Understanding Operators

The Jaeger Operator is an implementation of a Kubernetes Operator . Operators are pieces of software that ease the operational complexity of running another piece of software. More technically, Operators are a method of packaging, deploying, and managing a Kubernetes application.

A Kubernetes application is an application that is both deployed on Kubernetes and managed using the Kubernetes APIs and kubectl (kubernetes) or oc (OKD) tooling. To be able to make the most of Kubernetes, you need a set of cohesive APIs to extend in order to service and manage your apps that run on Kubernetes. Think of Operators as the runtime that manages this type of app on Kubernetes.

Installing the Operator

The Jaeger Operator can be installed in Kubernetes-based clusters and is able to watch for new Jaeger custom resources (CR) in specific namespaces, or across the entire cluster. There is typically only one Jaeger Operator per cluster, but there might be at most one Jaeger Operator per namespace in multi-tenant scenarios. When a new Jaeger CR is detected, an operator will attempt to set itself as the owner of the resource, setting a label jaegertracing.io/operated-by to the new CR, with the operator’s namespace and name as the label’s value.

While multiple operators might coexist watching the same set of namespaces, which operator will succeed in setting itself as the owner of the CR is undefined behavior. Automatic injection of the sidecars might also result in undefined behavior. Therefore, it’s highly recommended to have at most one operator watching each namespace. Note that namespaces might contain any number of Jaeger instances (CRs).

Install modes

The Jaeger Operator can be installed to watch for new Jaeger custom resources (CRs) either in the whole cluster or in specific namespaces. When configured for cluster-mode, the operator can:

• watch for events related to Jaeger resources in all namespaces
• watch the namespaces themselves looking for the sidecar.jaegertracing.io/inject annotation
• watch all deployments, to inject or remove sidecars based on the sidecar.jaegertracing.io/inject annotation
• create cluster role bindings, when necessary

When not using the cluster-wide resources (ClusterRole and ClusterRoleBinding), set the WATCH_NAMESPACE to the comma-separated list of namespaces that the Jaeger Operator should watch for events related to Jaeger resources. It is possible to have the Jaeger Operator running in a given namespace (like, observability) and manage Jaeger resources in another (like, myproject). For that, use a RoleBinding like the following for each namespace the operator should watch for resources:

kind: RoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: jaeger-operator-in-myproject
  namespace: myproject
subjects:
- kind: ServiceAccount
  name: jaeger-operator
  namespace: observability
roleRef:
  kind: Role
  name: jaeger-operator
  apiGroup: rbac.authorization.k8s.io

Installing the Operator on Kubernetes

The following instructions will create the observability namespace and install the Jaeger Operator there. By default, the operator will watch the same namespace in which it has been installed.

To install the operator, run:

kubectl create namespace observability # <1>
kubectl create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/crds/jaegertracing.io_jaegers_crd.yaml # <2>
kubectl create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/service_account.yaml
kubectl create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/role.yaml
kubectl create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/role_binding.yaml
kubectl create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/operator.yaml

<1> This creates the namespace used by default in the deployment files. If you want to install the Jaeger operator in a different namespace, you must edit the deployment files to change observability to the desired namespace value.

<2> This installs the “Custom Resource Definition” for the apiVersion: jaegertracing.io/v1

The operator will activate extra features if given cluster-wide permissions. To enable that, run:

kubectl create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/cluster_role.yaml
kubectl create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/cluster_role_binding.yaml

Note that you’ll need to download and customize the cluster_role_binding.yaml if you are using a namespace other than observability. You probably also want to download and customize the operator.yaml, setting the env var WATCH_NAMESPACE to have an empty value, so that it can watch for instances across all namespaces.

At this point, there should be a jaeger-operator deployment available. You can view it by running the following command:

$ kubectl get deployment jaeger-operator -n observability

NAME              DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
jaeger-operator   1         1         1            1           48s

The operator is now ready to create Jaeger instances.

Installing the Operator on OKD/OpenShift

The instructions from the previous section also work for installing the operator on OKD or OpenShift. Make sure you are logged in as a privileged user, when you install the role based access control (RBAC) rules, the custom resource definition, and the operator.

oc login -u <privileged user>

oc new-project observability # <1>
oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/crds/jaegertracing.io_jaegers_crd.yaml # <2>
oc create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/service_account.yaml
oc create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/role.yaml
oc create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/role_binding.yaml
oc create -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/operator.yaml

<1> This creates the namespace used by default in the deployment files. If you want to install the Jaeger operator in a different namespace, you must edit the deployment files to change observability to the desired namespace value.

<2> This installs the “Custom Resource Definition” for the apiVersion: jaegertracing.io/v1

The operator will activate extra features if given cluster-wide permissions. To enable that, run:

oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/cluster_role.yaml
oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/cluster_role_binding.yaml

Note that you’ll need to download and customize the cluster_role_binding.yaml if you are using a namespace other than observability. You probably also want to download and customize the operator.yaml, setting the env var WATCH_NAMESPACE to have an empty value, so that it can watch for instances across all namespaces.

Once the operator is installed, grant the role jaeger-operator to users who should be able to install individual Jaeger instances. The following example creates a role binding allowing the user developer to create Jaeger instances:

oc create \
  rolebinding developer-jaeger-operator \
  --role=jaeger-operator \
  --user=developer

After the role is granted, switch back to a non-privileged user.

Quick Start - Deploying the AllInOne image

The simplest possible way to create a Jaeger instance is by creating a YAML file like the following example. This will install the default AllInOne strategy, which deploys the “all-in-one” image (agent, collector, query, ingester, Jaeger UI) in a single pod, using in-memory storage by default.

