Multi-cluster services discovery & communication

Multi-cluster service communication using Flomesh Service Mesh

Demo Architecture

For demonstration purposes we will be creating 4 Kubernetes clusters and high-level architecture will look something like the below:

As a convention and for this demo we will be creating a separate stand-alone cluster to serve as a control plane cluster, but that isn’t strictly required as a separate cluster and it could be one of any existing cluster.

Pre-requisites

kubectx: for switching between multiple kubeconfig contexts (clusters)
k3d: for creating multiple k3s clusters locally using containers
FSM CLI: for deploying FSM
docker: required to run k3d
Have fsm CLI available for managing the service mesh.
FSM version >= v1.2.0.

Demo clusters & environment setup

In this demo, we will be using k3d a lightweight wrapper to run k3s (Rancher Lab’s minimal Kubernetes distribution) in docker, to create 4 separate clusters named control-plane, cluster-1, cluster-2, and cluster-3 respectively.

We will be using the HOST machine IP address and separate ports during the installation, for us to easily access the individual clusters. My demo host machine IP address is 192.168.1.110 (it might be different for your machine).

cluster	cluster ip	api-server port	LB external-port	description
control-plane	HOST_IP(192.168.1.110)	6444	N/A	control-plane cluster
cluster-1	HOST_IP(192.168.1.110)	6445	81	application-cluster
cluster-2	HOST_IP(192.168.1.110)	6446	82	Application Cluster
cluster-3	HOST_IP(192.168.1.110)	6447	83	Application Cluster

Network

Creates a docker bridge type network named multi-clusters, which run all containers.

docker network create multi-clusters

Find your machine host IP address, mine is 192.168.1.110, and export that as an environment variable to be used later.

export HOST_IP=192.168.1.110

Cluster creation

We are going to use k3d to create 4 clusters.

API_PORT=6444 #6444 6445 6446 6447
PORT=80 #81 82 83
for CLUSTER_NAME in control-plane cluster-1 cluster-2 cluster-3
do
  k3d cluster create ${CLUSTER_NAME} \
    --image docker.io/rancher/k3s:v1.23.8-k3s2 \
    --api-port "${HOST_IP}:${API_PORT}" \
    --port "${PORT}:80@server:0" \
    --servers-memory 4g \
    --k3s-arg "--disable=traefik@server:0" \
    --network multi-clusters \
    --timeout 120s \
    --wait
    ((API_PORT=API_PORT+1))
    ((PORT=PORT+1))
done

Install FSM

Install the service mesh FSM to the clusters cluster-1, cluster-2, and cluster-3. The control plane does not handle application traffic and does not need to be installed.

export FSM_NAMESPACE=fsm-system
export FSM_MESH_NAME=fsm
for CONFIG in kubeconfig_cp kubeconfig_c1 kubeconfig_c2 kubeconfig_c3; do
  DNS_SVC_IP="$(kubectl --kubeconfig ${!CONFIG} get svc -n kube-system -l k8s-app=kube-dns -o jsonpath='{.items[0].spec.clusterIP}')"
  CLUSTER_NAME=$(if [ "${CONFIG}" == "kubeconfig_c1" ]; then echo "cluster-1"; elif [ "${CONFIG}" == "kubeconfig_c2" ]; then echo "cluster-2"; else echo "cluster-3"; fi)
  desc "Installing fsm service mesh in cluster ${CLUSTER_NAME}"
  KUBECONFIG=${!CONFIG} $fsm_binary install \
    --mesh-name "$FSM_MESH_NAME" \
    --fsm-namespace "$FSM_NAMESPACE" \
    --set=fsm.certificateProvider.kind=tresor \
    --set=fsm.image.pullPolicy=Always \
    --set=fsm.sidecarLogLevel=error \
    --set=fsm.controllerLogLevel=warn \
    --set=fsm.fsmIngress.enabled=true \
    --timeout=900s \
    --set=fsm.localDNSProxy.enable=true \
    --set=fsm.localDNSProxy.primaryUpstreamDNSServerIPAddr="${DNS_SVC_IP}"

  kubectl --kubeconfig ${!CONFIG} wait --for=condition=ready pod --all -n $FSM_NAMESPACE --timeout=120s
done

We have our clusters ready, now we need to federate them together, but before we do that, let’s first understand the mechanics on how FSM is configured.

