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Observability

Red Hat Advanced Cluster Management for Kubernetes 2.10

Observability

Abstract

Enable the observability component to gain insight about your managed clusters.

Chapter 1. Observability service introduction

Observability can help you identify and assess performance problems without additional tests and support. The Red Hat Advanced Cluster Management for Kubernetes observability component is a service you can use to understand the health and utilization of clusters, and workloads across your fleet. By using the observability service, you are able to automate and manage the components that are within observability.

Observability service uses existing and widely-adopted observability tools from the open source community. By default, multicluster observability operator is enabled during the installation of Red Hat Advanced Cluster Management. Thanos is deployed within the hub cluster for long-term metrics storage. The observability-endpoint-operator is automatically deployed to each imported or created managed cluster. This controller starts a metrics collector that collects the data from Red Hat OpenShift Container Platform Prometheus, then sends the data to the Red Hat Advanced Cluster Management hub cluster.

Read the following documentation for more details about the observability component:

1.1. Observability architecture

The multiclusterhub-operator enables the multicluster-observability-operator pod by default. You must configure the multicluster-observability-operator pod.

1.1.1. Observability open source components

Observability service uses open source observability tools from community. View the following descriptions of the tools that are apart of the product observability service:

  • Thanos: A toolkit of components that you can use to perform global querying across multiple Prometheus instances. For long-term storage of Prometheus data, persist it in any S3 compatible storage. You can also compose a highly-available and scalable metrics system.
  • Prometheus: A monitoring and alerting tool that you can use to collect metrics from your application and store these metrics as time-series data. Store all scraped samples locally, run rules to aggregate and record new time series from existing data, and generate alerts.
  • Alertmanager: A tool to manage and receive alerts from Prometheus. Deduplicate, group, and route alerts to your integrations such as email, Slack, and PagerDuty. Configure Alertmanager to silence and inhibit specific alerts.

1.1.2. Observability architecture diagram

The following diagram shows the components of observability:

Multicluster observability architecture

The components of the observability architecture include the following items:

  • The multicluster hub operator, also known as the multiclusterhub-operator pod, deploys the multicluster-observability-operator pod. It sends hub cluster data to your managed clusters.
  • The observability add-on controller is the API server that automatically updates the log of the managed cluster.

  • The Thanos infrastructure includes the Thanos Compactor, which is deployed by the multicluster-observability-operator pod. The Thanos Compactor ensures that queries are performing well by using the retention configuration, and compaction of the data in storage.

    To help identify when the Thanos Compactor is experiencing issues, use the four default alerts that are monitoring its health. Read the following table of default alerts:

    Table 1.1. Table of default Thanos alerts
    AlertSeverityDescription

    ACMThanosCompactHalted

    critical

    An alert is sent when the compactor stops.

    ACMThanosCompactHighCompactionFailures

    warning

    An alert is sent when the compaction failure rate is greater than 5 percent.

    ACMThanosCompactBucketHighOperationFailures

    warning

    An alert is sent when the bucket operation failure rate is greater than 5%.

    ACMThanosCompactHasNotRun

    warning

    An alert is sent when the compactor has not uploaded anything in last 24 hours.

  • The observability component deploys an instance of Grafana to enable data visualization with dashboards (static) or data exploration. Red Hat Advanced Cluster Management supports version 8.5.20 of Grafana. You can also design your Grafana dashboard. For more information, see Designing your Grafana dashboard.
  • The Prometheus Alertmanager enables alerts to be forwarded with third-party applications. You can customize the observability service by creating custom recording rules or alerting rules. Red Hat Advanced Cluster Management supports version 0.25 of Prometheus Alertmanager.

1.1.3. Persistent stores used in the observability service

Important: Do not use the local storage operator or a storage class that uses local volumes for persistent storage. You can lose data if the pod relaunched on a different node after a restart. When this happens, the pod can no longer access the local storage on the node. Be sure that you can access the persistent volumes of the receive and rules pods to avoid data loss.

When you install Red Hat Advanced Cluster Management the following persistent volumes (PV) must be created so that Persistent Volume Claims (PVC) can attach to it automatically. As a reminder, you must define a storage class in the MultiClusterObservability custom resource when there is no default storage class specified or you want to use a non-default storage class to host the PVs. It is recommended to use Block Storage, similar to what Prometheus uses. Also each replica of alertmanager, thanos-compactor, thanos-ruler, thanos-receive-default and thanos-store-shard must have its own PV. View the following table:

Table 1.2. Table list of persistent volumes

Persistent volume name

Purpose

alertmanager

Alertmanager stores the nflog data and silenced alerts in its storage. nflog is an append-only log of active and resolved notifications along with the notified receiver, and a hash digest of contents that the notification identified.

thanos-compact

The compactor needs local disk space to store intermediate data for its processing, as well as bucket state cache. The required space depends on the size of the underlying blocks. The compactor must have enough space to download all of the source blocks, then build the compacted blocks on the disk. On-disk data is safe to delete between restarts and should be the first attempt to get crash-looping compactors unstuck. However, it is recommended to give the compactor persistent disks in order to effectively use bucket state cache in between restarts.

thanos-rule

The thanos ruler evaluates Prometheus recording and alerting rules against a chosen query API by issuing queries at a fixed interval. Rule results are written back to the disk in the Prometheus 2.0 storage format. The amount of hours or days of data retained in this stateful set was fixed in the API version observability.open-cluster-management.io/v1beta1. It has been exposed as an API parameter in observability.open-cluster-management.io/v1beta2: RetentionInLocal

thanos-receive-default

Thanos receiver accepts incoming data (Prometheus remote-write requests) and writes these into a local instance of the Prometheus TSDB. Periodically (every 2 hours), TSDB blocks are uploaded to the object storage for long term storage and compaction. The amount of hours or days of data retained in this stateful set, which acts a local cache was fixed in API Version observability.open-cluster-management.io/v1beta. It has been exposed as an API parameter in observability.open-cluster-management.io/v1beta2: RetentionInLocal

thanos-store-shard

It acts primarily as an API gateway and therefore does not need a significant amount of local disk space. It joins a Thanos cluster on startup and advertises the data it can access. It keeps a small amount of information about all remote blocks on local disk and keeps it in sync with the bucket. This data is generally safe to delete across restarts at the cost of increased startup times.

Note: The time series historical data is stored in object stores. Thanos uses object storage as the primary storage for metrics and metadata related to them. For more details about the object storage and downsampling, see Enabling observability service.

1.1.4. Additional resources

To learn more about observability and the integrated components, see the following topics:

1.2. Observability configuration

When the observability service is enabled, the hub cluster is always configured to collect and send metrics to the configured Thanos instance, regardless of whether hub self-management is enabled or not. When the hub cluster is self-managed, the disableHubSelfManagement parameter is set to false, which is the default setting. The multiclusterhub-operator enables the multicluster-observability-operator pod by default. You must configure the multicluster-observability-operator pod.

Metrics and alerts for the hub cluster appear in the local-cluster namespace. The local-cluster is only available if hub self-management is enabled. You can query the local-cluster metrics in the Grafana explorer. Continue reading to understand what metrics you can collect with the observability component, and for information about the observability pod capacity.

1.2.1. Metric types

By default, OpenShift Container Platform sends metrics to Red Hat using the Telemetry service. The acm_managed_cluster_info is available with Red Hat Advanced Cluster Management and is included with telemetry, but is not displayed on the Red Hat Advanced Cluster Management Observe environments overview dashboard.

View the following table of metric types that are supported by the framework:

Table 1.3. Parameter table
Metric nameMetric typeLabels/tagsStatus

acm_managed_cluster_info

Gauge

hub_cluster_id, managed_cluster_id, vendor, cloud, version, available, created_via, core_worker, socket_worker

Stable

config_policies_evaluation_duration_seconds_bucket

Histogram

None

Stable. Read Governance metric for more details.

config_policies_evaluation_duration_seconds_count

Histogram

None

Stable. Refer to Governance metric for more details.

config_policies_evaluation_duration_seconds_sum

Histogram

None

Stable. Read Governance metric for more details.

policy_governance_info

Gauge

type, policy, policy_namespace, cluster_namespace

Stable. Review Governance metric for more details.

policyreport_info

Gauge

managed_cluster_id, category, policy, result, severity

Stable. Read Managing insight _PolicyReports_ for more details.

search_api_db_connection_failed_total

Counter

None

Stable. See the Search components section in the Searching in the console introduction documentation.

search_api_dbquery_duration_seconds

Histogram

None

Stable. See the Search components section in the Searching in the console introduction documentation.

search_api_requests

Histogram

None

Stable. See the Search components section in the Searching in the console introduction documentation.

search_indexer_request_count

Counter

None

Stable. See the Search components section in the Searching in the console introduction documentation.

search_indexer_request_duration

Histogram

None

Stable. See the Search components section in the Searching in the console introduction documentation.

search_indexer_requests_in_flight

Gauge

None

Stable. See the Search components section in the Searching in the console introduction documentation.

search_indexer_request_size

Histogram

None

Stable. See the Search components section in the Searching in the console introduction documentation.

1.2.2. Observability pod capacity requests

Observability components require 2701mCPU and 11972Mi memory to install the observability service. The following table is a list of the pod capacity requests for five managed clusters with observability-addons enabled:

Table 1.4. Observability pod capacity requests
Deployment or StatefulSetContainer nameCPU (mCPU)Memory (Mi)ReplicasPod total CPUPod total memory

observability-alertmanager

alertmanager

4

200

3

12

600

config-reloader

4

25

3

12

75

alertmanager-proxy

1

20

3

3

60

observability-grafana

grafana

4

100

2

8

200

grafana-dashboard-loader

4

50

2

8

100

observability-observatorium-api

observatorium-api

20

128

2

40

256

observability-observatorium-operator

observatorium-operator

100

100

1

10

50

observability-rbac-query-proxy

rbac-query-proxy

20

100

2

40

200

oauth-proxy

1

20

2

2

40

observability-thanos-compact

thanos-compact

100

512

1

100

512

observability-thanos-query

thanos-query

300

1024

2

600

2048

observability-thanos-query-frontend

thanos-query-frontend

100

256

2

200

512

observability-thanos-query-frontend-memcached

memcached

45

128

3

135

384

exporter

5

50

3

15

150

observability-thanos-receive-controller

thanos-receive-controller

4

32

1

4

32

observability-thanos-receive-default

thanos-receive

300

512

3

900

1536

observability-thanos-rule

thanos-rule

50

512

3

150

1536

configmap-reloader

4

25

3

12

75

observability-thanos-store-memcached

memcached

45

128

3

135

384

exporter

5

50

3

15

150

observability-thanos-store-shard

thanos-store

100

1024

3

300

3072

1.2.3. Additional resources

1.3. Enabling the observability service

When you enable the observability service on your hub cluster, the multicluster-observability-operator watches for new managed clusters and automatically deploys metric and alert collection services to the managed clusters. You can use metrics and configure Grafana dashboards to make cluster resource information visible, help you save cost, and prevent service disruptions.

