In Part 1 we covered the motives, goals, and architecture of Flomesh Service Mesh and in this blog post we are going to demonstrate how to use FSM and lightweight SMI-compatible Service Mesh osm-edge to achieve multi-cluster service discovery & communication.
Kubernetes: Multi-cluster communication with Flomesh Service Mesh
Ali Naqvi γ» Nov 28 '22
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.
Demo Pre-requisites
-
kubectx
: for switching between multiplekubeconfig contexts
(clusters) -
k3d
: for creating multiplek3s
clusters locally using containers -
helm
: for deployingFSM
-
docker
: required to runk3d
Demo clusters & environment setup
In this demo, we will be using k3d a lightweight wrapper to run k3s (Rancher Labβs 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}:${API_PORT}" \
--port "${API_PORT}:6443@server:0" \
--port "${PORT}:80@server:0" \
--servers-memory 4g \
--k3s-arg "--disable=traefik@server:0" \
--network multi-clusters \
--timeout 120s \
--wait
((API_PORT=API_PORT+1))
((PORT=PORT+1))
done
Install FSM
Install FSM to newly created 4 clusters.
helm repo update
export FSM_NAMESPACE=flomesh
export FSM_VERSION=0.2.0-alpha.9
for CLUSTER_NAME in control-plane cluster-1 cluster-2 cluster-3
do
kubectx k3d-${CLUSTER_NAME}
sleep 1
helm install --namespace ${FSM_NAMESPACE} --create-namespace --version=${FSM_VERSION} --set fsm.logLevel=5 fsm fsm/fsm
sleep 1
kubectl wait --for=condition=ready pod --all -n $FSM_NAMESPACE
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.
In Part 1 of this series, we stated that FSM provides a set of Kubernetes custom resources (CRD) for cluster connectors, and makes use of KEP-1645 ServiceExport
and ServiceImport
API for exporting and importing services. So let's take a quick look at them
Cluster
CRD
When registering a cluster, we provide the following information.
- The address (e.g.
gatewayHost: cluster-A.host
) and port (e.g.gatewayPort: 80
) of the cluster -
kubeconfig
to access the cluster, containing the api-server address and information such as the certificate and secret key
apiVersion: flomesh.io/v1alpha1
kind: Cluster
metadata:
name: cluster-A
spec:
gatewayHost: cluster-A.host
gatewayPort: 80
kubeconfig: |+
---
apiVersion: v1
clusters:
- cluster:
certificate-authority-data:
server: https://cluster-A.host:6443
name: cluster-A
contexts:
- context:
cluster: cluster-A
user: admin@cluster-A
name: cluster-A
current-context: cluster-A
kind: Config
preferences: {}
users:
- name: admin@cluster-A
user:
client-certificate-data:
client-key-data:
ServiceExport
and ServiceImport
CRD
For cross-cluster service registration, FSM provides the ServiceExport
and ServiceImport
CRDs from KEP-1645: Multi-Cluster Services API for ServiceExports.flomesh.io
and ServiceImports.flomesh.io
. The former is used to register services with the control plane and declare that the application can provide services across clusters, while the latter is used to reference services from other clusters.
For clusters cluster-A
and cluster-B
that join the cluster federation, a Service
named foo
exists under the namespace bar
of cluster cluster-A
and a ServiceExport
foo
of the same name is created under the same namespace. A ServiceImport
resource with the same name is automatically created under the namespace bar
of cluster cluster-B
(if it does not exist, it is automatically created).
// in cluster-A
apiVersion: v1
kind: Service
metadata:
name: foo
namespace: bar
spec:
ports:
- port: 80
selector:
app: foo
---
apiVersion: flomesh.io/v1alpha1
kind: ServiceExport
metadata:
name: foo
namespace: bar
---
// in cluster-B
apiVersion: flomesh.io/v1alpha1
kind: ServiceImport
metadata:
name: foo
namespace: bar
The YAML snippet above shows how to register the foo
service to the control plane of a multi-cluster. In the following, we will walk through a slightly more complex scenario of cross-cluster service registration and traffic scheduling.
Okay that was a quick introduction to the CRDs, so let's continue with our demo.
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}
kubeconfig: |+
`k3d kubeconfig get ${CLUSTER_NAME} | sed 's|^| |g' | sed "s|0.0.0.0|$HOST_IP|g"`
EOF
((PORT=PORT+1))
done
Install osm-edge Service Mesh
Download osm CLI
Install the service mesh osm-edge 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 OSM_NAMESPACE=osm-system
export OSM_MESH_NAME=osm
for CLUSTER_NAME in cluster-1 cluster-2 cluster-3
do
kubectx k3d-${CLUSTER_NAME}
DNS_SVC_IP="$(kubectl get svc -n kube-system -l k8s-app=kube-dns -o jsonpath='{.items[0].spec.clusterIP}')"
osm install \
--mesh-name "$OSM_MESH_NAME" \
--osm-namespace "$OSM_NAMESPACE" \
--set=osm.certificateProvider.kind=tresor \
--set=osm.image.pullPolicy=Always \
--set=osm.sidecarLogLevel=error \
--set=osm.controllerLogLevel=warn \
--timeout=900s \
--set=osm.localDNSProxy.enable=true \
--set=osm.localDNSProxy.primaryUpstreamDNSServerIPAddr="${DNS_SVC_IP}"
done
Deploy Demo application
Deploying mesh-managed applications
Deploy the httpbin
application under the httpbin
namespace of clusters cluster-1
and cluster-3
(which are managed by the mesh and will inject sidecar). Here the httpbin
application is implemented by Pipy and will return the current cluster name.
export NAMESPACE=httpbin
for CLUSTER_NAME in cluster-1 cluster-3
do
kubectx k3d-${CLUSTER_NAME}
kubectl create namespace ${NAMESPACE}
osm 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}
osm 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 thehttpbin
application fails when we don't provide any global policy, because there is no such service in clustercluster-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}
osm 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
:
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!
Summary
This concludes our series of introductions to Flomesh Service Mesh, an open-source solution from Flomesh to help you achieve Kubernetes multi-cluster services discovery and communication.
In part 1 of this series, we briefly touched on the use cases for multi-cluster requirements and talked about the motivation and goals of FSM and its architecture. In this part of the series we demonstrated how to implement cross-cluster traffic scheduling and load balancing of services, and try three different global traffic policies: local cluster scheduling only, failover, and global load balancing.
If you are interested in learning more about SMI-compatible Service Mesh, FSM, and Programmable Proxy, visit our website Flomesh.io for more detailed documentation, tutorials, and use cases.
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