Metering

Metering has many resources which can be used to manage the deployment and installation of metering, as well as the reporting functionality metering provides.

Metering is managed using the following CustomResourceDefinitions (CRDs):

You can adjust the CPU, memory, or storage resource requests and/or limits for pods and volumes. The default-resource-limits.yaml below provides an example of setting resource request and limits for each component.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  reporting-operator:
    spec:
      resources:
        limits:
          cpu: 1
          memory: 500Mi
        requests:
          cpu: 500m
          memory: 100Mi
  presto:
    spec:
      coordinator:
        resources:
          limits:
            cpu: 4
            memory: 4Gi
          requests:
            cpu: 2
            memory: 2Gi

      worker:
        replicas: 0
        resources:
          limits:
            cpu: 8
            memory: 8Gi
          requests:
            cpu: 4
            memory: 2Gi

  hive:
    spec:
      metastore:
        resources:
          limits:
            cpu: 4
            memory: 2Gi
          requests:
            cpu: 500m
            memory: 650Mi
        storage:
          class: null
          create: true
          size: 5Gi
      server:
        resources:
          limits:
            cpu: 1
            memory: 1Gi
          requests:
            cpu: 500m
            memory: 500Mi

If you want to run the metering components on specific sets of nodes, then you can set nodeSelectors on each component to control where each component of metering is scheduled. The node-selectors.yaml file below provides an example of setting node selectors for each component.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  reporting-operator:
    spec:
      nodeSelector:
        "node-role.kubernetes.io/infra": "true"

  presto:
    spec:
      coordinator:
        nodeSelector:
          "node-role.kubernetes.io/infra": "true"
      worker:
        nodeSelector:
          "node-role.kubernetes.io/infra": "true"
  hive:
    spec:
      metastore:
        nodeSelector:
          "node-role.kubernetes.io/infra": "true"
      server:
        nodeSelector:
          "node-role.kubernetes.io/infra": "true"

Metering can use an existing Amazon S3 bucket or create a bucket for storage.

To use Amazon S3 for storage, edit the spec.storage section in the example s3-storage.yaml file below.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  storage:
    type: "hive"
    hive:
      type: "s3"
      s3:
        bucket: "bucketname/path/" 1
        region: "us-west-1" 2
        secretName: "my-aws-secret" 3
        # Set to false if you want to provide an existing bucket, instead of
        # having metering create the bucket on your behalf.
        createBucket: true 4

Use the example secret below as a template.

apiVersion: v1
kind: Secret
metadata:
  name: your-aws-secret
data:
  aws-access-key-id: "dGVzdAo="
  aws-secret-access-key: "c2VjcmV0Cg=="

You can use the following command to create the secret.

oc create secret -n openshift-metering generic your-aws-secret --from-literal=aws-access-key-id=your-access-key  --from-literal=aws-secret-access-key=your-secret-key

The aws-access-key-id and aws-secret-access-key credentials must have read and write access to the bucket. For an example of an IAM policy granting the required permissions, see the aws/read-write.json file below.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "1",
            "Effect": "Allow",
            "Action": [
                "s3:AbortMultipartUpload",
                "s3:DeleteObject",
                "s3:GetObject",
                "s3:HeadBucket",
                "s3:ListBucket",
                "s3:ListMultipartUploadParts",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::operator-metering-data/*",
                "arn:aws:s3:::operator-metering-data"
            ]
        }
    ]
}

If you left spec.storage.hive.s3.createBucket set to true, or unset, then you should use the aws/read-write-create.json file below, which contains permissions for creating and deleting buckets.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "1",
            "Effect": "Allow",
            "Action": [
                "s3:AbortMultipartUpload",
                "s3:DeleteObject",
                "s3:GetObject",
                "s3:HeadBucket",
                "s3:ListBucket",
                "s3:CreateBucket",
                "s3:DeleteBucket",
                "s3:ListMultipartUploadParts",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::operator-metering-data/*",
                "arn:aws:s3:::operator-metering-data"
            ]
        }
    ]
}