Monitor the status of your managed clusters with the observability component, also known as the multicluster-observability-operator pod.

Required access: Cluster administrator, the open-cluster-management:cluster-manager-admin role, or S3 administrator.

1.3.1. Prerequisites

  • You must install Red Hat Advanced Cluster Management for Kubernetes. See Installing while connected online for more information.
  • You must define a storage class in the MultiClusterObservability custom resource, if there is no default storage class specified.
  • Direct network access to the hub cluster is required. Network access to load balancers and proxies are not supported. For more information, see Networking.

  • You must configure an object store to create a storage solution.

    • Important: When you configure your object store, ensure that you meet the encryption requirements that are necessary when sensitive data is persisted. The observability service uses Thanos supported, stable object stores. You might not be able to share an object store bucket by multiple Red Hat Advanced Cluster Management observability installations. Therefore, for each installation, provide a separate object store bucket.

    • Red Hat Advanced Cluster Management supports the following cloud providers with stable object stores:

      • Amazon Web Services S3 (AWS S3)
      • Red Hat Ceph (S3 compatible API)
      • Google Cloud Storage
      • Azure storage
      • Red Hat OpenShift Data Foundation, formerly known as Red Hat OpenShift Container Storage
      • Red Hat OpenShift on IBM (ROKS)

1.3.2. Enabling observability from the command line interface

Enable the observability service by creating a MultiClusterObservability custom resource instance. Before you enable observability, see Observability pod capacity requests for more information.

Note:

  • When observability is enabled or disabled on OpenShift Container Platform managed clusters that are managed by Red Hat Advanced Cluster Management, the observability endpoint operator updates the cluster-monitoring-config config map by adding additional alertmanager configuration that automatically restarts the local Prometheus.
  • The observability endpoint operator updates the cluster-monitoring-config config map by adding additional alertmanager configurations that automatically restart the local Prometheus. When you insert the alertmanager configuration in the OpenShift Container Platform managed cluster, the configuration removes the settings that relate to the retention field of the Prometheus metrics.

Complete the following steps to enable the observability service:

  1. Log in to your Red Hat Advanced Cluster Management hub cluster.

  2. Create a namespace for the observability service with the following command: 

    oc create namespace open-cluster-management-observability

  3. Generate your pull-secret. If Red Hat Advanced Cluster Management is installed in the open-cluster-management namespace, run the following command: 

    DOCKER_CONFIG_JSON=`oc extract secret/multiclusterhub-operator-pull-secret -n open-cluster-management --to=-`

    If the multiclusterhub-operator-pull-secret is not defined in the namespace, copy the pull-secret from the openshift-config namespace into the open-cluster-management-observability namespace. Run the following command: 

    DOCKER_CONFIG_JSON=`oc extract secret/pull-secret -n openshift-config --to=-`

    Then, create the pull-secret in the open-cluster-management-observability namespace, run the following command: 

    oc create secret generic multiclusterhub-operator-pull-secret \
    -n open-cluster-management-observability \
    --from-literal=.dockerconfigjson="$DOCKER_CONFIG_JSON" \
    --type=kubernetes.io/dockerconfigjson

    Important: If you modify the global pull secret for your cluster by using the OpenShift Container Platform documentation, be sure to also update the global pull secret in the observability namespace. See Updating the global pull secret for more details.

  4. Create a secret for your object storage for your cloud provider. Your secret must contain the credentials to your storage solution. For example, run the following command: 

    oc create -f thanos-object-storage.yaml -n open-cluster-management-observability

    View the following examples of secrets for the supported object stores:

    • For Amazon S3 or S3 compatible, your secret might resemble the following file: 

      apiVersion: v1
kind: Secret
metadata:
  name: thanos-object-storage
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  thanos.yaml: |
    type: s3
    config:
      bucket: YOUR_S3_BUCKET
      endpoint: YOUR_S3_ENDPOINT 1
      insecure: true
      access_key: YOUR_ACCESS_KEY
      secret_key: YOUR_SECRET_KEY
      1
      Enter the URL without the protocol. Enter the URL for your Amazon S3 endpoint that might resemble the following URL: s3.us-east-1.amazonaws.com.

      For more details, see the Amazon Simple Storage Service user guide.

    • For Google Cloud Platform, your secret might resemble the following file: 

      apiVersion: v1
kind: Secret
metadata:
  name: thanos-object-storage
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  thanos.yaml: |
    type: GCS
    config:
      bucket: YOUR_GCS_BUCKET
      service_account: YOUR_SERVICE_ACCOUNT

      For more details, see Google Cloud Storage.

    • For Azure your secret might resemble the following file: 

      apiVersion: v1
kind: Secret
metadata:
  name: thanos-object-storage
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  thanos.yaml: |
    type: AZURE
    config:
      storage_account: YOUR_STORAGE_ACCT
      storage_account_key: YOUR_STORAGE_KEY
      container: YOUR_CONTAINER
      endpoint: blob.core.windows.net 1
      max_retries: 0
      1
      If you use the msi_resource path, the endpoint authentication is complete by using the system-assigned managed identity. Your value must resemble the following endpoint: https://<storage-account-name>.blob.core.windows.net.

      If you use the user_assigned_id path, endpoint authentication is complete by using the user-assigned managed identity. When you use the user_assigned_id, the msi_resource endpoint default value is https:<storage_account>.<endpoint>. For more details, see Azure Storage documentation.

      Note: If you use Azure as an object storage for a Red Hat OpenShift Container Platform cluster, the storage account associated with the cluster is not supported. You must create a new storage account.

    • For Red Hat OpenShift Data Foundation, your secret might resemble the following file: 

      apiVersion: v1
kind: Secret
metadata:
  name: thanos-object-storage
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  thanos.yaml: |
    type: s3
    config:
      bucket: YOUR_RH_DATA_FOUNDATION_BUCKET
      endpoint: YOUR_RH_DATA_FOUNDATION_ENDPOINT 1
      insecure: false
      access_key: YOUR_RH_DATA_FOUNDATION_ACCESS_KEY
      secret_key: YOUR_RH_DATA_FOUNDATION_SECRET_KEY
      1
      Enter the URL without the protocol. Enter the URL for your Red Hat OpenShift Data Foundation endpoint that might resemble the following URL: example.redhat.com:443.

      For more details, see Red Hat OpenShift Data Foundation.

    • For Red Hat OpenShift on IBM (ROKS), your secret might resemble the following file: 

      apiVersion: v1
kind: Secret
metadata:
  name: thanos-object-storage
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  thanos.yaml: |
    type: s3
    config:
      bucket: YOUR_ROKS_S3_BUCKET
      endpoint: YOUR_ROKS_S3_ENDPOINT 1
      insecure: true
      access_key: YOUR_ROKS_ACCESS_KEY
      secret_key: YOUR_ROKS_SECRET_KEY
      1
      Enter the URL without the protocol. Enter the URL for your Red Hat OpenShift Data Foundation endpoint that might resemble the following URL: example.redhat.com:443.

      For more details, follow the IBM Cloud documentation, Cloud Object Storage. Be sure to use the service credentials to connect with the object storage. For more details, follow the IBM Cloud documentation, Cloud Object Store and Service Credentials.

1.3.2.1. Configuring storage for AWS Security Token Service

For Amazon S3 or S3 compatible storage, you can also use short term, limited-privilege credentials that are generated with AWS Security Token Service (AWS STS). Refer to AWS Security Token Service documentation for more details.

Generating access keys using AWS Security Service require the following additional steps:

  1. Create an IAM policy that limits access to an S3 bucket.
  2. Create an IAM role with a trust policy to generate JWT tokens for OpenShift Container Platform service accounts.
  3. Specify annotations for the observability service accounts that requires access to the S3 bucket. You can find an example of how observability on Red Hat OpenShift Service on AWS (ROSA) cluster can be configured to work with AWS STS tokens in the Set environment step. See Red Hat OpenShift Service on AWS (ROSA) for more details, along with ROSA with STS explained for an in-depth description of the requirements and setup to use STS tokens.
1.3.2.2. Generating access keys using the AWS Security Service

Complete the following steps to generate access keys using the AWS Security Service:

  1. Set up the AWS environment. Run the following commands: 

    export POLICY_VERSION=$(date +"%m-%d-%y")
export TRUST_POLICY_VERSION=$(date +"%m-%d-%y")
export CLUSTER_NAME=<my-cluster>
export S3_BUCKET=$CLUSTER_NAME-acm-observability
export REGION=us-east-2
export NAMESPACE=open-cluster-management-observability
export SA=tbd
export SCRATCH_DIR=/tmp/scratch
export OIDC_PROVIDER=$(oc get authentication.config.openshift.io cluster -o json | jq -r .spec.serviceAccountIssuer| sed -e "s/^https:\/\///")
export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
export AWS_PAGER=""
rm -rf $SCRATCH_DIR
mkdir -p $SCRATCH_DIR

  2. Create an S3 bucket with the following command: 

    aws s3 mb s3://$S3_BUCKET

  3. Create a s3-policy JSON file for access to your S3 bucket. Run the following command: 

    {
    "Version": "$POLICY_VERSION",
    "Statement": [
        {
            "Sid": "Statement",
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket",
                "s3:GetObject",
                "s3:DeleteObject",
                "s3:PutObject",
                "s3:PutObjectAcl",
                "s3:CreateBucket",
                "s3:DeleteBucket"
            ],
            "Resource": [
                "arn:aws:s3:::$S3_BUCKET/*",
                "arn:aws:s3:::$S3_BUCKET"
            ]
        }
    ]
 }

  4. Apply the policy with the following command: 

    S3_POLICY=$(aws iam create-policy --policy-name $CLUSTER_NAME-acm-obs \
--policy-document file://$SCRATCH_DIR/s3-policy.json \
--query 'Policy.Arn' --output text)
echo $S3_POLICY

  5. Create a TrustPolicy JSON file. Run the following command: 

    {
 "Version": "$TRUST_POLICY_VERSION",
 "Statement": [
   {
     "Effect": "Allow",
     "Principal": {
       "Federated": "arn:aws:iam::${AWS_ACCOUNT_ID}:oidc-provider/${OIDC_PROVIDER}"
     },
     "Action": "sts:AssumeRoleWithWebIdentity",
     "Condition": {
       "StringEquals": {
         "${OIDC_PROVIDER}:sub": [
           "system:serviceaccount:${NAMESPACE}:observability-thanos-query",
           "system:serviceaccount:${NAMESPACE}:observability-thanos-store-shard",
           "system:serviceaccount:${NAMESPACE}:observability-thanos-compact"
           "system:serviceaccount:${NAMESPACE}:observability-thanos-rule",
           "system:serviceaccount:${NAMESPACE}:observability-thanos-receive",
         ]
       }
     }
   }
 ]
}

  6. Create a role for AWS Prometheus and CloudWatch with the following command: 

    S3_ROLE=$(aws iam create-role \
  --role-name "$CLUSTER_NAME-acm-obs-s3" \
  --assume-role-policy-document file://$SCRATCH_DIR/TrustPolicy.json \
  --query "Role.Arn" --output text)
echo $S3_ROLE

  7. Attach the policies to the role. Run the following command: 

    aws iam attach-role-policy \
  --role-name "$CLUSTER_NAME-acm-obs-s3" \
  --policy-arn $S3_POLICY

    Your secret might resemble the following file. The config section specifies signature_version2: false and does not specify access_key and secret_key: 

    apiVersion: v1
kind: Secret
metadata:
  name: thanos-object-storage
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  thanos.yaml: |
 type: s3
 config:
   bucket: $S3_BUCKET
   endpoint: s3.$REGION.amazonaws.com
   signature_version2: false
  8. Specify service account annotations when you the MultiClusterObservability custom resource as described in Creating the MultiClusterObservability custom resource section.