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simplest

The YAML file can then be used with kubectl:

kubectl apply -f simplest.yaml

In a few seconds, a new in-memory all-in-one instance of Jaeger will be available, suitable for quick demos and development purposes. To check the instances that were created, list the jaeger objects:

$ kubectl get jaegers
NAME        CREATED AT
simplest    28s

To get the pod name, query for the pods belonging to the simplest Jaeger instance:

$ kubectl get pods -l app.kubernetes.io/instance=simplest
NAME                        READY     STATUS    RESTARTS   AGE
simplest-6499bb6cdd-kqx75   1/1       Running   0          2m

Similarly, the logs can be queried either from the pod directly using the pod name obtained from the previous example, or from all pods belonging to our instance:

$ kubectl logs -l app.kubernetes.io/instance=simplest
...
{"level":"info","ts":1535385688.0951214,"caller":"healthcheck/handler.go:133","msg":"Health Check state change","status":"ready"}

$ kubectl logs -l app.kubernetes.io/instance=simplest -c jaeger
...
{"level":"info","ts":1535385688.0951214,"caller":"healthcheck/handler.go:133","msg":"Health Check state change","status":"ready"}

Configuring the operator

The Jaeger Operator can be configured via command-line interface parameters, via environment variables or configuration file. When the same var is specified at different levels, the precedence order is:

1. command-line parameter (flag)
2. environment variable
3. configuration file

Each item takes precedence over the item below it. The available options can be seen by running the operator with the --help flag, such as:

$ podman run jaegertracing/jaeger-operator:1.28.0 start --help

Examples

Setting the log-level parameter via flag of a given Jaeger Operator deployment (excerpt):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: jaeger-operator
spec:
  template:
    spec:
      containers:
      - name: jaeger-operator
        image: jaegertracing/jaeger-operator:1.28.0
        args: ["start", "--log-level=debug"]

Setting the log-level parameter via environment variable on a given Jaeger Operator deployment (excerpt):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: jaeger-operator
spec:
  template:
    spec:
      containers:
      - name: jaeger-operator
        image: jaegertracing/jaeger-operator:1.28.0
        args: ["start"]
        env:
        - name: LOG-LEVEL
          value: debug

Setting the log-level parameter in the configuration file:

log-level: debug

To use a configuration file, either create a file at ${HOME}/.jaeger-operator.yaml, or specify the location via --config.

Deployment Strategies

When you create a Jaeger instance, it is associated with a strategy. The strategy is defined in the custom resource file, and determines the architecture to be used for the Jaeger backend. The default strategy is allInOne. The other possible values are production and streaming.

The available strategies are described in the following sections.

AllInOne (Default) strategy

This strategy is intended for development, testing, and demo purposes.

The main backend components, agent, collector and query service, are all packaged into a single executable which is configured (by default) to use in-memory storage. This strategy cannot be scaled beyond one replica.

Production strategy

The production strategy is intended (as the name suggests) for production environments, where long term storage of trace data is important, as well as a more scalable and highly available architecture is required. Each of the backend components is therefore separately deployed.

The agent can be injected as a sidecar on the instrumented application or as a daemonset.

The collector can be configured to autoscale on demand. By default, when no value for .Spec.Collector.Replicas is provided, the Jaeger Operator will create a Horizontal Pod Autoscaler (HPA) configuration for the collector. We recommend setting an explicit value for .Spec.Collector.MaxReplicas, along with a reasonable value for the resources that the collector’s pod is expected to consume. When no .Spec.Collector.MaxReplicas is set, the operator will set 100 as its value. Read more about HPA on Kubernetes’ website . The feature can be explicitly disabled by setting .Spec.Collector.Autoscale to false. Here’s an example, setting the collector’s limits as well as the maximum number of replicas:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-prod
spec:
  strategy: production
  collector:
    maxReplicas: 5
    resources:
      limits:
        cpu: 100m
        memory: 128Mi

The query and collector services are configured with a supported storage type - currently Cassandra or Elasticsearch. Multiple instances of each of these components can be provisioned as required for performance and resilience purposes.

The main additional requirement is to provide the details of the storage type and options, for example:

    storage:
      type: elasticsearch
      options:
        es:
          server-urls: http://elasticsearch:9200

Streaming strategy

The streaming strategy is designed to augment the production strategy by providing a streaming capability that effectively sits between the collector and the backend storage (Cassandra or Elasticsearch). This provides the benefit of reducing the pressure on the backend storage, under high load situations, and enables other trace post-processing capabilities to tap into the real time span data directly from the streaming platform (Kafka).

The collector can be configured to autoscale on demand, as described in the “Production strategy” section.

