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Customize scraping of Prometheus metrics in Azure Monitor managed service for Prometheus

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This article provides instructions on customizing metrics scraping for a Kubernetes cluster with the metrics addon in Azure Monitor.

Configmaps

Four different configmaps can be configured to provide scrape configuration and other settings for the metrics add-on. All config-maps should be applied to kube-system namespace for any cluster.

Note

None of the four configmaps exist by default in the cluster when Managed Prometheus is enabled. Depending on what needs to be customized, you need to deploy any or all of these four configmaps with the same name specified, in kube-system namespace. AMA-Metrics pods will pick up these configmaps after you deploy them to kube-system namespace, and will restart in 2-3 minutes to apply the configuration settings specified in the configmap(s).

  1. ama-metrics-settings-configmap This config map has below simple settings that can be configured. You can take the configmap from the above git hub repo, change the settings are required and apply/deploy the configmap to kube-system namespace for your cluster
    • cluster alias (to change the value of cluster label in every time-series/metric that's ingested from a cluster)
    • enable/disable default scrape targets - Turn ON/OFF default scraping based on targets. Scrape configuration for these default targets are already pre-defined/built-in
    • enable pod annotation based scraping per namespace
    • metric keep-lists - this setting is used to control which metrics are listed to be allowed from each default target and to change the default behavior
    • scrape intervals for default/pre-definetargets. 30 secs is the default scrape frequency and it can be changed per default target using this configmap
    • debug-mode - turning this ON helps to debug missing metric/ingestion issues - see more on troubleshooting
  2. ama-metrics-prometheus-config This config map can be used to provide Prometheus scrape config for addon replica. Addon runs a singleton replica, and any cluster level services can be discovered and scraped by providing scrape jobs in this configmap. You can take the sample configmap from the above git hub repo, add scrape jobs that you would need and apply/deploy the config map to kube-system namespace for your cluster. Although this is supported, please note that the recommended way of scraping custom targets is using custom resources
  3. ama-metrics-prometheus-config-node (Advanced) This config map can be used to provide Prometheus scrape config for addon DaemonSet that runs on every Linux node in the cluster, and any node level targets on each node can be scraped by providing scrape jobs in this configmap. When you use this configmap, you can use $NODE_IP variable in your scrape config, which gets substituted by corresponding node's ip address in DaemonSet pod running on each node. This way you get access to scrape anything that runs on that node from the metrics addon DaemonSet. Please be careful when you use discoveries in scrape config in this node level config map, as every node in the cluster will setup & discover the target(s) and will collect redundant metrics. You can take the sample configmap from the above git hub repo, add scrape jobs that you would need and apply/deploy the config map to kube-system namespace for your cluster
  4. ama-metrics-prometheus-config-node-windows (Advanced) This config map can be used to provide Prometheus scrape config for addon DaemonSet that runs on every Windows node in the cluster, and node level targets on each node can be scraped by providing scrape jobs in this configmap. When you use this configmap, you can use $NODE_IP variable in your scrape config, which will be substituted by corresponding node's ip address in DaemonSet pod running on each node. This way you get access to scrape anything that runs on that node from the metrics addon DaemonSet. Please be careful when you use discoveries in scrape config in this node level config map, as every node in the cluster will setup & discover the target(s) and will collect redundant metrics. You can take the sample configmap from the above git hub repo, add scrape jobs that you would need and apply/deploy the config map to kube-system namespace for your cluster

Custom Resource Definitions

The Azure Monitor metrics add-on supports scraping Prometheus metrics using Prometheus - Pod Monitors and Service Monitors, similar to the OSS Prometheus operator. Enabling the add-on will deploy the Pod and Service Monitor custom resource definitions to allow you to create your own custom resources. Follow the instructions to create and apply custom resources on your cluster.

Metrics add-on settings configmap

The ama-metrics-settings-configmap can be downloaded, edited, and applied to the cluster to customize the out-of-the-box features of the metrics add-on.

Enable and disable default targets

The following table has a list of all the default targets that the Azure Monitor metrics add-on can scrape by default and whether it's initially enabled. Default targets are scraped every 30 seconds. A replica is deployed to scrape cluster-wide targets such as kube-state-metrics. A DaemonSet is also deployed to scrape node-wide targets such as kubelet.