  9. You can retrieve the S3 access key and secret key for your cloud providers with the following commands. You must decode, edit, and encode your base64 string in the secret: 

    YOUR_CLOUD_PROVIDER_ACCESS_KEY=$(oc -n open-cluster-management-observability get secret <object-storage-secret> -o jsonpath="{.data.thanos\.yaml}" | base64 --decode | grep access_key | awk '{print $2}')

echo $ACCESS_KEY

YOUR_CLOUD_PROVIDER_SECRET_KEY=$(oc -n open-cluster-management-observability get secret <object-storage-secret> -o jsonpath="{.data.thanos\.yaml}" | base64 --decode | grep secret_key | awk '{print $2}')

echo $SECRET_KEY

  10. Verify that observability is enabled by checking the pods for the following deployments and stateful sets. You might receive the following information: 

    observability-thanos-query (deployment)
observability-thanos-compact (statefulset)
observability-thanos-receive-default  (statefulset)
observability-thanos-rule   (statefulset)
observability-thanos-store-shard-x  (statefulsets)
1.3.2.3. Creating the MultiClusterObservability custom resource

Use the MultiClusterObservability custom resource to specify the persistent volume storage size for various components. You must set the storage size during the initial creation of the MultiClusterObservability custom resource. When you update the storage size values post-deployment, changes take effect only if the storage class supports dynamic volume expansion. For more information, see Expanding persistent volumes from the Red Hat OpenShift Container Platform documentation.

Complete the following steps to create the MultiClusterObservability custom resource on your hub cluster:

  1. Create the MultiClusterObservability custom resource YAML file named multiclusterobservability_cr.yaml.

    View the following default YAML file for observability: 

    apiVersion: observability.open-cluster-management.io/v1beta2
kind: MultiClusterObservability
metadata:
  name: observability
spec:
  observabilityAddonSpec: {}
  storageConfig:
    metricObjectStorage:
      name: thanos-object-storage
      key: thanos.yaml

    You might want to modify the value for the retentionConfig parameter in the advanced section. For more information, see Thanos Downsampling resolution and retention. Depending on the number of managed clusters, you might want to update the amount of storage for stateful sets. If your S3 bucket is configured to use STS tokens, annotate the service accounts to use STS with S3 role. View the following configuration: 

    spec:
  advanced:
    compact:
       serviceAccountAnnotations:
           eks.amazonaws.com/role-arn: $S3_ROLE
    store:
       serviceAccountAnnotations:
          eks.amazonaws.com/role-arn: $S3_ROLE
    rule:
       serviceAccountAnnotations:
          eks.amazonaws.com/role-arn: $S3_ROLE
    receive:
       serviceAccountAnnotations:
          eks.amazonaws.com/role-arn: $S3_ROLE
    query:
       serviceAccountAnnotations:
          eks.amazonaws.com/role-arn: $S3_ROLE

    See Observability API for more information.

  2. To deploy on infrastructure machine sets, you must set a label for your set by updating the nodeSelector in the MultiClusterObservability YAML. Your YAML might resemble the following content: 

      nodeSelector:
    node-role.kubernetes.io/infra:

    For more information, see Creating infrastructure machine sets.

  3. Apply the observability YAML to your cluster by running the following command: 

    oc apply -f multiclusterobservability_cr.yaml

    All the pods in open-cluster-management-observability namespace for Thanos, Grafana and Alertmanager are created. All the managed clusters connected to the Red Hat Advanced Cluster Management hub cluster are enabled to send metrics back to the Red Hat Advanced Cluster Management Observability service.

  4. Validate that the observability service is enabled and the data is populated by launching the Grafana dashboards.

  5. Click the Grafana link that is near the console header, from either the console Overview page or the Clusters page.

    1. Alternatively, access the OpenShift Container Platform 3.11 Grafana dashboards with the following URL: https://$ACM_URL/grafana/dashboards.
    2. To view the OpenShift Container Platform 3.11 dashboards, select the folder named OCP 3.11 .

  6. Access the multicluster-observability-operator deployment to verify that the multicluster-observability-operator pod is being deployed by the multiclusterhub-operator deployment. Run the following command: 

    oc get deploy multicluster-observability-operator -n open-cluster-management --show-labels

NAME                                  READY   UP-TO-DATE   AVAILABLE   AGE   LABELS
multicluster-observability-operator   1/1     1            1           35m   installer.name=multiclusterhub,installer.namespace=open-cluster-management

  7. View the labels section of the multicluster-observability-operator deployment for labels that are associated with the resource. The labels section might contain the following details: 

     labels:
    installer.name: multiclusterhub
    installer.namespace: open-cluster-management

. . Optional: If you want to exclude specific managed clusters from collecting the observability data, add the following cluster label to your clusters: observability: disabled.

The observability service is enabled. After you enable the observability service, the following functions are initiated:

  • All the alert managers from the managed clusters are forwarded to the Red Hat Advanced Cluster Management hub cluster.

  • All the managed clusters that are connected to the Red Hat Advanced Cluster Management hub cluster are enabled to send alerts back to the Red Hat Advanced Cluster Management observability service. You can configure the Red Hat Advanced Cluster Management Alertmanager to take care of deduplicating, grouping, and routing the alerts to the correct receiver integration such as email, PagerDuty, or OpsGenie. You can also handle silencing and inhibition of the alerts.

    Note: Alert forwarding to the Red Hat Advanced Cluster Management hub cluster feature is only supported by managed clusters with Red Hat OpenShift Container Platform version 4.13 or later. After you install Red Hat Advanced Cluster Management with observability enabled, alerts from OpenShift Container Platform 4.13 and later are automatically forwarded to the hub cluster. See Forwarding alerts to learn more.

1.3.3. Enabling observability from the Red Hat OpenShift Container Platform console

Optionally, you can enable observability from the Red Hat OpenShift Container Platform console, create a project named open-cluster-management-observability. Be sure to create an image pull-secret named, multiclusterhub-operator-pull-secret in the open-cluster-management-observability project.

Create your object storage secret named, thanos-object-storage in the open-cluster-management-observability project. Enter the object storage secret details, then click Create. See step four of the Enabling observability section to view an example of a secret.

Create the MultiClusterObservability custom resource instance. When you receive the following message, the observability service is enabled successfully from OpenShift Container Platform: Observability components are deployed and running.

1.3.3.1. Verifying the Thanos version

After Thanos is deployed on your cluster, verify the Thanos version from the command line interface (CLI).

After you log in to your hub cluster, run the following command in the observability pods to receive the Thanos version: 

thanos --version

The Thanos version is displayed.

1.3.4. Disabling observability

You can disable observability, which stops data collection on the Red Hat Advanced Cluster Management hub cluster.

1.3.4.1. Disabling observability on all clusters

Disable observability by removing observability components on all managed clusters. Update the multicluster-observability-operator resource by setting enableMetrics to false. Your updated resource might resemble the following change: 

spec:
  imagePullPolicy: Always
  imagePullSecret: multiclusterhub-operator-pull-secret
  observabilityAddonSpec: # The ObservabilityAddonSpec defines the global settings for all managed clusters which have observability add-on enabled
    enableMetrics: false #indicates the observability addon push metrics to hub server
1.3.4.2. Disabling observability on a single cluster

Disable observability by removing observability components on specific managed clusters. Add the observability: disabled label to the managedclusters.cluster.open-cluster-management.io custom resource. From the Red Hat Advanced Cluster Management console Clusters page, add the observability=disabled label to the specified cluster.

Note: When a managed cluster with the observability component is detached, the metrics-collector deployments are removed.

1.3.5. Removing observability

When you remove the MultiClusterObservability custom resource, you are disabling and uninstalling the observability service. From the OpenShift Container Platform console navigation, select Operators > Installed Operators > Advanced Cluster Manager for Kubernetes. Remove the MultiClusterObservability custom resource.

1.3.6. Additional resources

1.4. Customizing observability configuration

After you enable observability, customize the observability configuration to the specific needs of your environment. Manage and view cluster fleet data that the observability service collects.

Required access: Cluster administrator

1.4.1. Creating custom rules

Create custom rules for the observability installation by adding Prometheus recording rules and alerting rules to the observability resource.

To precalculate expensive expressions, use the recording rules abilities. The results are saved as a new set of time series. With the alerting rules, you can specify the alert conditions based on how you want to send an alert to an external service.

Note: When you update your custom rules, observability-thanos-rule pods restart automatically.

Define custom rules with Prometheus to create alert conditions, and send notifications to an external messaging service. View the following examples of custom rules:

  • Create a custom alert rule. Create a config map named thanos-ruler-custom-rules in the open-cluster-management-observability namespace. You must name the key, custom_rules.yaml, as shown in the following example. You can create multiple rules in the configuration.

    • Create a custom alert rule that notifies you when your CPU usage passes your defined value. Your YAML might resemble the following content: 

      data:
  custom_rules.yaml: |
    groups:
      - name: cluster-health
        rules:
        - alert: ClusterCPUHealth-jb
          annotations:
            summary: Notify when CPU utilization on a cluster is greater than the defined utilization limit
            description: "The cluster has a high CPU usage: {{ $value }} core for {{ $labels.cluster }} {{ $labels.clusterID }}."
          expr: |
            max(cluster:cpu_usage_cores:sum) by (clusterID, cluster, prometheus) > 0
          for: 5s
          labels:
            cluster: "{{ $labels.cluster }}"
            prometheus: "{{ $labels.prometheus }}"
            severity: critical
    • The default alert rules are in the thanos-ruler-default-rules config map of the open-cluster-management-observability namespace.

  • Create a custom recording rule within the thanos-ruler-custom-rules config map. Create a recording rule that gives you the ability to get the sum of the container memory cache of a pod. Your YAML might resemble the following content: 

    data:
  custom_rules.yaml: |
    groups:
      - name: container-memory
        rules:
        - record: pod:container_memory_cache:sum
          expr: sum(container_memory_cache{pod!=""}) BY (pod, container)

    Note: After you make changes to the config map, the configuration automatically reloads. The configuration reloads because of the config-reload within the observability-thanos-ruler sidecar.