Federate clusters

We will enroll clusters cluster-1, cluster-2, and cluster-3 into the management of control-plane cluster.

export HOST_IP=192.168.1.110
kubectx k3d-control-plane
sleep 1
PORT=81
for CLUSTER_NAME in cluster-1 cluster-2 cluster-3
do
  cat <<EOF
apiVersion: flomesh.io/v1alpha1
kind: Cluster
metadata:
  name: ${CLUSTER_NAME}
spec:
  gatewayHost: ${HOST_IP}
  gatewayPort: ${PORT}
  fsmMeshConfigName: ${FSM_NAMESPACE}
  kubeconfig: |+
`k3d kubeconfig get ${CLUSTER_NAME} | sed 's|^|    |g' | sed "s|0.0.0.0|$HOST_IP|g"`
EOF
((PORT=PORT+1))
done

Deploy Demo application

Deploying mesh-managed applications

Deploy the httpbin application under the httpbin namespace of clusters cluster-1 and cluster-3 (which are managed by the mesh and will inject sidecar). Here the httpbin application is implemented by Pipy and will return the current cluster name.

export NAMESPACE=httpbin
for CLUSTER_NAME in cluster-1 cluster-3
do
  kubectx k3d-${CLUSTER_NAME}
  kubectl create namespace ${NAMESPACE}
  fsm namespace add ${NAMESPACE}
  kubectl apply -n ${NAMESPACE} -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: httpbin
  labels:
    app: pipy
spec:
  replicas: 1
  selector:
    matchLabels:
      app: pipy
  template:
    metadata:
      labels:
        app: pipy
    spec:
      containers:
        - name: pipy
          image: flomesh/pipy:latest
          ports:
            - containerPort: 8080
          command:
            - pipy
            - -e
            - |
              pipy()
              .listen(8080)
              .serveHTTP(new Message('Hi, I am from ${CLUSTER_NAME} and controlled by mesh!\n'))
---
apiVersion: v1
kind: Service
metadata:
  name: httpbin
spec:
  ports:
    - port: 8080
      targetPort: 8080
      protocol: TCP
  selector:
    app: pipy
---
apiVersion: v1
kind: Service
metadata:
  name: httpbin-${CLUSTER_NAME}
spec:
  ports:
    - port: 8080
      targetPort: 8080
      protocol: TCP
  selector:
    app: pipy
EOF

  sleep 3
  kubectl wait --for=condition=ready pod -n ${NAMESPACE} --all --timeout=60s
done

Deploy the curl application under the namespace curl in cluster cluster-2, which is managed by the mesh.

export NAMESPACE=curl
kubectx k3d-cluster-2
kubectl create namespace ${NAMESPACE}
fsm namespace add ${NAMESPACE}
kubectl apply -n ${NAMESPACE} -f - <<EOF
apiVersion: v1
kind: ServiceAccount
metadata:
  name: curl
---
apiVersion: v1
kind: Service
metadata:
  name: curl
  labels:
    app: curl
    service: curl
spec:
  ports:
    - name: http
      port: 80
  selector:
    app: curl
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: curl
spec:
  replicas: 1
  selector:
    matchLabels:
      app: curl
  template:
    metadata:
      labels:
        app: curl
    spec:
      serviceAccountName: curl
      containers:
      - image: curlimages/curl
        imagePullPolicy: IfNotPresent
        name: curl
        command: ["sleep", "365d"]
EOF

sleep 3
kubectl wait --for=condition=ready pod -n ${NAMESPACE} --all --timeout=60s

Export Service

Let’s export services in cluster-1 and cluster-3

export NAMESPACE_MESH=httpbin
for CLUSTER_NAME in cluster-1 cluster-3
do
  kubectx k3d-${CLUSTER_NAME}
  kubectl apply -f - <<EOF
apiVersion: flomesh.io/v1alpha1
kind: ServiceExport
metadata:
  namespace: ${NAMESPACE_MESH}
  name: httpbin
spec:
  serviceAccountName: "*"
  rules:
    - portNumber: 8080
      path: "/${CLUSTER_NAME}/httpbin-mesh"
      pathType: Prefix
---
apiVersion: flomesh.io/v1alpha1
kind: ServiceExport
metadata:
  namespace: ${NAMESPACE_MESH}
  name: httpbin-${CLUSTER_NAME}
spec:
  serviceAccountName: "*"
  rules:
    - portNumber: 8080
      path: "/${CLUSTER_NAME}/httpbin-mesh-${CLUSTER_NAME}"
      pathType: Prefix
EOF
sleep 1
done

