since the collector configuration supports env variable substitution $ characters in your prometheus configuration are interpreted as environment variables. If you want to use $ characters in your prometheus configuration, you must escape them using $$.

Behind the scenes, the OpenTelemetry Operator will convert the Collector’s configuration after the reconciliation into the following:

    receivers:
      prometheus:
        target_allocator:
          endpoint: http://collector-with-ta-targetallocator:80
          interval: 30s
          collector_id: $POD_NAME

    exporters:
      debug:

    service:
      pipelines:
        metrics:
          receivers: [prometheus]
          processors: []
          exporters: [debug]

The OpenTelemetry Operator will also convert the Target Allocator's Prometheus configuration after the reconciliation into the following:

    config:
      scrape_configs:
      - job_name: otel-collector
        scrape_interval: 10s
        static_configs:
        - targets: [ '0.0.0.0:8888' ]
        metric_relabel_configs:
        - action: labeldrop
          regex: (id|name)
          replacement: $1
        - action: labelmap
          regex: label_(.+)
          replacement: $1

Note that in this case, the Operator replaces "$$" with a single "$" in the replacement keys. This is because the collector supports environment variable substitution, whereas the TA (Target Allocator) does not. Therefore, to ensure compatibility, the TA configuration should only contain a single "$" symbol.

More info on the TargetAllocator can be found here.

Using Prometheus Custom Resources for service discovery

The target allocator can use Custom Resources from the prometheus-operator ecosystem, like ServiceMonitors and PodMonitors, for service discovery, performing a function analogous to that of prometheus-operator itself. This is enabled via the prometheusCR section in the Collector CR.

See below for a minimal example:

apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: collector-with-ta-prometheus-cr
spec:
  mode: statefulset
  targetAllocator:
    enabled: true
    serviceAccount: everything-prometheus-operator-needs
    prometheusCR:
      enabled: true
  config: |
    receivers:
      prometheus:
        config:

    exporters:
      debug:

    service:
      pipelines:
        metrics:
          receivers: [prometheus]
          processors: []
          exporters: [debug]

Compatibility matrix

OpenTelemetry Operator vs. OpenTelemetry Collector

The OpenTelemetry Operator follows the same versioning as the operand (OpenTelemetry Collector) up to the minor part of the version. For example, the OpenTelemetry Operator v0.18.1 tracks OpenTelemetry Collector 0.18.0. The patch part of the version indicates the patch level of the operator itself, not that of OpenTelemetry Collector. Whenever a new patch version is released for OpenTelemetry Collector, we'll release a new patch version of the operator.

By default, the OpenTelemetry Operator ensures consistent versioning between itself and the managed OpenTelemetryCollector resources. That is, if the OpenTelemetry Operator is based on version 0.40.0, it will create resources with an underlying OpenTelemetry Collector at version 0.40.0.

When a custom Spec.Image is used with an OpenTelemetryCollector resource, the OpenTelemetry Operator will not manage this versioning and upgrading. In this scenario, it is best practice that the OpenTelemetry Operator version should match the underlying core version. Given a OpenTelemetryCollector resource with a Spec.Image configured to a custom image based on underlying OpenTelemetry Collector at version 0.40.0, it is recommended that the OpenTelemetry Operator is kept at version 0.40.0.

OpenTelemetry Operator vs. Kubernetes vs. Cert Manager

We strive to be compatible with the widest range of Kubernetes versions as possible, but some changes to Kubernetes itself require us to break compatibility with older Kubernetes versions, be it because of code incompatibilities, or in the name of maintainability. Every released operator will support a specific range of Kubernetes versions, to be determined at the latest during the release.

We use cert-manager for some features of this operator and the third column shows the versions of the cert-manager that are known to work with this operator's versions.

The OpenTelemetry Operator might work on versions outside of the given range, but when opening new issues, please make sure to test your scenario on a supported version.

OpenTelemetry Operator	Kubernetes	Cert-Manager
v0.88.0	v1.23 to v1.28	v1
v0.87.0	v1.23 to v1.28	v1
v0.86.0	v1.23 to v1.28	v1
v0.85.0	v1.19 to v1.28	v1
v0.84.0	v1.19 to v1.28	v1
v0.83.0	v1.19 to v1.27	v1
v0.82.0	v1.19 to v1.27	v1
v0.81.0	v1.19 to v1.27	v1
v0.80.0	v1.19 to v1.27	v1
v0.79.0	v1.19 to v1.27	v1
v0.78.0	v1.19 to v1.27	v1
v0.77.0	v1.19 to v1.26	v1
v0.76.1	v1.19 to v1.26	v1
v0.75.0	v1.19 to v1.26	v1
v0.74.0	v1.19 to v1.26	v1
v0.73.0	v1.19 to v1.26	v1
v0.72.0	v1.19 to v1.26	v1
v0.71.0	v1.19 to v1.25	v1
v0.70.0	v1.19 to v1.25	v1
v0.69.0	v1.19 to v1.25	v1
v0.68.0	v1.19 to v1.25	v1
v0.67.0	v1.19 to v1.25	v1
v0.66.0	v1.19 to v1.25	v1

Contributing and Developing

Please see CONTRIBUTING.md.

In addition to the core responsibilities the operator project requires approvers and maintainers to be responsible for releasing the project. See RELEASE.md for more information and release schedule.

Approvers (@open-telemetry/operator-approvers):

• Benedikt Bongartz, Red Hat
• Tyler Helmuth, Honeycomb
• Yuri Oliveira Sa, Red Hat
• Mikołaj Świątek, Sumo Logic

Emeritus Approvers:

• Anthony Mirabella, AWS
• Dmitrii Anoshin, Splunk
• Jay Camp, Splunk
• James Bebbington, Google
• Owais Lone, Splunk
• Pablo Baeyens, DataDog

Target Allocator Maintainers (@open-telemetry/operator-ta-maintainers):

• Anthony Mirabella, AWS
• Kristina Pathak, Lightstep
• Sebastian Poxhofer

Maintainers (@open-telemetry/operator-maintainers):

• Jacob Aronoff, Lightstep
• Pavol Loffay, Red Hat
• Vineeth Pothulapati, Timescale

Emeritus Maintainers

• Alex Boten, Lightstep
• Bogdan Drutu, Splunk
• Juraci Paixão Kröhling, Grafana Labs
• Tigran Najaryan, Splunk

Learn more about roles in the community repository.

Thanks to all the people who already contributed!

License

Apache 2.0 License.