  • To verify that the alert rules are functioning correctly, go to the Grafana dashboard, select the Explore page, and query ALERTS. The alert is only available in Grafana if you created the alert.

1.4.2. Adding custom metrics

To start collecting metrics from all of your managed clusters, add metrics to the metrics_list.yaml file. Complete the following steps:

  1. Before you add a custom metric, verify that mco observability is enabled with the following command: 

    oc get mco observability -o yaml

  2. Check for the following message in the status.conditions.message section reads: 

    Observability components are deployed and running

  3. Create the observability-metrics-custom-allowlist config map in the open-cluster-management-observability namespace with the following command: 

    oc apply -n open-cluster-management-observability -f observability-metrics-custom-allowlist.yaml

  4. Add the name of the custom metric to the metrics_list.yaml parameter. Your YAML for the config map might resemble the following content: 

    kind: ConfigMap
apiVersion: v1
metadata:
  name: observability-metrics-custom-allowlist
data:
  metrics_list.yaml: |
    names: 1
      - node_memory_MemTotal_bytes
    rules: 2
    - record: apiserver_request_duration_seconds:histogram_quantile_90
      expr: histogram_quantile(0.90,sum(rate(apiserver_request_duration_seconds_bucket{job=\"apiserver\",
        verb!=\"WATCH\"}[5m])) by (verb,le))
    1
    Optional: Add the name of the custom metrics that are to be collected from the managed cluster.
    2
    Optional: Enter only one value for the expr and record parameter pair to define the query expression. The metrics are collected as the name that is defined in the record parameter from your managed cluster. The metric value returned are the results after you run the query expression.

    You can use either one or both of the sections. For user workload metrics, see the Adding user workload metrics section.

    Note: You can also individually customize each managed cluster in the custom metrics allowlist instead of applying it across your entire fleet. You can create the same YAML directly on your managed cluster to customize it.

  5. Verify the data collection from your custom metric by querying the metric from the Grafana dashboard Explore page. You can also use the custom metrics in your own dashboard.
1.4.2.1. Adding user workload metrics

Collect OpenShift Container Platform user-defined metrics from workloads in OpenShift Container Platform to display the metrics from your Grafana dashboard. Complete the following steps:

  1. Enable monitoring on your OpenShift Container Platform cluster. See Enabling monitoring for user-defined projects in the Additional resources section.

    If you have a managed cluster with monitoring for user-defined workloads enabled, the user workloads are located in the test namespace and generate metrics. These metrics are collected by Prometheus from the OpenShift Container Platform user workload.

  2. Add user workload metrics to the observability-metrics-custom-allowlist config map to collect the metrics in the test namespace. View the following example: 

    kind: ConfigMap
apiVersion: v1
metadata:
  name: observability-metrics-custom-allowlist
  namespace: test
data:
  uwl_metrics_list.yaml: 1
    names: 2
      - sample_metrics
    1
    Enter the key for the config map data.
    2
    Enter the value of the config map data in YAML format. The names section includes the list of metric names, which you want to collect from the test namespace. After you create the config map, the observability collector collects and pushes the metrics from the target namespace to the hub cluster.
1.4.2.2. Removing default metrics

If you do not want to collect data for a specific metric from your managed cluster, remove the metric from the observability-metrics-custom-allowlist.yaml file. When you remove a metric, the metric data is not collected from your managed clusters. Complete the following steps to remove a default metric:

  1. Verify that mco observability is enabled by using the following command: 

    oc get mco observability -o yaml

  2. Add the name of the default metric to the metrics_list.yaml parameter with a hyphen - at the start of the metric name. View the following metric example: 

    -cluster_infrastructure_provider

  3. Create the observability-metrics-custom-allowlist config map in the open-cluster-management-observability namespace with the following command: 

    oc apply -n open-cluster-management-observability -f observability-metrics-custom-allowlist.yaml
  4. Verify that the observability service is not collecting the specific metric from your managed clusters. When you query the metric from the Grafana dashboard, the metric is not displayed.

1.4.3. Adding advanced configuration for retention

To update the retention for each observability component according to your need, add the advanced configuration section. Complete the following steps:

  1. Edit the MultiClusterObservability custom resource with the following command: 

    oc edit mco observability -o yaml

  2. Add the advanced section to the file. Your YAML file might resemble the following contents: 

    spec:
  advanced:
    retentionConfig:
      blockDuration: 2h
      deleteDelay: 48h
      retentionInLocal: 24h
      retentionResolutionRaw: 365d
      retentionResolution5m: 365d
      retentionResolution1h: 365d
    receive:
      resources:
        limits:
          memory: 4096Gi
      replicas: 3

    Notes:

    • For descriptions of all the parameters that can added into the advanced configuration, see the Observability API documentation.
    • The default retention for all resolution levels, such as retentionResolutionRaw, retentionResolution5m, or retentionResolution1h, is 365 days (365d). You must set an explicit value for the resolution retention in your MultiClusterObservability spec.advanced.retentionConfig parameter.

  3. If you upgraded from an earlier version and want to keep that version retention configuration, add the configuration previously mentioned. Complete the following steps:

    1. Go to your MultiClusterObservability resource by running the following command: 

      edit mco observability
    2. In the spec.advanced.retentionConfig parameter, apply the following configuration: 
    spec:
  advanced:
    retentionConfig:
      retentionResolutionRaw: 365d
      retentionResolution5m: 365d
      retentionResolution1h: 365d

1.4.4. Dynamic metrics for single-node OpenShift clusters

Dynamic metrics collection supports automatic metric collection based on certain conditions. By default, a single-node OpenShift cluster does not collect pod and container resource metrics. Once a single-node OpenShift cluster reaches a specific level of resource consumption, the defined granular metrics are collected dynamically. When the cluster resource consumption is consistently less than the threshold for a period of time, granular metric collection stops.

The metrics are collected dynamically based on the conditions on the managed cluster specified by a collection rule. Because these metrics are collected dynamically, the following Red Hat Advanced Cluster Management Grafana dashboards do not display any data. When a collection rule is activated and the corresponding metrics are collected, the following panels display data for the duration of the time that the collection rule is initiated:

  • Kubernetes/Compute Resources/Namespace (Pods)
  • Kubernetes/Compute Resources/Namespace (Workloads)
  • Kubernetes/Compute Resources/Nodes (Pods)
  • Kubernetes/Compute Resources/Pod
  • Kubernetes/Compute Resources/Workload A collection rule includes the following conditions:
  • A set of metrics to collect dynamically.
  • Conditions written as a PromQL expression.
  • A time interval for the collection, which must be set to true.
  • A match expression to select clusters where the collect rule must be evaluated.

By default, collection rules are evaluated continuously on managed clusters every 30 seconds, or at a specific time interval. The lowest value between the collection interval and time interval takes precedence. Once the collection rule condition persists for the duration specified by the for attribute, the collection rule starts and the metrics specified by the rule are automatically collected on the managed cluster. Metrics collection stops automatically after the collection rule condition no longer exists on the managed cluster, at least 15 minutes after it starts.

The collection rules are grouped together as a parameter section named collect_rules, where it can be enabled or disabled as a group. Red Hat Advanced Cluster Management installation includes the collection rule group, SNOResourceUsage with two default collection rules: HighCPUUsage and HighMemoryUsage. The HighCPUUsage collection rule begins when the node CPU usage exceeds 70%. The HighMemoryUsage collection rule begins if the overall memory utilization of the single-node OpenShift cluster exceeds 70% of the available node memory. Currently, the previously mentioned thresholds are fixed and cannot be changed. When a collection rule begins for more than the interval specified by the for attribute, the system automatically starts collecting the metrics that are specified in the dynamic_metrics section.

View the list of dynamic metrics that from the collect_rules section, in the following YAML file: 

collect_rules:
  - group: SNOResourceUsage
    annotations:
      description: >
        By default, a {sno} cluster does not collect pod and container resource metrics. Once a {sno} cluster
        reaches a level of resource consumption, these granular metrics are collected dynamically.
        When the cluster resource consumption is consistently less than the threshold for a period of time,
        collection of the granular metrics stops.
    selector:
      matchExpressions:
        - key: clusterType
          operator: In
          values: ["{sno}"]
    rules:
    - collect: SNOHighCPUUsage
      annotations:
        description: >
          Collects the dynamic metrics specified if the cluster cpu usage is constantly more than 70% for 2 minutes
      expr: (1 - avg(rate(node_cpu_seconds_total{mode=\"idle\"}[5m]))) * 100 > 70
      for: 2m
      dynamic_metrics:
        names:
          - container_cpu_cfs_periods_total
          - container_cpu_cfs_throttled_periods_total
          - kube_pod_container_resource_limits
          - kube_pod_container_resource_requests
          - namespace_workload_pod:kube_pod_owner:relabel
          - node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate
          - node_namespace_pod_container:container_cpu_usage_seconds_total:sum_rate
    - collect: SNOHighMemoryUsage
      annotations:
        description: >
          Collects the dynamic metrics specified if the cluster memory usage is constantly more than 70% for 2 minutes
      expr: (1 - sum(:node_memory_MemAvailable_bytes:sum) / sum(kube_node_status_allocatable{resource=\"memory\"})) * 100 > 70
      for: 2m
      dynamic_metrics:
        names:
          - kube_pod_container_resource_limits
          - kube_pod_container_resource_requests
          - namespace_workload_pod:kube_pod_owner:relabel
        matches:
          - __name__="container_memory_cache",container!=""
          - __name__="container_memory_rss",container!=""
          - __name__="container_memory_swap",container!=""
          - __name__="container_memory_working_set_bytes",container!=""

A collect_rules.group can be disabled in the custom-allowlist as shown in the following example. When a collect_rules.group is disabled, metrics collection reverts to the previous behavior. These metrics are collected at regularly, specified intervals: 

collect_rules:
  - group: -SNOResourceUsage

The data is only displayed in Grafana when the rule is initiated.

1.4.5. Updating the MultiClusterObservability custom resource replicas from the console

If your workload increases, increase the number of replicas of your observability pods. Navigate to the Red Hat OpenShift Container Platform console from your hub cluster. Locate the MultiClusterObservability custom resource, and update the replicas parameter value for the component where you want to change the replicas. Your updated YAML might resemble the following content: 

spec:
   advanced:
      receive:
         replicas: 6

For more information about the parameters within the mco observability custom resource, see the Observability API documentation.

1.4.6. Increasing and decreasing persistent volumes and persistent volume claims

Increase and decrease the persistent volume and persistent volume claims to change the amount of storage in your storage class. Complete the following steps:

  1. To increase the size of the persistent volume, update the MultiClusterObservability custom resource if the storage class support expanding volumes.

  2. To decrease the size of the persistent volumes remove the pods using the persistent volumes, delete the persistent volume and recreate them. You might experience data loss in the persistent volume. Complete the following steps:

    1. Pause the MultiClusterObservability operator by adding the annotation mco-pause: "true" to the MultiClusterObservability custom resource.