After exporting the services, FSM will automatically create Ingress rules for them, and with the rules, you can access these services through Ingress.


for CLUSTER_NAME_INDEX in 1 3
do
  CLUSTER_NAME=cluster-${CLUSTER_NAME_INDEX}
  ((PORT=80+CLUSTER_NAME_INDEX))
  kubectx k3d-${CLUSTER_NAME}
  echo "Getting service exported in cluster ${CLUSTER_NAME}"
  echo '-----------------------------------'
  kubectl get serviceexports.flomesh.io -A
  echo '-----------------------------------'
  curl -s "http://${HOST_IP}:${PORT}/${CLUSTER_NAME}/httpbin-mesh"
  curl -s "http://${HOST_IP}:${PORT}/${CLUSTER_NAME}/httpbin-mesh-${CLUSTER_NAME}"
  echo '-----------------------------------'
done

To view one of the ServiceExports resources.

kubectl get serviceexports httpbin -n httpbin -o jsonpath='{.spec}' | jq

{
  "loadBalancer": "RoundRobinLoadBalancer",
  "rules": [
    {
      "path": "/cluster-3/httpbin-mesh",
      "pathType": "Prefix",
      "portNumber": 8080
    }
  ],
  "serviceAccountName": "*"
}

The exported services can be imported into other managed clusters. For example, if we look at the cluster cluster-2, we can have multiple services imported.

kubectx k3d-cluster-2
kubectl get serviceimports -A

NAMESPACE   NAME                AGE
httpbin     httpbin-cluster-1   13m
httpbin     httpbin-cluster-3   13m
httpbin     httpbin             13m

Testing

Staying in the cluster-2 cluster (kubectx k3d-cluster-2), we test if we can access these imported services from the curl application in the mesh.

Get the pod of the curl application, from which we will later launch requests to simulate service access.

curl_client="$(kubectl get pod -n curl -l app=curl -o jsonpath='{.items[0].metadata.name}')"

At this point you will find that it is not accessible.

kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/

command terminated with exit code 7

Note that this is normal, by default no other cluster instance will be used to respond to requests, which means no calls to other clusters will be made by default. So how to access it, then we need to be clear about the global traffic policy GlobalTrafficPolicy.

Global Traffic Policy

Note that all global traffic policies are set on the user’s side, so this demo is about setting global traffic policies on the cluster cluster-2 side. So before you start, switch to cluster cluster-2: kubectx k3d-cluster-2.

The global traffic policy is set via CRD GlobalTrafficPolicy.

type GlobalTrafficPolicy struct {  
   metav1.TypeMeta   `json:",inline"`  
   metav1.ObjectMeta `json:"metadata,omitempty"`  
  
   Spec   GlobalTrafficPolicySpec   `json:"spec,omitempty"`  
   Status GlobalTrafficPolicyStatus `json:"status,omitempty"`  
}
type GlobalTrafficPolicySpec struct {  
   LbType LoadBalancerType `json:"lbType"`  
   LoadBalanceTarget []TrafficTarget `json:"targets"`  
}

Global load balancing types .spec.lbType There are three types.

Locality: uses only the services of this cluster, and is also the default type. This is why accessing the httpbin application fails when we don’t provide any global policy, because there is no such service in cluster cluster-2.
FailOver: proxies to other clusters only when access to this cluster fails, which is often referred to as failover, similar to primary backup.
ActiveActive: Proxy to other clusters under normal conditions, similar to multi-live.

The FailOver and ActiveActive policies are used with the targets field to specify the id of the standby cluster, which is the cluster that can be routed to in case of failure or load balancing. ** For example, if you look at the import service httpbin/httpbin in cluster cluster-2, you can see that it has two endpoints for the outer cluster, note that endpoints here is a different concept than the native endpoints.v1 and will contain more information. In addition, there is the cluster id clusterKey.