To use S3-compatible storage such as Noobaa, edit the spec.storage section in the example s3-compatible-storage.yaml file below.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  storage:
    type: "hive"
    hive:
      type: "s3Compatible"
      s3Compatible:
        bucket: "bucketname" 1
        endpoint: "http://example:port-number" 2
        secretName: "my-aws-secret" 3

Use the example secret below as a template.

apiVersion: v1
kind: Secret
metadata:
  name: your-aws-secret
data:
  aws-access-key-id: "dGVzdAo="
  aws-secret-access-key: "c2VjcmV0Cg=="

To store data in Azure blob storage you must use an existing container. Edit the spec.storage section in the example azure-blob-storage.yaml file below.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  storage:
    type: "hive"
    hive:
      type: "azure"
      azure:
        container: "bucket1" 1
        secretName: "my-azure-secret" 2
        rootDirectory: "/testDir" 3

Use the example secret below as a template.

apiVersion: v1
kind: Secret
metadata:
  name: your-azure-secret
data:
  azure-storage-account-name: "dGVzdAo="
  azure-secret-access-key: "c2VjcmV0Cg=="

You can use the following command to create the secret.

oc create secret -n openshift-metering generic your-azure-secret --from-literal=azure-storage-account-name=your-storage-account-name --from-literal=azure-secret-access-key-your-secret-key

To store your data in Google Cloud Storage you must use an existing bucket. Edit the spec.storage section in the example gcs-storage.yaml file below.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  storage:
    type: "hive"
    hive:
      type: "gcs"
      gcs:
        bucket: "metering-gcs/test1" 1
        secretName: "my-gcs-secret" 2

Use the example secret below as a template:

apiVersion: v1
kind: Secret
metadata:
  name: your-gcs-secret
data:
  gcs-service-account.json: "c2VjcmV0Cg=="

You can use the following command to create the secret.

oc create secret -n openshift-metering generic your-gcs-secret --from-file gcs-service-account.json=/path/to/your/service-account-key.json

Metering has no storage by default, but it can use any ReadWriteMany PersistentVolume or any StorageClass that provisions a ReadWriteMany PersistentVolume.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  storage:
    type: "hive"
    hive:
      type: "sharedPVC"
      sharedPVC:
        claimName: "metering-nfs" 1
        # uncomment the lines below to provision a new PVC using the specified 2
        # storageClass.
        # createPVC: true
        # storageClass: "my-nfs-storage-class"
        # size: 5Gi

Select one of the configuration options below:

By default, Hive requires one PersistentVolume to operate.

hive-metastore-db-data is the main PersistentVolumeClaim (PVC) required by default. This PVC is used by the Hive metastore to store metadata about tables, such as table name, columns, and location. Hive metastore is used by Presto and the Hive server to look up table metadata when processing queries. You remove this requirement by using MySQL or PostgreSQL for the Hive metastore database.

To install, Hive metastore requires that dynamic volume provisioning be enabled via a StorageClass, a persistent volume of the correct size must be manually pre-created, or that you use a pre-existing MySQL or PostgreSQL database.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  hive:
    spec:
      metastore:
        storage:
          # Default is null, which means using the default storage class if it exists.
          # If you wish to use a different storage class, specify it here
          # class: "null" 1
          size: "5Gi"

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  hive:
    spec:
      metastore:
        storage:
          # Default is null, which means using the default storage class if it exists.
          # If you wish to use a different storage class, specify it here
          # class: "null"
          size: "5Gi" 1

The default installation of metering configures Hive to use an embedded Java database called Derby. This is unsuited for larger environments and can be replaced with either a MySQL or PostgreSQL database. Use the following example configuration files if your deployment requires a MySQL or PostgreSQL database for Hive.

There are 4 configuration options you can use to control the database used by Hive metastore: url, driver, username, and password.

