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Version: v2.2.0

Kubernetes

Documentation for deploying Dragonfly on kubernetes using helm.

Runtime​

You can have a quick start following Helm Charts. It is recommended to use containerd.

RuntimeVersionDocument
containerdv1.1.0+Link
Dockerv20.0.1+Link
CRI-OAllLink

Setup kubernetes cluster​

Kind is recommended if no Kubernetes cluster is available for testing.

Create kind multi-node cluster configuration file kind-config.yaml, configuration content is as follows:

kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
  - role: control-plane
  - role: worker
  - role: worker

Create a kind multi-node cluster using the configuration file:

kind create cluster --config kind-config.yaml

Switch the context of kubectl to kind cluster:

kubectl config use-context kind-kind

Kind loads Dragonfly image​

Pull Dragonfly latest images:

docker pull dragonflyoss/scheduler:latest
docker pull dragonflyoss/manager:latest
docker pull dragonflyoss/client:latest
docker pull dragonflyoss/dfinit:latest

Kind cluster loads Dragonfly latest images:

kind load docker-image dragonflyoss/scheduler:latest
kind load docker-image dragonflyoss/manager:latest
kind load docker-image dragonflyoss/client:latest
kind load docker-image dragonflyoss/dfinit:latest

Create Dragonfly cluster based on helm charts​

Create helm charts configuration file charts-config.yaml, configuration content is as follows:

manager:
  image:
    repository: dragonflyoss/manager
    tag: latest
  metrics:
    enable: true
  config:
    verbose: true
    pprofPort: 18066

scheduler:
  image:
    repository: dragonflyoss/scheduler
    tag: latest
  metrics:
    enable: true
  config:
    verbose: true
    pprofPort: 18066

seedClient:
  image:
    repository: dragonflyoss/client
    tag: latest
  metrics:
    enable: true
  config:
    verbose: true

client:
  image:
    repository: dragonflyoss/client
    tag: latest
  metrics:
    enable: true
  config:
    verbose: true
  dfinit:
    enable: true
    image:
      repository: dragonflyoss/dfinit
      tag: latest
    config:
      containerRuntime:
        containerd:
          configPath: /etc/containerd/config.toml
          registries:
            - hostNamespace: docker.io
              serverAddr: https://index.docker.io
              capabilities: ['pull', 'resolve']

Create a Dragonfly cluster using the configuration file:

$ helm repo add dragonfly https://dragonflyoss.github.io/helm-charts/
$ helm install --wait --create-namespace --namespace dragonfly-system dragonfly dragonfly/dragonfly -f charts-config.yaml
NAME: dragonfly
LAST DEPLOYED: Tue Apr 16 11:23:00 2024
NAMESPACE: dragonfly-system
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
1. Get the scheduler address by running these commands:
  export SCHEDULER_POD_NAME=$(kubectl get pods --namespace dragonfly-system -l "app=dragonfly,release=dragonfly,component=scheduler" -o jsonpath={.items[0].metadata.name})
  export SCHEDULER_CONTAINER_PORT=$(kubectl get pod --namespace dragonfly-system $SCHEDULER_POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}")
  kubectl --namespace dragonfly-system port-forward $SCHEDULER_POD_NAME 8002:$SCHEDULER_CONTAINER_PORT
  echo "Visit http://127.0.0.1:8002 to use your scheduler"

2. Get the dfdaemon port by running these commands:
  export DFDAEMON_POD_NAME=$(kubectl get pods --namespace dragonfly-system -l "app=dragonfly,release=dragonfly,component=dfdaemon" -o jsonpath={.items[0].metadata.name})
  export DFDAEMON_CONTAINER_PORT=$(kubectl get pod --namespace dragonfly-system $DFDAEMON_POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}")
  You can use $DFDAEMON_CONTAINER_PORT as a proxy port in Node.

3. Configure runtime to use dragonfly:
  https://d7y.io/docs/getting-started/quick-start/kubernetes/

Check that Dragonfly is deployed successfully:

$ kubectl get po -n dragonfly-system
NAME                                 READY   STATUS     RESTARTS      AGE
dragonfly-client-dhqfc               1/1     Running    0             13m
dragonfly-client-h58x6               1/1     Running    0             13m
dragonfly-manager-7b4fd85458-fjtpk   1/1     Running    0             13m
dragonfly-mysql-0                    1/1     Running    0             13m
dragonfly-redis-master-0             1/1     Running    0             13m
dragonfly-redis-replicas-0           1/1     Running    0             13m
dragonfly-redis-replicas-1           1/1     Running    0             11m
dragonfly-redis-replicas-2           1/1     Running    0             10m
dragonfly-scheduler-0                1/1     Running    0             13m
dragonfly-seed-client-0              1/1     Running    2 (76s ago)   13m

Containerd downloads images through Dragonfly​

Pull alpine:3.19 image in kind-worker node:

docker exec -i kind-worker /usr/local/bin/crictl pull alpine:3.19

Verify​

You can execute the following command to check if the alpine:3.19 image is distributed via Dragonfly.