Key Type Enabled Pod Description
kubelet bool true Linux DaemonSet Scrape kubelet in every node in the K8s cluster without any extra scrape config.
cadvisor bool true Linux DaemonSet Scrape cadvisor in every node in the K8s cluster without any extra scrape config.
Linux only.
kubestate bool true Linux replica Scrape kube-state-metrics in the K8s cluster (installed as a part of the add-on) without any extra scrape config.
nodeexporter bool true Linux DaemonSet Scrape node metrics without any extra scrape config.
Linux only.
coredns bool false Linux replica Scrape coredns service in the K8s cluster without any extra scrape config.
kubeproxy bool false Linux DaemonSet Scrape kube-proxy in every Linux node discovered in the K8s cluster without any extra scrape config.
Linux only.
apiserver bool false Linux replica Scrape the Kubernetes API server in the K8s cluster without any extra scrape config.
windowsexporter bool false Windows DaemonSet Scrape windows-exporter in every node in the K8s cluster without any extra scrape config.
Windows only.
windowskubeproxy bool false Windows DaemonSet Scrape windows-kube-proxy in every node in the K8s cluster without any extra scrape config.
Windows only.
prometheuscollectorhealth bool false Linux replica Scrape information about the prometheus-collector container, such as the amount and size of time series scraped.

If you want to turn on the scraping of the default targets that aren't enabled by default, edit the configmap ama-metrics-settings-configmap to update the targets listed under default-scrape-settings-enabled to true. Apply the configmap to your cluster.

Enable pod annotation-based scraping

To scrape application pods without needing to create a custom Prometheus config, annotations can be added to the pods. The annotation prometheus.io/scrape: "true" is required for the pod to be scraped. The annotations prometheus.io/path and prometheus.io/port indicate the path and port that the metrics are hosted at on the pod. The annotations for a pod that is hosting metrics at <pod IP>:8080/metrics would be:

metadata:   
  annotations:
    prometheus.io/scrape: 'true'
    prometheus.io/path: '/metrics'
    prometheus.io/port: '8080'

Scraping these pods with specific annotations is disabled by default. To enable, in the ama-metrics-settings-configmap, add the regex for the namespace(s) of the pods with annotations you wish to scrape as the value of the field podannotationnamespaceregex.

For example, the following setting scrapes pods with annotations only in the namespaces kube-system and my-namespace:

pod-annotation-based-scraping: |-
    podannotationnamespaceregex = "kube-system|my-namespace"

To enable scraping for pods with annotations in all namespaces, use:

pod-annotation-based-scraping: |-
    podannotationnamespaceregex = ".*"

Warning

Scraping the pod annotations from many namespaces can generate a very large volume of metrics depending on the number of pods that have annotations.

Customize metrics collected by default targets

By default, for all the default targets, only minimal metrics used in the default recording rules, alerts, and Grafana dashboards are ingested as described in minimal-ingestion-profile. To collect all metrics from default targets, update the keep-lists in the settings configmap under default-targets-metrics-keep-list, and set minimalingestionprofile to false.

To allowlist more metrics in addition to default metrics that are listed to be allowed, for any default targets, edit the settings under default-targets-metrics-keep-list for the corresponding job you want to change.

For example, kubelet is the metric filtering setting for the default target kubelet. Use the following script to filter in metrics collected for the default targets by using regex-based filtering.

kubelet = "metricX|metricY"
apiserver = "mymetric.*"

Note

If you use quotation marks or backslashes in the regex, you need to escape them by using a backslash like the examples "test\'smetric\"s\"" and testbackslash\\*.

To further customize the default jobs to change properties like collection frequency or labels, disable the corresponding default target by setting the configmap value for the target to false. Then apply the job by using a custom configmap. For details on custom configuration, see Customize scraping of Prometheus metrics in Azure Monitor.

Cluster alias

The cluster label appended to every time series scraped uses the last part of the full AKS cluster's Azure Resource Manager resource ID. For example, if the resource ID is /subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/rg-name/providers/Microsoft.ContainerService/managedClusters/myclustername, the cluster label is myclustername.

To override the cluster label in the time series scraped, update the setting cluster_alias to any string under prometheus-collector-settings in the configmap ama-metrics-settings-configmap. You can create this configmap if it doesn't exist in the cluster or you can edit the existing one if it already exists in your cluster.

The new label also shows up in the cluster parameter dropdown in the Grafana dashboards instead of the default one.

Note

Only alphanumeric characters are allowed. Any other characters are replaced with _. This change is to ensure that different components that consume this label adhere to the basic alphanumeric convention. If you are enabling recording and alerting rules, please make sure to use the cluster alias name in the cluster name parameter of the rule onboarding template for the rules to work.

Debug mode

Warning

This mode can affect performance and should only be enabled for a short time for debugging purposes.

To view every metric that's being scraped for debugging purposes, the metrics add-on agent can be configured to run in debug mode by updating the setting enabled to true under the debug-mode setting in the configmap ama-metrics-settings-configmap. You can either create this configmap or edit an existing one. For more information, see the Debug mode section in Troubleshoot collection of Prometheus metrics.