Use the example configuration file below to use a MySQL database for Hive:

spec:
  hive:
    spec:
      metastore:
        storage:
          create: false
      config:
        db:
          url: "jdbc:mysql://mysql.example.com:3306/hive_metastore"
          driver: "com.mysql.jdbc.Driver"
          username: "REPLACEME"
          password: "REPLACEME"

You can pass additional JDBC parameters using the spec.hive.config.url. For more details see the MySQL Connector/J documentation.

Use the example configuration file below to use a PostgreSQL database for Hive:

spec:
  hive:
    spec:
      metastore:
        storage:
          create: false
      config:
        db:
          url: "jdbc:postgresql://postgresql.example.com:5432/hive_metastore"
          driver: "org.postgresql.Driver"
          username: "REPLACEME"
          password: "REPLACEME"

You can pass additional JDBC parameters using the URL. For more details see the PostgreSQL JDBC driver documentation.

When you install metering on OpenShift Container Platform, Prometheus is available at https://prometheus-k8s.openshift-monitoring.svc:9091/.

To secure the connection to Prometheus, the default metering installation uses the OpenShift Container Platform certificate authority. If your Prometheus instance uses a different CA, the CA can be injected through a ConfigMap. See the following example.

spec:
  reporting-operator:
    spec:
      config:
        prometheus:
          certificateAuthority:
            useServiceAccountCA: false
            configMap:
              enabled: true
              create: true
              name: reporting-operator-certificate-authority-config
              filename: "internal-ca.crt"
              value: |
                -----BEGIN CERTIFICATE-----
                (snip)
                -----END CERTIFICATE-----

Alternatively, to use the system certificate authorities for publicly valid certificates, set both useServiceAccountCA and configMap.enabled to false.

The reporting-operator can also be configured to use a specified bearer token to auth with Prometheus. See the following example.

spec:
  reporting-operator:
    spec:
      config:
        prometheus:
          metricsImporter:
            auth:
              useServiceAccountToken: false
              tokenSecret:
                enabled: true
                create: true
                value: "abc-123"

On OpenShift Container Platform the default metering installation automatically exposes a Route, making the reporting API available. This provides the following features:

Also, the default installation makes it possible to use the OpenShift service for serving certificates to protect the reporting API with TLS. The OpenShift OAuth proxy is deployed as a side-car container for reporting-operator, which protects the reporting API with authentication.

METERING_ROUTE_HOSTNAME=$(oc -n openshift-metering get routes metering -o json | jq -r '.status.ingress[].host')

TOKEN=$(oc -n openshift-metering serviceaccounts get-token reporting-operator)
curl -H "Authorization: Bearer $TOKEN" -k "https://$METERING_ROUTE_HOSTNAME/api/v1/reports/get?name=[Report Name]&namespace=openshift-metering&format=[Format]"

curl -u testuser:password123 -k "https://$METERING_ROUTE_HOSTNAME/api/v1/reports/get?name=[Report Name]&namespace=openshift-metering&format=[Format]"

$ openssl rand -base64 32 | head -c32; echo.

apiVersion: metering.openshift.io/v1
kind: MeteringConfig
metadata:
  name: "operator-metering"
spec:
  permissions:
    # anyone in the "metering-admins" group can create, update, delete, etc any
    # metering.openshift.io resources in the namespace.
    # This also grants permissions to get query report results from the reporting REST API.
    meteringAdmins:
    - kind: Group
      name: metering-admins
    # Same as above except read only access and for the metering-viewers group.
    meteringViewers:
    - kind: Group
      name: metering-viewers
    # the default serviceaccount in the namespace "my-custom-ns" can:
    # create, update, delete, etc reports.
    # This also gives permissions query the results from the reporting REST API.
    reportingAdmins:
    - kind: ServiceAccount
      name: default
      namespace: my-custom-ns
    # anyone in the group reporting-readers can get, list, watch reports, and
    # query report results from the reporting REST API.
    reportingViewers:
    - kind: Group
      name: reporting-readers
    # anyone in the group cluster-admins can query report results
    # from the reporting REST API. So can the user bob-from-accounting.
    reportExporters:
    - kind: Group
      name: cluster-admins
    - kind: User
      name: bob-from-accounting

