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RUN 1 - Running on Kubernetes

Prerequisites

In order to run this tutorial you will need to run a kubernetes cluster locally: Below are some possible options you could use:

Install and startup your desired solution for running a Kubernetes cluster locally.

Kubernetes (K8s) is an open-source system for automating deployment, scaling and management of containerised applications.

Deploy IPF Tutorial on Kubernetes

For running the IPF tutorial as a clustered application on Kubernetes, we will do the following:

  1. Step 1 - Create a Namespace

  2. Step 2 - Create Service Account

  3. Step 3 - Create Configmap

  4. Step 4 - Create imagePullSecret

  5. Step 5 - Create MongoDB

  6. Step 6 - Create Developer App

  7. Step 7 - Create a Deployment

Requirements

Step 1 - Create a Namespace

kubectl create namespace ipf-tutorial

Step 2 - Create Service Account

Create a 'serviceAccount.yaml' file and copy the following admin service account manifest.

---
kind: Role
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: ipf-tutorial
  namespace: ipf-tutorial
rules:
  - apiGroups: [""]
    resources: ["pods"]
    verbs: ["get", "watch", "list"]
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: ipf-tutorial
  namespace: ipf-tutorial
---
kind: RoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: ipf-tutorial
  namespace: ipf-tutorial
subjects:
  - kind: ServiceAccount
    name: ipf-tutorial
    namespace: ipf-tutorial
roleRef:
  kind: Role
  name: ipf-tutorial
  apiGroup: rbac.authorization.k8s.io

The 'serviceAccount.yaml' creates a ipf-tutorial role, 'ipf-tutorial' ServiceAccount and binds the 'Role' to the service account.

The 'ipf-tutorial' role has all the permissions to query the API for IPF pods running within the namespace.

Now create the service account using kubectl.

kubectl apply -f serviceAccount.yaml

Step 3 - Create Configmap

Create a Configmap file named 'configmap.yaml' and copy the following config manifest.

apiVersion: v1
kind: ConfigMap
metadata:
  name: ipf-tutorial-service-cm
  namespace: ipf-tutorial
data:
  application.conf: |
    akka {
      loglevel = "INFO"
      cluster {
        # undefine seed nodes to allow for Kubernetes to describe cluster topography
        seed-nodes = []
        sharding {
          distributed-data.majority-min-cap = 2
        }
      }
      # Use Kubernetes API to discover the cluster
      discovery {
        kubernetes-api {
          pod-label-selector = "app=%s"
        }
      }
      actor.provider = cluster
    }
    akka.remote.artery.canonical.hostname = ${POD_IP}
    akka.management {
      # available from Akka management >= 1.0.0
      health-checks {
        readiness-path  = "health/ready"
        liveness-path   = "health/alive"
      }
      # use the Kubernetes API to create the cluster
      cluster.bootstrap {
        contact-point-discovery {
          service-name              = "ipf-tutorial-service"
          discovery-method          = kubernetes-api
          required-contact-point-nr = 2
        }
      }
    }
    management.endpoints.web.exposure.include = "*"
    flow-restart-settings {
      min-backoff = 1s
      max-backoff = 5s
      random-factor = 0.25
      max-restarts = 5000
      max-restarts-within = 3h
    }
    ipf.behaviour.retries.initial-timeout = 3s
    ipf.behaviour.config.action-recovery-delay = 3s
    #Change the Mongo URL for your specific setup
    ipf {
      mongodb.url = "mongodb://ipf-mongo:27017/ipf"
      processing-data.egress {
        enabled = true
        transport = http
        http {
          client {
            host = "ipf-developer-service"
            port = 8081
            endpoint-url = "/ipf-processing-data"
          }
        }
      }
    }
  logback.xml: |
    <?xml version="1.0" encoding="UTF-8"?>
    <configuration>
      <appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
        <target>System.out</target>
        <encoder>
          <pattern>[%date{ISO8601}] [%level] [%logger] [%marker] [%thread] - %msg MDC: {%mdc}%n</pattern>
        </encoder>
      </appender>
      <appender name="ASYNC" class="ch.qos.logback.classic.AsyncAppender">
        <queueSize>8192</queueSize>
        <neverBlock>true</neverBlock>
        <appender-ref ref="CONSOLE" />
      </appender>
      <logger name="akka" level="WARN" />
      <root level="INFO">
        <appender-ref ref="ASYNC"/>
      </root>
    </configuration>