The ingester can also be configured to autoscale on demand. By default, when no value for .Spec.Ingester.Replicas is provided, the Jaeger Operator will create a Horizontal Pod Autoscaler (HPA) configuration for the ingester. We recommend setting an explicit value for .Spec.Ingester.MaxReplicas, along with a reasonable value for the resources that the ingester’s pod is expected to consume. When no .Spec.Ingester.MaxReplicas is set, the operator will set 100 as its value. Read more about HPA on Kubernetes’ website . The feature can be explicitly disabled by setting .Spec.Ingester.Autoscale to false. Here’s an example, setting the ingester’s limits as well as the maximum number of replicas:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-streaming
spec:
  strategy: streaming
  ingester:
    maxReplicas: 8
    resources:
      limits:
        cpu: 100m
        memory: 128Mi

Existing Kafka Cluster

The only additional information required is to provide the details for accessing the Kafka platform, which is configured in the collector component (as producer) and ingester component (as consumer):

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-streaming
spec:
  strategy: streaming
  collector:
    options:
      kafka: # <1>
        producer:
          topic: jaeger-spans
          brokers: my-cluster-kafka-brokers.kafka:9092
  ingester:
    options:
      kafka: # <1>
        consumer:
          topic: jaeger-spans
          brokers: my-cluster-kafka-brokers.kafka:9092
      ingester:
        deadlockInterval: 5s # <2>
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch:9200

<1> Identifies the Kafka configuration used by the collector, to produce the messages, and the ingester to consume the messages.

<2> The deadlock interval is disabled by default (set to 0), to avoid the ingester being terminated when no messages arrive, but can be configured to specify the number of minutes to wait for a message before terminating.

Self Provisioned Kafka Cluster

To use the self-provisioned approach, the producer/consumer brokers property should not be defined.

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: auto-provision-kafka
spec:
  strategy: streaming
  storage:
    type: elasticsearch
    options:
      es:
        # Note: This assumes elasticsearch is running in the "default" namespace.
        server-urls: http://elasticsearch.default.svc:9200

The self-provision of a Kafka cluster can be disabled by setting the flag --kafka-provision to false. The default value is auto, which will make the Jaeger Operator query the Kubernetes cluster for its ability to handle a Kafka custom resource. This is usually set by the Kafka Operator during its installation process, so, if the Kafka Operator is expected to run after the Jaeger Operator, the flag can be set to true.

Understanding Custom Resource Definitions

In the Kubernetes API, a resource is an endpoint that stores a collection of API objects of a certain kind. For example, the built-in Pods resource contains a collection of Pod objects. A Custom Resource Definition (CRD) object defines a new, unique object Kind in the cluster and lets the Kubernetes API server handle its entire lifecycle.

To create Custom Resource (CR) objects, cluster administrators must first create a Custom Resource Definition (CRD). The CRDs allow cluster users to create CRs to add the new resource types into their projects. An Operator watches for custom resource objects to be created, and when it sees a custom resource being created, it creates the application based on the parameters defined in the custom resource object.

For reference, here’s how you can create a more complex all-in-one instance:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: my-jaeger
spec:
  strategy: allInOne # <1>
  allInOne:
    image: jaegertracing/all-in-one:latest # <2>
    options: # <3>
      log-level: debug # <4>
  storage:
    type: memory # <5>
    options: # <6>
      memory: # <7>
        max-traces: 100000
  ingress:
    enabled: false # <8>
  agent:
    strategy: DaemonSet # <9>
  annotations:
    scheduler.alpha.kubernetes.io/critical-pod: "" # <10>

<1> The default strategy is allInOne. The other possible values are production and streaming.

<2> The image to use, in a regular Docker syntax.

<3> The (non-storage related) options to be passed verbatim to the underlying binary. Refer to the Jaeger documentation and/or to the --help option from the related binary for all the available options.

<4> The option is a simple key: value map. In this case, we want the option --log-level=debug to be passed to the binary.

<5> The storage type to be used. By default it will be memory, but can be any other supported storage type (Cassandra, Elasticsearch, Kafka).

<6> All storage related options should be placed here, rather than under the ‘allInOne’ or other component options.

<7> Some options are namespaced and we can alternatively break them into nested objects. We could have specified memory.max-traces: 100000.

<8> By default, an ingress object is created for the query service. It can be disabled by setting its enabled option to false. If deploying on OpenShift, this will be represented by a Route object.

<9> By default, the operator assumes that agents are deployed as sidecars within the target pods. Specifying the strategy as “DaemonSet” changes that and makes the operator deploy the agent as DaemonSet. Note that your tracer client will probably have to override the “JAEGER_AGENT_HOST” environment variable to use the node’s IP.

<10> Define annotations to be applied to all deployments (not services). These can be overridden by annotations defined on the individual components.

You can view example custom resources for different Jaeger configurations on GitHub .

Configuring the Custom Resource

You can use the simplest example (shown above) and create a Jaeger instance using the defaults, or you can create your own custom resource file.

Storage options

Cassandra storage

When the storage type is set to Cassandra, the operator will automatically create a batch job that creates the required schema for Jaeger to run. This batch job will block the Jaeger installation, so that it starts only after the schema is successfully created. The creation of this batch job can be disabled by setting the enabled property to false:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: cassandra-without-create-schema
spec:
  strategy: allInOne
  storage:
    type: cassandra
    cassandraCreateSchema:
      enabled: false # <1>

<1> Defaults to true

Further aspects of the batch job can be configured as well. An example with all the possible options is shown below:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: cassandra-with-create-schema
spec:
  strategy: allInOne # <1>
  storage:
    type: cassandra
    options: # <2>
      cassandra:
        servers: cassandra
        keyspace: jaeger_v1_datacenter3
    cassandraCreateSchema: # <3>
      datacenter: "datacenter3"
      mode: "test"

<1> The same works for production and streaming.

<2> These options are for the regular Jaeger components, like collector and query.

<3> The options for the create-schema job.

Elasticsearch storage

By default Elasticsearch storage does not require any initialization job to be run. However Elasticsearch storage requires a cron job to be run to clean old data from the storage.

When rollover (es.use-aliases) is enabled, Jaeger operator also deploys a job to initialize Elasticsearch storage and another two cron jobs to perform required index management actions.