  reporting-operator:
    spec:
      authProxy:
        # htpasswd.data can contain htpasswd file contents for allowing auth
        # using a static list of usernames and their password hashes.
        #
        # username is 'testuser' password is 'password123'
        # generated htpasswdData using: `htpasswd -nb -s testuser password123`
        # htpasswd:
        #   data: |
        #     testuser:{SHA}y/2sYAj5yrQIN4TL0YdPdmGNKpc=
        #
        # change REPLACEME to the output of your htpasswd command
        htpasswd:
          data: |
            REPLACEME

The Report custom resource is used to manage the execution and status of reports. Metering produces reports derived from usage data sources, which can be used in further analysis and filtering.

A single Report resource represents a job that manages a database table and updates it with new information according to a schedule. The Report exposes the data in that table via the reporting-operator HTTP API. Reports with a spec.schedule field set are always running and track what time periods it has collected data for. This ensures that if metering is shutdown or unavailable for an extended period of time, it will backfill the data starting where it left off. If the schedule is unset, then the Report will run once for the time specified by the reportingStart and reportingEnd. By default, reports wait for ReportDataSources to have fully imported any data covered in the reporting period. If the Report has a schedule, it will wait to run until the data in the period currently being processed has finished importing.

apiVersion: metering.openshift.io/v1
kind: Report
metadata:
  name: pod-cpu-request-hourly
spec:
  query: "pod-cpu-request"
  reportingStart: "2019-07-01T00:00:00Z"
  schedule:
    period: "hourly"
    hourly:
      minute: 0
      second: 0

apiVersion: metering.openshift.io/v1
kind: Report
metadata:
  name: pod-cpu-request-hourly
spec:
  query: "pod-cpu-request"
  reportingStart: "2019-07-01T00:00:00Z"
  reportingEnd: "2019-07-31T00:00:00Z"

$ oc -n openshift-metering get reportqueries
NAME                                         AGE
cluster-cpu-capacity                         23m
cluster-cpu-capacity-raw                     23m
cluster-cpu-usage                            23m
cluster-cpu-usage-raw                        23m
cluster-cpu-utilization                      23m
cluster-memory-capacity                      23m
cluster-memory-capacity-raw                  23m
cluster-memory-usage                         23m
cluster-memory-usage-raw                     23m
cluster-memory-utilization                   23m
cluster-persistentvolumeclaim-request        23m
namespace-cpu-request                        23m
namespace-cpu-usage                          23m
namespace-cpu-utilization                    23m
namespace-memory-request                     23m
namespace-memory-usage                       23m
namespace-memory-utilization                 23m
namespace-persistentvolumeclaim-request      23m
namespace-persistentvolumeclaim-usage        23m
node-cpu-allocatable                         23m
node-cpu-allocatable-raw                     23m
node-cpu-capacity                            23m
node-cpu-capacity-raw                        23m
node-cpu-utilization                         23m
node-memory-allocatable                      23m
node-memory-allocatable-raw                  23m
node-memory-capacity                         23m
node-memory-capacity-raw                     23m
node-memory-utilization                      23m
persistentvolumeclaim-capacity               23m
persistentvolumeclaim-capacity-raw           23m
persistentvolumeclaim-phase-raw              23m
persistentvolumeclaim-request                23m
persistentvolumeclaim-request-raw            23m
persistentvolumeclaim-usage                  23m
persistentvolumeclaim-usage-raw              23m
persistentvolumeclaim-usage-with-phase-raw   23m
pod-cpu-request                              23m
pod-cpu-request-raw                          23m
pod-cpu-usage                                23m
pod-cpu-usage-raw                            23m
pod-memory-request                           23m
pod-memory-request-raw                       23m
pod-memory-usage                             23m
pod-memory-usage-raw                         23m