kubectl get serviceimports httpbin -n httpbin -o jsonpath='{.spec}' | jq

{
  "ports": [
    {
      "endpoints": [
        {
          "clusterKey": "default/default/default/cluster-1",
          "target": {
            "host": "192.168.1.110",
            "ip": "192.168.1.110",
            "path": "/cluster-1/httpbin-mesh",
            "port": 81
          }
        },
        {
          "clusterKey": "default/default/default/cluster-3",
          "target": {
            "host": "192.168.1.110",
            "ip": "192.168.1.110",
            "path": "/cluster-3/httpbin-mesh",
            "port": 83
          }
        }
      ],
      "port": 8080,
      "protocol": "TCP"
    }
  ],
  "serviceAccountName": "*",
  "type": "ClusterSetIP"
}

Routing Type - `Locality`

The default routing type is Locality, and as tested above, traffic cannot be dispatched to other clusters.

kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/
command terminated with exit code 7

Routing Type - `FailOver`

Since setting a global traffic policy for causes access failure, we start by enabling FailOver mode. Note that the global policy traffic, to be consistent with the target service name and namespace. For example, if we want to access http://httpbin.httpbin:8080/, we need to create GlobalTrafficPolicy resource named httpbin under the namespace httpbin.

kubectl apply -n httpbin -f  - <<EOF
apiVersion: flomesh.io/v1alpha1
kind: GlobalTrafficPolicy
metadata:
  name: httpbin
spec:
  lbType: FailOver
  targets:
    - clusterKey: default/default/default/cluster-1
    - clusterKey: default/default/default/cluster-3
EOF

After setting the policy, let’s try it again by requesting.

kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/
Hi, I am from cluster-1!

The request is successful and the request is proxied to the service in cluster cluster-1. Another request is made, and it is proxied to cluster cluster-3, as expected for load balancing.

kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/
Hi, I am from cluster-3!

What will happen if we deploy the application httpbin in the namespace httpbin of the cluster cluster-2?

export NAMESPACE=httpbin
export CLUSTER_NAME=cluster-2

kubectx k3d-${CLUSTER_NAME}
kubectl create namespace ${NAMESPACE}
fsm namespace add ${NAMESPACE}
kubectl apply -n ${NAMESPACE} -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: httpbin
  labels:
    app: pipy
spec:
  replicas: 1
  selector:
    matchLabels:
      app: pipy
  template:
    metadata:
      labels:
        app: pipy
    spec:
      containers:
        - name: pipy
          image: flomesh/pipy:latest
          ports:
            - containerPort: 8080
          command:
            - pipy
            - -e
            - |
              pipy()
              .listen(8080)
              .serveHTTP(new Message('Hi, I am from ${CLUSTER_NAME}!\n'))
---
apiVersion: v1
kind: Service
metadata:
  name: httpbin
spec:
  ports:
    - port: 8080
      targetPort: 8080
      protocol: TCP
  selector:
    app: pipy
---
apiVersion: v1
kind: Service
metadata:
  name: httpbin-${CLUSTER_NAME}
spec:
  ports:
    - port: 8080
      targetPort: 8080
      protocol: TCP
  selector:
    app: pipy
EOF

sleep 3
kubectl wait --for=condition=ready pod -n ${NAMESPACE} --all --timeout=60s

After the application is running normally, this time we send the request to test again. From the results, it looks like the request is processed in the current cluster.

kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/ 
Hi, I am from cluster-2!

Even if the request is repeated multiple times, it will always return Hi, I am from cluster-2!, which indicates that the services of same cluster are used in preference to the services imported from other clusters.

In some cases, we also want other clusters to participate in the service as well, because the resources of other clusters are wasted if only the services of this cluster are used. This is where the ActiveActive routing type comes into play.

Routing Type - `ActiveActive`

Moving on from the status above, let’s test the ActiveActive type by modifying the policy created earlier and updating it to ActiveActive: `ActiveActive

kubectl apply -n httpbin -f  - <<EOF
apiVersion: flomesh.io/v1alpha1
kind: GlobalTrafficPolicy
metadata:
  name: httpbin
spec:
  lbType: ActiveActive
  targets:
    - clusterKey: default/default/default/cluster-1
    - clusterKey: default/default/default/cluster-3
EOF

Multiple requests will show that httpbin from all three clusters will participate in the service. This indicates that the load is being proxied to multiple clusters in a balanced manner.

kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/
Hi, I am from cluster-1 and controlled by mesh!
kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/
Hi, I am from cluster-2!
kubectl exec "${curl_client}" -n curl -c curl -- curl -s http://httpbin.httpbin:8080/
Hi, I am from cluster-3 and controlled by mesh!

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Last modified June 18, 2024: fix workflow issue (c83135d)