External Elasticsearch

Jaeger can be used with an external Elasticsearch cluster. The following example shows a Jaeger CR using an external Elasticsearch cluster (created by Elasticsearch Operator ) with TLS CA certificate mounted from a volume and user/password stored in a secret.

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-prod
spec:
  strategy: production
  storage:
    type: elasticsearch # <1>
    options:
      es:
        server-urls: https://quickstart-es-http.default.svc:9200 # <2>
        index-prefix: my-prefix
        tls: # <3>
          ca: /es/certificates/ca.crt
    secretName: jaeger-secret # <4>
  volumeMounts: # <5>
    - name: certificates
      mountPath: /es/certificates/
      readOnly: true
  volumes:
    - name: certificates
      secret:
        secretName: quickstart-es-http-certs-public

<1> Storage type Elasticsearch.

<2> Url to Elasticsearch service running in default namespace.

<3> TLS configuration. In this case only CA certificate, but it can also contain es.tls.key and es.tls.cert when using mutual TLS.

<4> Secret which defines environment variables ES_PASSWORD and ES_USERNAME. Created by kubectl create secret generic jaeger-secret --from-literal=ES_PASSWORD=changeme --from-literal=ES_USERNAME=elastic

<5> Volume mounts and volumes which are mounted into all storage components.

Self provisioned

Under some circumstances, the Jaeger Operator can make use of the Elasticsearch Operator  to provision a suitable Elasticsearch cluster. Jaeger CR exposes the same configuration as OpenShift Cluster Logging .

When there is no es.server-urls option as part of a Jaeger production instance and elasticsearch is set as the storage type, the Jaeger Operator creates an Elasticsearch cluster via the Elasticsearch Operator by creating a Custom Resource based on the configuration provided in storage section. The Elasticsearch cluster is meant to be dedicated for a single Jaeger instance.

Follows an example of Jaeger with a single node Elasticsearch cluster with AWS gp2 persistent storage:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-prod
spec:
  strategy: production
  storage:
    type: elasticsearch
    elasticsearch:
      nodeCount: 1 # <1>
      storage: # <2>
        storageClassName: gp2
        size: 5Gi
      resources: # <3>
        requests:
          cpu: 200m
          memory: 4Gi
        limits:
          memory: 4Gi
      redundancyPolicy: ZeroRedundancy # <4>

<1> Number of Elasticsearch nodes. For high availability use at least 3 nodes. Do not use 2 nodes as “split brain” problem can happen.

<2> Persistent storage configuration. In this case AWS gp2 with 5Gi size. When omitted emptyDir is used. Elasticsearch operator provisions PersistentVolumeClaim and PersistentVolume which are not removed with Jaeger instance. The same volumes can be mounted if Jaeger with the same name and namespace is crated. Some storages might fail in default namespace because of OpenShift SCC policy.

<3> Resources for Elasticsearch nodes. In this case 4Gi which results to by default required 2Gi of heap space. Refer to Elasticsearch documentation  for memory recommendations.

<4> Data replication policy defines how Elasticsearch shards are replicated across data nodes in the cluster. If not specified Jaeger Operator automatically determines the most appropriate replication based on number of nodes.

• FullRedundancy Elasticsearch fully replicates the primary shards for each index to every data node. This provides the highest safety, but at the cost of the highest amount of disk required and the poorest performance.
• MultipleRedundancy Elasticsearch fully replicates the primary shards for each index to half of the data nodes. This provides a good tradeoff between safety and performance.
• SingleRedundancy Elasticsearch makes one copy of the primary shards for each index. Data are always available and recoverable as long as at least two data nodes exist. Better performance than MultipleRedundancy, when using 5 or more nodes. You cannot apply this policy on deployments of single Elasticsearch node.
• ZeroRedundancy Elasticsearch does not make copies of the primary shards. Data might be unavailable or lost in the event a node is down or fails. Use this mode when you are more concerned with performance than safety, or have implemented your own disk/PVC backup/restore strategy.

The self-provision of an Elasticsearch cluster can be disabled by setting the flag --es-provision to false. The default value is auto, which will make the Jaeger Operator query the Kubernetes cluster for its ability to handle a Elasticsearch custom resource. This is usually set by the Elasticsearch Operator during its installation process, so, if the Elasticsearch Operator is expected to run after the Jaeger Operator, the flag can be set to true.

Elasticsearch index cleaner job

When using elasticsearch storage by default a cron job is created to clean old traces from it, the options for it are listed below so you can configure it to your use case. The connection configuration is derived from the storage options.

storage:
  type: elasticsearch
  esIndexCleaner:
    enabled: true                                 // turn the cron job deployment on and off
    numberOfDays: 7                               // number of days to wait before deleting a record
    schedule: "55 23 * * *"                       // cron expression for it to run

The connection configuration to storage is derived from storage options.

Elasticsearch rollover

This index management strategy is more complicated than using the default daily indices and it requires an initialization job to prepare the storage and two cron jobs to manage indices. The first cron job is used for rolling-over to a new index and the second for removing indices from read alias. The rollover feature is used when storage option es.use-aliases is enabled.

To learn more about rollover index management in Jaeger refer to this article .

storage:
  type: elasticsearch
  options:
    es:
      use-aliases: true
  esRollover:
    conditions: "{\"max_age\": \"2d\"}"          // conditions when to rollover to a new index
    readTTL: 168h                                // how long should be old data available for reading (7 days)
    schedule: "55 23 * * *"                      // cron expression for it to run

The connection configuration to storage is derived from storage options.