$ oc -n openshift-metering get reportqueries namespace-memory-request -o yaml

apiVersion: metering.openshift.io/v1
kind: ReportQuery
metadata:
  name: namespace-memory-request
  labels:
    operator-metering: "true"
spec:
  columns:
  - name: period_start
    type: timestamp
    unit: date
  - name: period_end
    type: timestamp
    unit: date
  - name: namespace
    type: varchar
    unit: kubernetes_namespace
  - name: pod_request_memory_byte_seconds
    type: double
    unit: byte_seconds

...
  schedule:
    period: "weekly"
    weekly:
      dayOfWeek: "wednesday"
      hour: 13
...

apiVersion: metering.openshift.io/v1
kind: Report
metadata:
  name: pod-cpu-request-hourly
spec:
  query: "pod-cpu-request"
  schedule:
    period: "hourly"
  reportingStart: "2019-01-01T00:00:00Z"

apiVersion: metering.openshift.io/v1
kind: Report
metadata:
  name: pod-cpu-request-hourly
spec:
  query: "pod-cpu-request"
  schedule:
    period: "weekly"
  reportingStart: "2019-07-01T00:00:00Z"
  reportingEnd: "2019-07-31T00:00:00Z"

spec:
  inputs:
  - name: "NamespaceCPUUsageReportName"
    value: "namespace-cpu-usage-hourly"

# Taken from pod-cpu.yaml
spec:
...
  inputs:
  - name: ReportingStart
    type: time
  - name: ReportingEnd
    type: time
  - name: NamespaceCPUUsageReportName
    type: Report
  - name: PodCpuUsageRawDataSourceName
    type: ReportDataSource
    default: pod-cpu-usage-raw
...

  query: |
...
    {|- if .Report.Inputs.NamespaceCPUUsageReportName |}
      namespace,
      sum(pod_usage_cpu_core_seconds) as pod_usage_cpu_core_seconds
    FROM {| .Report.Inputs.NamespaceCPUUsageReportName | reportTableName |}
...

# aggregated-report.yaml
spec:
  query: "namespace-cpu-usage"
  inputs:
  - name: "NamespaceCPUUsageReportName"
    value: "namespace-cpu-usage-hourly"

This first example is what the built-in local storage option looks like. It is configured to use Hive, and by default data is stored wherever Hive is configured to use storage (HDFS, S3, or a ReadWriteMany PVC).

apiVersion: metering.openshift.io/v1
kind: StorageLocation
metadata:
  name: hive
  labels:
    operator-metering: "true"
spec:
  hive: 1
    databaseName: metering 2
    unmanagedDatabase: false 3

The next example uses an AWS S3 bucket for storage. The prefix is appended to the bucket name when constructing the path to use.

apiVersion: metering.openshift.io/v1
kind: StorageLocation
metadata:
  name: example-s3-storage
  labels:
    operator-metering: "true"
spec:
  hive:
    databaseName: example_s3_storage
    unmanagedDatabase: false
    location: "s3a://bucket-name/path/within/bucket" 1

There are some additonal optional fields that can be specified in the hive section:

If an annotation storagelocation.metering.openshift.io/is-default exists and is set to true on a StorageLocation resource, then that resource becomes the default storage resource. Any components with a storage configuration option where StorageLocation is not specified will use the default storage resource. There can only be on default storage resource. If more than one resource with the annotation exists, an error will be logged and the operator will consider there to be no default.

apiVersion: metering.openshift.io/v1
kind: StorageLocation
metadata:
  name: example-s3-storage
  labels:
    operator-metering: "true"
  annotations:
    storagelocation.metering.openshift.io/is-default: "true"
spec:
  hive:
    databaseName: example_s3_storage
    unmanagedDatabase: false
    location: "s3a://bucket-name/path/within/bucket"

A common issue when installing or running metering is lack of compute resources. Ensure that metering is allocated the minimum resource requirements described in the installation prerequisites.