    2. Look for the stateful sets or deployments of the desired component. Change their replica count to 0. This initiates a shutdown, which involves uploading local data when applicable to avoid data loss. For example, the Thanos Receive stateful set is named observability-thanos-receive-default and has three replicas by default. Therefore, you are looking for the following persistent volume claims:

      • data-observability-thanos-receive-default-0
      • data-observability-thanos-receive-default-1
      • data-observability-thanos-receive-default-2
    3. Delete the persistent volumes and persistent volume claims used by the desired component.
    4. In the MultiClusterObservability custom resource, edit the storage size in the configuration of the component to the desired amount in the storage size field. Prefix with the name of the component.
    5. Unpause the MultiClusterObservability operator by removing the previously added annotation.
    6. To initiate a reconcilation after having the operator paused, delete the multicluster-observability-operator and observatorium-operator pods. The pods are recreated and reconciled immediately.
  3. Verify that persistent volume and volume claims are updated by checking the MultiClusterObservability custom resource.

1.4.7. Customizing route certificate

If you want to customize the OpenShift Container Platform route certification, you must add the routes in the alt_names section. To ensure your OpenShift Container Platform routes are accessible, add the following information: alertmanager.apps.<domainname>, observatorium-api.apps.<domainname>, rbac-query-proxy.apps.<domainname>.

For more details, see Replacing certificates for alertmanager route in the Governance documentation.

Note: Users are responsible for certificate rotations and updates.

1.4.8. Customizing certificates for accessing the object store

You can configure secure connections with the observability object store by creating a Secret resource that contains the certificate authority and configuring the MultiClusterObservability custom resource. Complete the following steps:

  1. To validate the object store connection, create the Secret object in the file that contains the certificate authority by using the following command: 

    oc create secret generic <tls_secret_name> --from-file=ca.crt=<path_to_file> -n open-cluster-management-observability
    1. Alternatively, you can apply the following YAML to create the secret: 
    apiVersion: v1
kind: Secret
metadata:
  name: <tls_secret_name>
  namespace: open-cluster-management-observability
type: Opaque
data:
  ca.crt: <base64_encoded_ca_certificate>

    Optional: If you want to enable mutual TLS, you need to add the public.crt and private.key keys in the previous secret.

  2. Add the TLS secret details to the metricObjectStorage section by using the following command: 

    oc edit mco observability -o yaml

    Your file might resemble the following YAML: 

    metricObjectStorage:
  key: thanos.yaml
  name: thanos-object-storage
  tlsSecretName: tls-certs-secret 1
  tlsSecretMountPath: /etc/minio/certs 2
    1
    The value for tlsSecretName is the name of the Secret object that you previously created.
    2
    The /etc/minio/certs/ path defined for the tlsSecretMountPath parameter specifies where the certificates are mounted in the Observability components. This path is required for the next step.

  3. Update the thanos.yaml definition in the thanos-object-storage secret by adding the http_config.tls_config section with the certificate details. View the following example: 

    thanos.yaml: |
   type: s3
   config:
     bucket: "thanos"
     endpoint: "minio:9000"
     insecure: false 1
     access_key: "minio"
     secret_key: "minio123"
     http_config:
       tls_config:
         ca_file: /etc/minio/certs/ca.crt 2
         insecure_skip_verify: false
    1
    Set the insecure parameter to false to enable HTTPS.
    2
    The path for the ca_file parameter must match the tlsSecretMountPath from the MultiClusterObservability custom resource. The ca.crt must match the key in the <tls_secret_name> Secret resource.

    Optional: If you want to enable mutual TLS, you need to add the cert_file and key_file keys to the tls_config section. See the following example: 

     thanos.yaml: |
    type: s3
    config:
      bucket: "thanos"
      endpoint: "minio:9000"
      insecure: false
      access_key: "minio"
      secret_key: "minio123"
      http_config:
        tls_config:
          ca_file: /etc/minio/certs/ca.crt 1
          cert_file: /etc/minio/certs/public.crt
          key_file: /etc/minio/certs/private.key
          insecure_skip_verify: false
    1
    The path for ca_file, cert_file, and key_file must match the tlsSecretMountPath from the MultiClusterObservability custom resource. The ca.crt, public.crt, and private.crt must match the respective key in the tls_secret_name> Secret resource.

  4. To verify that you can access the object store, check that the pods are deployed. Run the following command: 

    oc -n open-cluster-management-observability get pods -l app.kubernetes.io/name=thanos-store

1.4.9. Configuring proxy settings for observability add-ons

Configure the proxy settings to allow the communications from the managed cluster to access the hub cluster through a HTTP and HTTPS proxy server. Typically, add-ons do not need any special configuration to support HTTP and HTTPS proxy servers between a hub cluster and a managed cluster. But if you enabled the observability add-on, you must complete the proxy configuration.

1.4.10. Prerequisite

  • You have a hub cluster.
  • You have enabled the proxy settings between the hub cluster and managed cluster.

Complete the following steps to configure the proxy settings for the observability add-on:

  1. Go to the cluster namespace on your hub cluster.

  2. Create an AddOnDeploymentConfig resource with the proxy settings by adding a spec.proxyConfig parameter. View the following YAML example: 

    apiVersion: addon.open-cluster-management.io/v1alpha1
kind: AddOnDeploymentConfig
metadata:
  name: <addon-deploy-config-name>
  namespace: <managed-cluster-name>
spec:
  agentInstallNamespace: open-cluster-managment-addon-observability
  proxyConfig:
    httpsProxy: "http://<username>:<password>@<ip>:<port>" 1
    noProxy: ".cluster.local,.svc,172.30.0.1" 2
    1
    For this field, you can specify either a HTTP proxy or a HTTPS proxy.
    2
    Include the IP address of the kube-apiserver.

  3. To get the IP address, run following command on your managed cluster: 

    oc -n default describe svc kubernetes | grep IP:

  4. Go to the ManagedClusterAddOn resource and update it by referencing the AddOnDeploymentConfig resource that you made. View the following YAML example: 

    apiVersion: addon.open-cluster-management.io/v1alpha1
kind: ManagedClusterAddOn
metadata:
  name: observability-controller
  namespace: <managed-cluster-name>
spec:
  installNamespace: open-cluster-managment-addon-observability
  configs:
  - group: addon.open-cluster-management.io
    resource: AddonDeploymentConfig
    name: <addon-deploy-config-name>
    namespace: <managed-cluster-name>

  5. Verify the proxy settings. If you successfully configured the proxy settings, the metric collector deployed by the observability add-on agent on the managed cluster sends the data to the hub cluster. Complete the following steps:

    1. Go to the hub cluster then the managed cluster on the Grafana dashboard.
    2. View the metrics for the proxy settings.

1.4.11. Disabling proxy settings for observability add-ons

If your development needs change, you might need to disable the proxy setting for the observability add-ons you configured for the hub cluster and managed cluster. You can disable the proxy settings for the observability add-on at any time. Complete the following steps:

  1. Go to the ManagedClusterAddOn resource.
  2. Remove the referenced AddOnDeploymentConfig resource.

1.4.12. Additional resources

1.5. Using observability

Use the observability service to view the utilization of clusters across your fleet.

1.5.1. Querying metrics using the observability API

Observability provides an external API for metrics to be queried through the OpenShift route, rbac-query-proxy. See the following options to get your queries for the rbac-query-proxy route:

  • You can get the details of the route with the following command: 

    oc get route rbac-query-proxy -n open-cluster-management-observability
  • You can also access the rbac-query-proxy route with your OpenShift OAuth access token. The token should be associated with a user or service account, which has permission to get namespaces. For more information, see Managing user-owned OAuth access tokens.

Complete the following steps to create proxy-byo-cert secrets for observability:

  1. Get the default CA certificate and store the content of the key tls.crt in a local file. Run the following command: 

    oc -n openshift-ingress get secret router-certs-default -o jsonpath="{.data.tls\.crt}" | base64 -d > ca.crt

  2. Run the following command to query metrics: 

    curl --cacert ./ca.crt -H "Authorization: Bearer {TOKEN}" https://{PROXY_ROUTE_URL}/api/v1/query?query={QUERY_EXPRESSION}

    Note: The QUERY_EXPRESSION is the standard Prometheus query expression. For example, query the metrics cluster_infrastructure_provider by replacing the URL in the previously mentioned command with the following URL: https://{PROXY_ROUTE_URL}/api/v1/query?query=cluster_infrastructure_provider. For more details, see Querying Prometheus.

  3. Run the following command to create proxy-byo-ca secrets using the generated certificates: 

    oc -n open-cluster-management-observability create secret tls proxy-byo-ca --cert ./ca.crt --key ./ca.key

  4. Create proxy-byo-cert secrets using the generated certificates by using the following command: 

    oc -n open-cluster-management-observability create secret tls proxy-byo-cert --cert ./ingress.crt --key ./ingress.key

1.5.2. Exporting metrics to external endpoints

Export metrics to external endpoints, which support the Prometheus Remote-Write specification in real time. Complete the following steps to export metrics to external endpoints:

  1. Create the Kubernetes secret for an external endpoint with the access information of the external endpoint in the open-cluster-management-observability namespace. View the following example secret: 

    apiVersion: v1
kind: Secret
metadata:
  name: victoriametrics
  namespace: open-cluster-management-observability
type: Opaque
stringData:
  ep.yaml: |
    url: http://victoriametrics:8428/api/v1/write
    http_client_config:
      basic_auth:
        username: test
        password: test

    The ep.yaml is the key of the content and is used in the MultiClusterObservability custom resource in next step. Currently, observability supports exporting metrics to endpoints without any security checks, with basic authentication or with tls enablement. View the following tables for a full list of supported parameters:

    NameDescriptionSchema

    url
    required

    URL for the external endpoint.

    string

    http_client_config
    optional

    Advanced configuration for the HTTP client.

    HttpClientConfig

    HttpClientConfig

    NameDescriptionSchema

    basic_auth
    optional

    HTTP client configuration for basic authentication.

    BasicAuth

    tls_config
    optional

    HTTP client configuration for TLS.

    TLSConfig

    BasicAuth

    NameDescriptionSchema

    username
    optional

    User name for basic authorization.

    string

    password
    optional

    Password for basic authorization.

    string

    TLSConfig

    Name

    Description

    Schema

    secret_name
    required

    Name of the secret that contains certificates.

    string

    ca_file_key
    optional

    Key of the CA certificate in the secret (only optional if insecure_skip_verify is set to true).

    string

    cert_file_key
    required

    Key of the client certificate in the secret.

    string

    key_file_key
    required

    Key of the client key in the secret.

    string

    insecure_skip_verify
    optional

    Parameter to skip the verification for target certificate.

    bool

  2. Add the writeStorage parameter to the MultiClusterObservability custom resource for adding a list of external endppoints that you want to export. View the following example: 

    spec:
  storageConfig:
    writeStorage: 1
    - key: ep.yaml
      name: victoriametrics
    1
    Each item contains two attributes: name and key. Name is the name of the Kubernetes secret that contains endpoint access information, and key is the key of the content in the secret. If you add more than one item to the list, then the metrics are exported to multiple external endpoints.