Elasticsearch index lifecycle management

Index lifecycle management  (ILM) is an Elasticsearch feature from X-Pack plugin that manages lifecycle of indices. In the context of the Operator it means that ILM can be used instead of rollover cron jobs. The Jaeger project does not provide a direct integration with the ILM, however the Jaeger instance can be configured to use index aliases (required by ILM) and disable index template creation and rollover cron jobs. This allows users to configure ILM in custom index templates before Jaeger is deployed.

spec:
  strategy: production
  collector:
    options:
      es:
        use-aliases: true # <1>
  query:
    options:
      es:
        use-aliases: true  # <1>
  storage:
    type: elasticsearch
    options:
      es:
        create-index-templates: false  # <2>
        server-urls: http://elasticsearch:9200

<1> Configures query and collector to use read and write index aliases.

<2> Disables creation of default index templates.

Storage plugin

Setting spec.storage.type to grpc-plugin enables using Jaeger with 3rd party storage implementations.

The following is an example of a Jaeger CR using the allInOne deployment strategy and the Clickhouse storage plugin. Refer to jaeger-clickhouse  for documentation and a fully functional example.

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: clickhouse-grpc-plugin
spec:
  strategy: allInOne
  storage:
    type: grpc-plugin # <1>
    grpcPlugin:
      image: ghcr.io/pavolloffay/jaeger-clickhouse:0.5.1 # <2>
    options:
      grpc-storage-plugin: # <3>
        binary: /plugin/jaeger-clickhouse
        configuration-file: /plugin-config/config.yaml
        log-level: debug
  volumeMounts:
    - name: plugin-config
      mountPath: /plugin-config
  volumes:
    - name: plugin-config
      configMap:
        name: jaeger-clickhouse # <4>

<1> Storage type set to grpc-plugin.

<2> Image with the plugin binary. The image is used as init-container to copy the binary into volume that is available to the Jaeger process. The image has to copy the binary into /plugin directory.

<3> Configuration options for the grpc-plugin.

<4> User created config map with the plugin configuration.

Deriving dependencies

The processing to derive dependencies will collect spans from storage, analyzes links between services and store them for later presentation in the UI. This job can only be used with the production strategy and storage type cassandra or elasticsearch.

storage:
  type: elasticsearch
  dependencies:
    enabled: true                                 # turn the job deployment on and off
    schedule: "55 23 * * *"                       # cron expression for it to run
    sparkMaster:                                  # spark master connection string, when empty spark runs in embedded local mode
    resources:
      requests:
        memory: 4096Mi
      limits:
        memory: 4096Mi

The connection configuration to storage is derived from storage options.

Auto-injecting Jaeger Agent Sidecars

The operator can inject Jaeger Agent sidecars in Deployment workloads, provided that the deployment or its namespace has the annotation sidecar.jaegertracing.io/inject with a suitable value. The values can be either "true" (as string), or the Jaeger instance name, as returned by kubectl get jaegers. When "true" is used, there should be exactly one Jaeger instance for the same namespace as the deployment, otherwise, the operator can’t figure out automatically which Jaeger instance to use. A specific Jaeger instance name on a deployment has a higher precedence than true applied on its namespace.

The following snippet shows a simple application that will get a sidecar injected, with the Jaeger Agent pointing to the single Jaeger instance available in the same namespace:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  annotations:
    "sidecar.jaegertracing.io/inject": "true" # <1>
spec:
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp
        image: acme/myapp:myversion

<1> Either "true" (as string) or the Jaeger instance name.

A complete sample deployment is available at deploy/examples/business-application-injected-sidecar.yaml .

When the sidecar is injected, the Jaeger Agent can then be accessed at its default location on localhost.

Deployment-level Configurations for Injected Sidecars

Since the sidecar may be injected in Deployments that are not managed by the jaeger-operator, many configurations that apply at the Deployment-level are not applied to a sidecar’s Deployment unless they are specified under the agent node. The following configurations are supported for the sidecar’s Deployment:

• Volumes (& VolumeMounts)
• ImagePullSecrets

E.g. the following Jaeger configuration will add the agent-volume and agent-imagePullSecrets to the sidecar’s deployment.

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: my-jaeger
spec:
  agent:
    volumeMounts:
    - name: agent-volume
      mountPath: /tmp/agent
      readOnly: true
    volumes:
      - name: agent-volume
        secret:
          secretName: agent-secret
    imagePullSecrets:
    - name: agent-imagePullSecret

Manually Defining Jaeger Agent Sidecars

For controller types other than Deployments (e.g. StatefulSets, DaemonSets, etc), the Jaeger Agent sidecar can be manually defined in your specification.

The following snippet shows the manual definition you can include in your containers section for a Jaeger Agent sidecar:

- name: jaeger-agent
  image: jaegertracing/jaeger-agent:latest
  imagePullPolicy: IfNotPresent
  ports:
    - containerPort: 5775
      name: zk-compact-thrift
      protocol: UDP
    - containerPort: 5778
      name: config-rest
      protocol: TCP
    - containerPort: 6831
      name: jg-compact-thrift
      protocol: UDP
    - containerPort: 6832
      name: jg-binary-thrift
      protocol: UDP
    - containerPort: 14271
      name: admin-http
      protocol: TCP
  args:
    - --reporter.grpc.host-port=dns:///jaeger-collector-headless.observability:14250
    - --reporter.type=grpc

A complete sample StatefulSet is available at deploy/examples/statefulset-manual-sidecar.yaml .