To determine if the issue is with resources or scheduling, follow the troubleshooting instructions included in the Kubernetes document Managing Compute Resources for Containers.

Metering requires that a default StorageClass be configured for dynamic provisioning.

See the documentation on configuring metering for information on how to check if there are any StorageClasses configured for the cluster, how to set the default, and how to configure metering to use a StorageClass other than the default.

A common issue with metering is providing the incorrect secret when configuring your persistent storage. Be sure to review the example configuration files and create you secret according to the guidelines for your storage provider.

The command below will follow the logs of the reporting-operator.

$ oc -n openshift-metering logs -f "$(oc -n openshift-metering get pods -l app=reporting-operator -o name | cut -c 5-)" -c reporting-operator

The following command opens an interactive presto-cli session where you can query Presto. This session runs in the same container as Presto and launches an additional Java instance, which can create memory limits for the Pod. If this occurs, you should increase the memory request and limits of the Presto Pod.

By default, Presto is configured to communicate using TLS. You must to run the following command to run Presto queries:

$ oc -n openshift-metering exec -it "$(oc -n openshift-metering get pods -l app=presto,presto=coordinator -o name | cut -d/ -f2)"  -- /usr/local/bin/presto-cli --server https://presto:8080 --catalog hive --schema default --user root --keystore-path /opt/presto/tls/keystore.pem

Once you run this command, a prompt appears where you can run queries. Use the show tables from metering; query to view the list of tables:

$ presto:default> show tables from metering;

                                 Table

 datasource_your_namespace_cluster_cpu_capacity_raw
 datasource_your_namespace_cluster_cpu_usage_raw
 datasource_your_namespace_cluster_memory_capacity_raw
 datasource_your_namespace_cluster_memory_usage_raw
 datasource_your_namespace_node_allocatable_cpu_cores
 datasource_your_namespace_node_allocatable_memory_bytes
 datasource_your_namespace_node_capacity_cpu_cores
 datasource_your_namespace_node_capacity_memory_bytes
 datasource_your_namespace_node_cpu_allocatable_raw
 datasource_your_namespace_node_cpu_capacity_raw
 datasource_your_namespace_node_memory_allocatable_raw
 datasource_your_namespace_node_memory_capacity_raw
 datasource_your_namespace_persistentvolumeclaim_capacity_bytes
 datasource_your_namespace_persistentvolumeclaim_capacity_raw
 datasource_your_namespace_persistentvolumeclaim_phase
 datasource_your_namespace_persistentvolumeclaim_phase_raw
 datasource_your_namespace_persistentvolumeclaim_request_bytes
 datasource_your_namespace_persistentvolumeclaim_request_raw
 datasource_your_namespace_persistentvolumeclaim_usage_bytes
 datasource_your_namespace_persistentvolumeclaim_usage_raw
 datasource_your_namespace_persistentvolumeclaim_usage_with_phase_raw
 datasource_your_namespace_pod_cpu_request_raw
 datasource_your_namespace_pod_cpu_usage_raw
 datasource_your_namespace_pod_limit_cpu_cores
 datasource_your_namespace_pod_limit_memory_bytes
 datasource_your_namespace_pod_memory_request_raw
 datasource_your_namespace_pod_memory_usage_raw
 datasource_your_namespace_pod_persistentvolumeclaim_request_info
 datasource_your_namespace_pod_request_cpu_cores
 datasource_your_namespace_pod_request_memory_bytes
 datasource_your_namespace_pod_usage_cpu_cores
 datasource_your_namespace_pod_usage_memory_bytes
(32 rows)

Query 20190503_175727_00107_3venm, FINISHED, 1 node
Splits: 19 total, 19 done (100.00%)
0:02 [32 rows, 2.23KB] [19 rows/s, 1.37KB/s]

presto:default>

The following opens an interactive beeline session where you can query Hive. This session runs in the same container as Hive and launches an additional Java instance, which can create memory limits for the Pod. If this occurs, you should increase the memory request and limits of the Hive Pod.

