Grafana Phlare Deploy on Kubernetes
Open source

Deploy Grafana Phlare using the Helm chart

In March 2023, Grafana Labs acquired Pyroscope, the company behind the eponymous open source continuous profiling project. As a result, the Pyroscope and Grafana Phlare projects will be merged under the new name Grafana Pyroscope. To learn more, read our recent blog post about the news.

The Helm chart allows you to configure, install, and upgrade Grafana Phlare within a Kubernetes cluster.

Before you begin

The instructions that follow are common across any flavor of Kubernetes and assume that you know how to install, configure, and operate a Kubernetes cluster. And that you know how to use kubectl.

Caution: Do not use this getting started procedure in a production environment.

Hardware requirements:

  • A single Kubernetes node with a minimum of 4 cores and 16GiB RAM

Software requirements:

  • Kubernetes 1.20 or higher
  • The kubectl command for your version of Kubernetes
  • Helm 3 or higher

Verify that you have:

  • Access to the Kubernetes cluster
  • Persistent storage is enabled in the Kubernetes cluster, which has a default storage class set up. You can change the default StorageClass.
  • DNS service works in the Kubernetes cluster

Install the Helm chart in a custom namespace

Use a custom namespace so that you do not have to overwrite the default namespace later in the procedure.

  1. Create a unique Kubernetes namespace, for example phlare-test:

    kubectl create namespace phlare-test

    For more details, see the Kubernetes documentation about Creating a new namespace.

  2. Set up a Helm repository using the following commands:

    helm repo add grafana
    helm repo update

    Note: The Helm chart at is a publication of the source code at grafana/phlare.

  3. Install Grafana Phlare using the Helm chart using one of the following options:

    • Option A: Install Grafana Phlare as single binary
    helm -n phlare-test install phlare grafana/phlare
    • Option B: Install Grafana Phlare as micro-services
    # Gather the default config for micro-services
    curl -LO values-micro-services.yaml
    helm -n phlare-test install phlare grafana/phlare --values values-micro-services.yaml

    Note: The output of the command contains the query URLs necessary for the following steps, so for a micro-service setup it will look like this:

    The in-cluster query URL is:
  4. Check the statuses of the Phlare pods:

    kubectl -n phlare-test get pods

    The results look similar to this when you are in micro-services mode:

    kubectl -n phlare-test get pods
    NAME                                 READY   STATUS    RESTARTS   AGE
    phlare-agent-7d75b4f9dc-xwpsw        1/1     Running   0          3m23s
    phlare-distributor-7c474947c-2p5cc   1/1     Running   0          3m23s
    phlare-distributor-7c474947c-xbszv   1/1     Running   0          3m23s
    phlare-ingester-0                    1/1     Running   0          5s
    phlare-ingester-1                    1/1     Running   0          37s
    phlare-ingester-2                    1/1     Running   0          69s
    phlare-minio-0                       1/1     Running   0          3m23s
    phlare-querier-66bf58dfcc-89gb8      1/1     Running   0          3m23s
    phlare-querier-66bf58dfcc-p7lnc      1/1     Running   0          3m23s
    phlare-querier-66bf58dfcc-zbggm      1/1     Running   0          3m23s
  5. Wait until all of the pods have a status of Running or Completed, which might take a few minutes.

Query profiles in Grafana

  1. Install Grafana in the same Kubernetes cluster where you installed Phlare.

    helm upgrade -n phlare-test --install grafana grafana/grafana \
      --set image.repository=grafana/grafana \
      --set image.tag=main \
      --set env.GF_FEATURE_TOGGLES_ENABLE=flameGraph \
      --set env.GF_AUTH_ANONYMOUS_ENABLED=true \
      --set env.GF_AUTH_ANONYMOUS_ORG_ROLE=Admin \
      --set env.GF_DIAGNOSTICS_PROFILING_PORT=6060 \
      --set-string 'podAnnotations.phlare\.grafana\.com/scrape=true' \
      --set-string 'podAnnotations.phlare\.grafana\.com/port=6060'

    For details, see Deploy Grafana on Kubernetes.

  2. Port-forward Grafana to localhost, by using the kubectl command:

    kubectl port-forward -n phlare-test service/grafana 3000:80
  3. In a browser, go to the Grafana server at http://localhost:3000.

  4. On the left-hand side, go to Configuration > Data sources.

  5. Configure a new Grafana Phlare data source to query the Grafana Phlare server, by using the following settings:


    To add a data source, see Add a data source.

  6. Verify success:

    You should be able to query profiles in Grafana Explore, as well as create dashboard panels by using your newly configured Phlare data source.

Optional: Persistently add data source

The deployment of Grafana has no persistent database, so it will not retain settings like the data source configuration across restarts.

To ensure the data source gets provisioned at start-up, create the following datasources.yaml file:

   apiVersion: 1
   - name: Phlare
     type: phlare
     uid: phlare-test
     url: http://phlare-querier.phlare-test.svc.cluster.local.:4100/

Modify the Helm deployment by running:

   helm upgrade -n phlare-test --reuse-values grafana grafana/grafana \
     --values datasources.yaml

Optional: Scrape your own workload’s profiles

The Phlare chart uses a default configuration that causes its agent to scrape Pods, provided they have the correct annotations. This functionality uses relabel_config and kubernetes_sd_config you might be familiar with the Prometheus or Grafana Agent config.

To get Phlare to scrape pods, you must add the following annotations to the pods:

  annotations: "true" "8080" "true" "8080" "true" "8080"

The above example will scrape the memory, cpu and goroutine profiles from the 8080 port of the pod.

Each profile type has a set of corresponding annotations which allows customization of scraping per profile type.

  annotations:<profile-type>.scrape: "true"<profile-type>.port: "<port>"<profile-type>.port_name: "<port-name>"<profile-type>.scheme: "<scheme>"<profile-type>.path: "<profile_path>"

The full list of profile types supported by annotations is cpu, memory, goroutine, block and mutex.

The following table describes the annotations:

AnnotationDescriptionDefault<profile-type>.scrapeWhether to scrape the profile type.false<profile-type>.portThe port to scrape the profile type from.``<profile-type>.port_nameThe port name to scrape the profile type from.``<profile-type>.schemeThe scheme to scrape the profile type from.http<profile-type>.pathThe path to scrape the profile type from.default golang path

By default, the port will be discovered using named port http2 or ending with -metrics or -profiles. This means that if you don’t have a named port the scraping target will be dropped.

If you don’t want to use the port name then you can use the<profile-type>.port annotation to statically specify the port number.