Now create the config map using kubectl.

kubectl apply -f configmap.yaml

Step 4 - Create imagePullSecret

Substitute docker-server, docker-username and docker-password for appropriate values 
kubectl create secret docker-registry registrysecret --docker-server=**** --docker-username=********* --docker-password=******* --namespace ipf-tutorial

Step 5 - Create MongoDB

Create a statefulset file named 'infrastructure.yaml' and copy the following manifest. This creates MongoDB which is required by the tutorial application

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: mongo
  namespace: ipf-tutorial
spec:
  selector:
    matchLabels:
      role: mongo
  serviceName: "ipf-mongo"
  replicas: 1
  template:
    metadata:
      labels:
        role: mongo
    spec:
      imagePullSecrets:
        - name: "registrysecret"
      terminationGracePeriodSeconds: 10
      containers:
        - name: mongo
          image: mongo:latest
          imagePullPolicy: Always
          ports:
            - containerPort: 27017
        - name: mongo-exporter
          image: bitnami/mongodb-exporter:0.11.2
          imagePullPolicy: IfNotPresent
          ports:
            - name: mongo-exporter
              containerPort: 9216
              protocol: TCP
          env:
            - name: MONGODB_URI
              value: "mongodb://localhost:27017"
---
apiVersion: v1
kind: Service
metadata:
  name: ipf-mongo
  namespace: ipf-tutorial
  labels:
    name: ipf-mongo
    type: mongo
spec:
  ports:
    - port: 27017
      name: mongo
      protocol: TCP
      targetPort: 27017
  selector:
    role: mongo
kubectl apply -f infrastructure.yaml

Step 6 - Create Developer App

Create a deployment file named developerApp.yaml and copy the following manifest. This creates the developer application which is required by the tutorial application to view flow events