The Jaeger Agent can then be accessed at its default location on localhost.

Installing the Agent as DaemonSet

By default, the Operator expects the agents to be deployed as sidecars to the target applications. This is convenient for several purposes, like in a multi-tenant scenario or to have better load balancing, but there are scenarios where you might want to install the agent as a DaemonSet. In that case, specify the Agent’s strategy to DaemonSet, as follows:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: my-jaeger
spec:
  agent:
    strategy: DaemonSet

Your tracer client will then most likely need to be told where the agent is located. This is usually done by setting the environment variable JAEGER_AGENT_HOST to the value of the Kubernetes node’s IP, for example:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp
        image: acme/myapp:myversion
        env:
        - name: JAEGER_AGENT_HOST
          valueFrom:
            fieldRef:
              fieldPath: status.hostIP

OpenShift

In OpenShift, a HostPort can only be set when a special security context is set. A separate service account can be used by the Jaeger Agent with the permission to bind to HostPort, as follows:

oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/examples/openshift/hostport-scc-daemonset.yaml # <1>
oc new-project myproject
oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/examples/openshift/service_account_jaeger-agent-daemonset.yaml # <2>
oc adm policy add-scc-to-user daemonset-with-hostport -z jaeger-agent-daemonset # <3>
oc apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/examples/openshift/agent-as-daemonset.yaml # <4>

<1> The SecurityContextConstraints with the allowHostPorts policy

<2> The ServiceAccount to be used by the Jaeger Agent

<3> Adds the security policy to the service account

<4> Creates the Jaeger Instance using the serviceAccount created in the steps above

After a few seconds, the DaemonSet should be up and running:

$ oc get daemonset agent-as-daemonset-agent-daemonset
NAME                                 DESIRED   CURRENT   READY     UP-TO-DATE   AVAILABLE
agent-as-daemonset-agent-daemonset   1         1         1         1            1

Secrets Support

The Operator supports passing secrets to the Collector, Query and All-In-One deployments. This can be used for example, to pass credentials (username/password) to access the underlying storage backend (for example: Elasticsearch). The secrets are available as environment variables in the (Collector/Query/All-In-One) nodes.

    storage:
      type: elasticsearch
      options:
        es:
          server-urls: http://elasticsearch:9200
      secretName: jaeger-secrets

The secret itself would be managed outside of the jaeger-operator custom resource.

Configuring the UI

Information on various configuration options for the UI can be found here, defined in json format.

To apply UI configuration changes within the Custom Resource, the same information can be included in yaml format as shown below:

    ui:
      options:
        dependencies:
          menuEnabled: false
        tracking:
          gaID: UA-000000-2
        menu:
        - label: "About Jaeger"
          items:
            - label: "Documentation"
              url: "https://www.jaegertracing.io/docs/latest"
        linkPatterns:
        - type: "logs"
          key: "customer_id"
          url: /search?limit=20&lookback=1h&service=frontend&tags=%7B%22customer_id%22%3A%22#{customer_id}%22%7D
          text: "Search for other traces for customer_id=#{customer_id}"

Defining Sampling Strategies

The operator can be used to define sampling strategies that will be supplied to tracers that have been configured to use a remote sampler:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: with-sampling
spec:
  strategy: allInOne
  sampling:
    options:
      default_strategy:
        type: probabilistic
        param: 0.5

This example defines a default sampling strategy that is probabilistic, with a 50% chance of the trace instances being sampled.

Refer to the Jaeger documentation on Collector Sampling Configuration  to see how service and endpoint sampling can be configured. The JSON representation described in that documentation can be used in the operator by converting to YAML.

Finer grained configuration

The custom resource can be used to define finer grained Kubernetes configuration applied to all Jaeger components or at the individual component level.

When a common definition (for all Jaeger components) is required, it is defined under the spec node. When the definition relates to an individual component, it is placed under the spec/<component> node.

The types of supported configuration include:

• affinity  to determine which nodes a pod can be allocated to

• annotations 

• labels 

• resources  to limit cpu and memory

• tolerations  in conjunction with taints to enable pods to avoid being repelled from a node

• volumes  and volume mounts

• serviceAccount  to run each component with separate identity

• securityContext  to define privileges of running components

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-prod
spec:
  strategy: production
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch:9200
  annotations:
    key1: value1
  labels:
    key2: value2
  resources:
    requests:
      memory: "64Mi"
      cpu: "250m"
    limits:
      memory: "128Mi"
      cpu: "500m"
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: kubernetes.io/e2e-az-name
            operator: In
            values:
            - e2e-az1
            - e2e-az2
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: another-node-label-key
            operator: In
            values:
            - another-node-label-value
  tolerations:
    - key: "key1"
      operator: "Equal"
      value: "value1"
      effect: "NoSchedule"
    - key: "key1"
      operator: "Equal"
      value: "value1"
      effect: "NoExecute"
  serviceAccount: nameOfServiceAccount
  securityContext:
    runAsUser: 1000
  volumeMounts:
    - name: config-vol
      mountPath: /etc/config
  volumes:
    - name: config-vol
      configMap:
        name: log-config
        items:
          - key: log_level
            path: log_level

Note: If necessary, imagePullSecrets can be configured for components through their serviceAccounts (see https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/#add-image-pull-secret-to-service-account) . For the sidecar, see the Deployment-level Configurations for Injected Sidecars section.