$ oc -n openshift-metering exec -it $(oc -n openshift-metering get pods -l app=hive,hive=server -o name | cut -d/ -f2) -c hiveserver2 -- beeline -u 'jdbc:hive2://127.0.0.1:10000/default;auth=noSasl'

Once you run this command, a prompt appears where you can run queries. Use the show tables; query to view the list of tables:

$ 0: jdbc:hive2://127.0.0.1:10000/default> show tables from metering;
+----------------------------------------------------+
|                      tab_name                      |
+----------------------------------------------------+
| datasource_your_namespace_cluster_cpu_capacity_raw |
| datasource_your_namespace_cluster_cpu_usage_raw  |
| datasource_your_namespace_cluster_memory_capacity_raw |
| datasource_your_namespace_cluster_memory_usage_raw |
| datasource_your_namespace_node_allocatable_cpu_cores |
| datasource_your_namespace_node_allocatable_memory_bytes |
| datasource_your_namespace_node_capacity_cpu_cores |
| datasource_your_namespace_node_capacity_memory_bytes |
| datasource_your_namespace_node_cpu_allocatable_raw |
| datasource_your_namespace_node_cpu_capacity_raw  |
| datasource_your_namespace_node_memory_allocatable_raw |
| datasource_your_namespace_node_memory_capacity_raw |
| datasource_your_namespace_persistentvolumeclaim_capacity_bytes |
| datasource_your_namespace_persistentvolumeclaim_capacity_raw |
| datasource_your_namespace_persistentvolumeclaim_phase |
| datasource_your_namespace_persistentvolumeclaim_phase_raw |
| datasource_your_namespace_persistentvolumeclaim_request_bytes |
| datasource_your_namespace_persistentvolumeclaim_request_raw |
| datasource_your_namespace_persistentvolumeclaim_usage_bytes |
| datasource_your_namespace_persistentvolumeclaim_usage_raw |
| datasource_your_namespace_persistentvolumeclaim_usage_with_phase_raw |
| datasource_your_namespace_pod_cpu_request_raw    |
| datasource_your_namespace_pod_cpu_usage_raw      |
| datasource_your_namespace_pod_limit_cpu_cores    |
| datasource_your_namespace_pod_limit_memory_bytes |
| datasource_your_namespace_pod_memory_request_raw |
| datasource_your_namespace_pod_memory_usage_raw   |
| datasource_your_namespace_pod_persistentvolumeclaim_request_info |
| datasource_your_namespace_pod_request_cpu_cores  |
| datasource_your_namespace_pod_request_memory_bytes |
| datasource_your_namespace_pod_usage_cpu_cores    |
| datasource_your_namespace_pod_usage_memory_bytes |
+----------------------------------------------------+
32 rows selected (13.101 seconds)
0: jdbc:hive2://127.0.0.1:10000/default>

Run the following command:

$ oc -n openshift-metering port-forward hive-server-0 10002

You can now open http://127.0.0.1:10002 in your browser window to view the Hive web interface.

To the namenode:

$ oc -n openshift-metering port-forward hdfs-namenode-0 9870

You can now open http://127.0.0.1:9870 in your browser window to view the HDFS web interface.

To the first datanode:

$ oc -n openshift-metering port-forward hdfs-datanode-0 9864

To check other datanodes, run the above command, replacing hdfs-datanode-0 with the Pod you want to view information on.

Metering uses the Ansible Operator to watch and reconcile resources in a cluster environment. When debugging a failed metering installation, it can be helpful to view the Ansible logs or status of your MeteringConfig custom resource.

$ oc -n openshift-metering logs $(oc -n openshift-metering get pods -l app=metering-operator -o name | cut -d/ -f2) -c ansible

$ oc -n openshift-metering get meteringconfig operator-metering -o json | jq '.status'