The OpenTelemetry Operator is an implementation of a Kubernetes Operator.
The operator manages:
- OpenTelemetry Collector
- auto-instrumentation of the workloads using OpenTelemetry instrumentation libraries
You can install Opentelemetry Operator via Helm Chart from the opentelemetry-helm-charts repository. More information is available in here.
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/v1beta1
kind: OpenTelemetryCollector
metadata:
name: simplest
spec:
config:
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
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: [memory_limiter, batch]
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.
🚨 Note: For private GKE clusters, you will need to either add a firewall rule that allows master nodes access to port
9443/tcp
on worker nodes, or change the existing rule that allows access to port80/tcp
,443/tcp
and10254/tcp
to also allow access to port9443/tcp
. More information can be found in the Official GCP Documentation. See the GKE documentation on adding rules and the Kubernetes issue for more detail.
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.
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
.
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:
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/v1beta1
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]
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_ATTRIBUTES
set with Kubernetes resource attributes, ready to be consumed by the resourcedetection processor.
The OpenTelemetry Collector defines a ServiceAccount field which could be set to run collector instances with a specific Service and their properties (e.g. imagePullSecrets). Therefore, if you have a constraint to run your collector with a private container registry, you should follow the procedure below:
- Create Service Account.
kubectl create serviceaccount <service-account-name>
- Create an imagePullSecret.
kubectl create secret docker-registry <secret-name> --docker-server=<registry name> \
--docker-username=DUMMY_USERNAME --docker-password=DUMMY_DOCKER_PASSWORD \
--docker-email=DUMMY_DOCKER_EMAIL
- Add image pull secret to service account
kubectl patch serviceaccount <service-account-name> -p '{"imagePullSecrets": [{"name": "<secret-name>"}]}'
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
go:
env:
# Required if endpoint is set to 4317.
# Go 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
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
environment 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 instrumentation will automatically inject OTEL_NODE_IP
and OTEL_POD_IP
environment variables should you need to reference either value in an endpoint.
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 will 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:
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 andInstrumentation
resource from the namespace."my-instrumentation"
- name ofInstrumentation
CR instance in the current namespace."my-other-namespace/my-instrumentation"
- name and namespace ofInstrumentation
CR instance in another namespace."false"
- do not inject
Note: For
DotNet
auto-instrumentation, by default, operator sets theOTEL_DOTNET_AUTO_TRACES_ENABLED_INSTRUMENTATIONS
environment variable which specifies the list of traces source instrumentations you want to enable. The value that is set by default by the operator is all available instrumentations supported by theopenTelemery-dotnet-instrumentation
release consumed in the image, i.e.AspNet,HttpClient,SqlClient
. This value can be overriden by configuring the environment variable explicitly.
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.
Works only when enable-multi-instrumentation
flag is true
.
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"
NGINX:
instrumentation.opentelemetry.io/inject-nginx-container-names: "nginx1,nginx2"
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.
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.
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
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
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"
The operator allows specifying, via the flags, which languages the Instrumentation resource may instrument.
If a language is enabled by default its gate only needs to be supplied when disabling the gate.
Language support can be disabled by passing the flag with a value of false
.
Language | Gate | Default Value |
---|---|---|
Java | enable-java-instrumentation |
true |
NodeJS | enable-nodejs-instrumentation |
true |
Python | enable-python-instrumentation |
true |
DotNet | enable-dotnet-instrumentation |
true |
ApacheHttpD | enable-apache-httpd-instrumentation |
true |
Go | enable-go-instrumentation |
false |
Nginx | enable-nginx-instrumentation |
false |
OpenTelemetry Operator allows to instrument multiple containers using multiple language specific instrumentations.
These features can be enabled using the enable-multi-instrumentation
flag. By default flag is false
.
For more information about multi-instrumentation feature capabilities please see Multi-container pods with multiple instrumentations.
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:
kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1beta1
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)
- action: labelmap
regex: label_(.+)
replacement: $$1
exporters:
debug: {}
service:
pipelines:
metrics:
receivers: [prometheus]
exporters: [debug]
EOF
The usage of $$
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 $$.