  3. View the status of metric export after the metrics export is enabled by checking the acm_remote_write_requests_total metric.

    1. From the OpenShift Container Platform console of your hub cluster, navigate to the Metrics page by clicking Metrics in the Observe section.
    2. Then query the acm_remote_write_requests_total metric. The value of that metric is the total number of requests with a specific response for one external endpoint, on one observatorium API instance. The name label is the name for the external endpoint. The code label is the return code of the HTTP request for the metrics export.

1.5.3. Viewing and exploring data by using dashboards

View the data from your managed clusters by accessing Grafana from the hub cluster. You can query specific alerts and add filters for the query.

For example, to explore the cluster_infrastructure_provider alert from a single-node OpenShift cluster, use the following query expression: cluster_infrastructure_provider{clusterType="SNO"}

Note: Do not set the ObservabilitySpec.resources.CPU.limits parameter if observability is enabled on single node managed clusters. When you set the CPU limits, it causes the observability pod to be counted against the capacity for your managed cluster. See the reference for Management Workload Partitioning in the Additional resources section.

1.5.3.1. Viewing historical data

When you query historical data, manually set your query parameter options to control how much data is displayed from the dashboard. Complete the following steps:

  1. From your hub cluster, select the Grafana link that is in the console header.
  2. Edit your cluster dashboard by selecting Edit Panel.
  3. From the Query front-end data source in Grafana, click the Query tab.
  4. Select $datasource.
  5. If you want to see more data, increase the value of the Step parameter section. If the Step parameter section is empty, it is automatically calculated.
  6. Find the Custom query parameters field and select max_source_resolution=auto.
  7. To verify that the data is displayed, refresh your Grafana page.

Your query data appears from the Grafana dashboard.

1.5.3.2. Viewing Red Hat Advanced Cluster Management dashboards

When you enable the Red Hat Advanced Cluster Management observability service, three dashboards become available. the following dashboard descriptions:

  • Alert Analysis: Overview dashboard of the alerts being generated within the managed cluster fleet.
  • Clusters by Alert: Alert dashboard where you can filter by the alert name.
  • Alerts by Cluster: Alert dashboard where you can filter by cluster, and view real-time data for alerts that are initiated or pending within the cluster environment.
1.5.3.3. Viewing the etcd table

You can also view the etcd table from the hub cluster dashboard in Grafana to learn the stability of the etcd as a data store. Select the Grafana link from your hub cluster to view the etcd table data, which is collected from your hub cluster. The Leader election changes across managed clusters are displayed.

1.5.3.4. Viewing the Kubernetes API server dashboard

View the following options to view the Kubernetes API server dashboards:

  • View the cluster fleet Kubernetes API service-level overview from the hub cluster dashboard in Grafana.

    1. Navigate to the Grafana dashboard.

    2. Access the managed dashboard menu by selecting Kubernetes > Service-Level Overview > API Server. The Fleet Overview and Top Cluster details are displayed.

      The total number of clusters that are exceeding or meeting the targeted service-level objective (SLO) value for the past seven or 30-day period, offending and non-offending clusters, and API Server Request Duration is displayed.

  • View the Kubernetes API service-level overview table from the hub cluster dashboard in Grafana.

    1. Navigate to the Grafana dashboard from your hub cluster.

    2. Access the managed dashboard menu by selecting Kubernetes > Service-Level Overview > API Server. The Fleet Overview and Top Cluster details are displayed.

      The error budget for the past seven or 30-day period, the remaining downtime, and trend are displayed.

1.5.4. Additional resources

1.5.5. Using Grafana dashboards

Use Grafana dashboards to view hub cluster and managed cluster metrics. The data displayed in the Grafana alerts dashboard relies on alerts metrics, originating from managed clusters. The alerts metric does not affect managed clusters forwarding alerts to Red Hat Advanced Cluster Management alert manager on the hub cluster. Therefore, the metrics and alerts have distinct propagation mechanisms and follow separate code paths.

Even if you see data in the Grafana alerts dashboard, that does not guarantee that the managed cluster alerts are successfully forwarding to the Red Hat Advanced Cluster Management hub cluster alert manager. If the metrics are propagated from the managed clusters, you can view the data displayed in the Grafana alerts dashboard.

To use the Grafana dashboards for your development needs, complete the following:

1.5.5.1. Setting up the Grafana developer instance

You can design your Grafana dashboard by creating a grafana-dev instance. Be sure to use the most current grafana-dev instance.

Complete the following steps to set up the Grafana developer instance:

  1. Clone the open-cluster-management/multicluster-observability-operator/ repository, so that you are able to run the scripts that are in the tools folder.

  2. Run the setup-grafana-dev.sh to setup your Grafana instance. When you run the script the following resources are created: secret/grafana-dev-config, deployment.apps/grafana-dev, service/grafana-dev, ingress.extensions/grafana-dev, persistentvolumeclaim/grafana-dev: 

    ./setup-grafana-dev.sh --deploy
secret/grafana-dev-config created
deployment.apps/grafana-dev created
service/grafana-dev created
serviceaccount/grafana-dev created
clusterrolebinding.rbac.authorization.k8s.io/open-cluster-management:grafana-crb-dev created
route.route.openshift.io/grafana-dev created
persistentvolumeclaim/grafana-dev created
oauthclient.oauth.openshift.io/grafana-proxy-client-dev created
deployment.apps/grafana-dev patched
service/grafana-dev patched
route.route.openshift.io/grafana-dev patched
oauthclient.oauth.openshift.io/grafana-proxy-client-dev patched
clusterrolebinding.rbac.authorization.k8s.io/open-cluster-management:grafana-crb-dev patched

  3. Switch the user role to Grafana administrator with the switch-to-grafana-admin.sh script.

    1. Select the Grafana URL, https:grafana-dev-open-cluster-management-observability.{OPENSHIFT_INGRESS_DOMAIN}, and log in.

    2. Then run the following command to add the switched user as Grafana administrator. For example, after you log in using kubeadmin, run following command: 

      ./switch-to-grafana-admin.sh kube:admin
User <kube:admin> switched to be grafana admin

The Grafana developer instance is set up.

1.5.5.1.1. Verifying Grafana version

Verify the Grafana version from the command line interface (CLI) or from the Grafana user interface.

After you log in to your hub cluster, access the observabilty-grafana pod terminal. Run the following command: 

grafana-cli

The Grafana version that is currently deployed within the cluster environment is displayed.

Alternatively, you can navigate to the Manage tab in the Grafana dashboard. Scroll to the end of the page, where the version is listed.

1.5.5.2. Designing your Grafana dashboard

After you set up the Grafana instance, you can design the dashboard. Complete the following steps to refresh the Grafana console and design your dashboard:

  1. From the Grafana console, create a dashboard by selecting the Create icon from the navigation panel. Select Dashboard, and then click Add new panel.
  2. From the New Dashboard/Edit Panel view, navigate to the Query tab.
  3. Configure your query by selecting Observatorium from the data source selector and enter a PromQL query.
  4. From the Grafana dashboard header, click the Save icon that is in the dashboard header.
  5. Add a descriptive name and click Save.
1.5.5.2.1. Designing your Grafana dashboard with a ConfigMap

Design your Grafana dashboard with a ConfigMap. You can use the generate-dashboard-configmap-yaml.sh script to generate the dashboard ConfigMap, and to save the ConfigMap locally: 

./generate-dashboard-configmap-yaml.sh "Your Dashboard Name"
Save dashboard <your-dashboard-name> to ./your-dashboard-name.yaml

If you do not have permissions to run the previously mentioned script, complete the following steps:

  1. Select a dashboard and click the Dashboard settings icon.
  2. Click the JSON Model icon from the navigation panel.
  3. Copy the dashboard JSON data and paste it in the data section.

  4. Modify the name and replace $your-dashboard-name. Enter a universally unique identifier (UUID) in the uid field in data.$your-dashboard-name.json.$$your_dashboard_json. You can use a program such as uuidegen to create a UUID. Your ConfigMap might resemble the following file: 

    kind: ConfigMap
apiVersion: v1
metadata:
  name: $your-dashboard-name
  namespace: open-cluster-management-observability
  labels:
    grafana-custom-dashboard: "true"
data:
  $your-dashboard-name.json: |-
    $your_dashboard_json

    Notes:

    • If your dashboard is created within the grafana-dev instance, you can take the name of the dashboard and pass it as an argument in the script. For example, a dashboard named Demo Dashboard is created in the grafana-dev instance. From the CLI, you can run the following script: 

      ./generate-dashboard-configmap-yaml.sh "Demo Dashboard"

      After running the script, you might receive the following message: 

      Save dashboard <demo-dashboard> to ./demo-dashboard.yaml

    • If your dashboard is not in the General folder, you can specify the folder name in the annotations section of this ConfigMap: 

      annotations:
  observability.open-cluster-management.io/dashboard-folder: Custom

      After you complete your updates for the ConfigMap, you can install it to import the dashboard to the Grafana instance.

Verify that the YAML file is created by applying the YAML from the CLI or OpenShift Container Platform console. A ConfigMap within the open-cluster-management-observability namespace is created. Run the following command from the CLI: 

oc apply -f demo-dashboard.yaml

From the OpenShift Container Platform console, create the ConfigMap using the demo-dashboard.yaml file. The dashboard is located in the Custom folder.

1.5.5.3. Uninstalling the Grafana developer instance

When you uninstall the instance, the related resources are also deleted. Run the following command: 

./setup-grafana-dev.sh --clean
secret "grafana-dev-config" deleted
deployment.apps "grafana-dev" deleted
serviceaccount "grafana-dev" deleted
route.route.openshift.io "grafana-dev" deleted
persistentvolumeclaim "grafana-dev" deleted
oauthclient.oauth.openshift.io "grafana-proxy-client-dev" deleted
clusterrolebinding.rbac.authorization.k8s.io "open-cluster-management:grafana-crb-dev" deleted
1.5.5.4. Additional resources

1.5.6. Using managed cluster labels in Grafana

Enable managed cluster labels to use them with Grafana dashboards. When observability is enabled in the hub cluster, the observability-managed-cluster-label-allowlist ConfigMap is created in the open-cluster-management-observability namespace. The ConfigMap contains a list of managed cluster labels maintained by the observabilty-rbac-query-proxy pod, to populate a list of label names to filter from within the ACM - Cluster Overview Grafana dashboard. By default, observability ignores a subset of labels in the observability-managed-cluster-label-allowlist ConfigMap.