Scrape interval settings

To update the scrape interval settings for any target, you can update the duration in the setting default-targets-scrape-interval-settings for that target in the configmap ama-metrics-settings-configmap. You have to set the scrape intervals in the correct format specified in this website. Otherwise, the default value of 30 seconds is applied to the corresponding targets. For example - If you want to update the scrape interval for the kubelet job to 60s then you can update the following section in the YAML:

default-targets-scrape-interval-settings: |-
    kubelet = "60s"
    coredns = "30s"
    cadvisor = "30s"
    kubeproxy = "30s"
    apiserver = "30s"
    kubestate = "30s"
    nodeexporter = "30s"
    windowsexporter = "30s"
    windowskubeproxy = "30s"
    kappiebasic = "30s"
    prometheuscollectorhealth = "30s"
    podannotations = "30s"

and apply the YAML using the following command: kubectl apply -f .\ama-metrics-settings-configmap.yaml

Configure custom Prometheus scrape jobs

You can scrape Prometheus metrics using Prometheus - Pod Monitors and Service Monitors(Recommended), similar to the OSS Prometheus operator. Follow the instructions to create and apply custom resources on your cluster.

Additionally, you can follow the instructions to create, validate, and apply the configmap for your cluster. The configuration format is similar to Prometheus configuration file.

Prometheus configuration tips and examples

Learn some tips from examples in this section.

Use the Pod and Service Monitor templates and follow the API specification to create your custom resources(PodMonitor and Service Monitor). Note that the only change required to the existing OSS CRs for being picked up by the Managed Prometheus is the API group - azmonitoring.coreos.com/v1. See here to learn more

Note

When custom scrape configuration fails to apply because of validation errors, default scrape configuration continues to be used.

If you want to use global settings that apply to all the scrape jobs, and only have Custom Resources you would still need to create a configmap with just the global settings(Settings for each of these in the custom resources will override the ones in the global section)

Scrape configs

Currently, the supported methods of target discovery for custom resources are pod and service monitor

Pod and Service Monitors

Targets discovered using pod and service monitors have different __meta_* labels depending on what monitor is used. You can use the labels in the relabelings section to filter targets or replace labels for the targets.

See the Pod and Service Monitor examples of pod and service monitors.

Relabelings

The relabelings section is applied at the time of target discovery and applies to each target for the job. The following examples show ways to use relabelings.

Add a label

Add a new label called example_label with the value example_value to every metric of the job. Use __address__ as the source label only because that label always exists and adds the label for every target of the job.

relabelings:
- sourceLabels: [__address__]
  targetLabel: example_label
  replacement: 'example_value'

Use Pod or Service Monitor labels

Targets discovered using pod and service monitors have different __meta_* labels depending on what monitor is used. The __* labels are dropped after discovering the targets. To filter by using them at the metrics level, first keep them using relabelings by assigning a label name. Then use metricRelabelings to filter.

# Use the kubernetes namespace as a label called 'kubernetes_namespace'
relabelings:
- sourceLabels: [__meta_kubernetes_namespace]
  action: replace
  targetLabel: kubernetes_namespace

# Keep only metrics with the kubernetes namespace 'default'
metricRelabelings:
- sourceLabels: [kubernetes_namespace]
  action: keep
  regex: 'default'

Job and instance relabeling

You can change the job and instance label values based on the source label, just like any other label.

# Replace the job name with the pod label 'k8s app'
relabelings:
- sourceLabels: [__meta_kubernetes_pod_label_k8s_app]
  targetLabel: job

# Replace the instance name with the node name. This is helpful to replace a node IP
# and port with a value that is more readable
relabelings:
- sourceLabels: [__meta_kubernetes_node_name]]
  targetLabel: instance

Note

If you have relabeling configs, ensure that the relabeling does not filter out the targets, and the labels configured correctly match the targets.

Metric Relabelings

Metric relabelings are applied after scraping and before ingestion. Use the metricRelabelings section to filter metrics after scraping. The following examples show how to do so.

Drop metrics by name

# Drop the metric named 'example_metric_name'
metricRelabelings:
- sourceLabels: [__name__]
  action: drop
  regex: 'example_metric_name'

Keep only certain metrics by name

# Keep only the metric named 'example_metric_name'
metricRelabelings:
- sourceLabels: [__name__]
  action: keep
  regex: 'example_metric_name'

# Keep only metrics that start with 'example_'
metricRelabelings:
- sourceLabels: [__name__]
  action: keep
  regex: '(example_.*)'

Rename metrics

Metric renaming isn't supported.