Accessing the Jaeger Console (UI)

Kubernetes

The operator creates a Kubernetes ingress  route, which is the Kubernetes’ standard for exposing a service to the outside world, but by default it does not come with Ingress providers. Check the Kubernetes documentation  for the most appropriate way to achieve an Ingress provider for your platform. The following command enables the Ingress provider on minikube:

minikube addons enable ingress

Once Ingress is enabled, the address for the Jaeger console can be found by querying the Ingress object:

$ kubectl get ingress
NAME             HOSTS     ADDRESS          PORTS     AGE
simplest-query   *         192.168.122.34   80        3m

In this example, the Jaeger UI is available at http://192.168.122.34.

To enable TLS in the Ingress, pass a secretName with the name of a Secret  containing the TLS certificate:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: ingress-with-tls
spec:
  ingress:
    secretName: my-tls-secret

OpenShift

When the Operator is running on OpenShift, the Operator will automatically create a Route object for the query services. Use the following command to check the hostname/port:

oc get routes

By default, the Jaeger UI is protected with OpenShift’s OAuth service and any valid user is able to login. To disable this feature and leave the Jaeger UI unsecured, set the Ingress property security to none in the custom resource file:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: disable-oauth-proxy
spec:
  ingress:
    security: none

Custom SAR and Delegate URL values can be specified as part of the .Spec.Ingress.OpenShift.SAR and .Spec.Ingress.Openshift.DelegateURLs, as follows:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: custom-sar-oauth-proxy
spec:
  ingress:
    openshift:
      sar: '{"namespace": "default", "resource": "pods", "verb": "get"}'
      delegateUrls: '{"/":{"namespace": "default", "resource": "pods", "verb": "get"}}'

When the delegateUrls is set, the Jaeger Operator needs to create a new ClusterRoleBinding between the service account used by the UI Proxy ({InstanceName}-ui-proxy) and the role system:auth-delegator, as required by the OpenShift OAuth Proxy. Because of that, the service account used by the operator itself needs to have the same cluster role binding. To accomplish that, a ClusterRoleBinding such as the following has to be created:

kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: jaeger-operator-with-auth-delegator
  namespace: observability
subjects:
- kind: ServiceAccount
  name: jaeger-operator
  namespace: observability
roleRef:
  kind: ClusterRole
  name: system:auth-delegator
  apiGroup: rbac.authorization.k8s.io

Cluster administrators not comfortable in letting users deploy Jaeger instances with this cluster role are free to not add this cluster role to the operator’s service account. In that case, the Operator will auto-detect that the required permissions are missing and will log a message similar to: the requested instance specifies the delegateUrls option for the OAuth Proxy, but this operator cannot assign the proper cluster role to it (system:auth-delegator). Create a cluster role binding between the operator's service account and the cluster role 'system:auth-delegator' in order to allow instances to use 'delegateUrls'.

The Jaeger Operator also supports authentication using htpasswd files via the OpenShift OAuth Proxy. To make use of that, specify the htpasswdFile option within the OpenShift-specific entries, pointing to the file htpasswd file location in the local disk. The htpasswd file can be created using the htpasswd utility:

$ htpasswd -cs /tmp/htpasswd jdoe
New password:
Re-type new password:
Adding password for user jdoe

This file can then be used as the input for the kubectl create secret command:

$ kubectl create secret generic htpasswd --from-file=htpasswd=/tmp/htpasswd
secret/htpasswd created

Once the secret is created, it can be specified in the Jaeger CR as a volume/volume mount:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: with-htpasswd
spec:
  ingress:
    openshift:
      sar: '{"namespace": "default", "resource": "pods", "verb": "get"}'
      htpasswdFile: /usr/local/data/htpasswd
  volumeMounts:
  - name: htpasswd-volume
    mountPath: /usr/local/data
  volumes:
  - name: htpasswd-volume
    secret:
      secretName: htpasswd

Upgrading the Operator and its managed instances

Each version of the Jaeger Operator follows one Jaeger version. Whenever a new version of the Jaeger Operator is installed, all the Jaeger instances managed by the operator will be upgraded to the Operator’s supported version. For example, an instance named simplest that was created with Jaeger Operator 1.12.0 will be running Jaeger 1.12.0. Once the Jaeger Operator is upgraded to 1.13.0, the instance simplest will be upgraded to the version 1.13.0, following the official upgrade instructions from the Jaeger project.

The Jaeger Operator can be upgraded manually by changing the deployment (kubectl edit deployment jaeger-operator), or via specialized tools such as the Operator Lifecycle Manager (OLM) .

Updating a Jaeger instance (experimental)

A Jaeger instance can be updated by changing the CustomResource, either via kubectl edit jaeger simplest, where simplest is the Jaeger’s instance name, or by applying the updated YAML file via kubectl apply -f simplest.yaml.

Simpler changes such as changing the replica sizes can be applied without much concern, whereas changes to the strategy should be watched closely and might potentially cause an outage for individual components (collector/query/agent).

While changing the backing storage is supported, migration of the data is not.