When a cluster is imported into the managed cluster fleet or modified, the observability-rbac-query-proxy pod watches for any changes in reference to the managed cluster labels and automatically updates the observability-managed-cluster-label-allowlist ConfigMap to reflect the changes. The ConfigMap contains only unique label names, which are either included in the ignore_labels or labels list. Your observability-managed-cluster-label-allowlist ConfigMap might resemble the following YAML file: 

data:
  managed_cluster.yaml: |
    ignore_labels: 1
      - clusterID
      - cluster.open-cluster-management.io/clusterset
      - feature.open-cluster-management.io/addon-application-manager
      - feature.open-cluster-management.io/addon-cert-policy-controller
      - feature.open-cluster-management.io/addon-cluster-proxy
      - feature.open-cluster-management.io/addon-config-policy-controller
      - feature.open-cluster-management.io/addon-governance-policy-framework
      - feature.open-cluster-management.io/addon-iam-policy-controller
      - feature.open-cluster-management.io/addon-observability-controller
      - feature.open-cluster-management.io/addon-search-collector
      - feature.open-cluster-management.io/addon-work-manager
      - installer.name
      - installer.namespace
      - local-cluster
      - name
    labels: 2
      - cloud
      - vendor

+ <1> Any label that is listed in the ignore_labels keylist of the ConfigMap is removed from the drop-down filter on the ACM - Clusters Overview Grafana dashboard. <2> The labels that are enabled are displayed in the drop-down filter on the ACM - Clusters Overview Grafana dashboard. The values are from the acm_managed_cluster_labels metric, depending on the label key value that is selected.

Continue reading how to use managed cluster labels in Grafana:

1.5.6.1. Adding managed cluster labels

When you add a managed cluster label to the observability-managed-cluster-label-allowlist ConfigMap, the label becomes available as a filter option in Grafana. Add a unique label to the hub cluster, or managed cluster object that is associated with the managed cluster fleet. For example, if you add the label, department=finance to a managed cluster, the ConfigMap is updated and might resemble the following changes: 

data:
  managed_cluster.yaml: |
    ignore_labels:
      - clusterID
      - cluster.open-cluster-management.io/clusterset
      - feature.open-cluster-management.io/addon-application-manager
      - feature.open-cluster-management.io/addon-cert-policy-controller
      - feature.open-cluster-management.io/addon-cluster-proxy
      - feature.open-cluster-management.io/addon-config-policy-controller
      - feature.open-cluster-management.io/addon-governance-policy-framework
      - feature.open-cluster-management.io/addon-iam-policy-controller
      - feature.open-cluster-management.io/addon-observability-controller
      - feature.open-cluster-management.io/addon-search-collector
      - feature.open-cluster-management.io/addon-work-manager
      - installer.name
      - installer.namespace
      - local-cluster
      - name
    labels:
      - cloud
      - department
      - vendor
1.5.6.2. Enabling managed cluster labels

Enable a managed cluster label that is already disabled by removing the label from the ignore_labels list in the observability-managed-cluster-label-allowlist ConfigMap.

For example, enable the local-cluster and name labels. Your observability-managed-cluster-label-allowlist ConfigMap might resemble the following content: 

data:
  managed_cluster.yaml: |
    ignore_labels:
      - clusterID
      - installer.name
      - installer.namespace
    labels:
      - cloud
      - vendor
      - local-cluster
      - name

The ConfigMap resyncs after 30 seconds to ensure that the cluster labels are updated. After you update the ConfigMap, check the observability-rbac-query-proxy pod logs in the open-cluster-management-observability namespace to verify where the label is listed. The following information might be displayed in the pod log: 

enabled managedcluster labels: <label>

From the Grafana dashboard, verify that the label is listed as a value in the Label drop-down menu.

1.5.6.3. Disabling managed cluster labels

Exclude a managed cluster label from being listed in the Label drop-down filter. Add the label name to the ignore_labels list. For example, your YAML might resemble the following file if you add local-cluster and name back into the ignore_labels list: 

data:
  managed_cluster.yaml: |
    ignore_labels:
      - clusterID
      - installer.name
      - installer.namespace
      - local-cluster
      - name
    labels:
      - cloud
      - vendor

Check the observability-rbac-query-proxy pod logs in the open-cluster-management-observability namespace to verify where the label is listed. The following information might be displayed in the pod log: 

disabled managedcluster label: <label>
1.5.6.4. Additional resources

Chapter 2. Managing observability alerts

Receive and define alerts for the observability service to be notified of hub cluster and managed cluster changes.

2.1. Configuring Alertmanager

Integrate external messaging tools such as email, Slack, and PagerDuty to receive notifications from Alertmanager. You must override the alertmanager-config secret in the open-cluster-management-observability namespace to add integrations, and configure routes for Alertmanager. Complete the following steps to update the custom receiver rules:

  1. Extract the data from the alertmanager-config secret. Run the following command: 

    oc -n open-cluster-management-observability get secret alertmanager-config --template='{{ index .data "alertmanager.yaml" }}' |base64 -d > alertmanager.yaml

  2. Edit and save the alertmanager.yaml file configuration by running the following command: 

    oc -n open-cluster-management-observability create secret generic alertmanager-config --from-file=alertmanager.yaml --dry-run -o=yaml |  oc -n open-cluster-management-observability replace secret --filename=-

    Your updated secret might resemble the following content: 

    global
  smtp_smarthost: 'localhost:25'
  smtp_from: 'alertmanager@example.org'
  smtp_auth_username: 'alertmanager'
  smtp_auth_password: 'password'
templates:
- '/etc/alertmanager/template/*.tmpl'
route:
  group_by: ['alertname', 'cluster', 'service']
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 3h
  receiver: team-X-mails
  routes:
  - match_re:
      service: ^(foo1|foo2|baz)$
    receiver: team-X-mails

Your changes are applied immediately after it is modified. For an example of Alertmanager, see prometheus/alertmanager.

2.2. Forwarding alerts

After you enable observability, alerts from your OpenShift Container Platform managed clusters are automatically sent to the hub cluster. You can use the alertmanager-config YAML file to configure alerts with an external notification system.

View the following example of the alertmanager-config YAML file: 

global:
  slack_api_url: '<slack_webhook_url>'

route:
  receiver: 'slack-notifications'
  group_by: [alertname, datacenter, app]

receivers:
- name: 'slack-notifications'
  slack_configs:
  - channel: '#alerts'
    text: 'https://internal.myorg.net/wiki/alerts/{{ .GroupLabels.app }}/{{ .GroupLabels.alertname }}'

If you want to configure a proxy for alert forwarding, add the following global entry to the alertmanager-config YAML file: 

global:
  slack_api_url: '<slack_webhook_url>'
  http_config:
    proxy_url: http://****

2.2.1. Disabling alert forwarding for managed clusters

To disable alert forwarding for managed clusters, add the following annotation to the MultiClusterObservability custom resource: 

metadata:
      annotations:
        mco-disable-alerting: "true"

When you set the annotation, the alert forwarding configuration on the managed clusters is reverted. Any changes made to the ocp-monitoring-config config map in the openshift-monitoring namespace are also reverted. Setting the annotation ensures that the ocp-monitoring-config config map is no longer managed or updated by the observability operator endpoint. After you update the configuration, the Prometheus instance on your managed cluster restarts.

Important: Metrics on your managed cluster are lost if you have a Prometheus instance with a persistent volume for metrics, and the Prometheus instance restarts. Metrics from the hub cluster are not affected.

When the changes are reverted, a ConfigMap named cluster-monitoring-reverted is created in the open-cluster-management-addon-observability namespace. Any new, manually added alert forward configurations are not reverted from the ConfigMap.

Verify that the hub cluster alert manager is no longer propagating managed cluster alerts to third-party messaging tools. See the previous section, Configuring Alertmanager.

2.3. Silencing alerts

Add alerts that you do not want to receive. You can silence alerts by the alert name, match label, or time duration. After you add the alert that you want to silence, an ID is created. Your ID for your silenced alert might resemble the following string, d839aca9-ed46-40be-84c4-dca8773671da.

Continue reading for ways to silence alerts:

  • To silence a Red Hat Advanced Cluster Management alert, you must have access to the alertmanager-main pod in the open-cluster-management-observability namespace. For example, enter the following command in the pod terminal to silence SampleAlert: 

    amtool silence add --alertmanager.url="http://localhost:9093" --author="user" --comment="Silencing sample alert" alertname="SampleAlert"

  • Silence an alert by using multiple match labels. The following command uses match-label-1 and match-label-2: 

    amtool silence add --alertmanager.url="http://localhost:9093" --author="user" --comment="Silencing sample alert" <match-label-1>=<match-value-1> <match-label-2>=<match-value-2>

  • If you want to silence an alert for a specific period of time, use the --duration flag. Run the following command to silence the SampleAlert for an hour: 

    amtool silence add --alertmanager.url="http://localhost:9093" --author="user" --comment="Silencing sample alert" --duration="1h" alertname="SampleAlert"

    You can also specify a start or end time for the silenced alert. Enter the following command to silence the SampleAlert at a specific start time: 

    amtool silence add --alertmanager.url="http://localhost:9093" --author="user" --comment="Silencing sample alert" --start="2023-04-14T15:04:05-07:00" alertname="SampleAlert"

  • To view all silenced alerts that are created, run the following command: 

    amtool silence --alertmanager.url="http://localhost:9093"

  • If you no longer want an alert to be silenced, end the silencing of the alert by running the following command: 

    amtool silence expire --alertmanager.url="http://localhost:9093" "d839aca9-ed46-40be-84c4-dca8773671da"

  • To end the silencing of all alerts, run the following command: 

    amtool silence expire --alertmanager.url="http://localhost:9093" $(amtool silence query --alertmanager.url="http://localhost:9093" -q)

2.3.1. Migrating observability storage

If you use alert silencers, you can migrate observability storage while retaining the silencers from its earlier state. To do this, migrate your Red Hat Advanced Cluster Management observability storage by creating new StatefulSets and PersistentVolumes (PV) resources that use your chosen StorageClass resource.

Note: The storage for PVs is different from the object storage used to store the metrics collected from your clusters.

When you use StatefulSets and PVs to migrate your observability data to new storage, it stores the following data components:

  • Observatorium or Thanos: Receives data then uploads it to object storage. Some of its components store data in PVs. For this data, the Observatorium or Thanos automatically regenerates the object storage on a startup, so there is no consequence if you lose this data.
  • Alertmanager: Only stores silenced alerts. If you want to keep these silenced alerts, you must migrate that data to the new PV.

To migrate your observability storage, complete the following steps:

  1. In the MultiClusterObservability, set the .spec.storageConfig.storageClass field to the new storage class.
  2. To ensure the data of the earlier PersistentVolumes is retained even when you delete the PersistentVolumeClaim, go to all your existing PersistentVolumes.
  3. Change the reclaimPolicy to "Retain": `oc patch pv <your-pv-name> -p '{"spec":{"persistentVolumeReclaimPolicy":"Retain"}}'.
  4. Optional: To avoid losing data, see Migrate persistent data to another Storage Class in DG 8 Operator in OCP 4.

