Getting Started



The goal for this tutorial is to gather our first profile from a running pod via kubectl profefe plugin. We will store the profile locally, and we will push one to profefe


If you have those things already done move to the chapter “getting started”

  1. Have a Kubernetes cluster up and running
  2. Deploy profefe inside or outside your Kubernetes cluster. But it has to be reachable from the Kubernetes cluster network. I will assume that the URL for it is
  3. You should have the kubectl profefe plugin already installed (checkout the installation doc)

Deploy kubernetes cluster with KinD

$ kind create cluster
Creating cluster "kind" ...
 ✓ Ensuring node image (kindest/node:v1.15.3) đŸ–ŧ
 ✓ Preparing nodes đŸ“Ļ
 ✓ Creating kubeadm config 📜
 ✓ Starting control-plane 🕹ī¸
 ✓ Installing CNI 🔌
 ✓ Installing StorageClass 💾
Cluster creation complete. You can now use the cluster with:

export KUBECONFIG="$(kind get kubeconfig-path --name="kind")"
kubectl cluster-info

$ export KUBECONFIG="$(kind get kubeconfig-path --name="kind")"
✔ ~/git/kube-profefe [docs/getting-start L|✚ 1â€Ļ1]

$ kubectl cluster-info
Kubernetes master is running at
KubeDNS is running at

To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.

Deploy profefe

Apply this spec via kubectl apply -f <path file.yaml>. It will create a profefe namespace, and it will deploy the profefe as a deployment with its service.

This installation is not for production.

apiVersion: v1
kind: Namespace
  name: profefe
apiVersion: extensions/v1beta1
kind: Deployment
    component: profefe
  name: profefe
  namespace: profefe
  replicas: 1
      component: profefe
        component: profefe
      - args:
        - -badger.dir
        - /tmp/profefe-data
        image: profefe/profefe:git-668a19d
        imagePullPolicy: IfNotPresent
        name: profefe
        - containerPort: 10100
apiVersion: v1
kind: Service
    component: profefe
  name: profefe-collector
  namespace: profefe
  - name: collector
    port: 10100
    protocol: TCP
    targetPort: 10100
    component: profefe
  type: ClusterIP

Getting Started

Do you have the kubectl plugin? Check it out with:

$ kubectl profefe --help
It is a kubectl plugin that you can use to retrieve and manage profiles in Go.

  kubectl-profefe [flags]
  kubectl-profefe [command]

Available Commands:
  capture     Capture gathers profiles for a pod or a set of them. If can filter by namespace and via label selector.
  get         Display one or many resources
  help        Help about any command
  load        Load a profile you have locally to profefe

  -A, --all-namespaces                 If present, list the requested object(s) across all namespaces. Namespace in current context is ignored even if specified with --namespace.
      --as string                      Username to impersonate for the operation
      --as-group stringArray           Group to impersonate for the operation, this flag can be repeated to specify multiple groups.
      --cache-dir string               Default HTTP cache directory (default "/home/gianarb/.kube/http-cache")
      --certificate-authority string   Path to a cert file for the certificate authority
      --client-certificate string      Path to a client certificate file for TLS
      --client-key string              Path to a client key file for TLS
      --cluster string                 The name of the kubeconfig cluster to use
      --context string                 The name of the kubeconfig context to use
  -f, --filename strings               identifying the resource.
  -h, --help                           help for kubectl-profefe
      --insecure-skip-tls-verify       If true, the server's certificate will not be checked for validity. This will make your HTTPS connections insecure
      --kubeconfig string              Path to the kubeconfig file to use for CLI requests.
  -n, --namespace string               If present, the namespace scope for this CLI request
  -R, --recursive                      Process the directory used in -f, --filename recursively. Useful when you want to manage related manifests organized within the same directory. (default true)
      --request-timeout string         The length of time to wait before giving up on a single server request. Non-zero values should contain a corresponding time unit (e.g. 1s, 2m, 3h). A value of zero means don't timeout requests. (default "0")
  -l, --selector string                Selector (label query) to filter on, supports '=', '==', and '!='.(e.g. -l key1=value1,key2=value2)
  -s, --server string                  The address and port of the Kubernetes API server
      --token string                   Bearer token for authentication to the API server
      --user string                    The name of the kubeconfig user to use

Use "kubectl-profefe [command] --help" for more information about a command.

Deploy InfluxDB v2 pod, it is a test one. That’s the one we will take profile from

apiVersion: v1
kind: Pod
  name: influxdb-v2
    "": "true"
    "": "9999"
  - name: influxdb
    - containerPort: 9999

Now you can capture the profiles:

kubectl profefe capture influxdb-v2

The profiles are stored inside your /tmp directory (you can change it with --output-dir), so you can read it with go tool pprof:

go tool pprof /tmp/profile-goroutine-influxdb-v2-1575552135.pb.gz

If you have a profefe up and running you can push your profiles there other than locally:

kubectl profefe capture influxdb-v2 --profefe-hostport http://localhost:10100

If you used kind as explained above, in order to have profefe reachable you have to use port forwarding:

$ kubectl port-forward -n profefe svc/profefe-collector 10100