Removing a Jaeger instance

To remove an instance, use the delete command with the custom resource file used when you created the instance:

kubectl delete -f simplest.yaml

Alternatively, you can remove a Jaeger instance by running:

kubectl delete jaeger simplest

Tracing and debugging the operator

Starting from version 1.16.0, the Jaeger Operator is able to generate spans related to its own operations. To take advantage of that, the operator.yaml has to be configured to enable tracing by setting the flag --tracing-enabled=true to the args of the container and to add a Jaeger Agent as sidecar to the pod. Here’s an excerpt from an operator.yaml that has tracing enabled and assumes that the Jaeger instance is at the same namespace as the Jaeger Operator:

...
        # .Spec.Template.Spec.Containers[0].Args
        args: ["start", "--tracing-enabled=true"]
...
      # add as a second container to .Spec.Template.Spec.Containers
      - name: jaeger-agent
        image: jaegertracing/jaeger-agent:latest # it's best to keep this version in sync with the operator's
        env:
        - name: POD_NAMESPACE
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
        args:
        - --reporter.grpc.host-port=dns:///jaeger-collector-headless.$(POD_NAMESPACE).svc.cluster.local:14250
        ports:
        - containerPort: 6831
          name: jg-compact-thrift
          protocol: UDP

Note that you must also manually provision the Jaeger instance. You can do this after the Jaeger Operator has been initialized. The Jaeger Agent will keep the Operator spans in the internal buffer until it makes a connection to the Jaeger instance. The following Jaeger CR can be used to provision a Jaeger instance suitable for non-production purposes:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger

The Jaeger Operator also provides extensive logging when the flag --log-level is set to debug. Here’s an excerpt from an operator.yaml that has the logging level set to debug:

        # .Spec.Template.Spec.Containers[0].Args
        args: ["start", "--log-level=debug"]

Note that tracing and logging at debug level can be both enabled at the same time.

When using OLM, the Jaeger Operator can be configured by changing the Subscription’s config property. To set the log-level parameter, this is how a subscription would look like (excerpt):

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: jaeger
  namespace: openshift-operators
spec:
  channel: stable
  config:
    env:
      - name: LOG-LEVEL
        value: debug
  installPlanApproval: Automatic
  name: jaeger
  source: community-operators
  sourceNamespace: openshift-marketplace

Monitoring the operator

The Jaeger Operator starts a Prometheus-compatible endpoint on 0.0.0.0:8383/metrics with internal metrics that can be used to monitor the process. Interesting metrics to watch are:

# Total number of reconciliations and their outcomes (cumulative)
controller_runtime_reconcile_total{controller="jaeger-controller",result="error"}
controller_runtime_reconcile_total{controller="jaeger-controller",result="success"}

# Total number of retries (cumulative)
workqueue_retries_total{name="jaeger-controller"}

# Current number of reconciliation loops in progress (gauge)
workqueue_depth{name="jaeger-controller"}

# How long (in seconds) the oldest reconciliation loop is taking. (gauge)
workqueue_unfinished_work_seconds{name="jaeger-controller"}

# How long (in seconds) it takes to process an item from the workqueue. (bucket)
workqueue_work_duration_seconds_bucket{name="jaeger-controller"}

A low number of reconciliation errors is normal (controller_runtime_reconcile_total{controller="jaeger-controller",result="error"}), as there might be several processes changing resources at the same time for different reasons. Whenever there’s a failure, the operator will attempt to reconcile again, increasing the workqueue_retries_total{name="jaeger-controller"} metric. However, if the rate of errors over time keeps increasing, or is beyond a reasonable threshold, an investigation might be required. The reasonable threshold might differ from cluster to cluster depending on what’s happening in it, but 10% is a good starting point.

As of v1.17.0, the Jaeger Operator will trigger a reconciliation loop only when the custom resource has changed or when the previous reconciliation has failed. Therefore, it’s normal to have this metric (controller_runtime_reconcile_total{controller="jaeger-controller"}) with the same value over a long period of time: it only indicates that the custom resource hasn’t changed. If this number keeps changing every second, it’s indicative that something in the cluster is periodically changing the custom resource, or that the Jaeger Operator is undoing a change that is being done by a different component. Future versions of the Jaeger Operator might trigger a periodic reconciliation loop.

The work queue depth (workqueue_depth{name="jaeger-controller"}) indicates the number of currently active reconciliation loops. For small clusters, or clusters where provisioning of Jaeger instances aren’t that frequent, this number should remain close to zero for most of the time. Any value that is higher than 0 for a sustained amount of time is an indication of a reconciliation loop that got stuck. If that happens, the metric workqueue_unfinished_work_seconds{name="jaeger-controller"} will also continually increase. This situation indicates a bug in the Jaeger Operator. As a rule of thumb, a reconciliation has to finish in a couple of minutes, except when provisioning of Elasticsearch or Kafka is involved. A reconciliation loop that takes more than 10 minutes can be considered as “stuck”. Provisioning of Elasticsearch or Kafka might take several minutes. In the usual case, reconciliation loops will take under one minute to complete.

The work queue buckets (workqueue_unfinished_work_seconds{name="jaeger-controller"} and workqueue_work_duration_seconds_bucket{name="jaeger-controller"}) are directly related to the time spent processing each reconciliation loop. It’s normal that one of the first 3 loops of a new Jaeger instance will take far more time than the subsequent ones, especially if the container images for the underlying components aren’t cached yet by the cluster. Using the auto-provisioning feature to create an Elasticsearch and/or Kafka cluster will also affects this metric. The general rule is: a few long-running reconciliation loops are normal, especially if they occur around the same time that the metric controller_runtime_reconcile_total{controller="jaeger-controller"} was increased.

Uninstalling the operator

To uninstall the operator, run the following commands:

kubectl delete -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/operator.yaml
kubectl delete -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/role_binding.yaml
kubectl delete -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/role.yaml
kubectl delete -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/service_account.yaml
kubectl delete -n observability -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/v1.28.0/deploy/crds/jaegertracing.io_jaegers_crd.yaml