  5. Delete both the StatefulSet and the PersistentVolumeClaim in the following StatefulSet resources:

    1. alertmanager-db-observability-alertmanager
    2. data-observability-thanos-compact
    3. data-observability-thanos-receive-default
    4. data-observability-thanos-store-shard
    5. Important: You might need to delete, then re-create, the MultiClusterObservability operator pod so that you can create the new StatefulSet.
  6. Re-create a new PersistentVolumeClaim with the same name but the correct StorageClass.
  7. Create a new PersistentVolumeClaim referring to the old PersistentVolume.
  8. Verify that the new StatefulSet and PersistentVolumes use the new StorageClass that you chose.

2.4. Suppressing alerts

Suppress Red Hat Advanced Cluster Management alerts across your clusters globally that are less severe. Suppress alerts by defining an inhibition rule in the alertmanager-config in the open-cluster-management-observability namespace.

An inhibition rule mutes an alert when there is a set of parameter matches that match another set of existing matchers. In order for the rule to take effect, both the target and source alerts must have the same label values for the label names in the equal list. Your inhibit_rules might resemble the following: 

global:
  resolve_timeout: 1h
inhibit_rules:1
  - equal:
      - namespace
    source_match:2
      severity: critical
    target_match_re:
      severity: warning|info
1 1
The inhibit_rules parameter section is defined to look for alerts in the same namespace. When a critical alert is initiated within a namespace and if there are any other alerts that contain the severity level warning or info in that namespace, only the critical alerts are routed to the Alertmanager receiver. The following alerts might be displayed when there are matches: 
ALERTS{alertname="foo", namespace="ns-1", severity="critical"}
ALERTS{alertname="foo", namespace="ns-1", severity="warning"}
2 2
If the value of the source_match and target_match_re parameters do not match, the alert is routed to the receiver: 
ALERTS{alertname="foo", namespace="ns-1", severity="critical"}
ALERTS{alertname="foo", namespace="ns-2", severity="warning"}

  • To view suppressed alerts in Red Hat Advanced Cluster Management, enter the following command: 

    amtool alert --alertmanager.url="http://localhost:9093" --inhibited

2.5. Additional resources

Chapter 3. Searching in the console introduction

For Red Hat Advanced Cluster Management for Kubernetes, search provides visibility into your Kubernetes resources across all of your clusters. Search also indexes the Kubernetes resources and the relationships to other resources.

3.1. Search components

The search architecture is composed of the following components:

Table 3.1. Search component table
Component nameMetricsMetric typeDescription

search-collector

  

Watches the Kubernetes resources, collects the resource metadata, computes relationships for resources across all of your managed clusters, and sends the collected data to the search-indexer. The search-collector on your managed cluster runs as a pod named, klusterlet-addon-search.

search-indexer

Receives resource metadata from the collectors and writes to PostgreSQL database. The search-indexer also watches resources in the hub cluster to keep track of active managed clusters.

search_indexer_request_duration

Histogram

Time (seconds) the search indexer takes to process a request (from managed cluster).

search_indexer_request_size

Histogram

Total changes (add, update, delete) in the search indexer request (from managed cluster).

search_indexer_request_count

Counter

Total requests received by the search indexer (from managed clusters).

search_indexer_requests_in_flight

Gauge

Total requests the search indexer is processing at a given time.

search-api

Provides access to all cluster data in the search-indexer through GraphQL and enforces role-based access control (RBAC).

search_api_requests

Histogram

Histogram of HTTP requests duration in seconds.

search_dbquery_duration_seconds

Histogram

Latency of database requests in seconds.

search_api_db_connection_failed_total

Counter

The total number of database connection attempts that failed.

search-postgres

  

Stores collected data from all managed clusters in an instance of the PostgreSQL database.

Search is configured by default on the hub cluster. When you provision or manually import a managed cluster, the klusterlet-addon-search is enabled. If you want to disable search on your managed cluster, see Modifying the klusterlet add-ons settings of your cluster for more information.

3.2. Search customization and configurations

You can modify the default values in the search-v2-operator custom resource. To view details of the custom resource, run the following command: 

oc get search search-v2-operator -o yaml

The search operator watches the search-v2-operator custom resource, reconciles the changes and updates active pods. View the following descriptions of the configurations:

  • PostgreSQL database storage:

    When you install Red Hat Advanced Cluster Management, the PostgreSQL database is configured to save the PostgreSQL data in an empty directory (emptyDir) volume. If the empty directory size is limited, you can save the PostgreSQL data on a Persistent Volume Claim (PVC) to improve search performance. You can select a storageclass from your Red Hat Advanced Cluster Management hub cluster to back up your search data. For example, if you select the gp2 storageclass your configuration might resemble the following example: 

    apiVersion: search.open-cluster-management.io/v1alpha1
kind: Search
metadata:
  name: search-v2-operator
  namespace: open-cluster-management
  labels:
    cluster.open-cluster-management.io/backup: ""
spec:
  dbStorage:
    size: 10Gi
    storageClassName: gp2

    This configuration creates a PVC named gp2-search and is mounted to the search-postgres pod. By default, the storage size is 10Gi. You can modify the storage size. For example, 20Gi might be sufficient for about 200 managed clusters.

  • Optimize cost by tuning the pod memory or CPU requirements, replica count, and update log levels for any of the four search pods (indexer, database, queryapi, or collector pod). Update the deployment section of the search-v2-operator custom resource. There are four deployments managed by the search-v2-operator, which can be updated individually. Your search-v2-operator custom resource might resemble the following file: 

    apiVersion: search.open-cluster-management.io/v1alpha1
kind: Search
metadata:
  name: search-v2-operator
  namespace: open-cluster-management
spec:
  deployments:
    collector:
      resources: 1
        limits:
          cpu: 500m
          memory: 128Mi
        requests:
          cpu: 250m
          memory: 64Mi
    indexer:
      replicaCount: 3
    database: 2
        envVar:
          - name: POSTGRESQL_EFFECTIVE_CACHE_SIZE
            value: 1024MB
          - name: POSTGRESQL_SHARED_BUFFERS
            value: 512MB
          - name: WORK_MEM
            value: 128MB
    queryapi:
      arguments: 3
      - -v=3
    1
    You can apply resources to an indexer, database, queryapi, or collector pod.
    2
    You can add multiple environment variables in the envVar section to specify a value for each variable that you name.
    3
    You can control the log level verbosity for any of the previous four pods by adding the - -v=3 argument.

    See the following example where memory resources are applied to the indexer pod: 

        indexer:
      resources:
        limits:
          memory: 5Gi
        requests:
          memory: 1Gi

  • Node placement for search pods:

    You can update the Placement of search pods by using the nodeSelector parameter, or the tolerations parameter. View the following example configuration: 

    spec:
 dbStorage:
  size: 10Gi
 deployments:
  collector: {}
  database: {}
  indexer: {}
  queryapi: {}
 nodeSelector:
  node-role.kubernetes.io/infra: ""
 tolerations:
 - effect: NoSchedule
  key: node-role.kubernetes.io/infra
  operator: Exists

Chapter 4. Using observability with Red Hat Insights

Red Hat Insights is integrated with Red Hat Advanced Cluster Management observability, and is enabled to help identify existing or potential problems in your clusters. Red Hat Insights helps you to identify, prioritize, and resolve stability, performance, network, and security risks. Red Hat OpenShift Container Platform offers cluster health monitoring through OpenShift Cluster Manager. OpenShift Cluster Manager collects anonymized, aggregated information about the health, usage, and size of the clusters. For more information, see Red Hat Insights product documentation.

When you create or import an OpenShift cluster, anonymized data from your managed cluster is automatically sent to Red Hat. This information is used to create insights, which provide cluster health information. Red Hat Advanced Cluster Management administrator can use this health information to create alerts based on severity.

Required access: Cluster administrator

4.1. Prerequisites

  • Ensure that Red Hat Insights is enabled. For more information, see Modifying the global cluster pull secret to disable remote health reporting.
  • Install OpenShift Container Platform version 4.0 or later.
  • Hub cluster user, who is registered to OpenShift Cluster Manager, must be able to manage all the Red Hat Advanced Cluster Management managed clusters in OpenShift Cluster Manager.

4.2. Red Hat Insights from the Red Hat Advanced Cluster Management console

Continue reading to view functionality descriptions of the integration:

  • When you select a cluster from the Clusters page, you can select the Number of identified issues from the Status card. The Status card displays information about Nodes, Applications, Policy violations, and Identified issues. The Identified issues card represents the information from Red Hat insights. The Identified issues status displays the number of issues by severity. The triage levels used for the issues are the following severity categories: Critical, Major, Low, and Warning.
  • After you click the number, the Potential issue side panel is displayed. A summary and chart of the total issues are displayed in the panel. You can also use the search feature to search for recommended remediations. The remediation option displays the Description of the vulnerability, Category that vulnerability is associated with, and the Total risk.
  • From the Description section, you can select the link to the vulnerability. View steps to resolve your vulnerability by selecting the How to remediate tab. You can also view why the vulnerability occurred by clicking the Reason tab.

See Managing insight PolicyReports for more information.

4.3. Managing insight PolicyReports

Red Hat Advanced Cluster Management for Kubernetes PolicyReports are violations that are generated by the insights-client. The PolicyReports are used to define and configure alerts that are sent to incident management systems. When there is a violation, alerts from a PolicyReport are sent to incident management system.

4.3.1. Searching for insight policy reports

You can search for a specific insight PolicyReport that has a violation, across your managed clusters. Complete the following steps:

  1. Log in to your Red Hat Advanced Cluster Management hub cluster.
  2. Click the Search icon in the console header to navigate to the Search page.

  3. Enter the following query: kind:policyreport.

    Note: The PolicyReport name matches the name of the cluster.

  4. You can specify your query with the insight policy violation and categories. When you select a PolicyReport name, you are redirected to the Details page of the associated cluster. The Insights sidebar is automatically displayed.

  5. If the search service is disabled and you want to search for an insight, run the following command from your hub cluster: 

    oc get policyreport --all-namespaces

4.3.2. Viewing identified issues from the console

You can view the identified issues on a specific cluster. Complete the following steps:

  1. Log in to your Red Hat Advanced Cluster Management cluster.
  2. Select Overview from the navigation menu.

  3. Select a severity to view the PolicyReports that are associated with that severity. Details of the cluster issues and the severities are displayed from the Cluster issues summary card.

    1. Alternatively, you can select Clusters from the navigation menu.
    2. Select a managed cluster from the table to view more details.
    3. From the Status card, view the number of identified issues.
  4. Select the number of potential issues to view the severity chart and recommended remediations for the issues.

  5. Click the link to the vulnerability to view steps on How to remediate and the Reason for the vulnerability.

    Note: After the issue is resolved, the Red Hat Insights are received by Red Hat Advanced Cluster Management every 30 minutes, and Red Hat Insights is updated every two hours.

  6. Be sure to verify which component sent the alert message from the PolicyReport.

    1. Navigate to the Governance page and select a specific PolicyReport.
    2. Select the Status tab and click the View details link to view the PolicyReport YAML file.
    3. Locate the source parameter, which informs you of the component that sent the violation. The value options are grc and insights.

4.3.3. Additional resources

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