Behind the scenes, the OpenTelemetry Operator will convert the Collector’s configuration after the reconciliation into the following:
receivers:
prometheus:
target_allocator:
endpoint: http://collector-with-ta-targetallocator:80
interval: 30s
collector_id: $POD_NAME
exporters:
debug:
service:
pipelines:
metrics:
receivers: [prometheus]
exporters: [debug]
The OpenTelemetry Operator will also convert the Target Allocator's Prometheus configuration after the reconciliation into the following:
config:
scrape_configs:
- job_name: otel-collector
scrape_interval: 10s
static_configs:
- targets: ["0.0.0.0:8888"]
metric_relabel_configs:
- action: labeldrop
regex: (id|name)
- action: labelmap
regex: label_(.+)
replacement: $1
Note that in this case, the Operator replaces "$$" 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.
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:
kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1beta1
kind: OpenTelemetryCollector
metadata:
name: collector-with-ta-prometheus-cr
spec:
mode: statefulset
targetAllocator:
enabled: true
serviceAccount: everything-prometheus-operator-needs
prometheusCR:
enabled: true
serviceMonitorSelector: {}
podMonitorSelector: {}
config:
receivers:
prometheus:
config: {}
exporters:
debug: {}
service:
pipelines:
metrics:
receivers: [prometheus]
exporters: [debug]
EOF
This example shows a pod configuration with OpenTelemetry annotations using the resource.opentelemetry.io/
prefix. These annotations can be used to add resource attributes to data produced by OpenTelemetry instrumentation.
apiVersion: v1
kind: Pod
metadata:
name: example-pod
annotations:
resource.opentelemetry.io/service.name: "my-service"
resource.opentelemetry.io/service.version: "1.0.0"
resource.opentelemetry.io/environment: "production"
spec:
containers:
- name: main-container
image: your-image:tag
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
.
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 Target Allocator supports prometheus-operator CRDs like ServiceMonitor, and it does so by using packages imported from prometheus-operator itself. The table shows which version is shipped with a given operator version. Generally speaking, these are backwards compatible, but specific features require the appropriate package 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 | Prometheus-Operator |
---|---|---|---|
v0.106.0 | v1.23 to v1.30 | v1 | v0.75.0 |
v0.105.0 | v1.23 to v1.30 | v1 | v0.74.0 |
v0.104.0 | v1.23 to v1.30 | v1 | v0.74.0 |
v0.103.0 | v1.23 to v1.30 | v1 | v0.74.0 |
v0.102.0 | v1.23 to v1.30 | v1 | v0.71.2 |
v0.101.0 | v1.23 to v1.30 | v1 | v0.71.2 |
v0.100.0 | v1.23 to v1.29 | v1 | v0.71.2 |
v0.99.0 | v1.23 to v1.29 | v1 | v0.71.2 |
v0.98.0 | v1.23 to v1.29 | v1 | v0.71.2 |
v0.97.0 | v1.23 to v1.29 | v1 | v0.71.2 |
v0.96.0 | v1.23 to v1.29 | v1 | v0.71.2 |
v0.95.0 | v1.23 to v1.29 | v1 | v0.71.2 |
v0.94.0 | v1.23 to v1.29 | v1 | v0.71.0 |
v0.93.0 | v1.23 to v1.29 | v1 | v0.71.0 |
v0.92.0 | v1.23 to v1.29 | v1 | v0.71.0 |
v0.91.0 | v1.23 to v1.29 | v1 | v0.70.0 |
v0.90.0 | v1.23 to v1.28 | v1 | v0.69.1 |
v0.89.0 | v1.23 to v1.28 | v1 | v0.69.1 |
v0.88.0 | v1.23 to v1.28 | v1 | v0.68.0 |
v0.87.0 | v1.23 to v1.28 | v1 | v0.68.0 |
v0.86.0 | v1.23 to v1.28 | v1 | v0.68.0 |
v0.85.0 | v1.19 to v1.28 | v1 | v0.67.1 |
v0.84.0 | v1.19 to v1.28 | v1 | v0.67.1 |
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
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):
- Kristina Pathak, Lightstep
- Sebastian Poxhofer
Emeritus Target Allocator Maintainers
- Anthony Mirabella, AWS
Maintainers (@open-telemetry/operator-maintainers):
- Jacob Aronoff, Lightstep
- Mikołaj Świątek, Sumo Logic
- Pavol Loffay, Red Hat
Emeritus Maintainers
- Alex Boten, Lightstep
- Bogdan Drutu, Splunk
- Juraci Paixão Kröhling, Grafana Labs
- Tigran Najaryan, Splunk
- Vineeth Pothulapati, Timescale
Learn more about roles in the community repository.
Thanks to all the people who already contributed!