Filter metrics by labels

# Keep metrics only where example_label = 'example'
metricRelabelings:
- sourceLabels: [example_label]
  action: keep
  regex: 'example'

# Keep metrics only if `example_label` equals `value_1` or `value_2`
metricRelabelings:
- sourceLabels: [example_label]
  action: keep
  regex: '(value_1|value_2)'

# Keep metrics only if `example_label_1 = value_1` and `example_label_2 = value_2`
metricRelabelings:
- sourceLabels: [example_label_1, example_label_2]
  separator: ';'
  action: keep
  regex: 'value_1;value_2'

# Keep metrics only if `example_label` exists as a label
metricRelabelings:
- sourceLabels: [example_label_1]
  action: keep
  regex: '.+'

Basic Authentication

If you are using basic_auth setting in your prometheus configuration, please follow the steps -

  1. Create a secret in the kube-system namespace named ama-metrics-mtls-secret

The value for password1 is base64encoded.

The key password1 can be anything, but just needs to match your scrapeconfig password_file filepath.

apiVersion: v1
kind: Secret
metadata:
  name: ama-metrics-mtls-secret
  namespace: kube-system
type: Opaque
data:
  password1: <base64-encoded-string>

The ama-metrics-mtls-secret secret is mounted on to the ama-metrics containers at path - /etc/prometheus/certs/ and is made available to the process that is scraping prometheus metrics. The key( ex - password1) in the above example will be the file name and the value is base64 decoded and added to the contents of the file within the container and the prometheus scraper uses the contents of this file to get the value that is used as the password used to scrape the endpoint.

  1. In the configmap for the custom scrape configuration use the following setting. The username field should contain the actual username string. The password_file field should contain the path to a file that contains the password.
basic_auth:
  username: <username string>
  password_file: /etc/prometheus/certs/password1

By providing the path to the password_file above, the prometheus scraper uses the contents of the file named password1 in the path /etc/prometheus/certs as the value of password for basic auth based scraping.

If you are using both basic auth and tls auth, please refer to the section below. For more details, refer to the note section below.

TLS based scraping

If you have a Prometheus instance served with TLS and you want to scrape metrics from it, you need to set scheme to https and set the TLS settings in your configmap or respective CRD. Please follow the below steps.

  1. Create a secret in the kube-system namespace named ama-metrics-mtls-secret. Each key-value pair specified in the data section of the secret object will be mounted as a separate file in this /etc/prometheus/certs location with filename(s) same as key(s) specified in the data section. The secret values should be base64 encoded before putting them under the data section, like below.

    Below is an example of creating secret through YAML.

    apiVersion: v1
kind: Secret
metadata:
  name: ama-metrics-mtls-secret
  namespace: kube-system
type: Opaque
data:
  <certfile>: base64_cert_content    
  <keyfile>: base64_key_content

    The ama-metrics-mtls-secret secret is mounted on to the ama-metrics containers at path - /etc/prometheus/certs/ and is made available to the process that is scraping prometheus metrics. The key( ex - certfile) in the above example will be the file name and the value is base64 decoded and added to the contents of the file within the container and the prometheus scraper uses the contents of this file to get the value that is used as the password used to scrape the endpoint.

  2. Below are the details about how to provide the TLS config settings through a configmap or CRD.

  • To provide the TLS config setting in a configmap, please follow the below example.
tls_config:
    ca_file: /etc/prometheus/certs/<certfile> # since it is self-signed
    cert_file: /etc/prometheus/certs/<certfile>
    key_file: /etc/prometheus/certs/<keyfile>
    insecure_skip_verify: false

Basic Auth and TLS

If you want to use both basic and Tls authentication settings in your configmap/CRD, just make sure that the secret ama-metrics-mtls-secret includes all the files(keys) under the data section with their corresponding base 64 encoded values, as shown below.

apiVersion: v1
kind: Secret
metadata:
  name: ama-metrics-mtls-secret
  namespace: kube-system
type: Opaque
data:
  certfile: base64_cert_content    # used for Tls
  keyfile: base64_key_content      # used for Tls
  password1: base64-encoded-string # used for basic auth
  password2: base64-encoded-string # used for basic auth

Note

Note

The /etc/prometheus/certs/ path is mandatory, but password1 can be any string and needs to match the key for the data in the secret created above. This is because the secret ama-metrics-mtls-secret is mounted in the path /etc/prometheus/certs/ within the container.

The base64 encoded value is automatically decoded by the agent pods when the secret is mounted as file.

Make sure the secret name is ama-metrics-mtls-secret and it is in kube-system namespace.

The secret should be created and then the configmap/CRD should be created in kube-system namespace. The order of secret creation matters. When there's no secret but a valid CRD/config map, you will find errors in collector log -> no file found for cert....

To read more on TLS configuration settings, please follow this Configurations.

Next steps

Setup Alerts on Prometheus metrics
Query Prometheus metrics
Learn more about collecting Prometheus metrics