# Find pod name.
export POD_NAME=$(kubectl get pods --namespace dragonfly-system -l "app=dragonfly,release=dragonfly,component=client" -o=jsonpath='{.items[?(@.spec.nodeName=="kind-worker")].metadata.name}' | head -n 1 )

# Find task id.
export TASK_ID=$(kubectl -n dragonfly-system exec ${POD_NAME} -- sh -c "grep -hoP 'library/alpine.*task_id=\"\K[^\"]+' /var/log/dragonfly/dfdaemon/* | head -n 1")

# Check logs.
kubectl -n dragonfly-system exec -it ${POD_NAME} -- sh -c "grep ${TASK_ID} /var/log/dragonfly/dfdaemon/* | grep 'download task succeeded'"

# Download logs.
kubectl -n dragonfly-system exec ${POD_NAME} -- sh -c "grep ${TASK_ID} /var/log/dragonfly/dfdaemon/*" > dfdaemon.log

The expected output is as follows:

{
  2024-04-19T02:44:09.259458Z  INFO
  "download_task":"dragonfly-client/src/grpc/dfdaemon_download.rs:276":: "download task succeeded"
  "host_id": "172.18.0.3-kind-worker",
  "task_id": "a46de92fcb9430049cf9e61e267e1c3c9db1f1aa4a8680a048949b06adb625a5",
  "peer_id": "172.18.0.3-kind-worker-86e48d67-1653-4571-bf01-7e0c9a0a119d"
}

Performance testing​

Containerd pull image back-to-source for the first time through Dragonfly​

Pull alpine:3.19 image in kind-worker node:

time docker exec -i kind-worker /usr/local/bin/crictl pull alpine:3.19

When pull image back-to-source for the first time through Dragonfly, it takes 37.852s to download the alpine:3.19 image.

Containerd pull image hits the cache of remote peer​

Delete the client whose Node is kind-worker to clear the cache of Dragonfly local Peer.

# Find pod name.
export POD_NAME=$(kubectl get pods --namespace dragonfly-system -l "app=dragonfly,release=dragonfly,component=client" -o=jsonpath='{.items[?(@.spec.nodeName=="kind-worker")].metadata.name}' | head -n 1 )

# Delete pod.
kubectl delete pod ${POD_NAME} -n dragonfly-system

Delete alpine:3.19 image in kind-worker node:

docker exec -i kind-worker /usr/local/bin/crictl rmi alpine:3.19

Pull alpine:3.19 image in kind-worker node:

time docker exec -i kind-worker /usr/local/bin/crictl pull alpine:3.19

When pull image hits cache of remote peer, it takes 6.942s to download the alpine:3.19 image.

Containerd pull image hits the cache of local peer​

Delete alpine:3.19 image in kind-worker node:

docker exec -i kind-worker /usr/local/bin/crictl rmi alpine:3.19

Pull alpine:3.19 image in kind-worker node:

time docker exec -i kind-worker /usr/local/bin/crictl pull alpine:3.19

When pull image hits cache of local peer, it takes 5.540s to download the alpine:3.19 image.

Preheat image​

Expose manager's port 8080:

kubectl --namespace dragonfly-system port-forward service/dragonfly-manager 8080:8080

Please create personal access Token before calling Open API, and select job for access scopes, refer to personal-access-tokens.

Use Open API to preheat the image alpine:3.19 to Seed Peer, refer to preheat.

curl --location --request POST 'http://127.0.0.1:8080/oapi/v1/jobs' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer your_personal_access_token' \
--data-raw '{
    "type": "preheat",
    "args": {
        "type": "image",
        "url": "https://index.docker.io/v2/library/alpine/manifests/3.19",
        "filteredQueryParams": "Expires&Signature",
        "username": "your_registry_username",
        "password": "your_registry_password"
    }
}'

The command-line log returns the preheat job id:

{
  "id": 1,
  "created_at": "2024-04-18T08:51:55Z",
  "updated_at": "2024-04-18T08:51:55Z",
  "task_id": "group_2717f455-ff0a-435f-a3a7-672828d15a2a",
  "type": "preheat",
  "state": "SUCCESS",
  "args": {
    "filteredQueryParams": "Expires&Signature",
    "headers": null,
    "password": "",
    "pieceLength": 4194304,
    "platform": "",
    "tag": "",
    "type": "image",
    "url": "https://index.docker.io/v2/library/alpine/manifests/3.19",
    "username": ""
  },
  "scheduler_clusters": [
    {
      "id": 1,
      "created_at": "2024-04-18T08:29:15Z",
      "updated_at": "2024-04-18T08:29:15Z",
      "name": "cluster-1"
    }
  ]
}

Polling the preheating status with job id:

curl --request GET 'http://127.0.0.1:8080/oapi/v1/jobs/1' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer your_personal_access_token'

If the status is SUCCESS, the preheating is successful:

{
  "id": 1,
  "created_at": "2024-04-18T08:51:55Z",
  "updated_at": "2024-04-18T08:51:55Z",
  "task_id": "group_2717f455-ff0a-435f-a3a7-672828d15a2a",
  "type": "preheat",
  "state": "PENDING",
  "args": {
    "filteredQueryParams": "Expires&Signature",
    "headers": null,
    "password": "",
    "pieceLength": 4194304,
    "platform": "",
    "tag": "",
    "type": "image",
    "url": "https://index.docker.io/v2/library/alpine/manifests/3.19",
    "username": ""
  },
  "scheduler_clusters": [
    {
      "id": 1,
      "created_at": "2024-04-18T08:29:15Z",
      "updated_at": "2024-04-18T08:29:15Z",
      "name": "cluster-1"
    }
  ]
}

Pull alpine:3.19 image in kind-worker node:

time docker exec -i kind-worker /usr/local/bin/crictl pull alpine:3.19

When pull image hits preheat cache, it takes 2.952s to download the alpine:3.19 image.