${registry_service} - substitute for the location of the docker registry

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ipf-developer-service
  namespace: ipf-tutorial
  annotations:
    prometheus.io/scrape: "true"
    prometheus.io/path: "/"
    prometheus.io/port: "9001"
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ipf-developer-service
      product: ipfv2
  template:
    metadata:
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/path: "/"
        prometheus.io/port: "9001"
      labels:
        app: ipf-developer-service
        product: ipfv2
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            - labelSelector:
                matchExpressions:
                  - key: app
                    operator: In
                    values:
                      - ipf-developer-service
              topologyKey: kubernetes.io/hostname
      securityContext:
        fsGroup: 1000
        runAsUser: 1000
      serviceAccountName: ipf-tutorial
      imagePullSecrets:
        - name: "registrysecret"
      containers:
        - name: ipf-developer-service
          image: $\{registry_service}/ipf-developer-app:latest
          imagePullPolicy: Always
          ports:
            - name: actuator
              containerPort: 8081
          env:
            - name: "POD_NAME"
              valueFrom:
                fieldRef:
                  fieldPath: metadata.name
            - name: "POD_IP"
              valueFrom:
                fieldRef:
                  fieldPath: status.podIP
            - name: "KUBERNETES_NAMESPACE"
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
            - name: "IPF_JAVA_ARGS"
              value: "-Dma.glasnost.orika.writeClassFiles=false -Dma.glasnost.orika.writeSourceFiles=false"
          resources:
            limits:
              memory: "2Gi"
            requests:
              memory: "2Gi"
              cpu: "1000m"
          volumeMounts:
            - mountPath: /ipf-developer-app/conf/logback.xml
              name: config-volume
              subPath: logback.xml
            - mountPath: /ipf-developer-app/conf/application.conf
              name: config-volume
              subPath: application.conf
      volumes:
        - name: config-volume
          configMap:
            name: ipf-developer-service-cm
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: ipf-developer-service-cm
  namespace: ipf-tutorial
data:
  application.conf: |
    flow-restart-settings {
     min-backoff = 1s
     max-backoff = 5s
     random-factor = 0.25
     max-restarts = 5
     max-restarts-within = 10m
    }
    spring.data.mongodb.uri = ${?ipf.mongodb.url}
    actor-system-name = ipf-developer
    ipf.mongodb.url = "mongodb://ipf-mongo:27017/ipf"
    ods.security.oauth.enabled = false
    application.write.url="http://localhost:8080"
    ipf.processing-data.ingress.transport=http
  logback.xml: |
    <?xml version="1.0" encoding="UTF-8"?>
    <configuration>
      <appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
        <target>System.out</target>
        <encoder>
          <pattern>[%date{ISO8601}] [%level] [%logger] [%marker] [%thread] - %msg MDC: {%mdc}%n</pattern>
        </encoder>
      </appender>
      <appender name="ASYNC" class="ch.qos.logback.classic.AsyncAppender">
        <queueSize>8192</queueSize>
        <neverBlock>true</neverBlock>
        <appender-ref ref="CONSOLE" />
      </appender>
      <logger name="akka" level="WARN" />
      <root level="INFO">
        <appender-ref ref="ASYNC"/>
      </root>
    </configuration>
---
apiVersion: v1
kind: Service
metadata:
  name: ipf-developer-service
  namespace: ipf-tutorial
  labels:
    name: ipf-developer-service
spec:
  type: NodePort
  ports:
    - protocol: TCP
      port: 8081
      targetPort: 8081
      nodePort: 30200
      name: ipf-developer-service
  selector:
    app: ipf-developer-service

Step 7 - Create a Deployment

Create a Deployment file named 'deployment.yaml' and copy the following deployment manifest.

${registry_service} - substitute for the location of the docker registry ${tutorial-service-version} - version of tutorial app

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ipf-tutorial-service
  namespace: ipf-tutorial
  annotations:
    prometheus.io/scrape: "true"
    prometheus.io/path: "/"
    prometheus.io/port: "9001"
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ipf-tutorial-service
      product: ipfv2
  template:
    metadata:
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/path: "/"
        prometheus.io/port: "9001"
      labels:
        app: ipf-tutorial-service
        product: ipfv2
    spec:
      #      affinity:
      #        podAntiAffinity:
      #          requiredDuringSchedulingIgnoredDuringExecution:
      #            - labelSelector:
      #                matchExpressions:
      #                  - key: app
      #                    operator: In
      #                    values:
      #                      - ipf-tutorial-service
      #              topologyKey: kubernetes.io/hostname
      securityContext:
        fsGroup: 1000
        runAsUser: 1000
      serviceAccountName: ipf-tutorial
      imagePullSecrets:
        - name: "registrysecret"
      containers:
        - name: ipf-tutorial-service
          image: ${registry_service}/ipf-tutorial-app:${tutorial-service-version}
          imagePullPolicy: Always
          ports:
            - name: actuator
              containerPort: 8080
            - name: akka-artery
              containerPort: 55001
            - name: akka-management
              containerPort: 8558
            - name: akka-metrics
              containerPort: 9001
          livenessProbe:
            failureThreshold: 5
            httpGet:
              path: /health/alive
              port: akka-management
              scheme: HTTP
            initialDelaySeconds: 30
            periodSeconds: 10
            successThreshold: 1
            timeoutSeconds: 1
          readinessProbe:
            failureThreshold: 3
            httpGet:
              path: /health/ready
              port: akka-management
              scheme: HTTP
            initialDelaySeconds: 30
            periodSeconds: 10
            successThreshold: 10
            timeoutSeconds: 1
          env:
            - name: "POD_NAME"
              valueFrom:
                fieldRef:
                  fieldPath: metadata.name
            - name: "POD_IP"
              valueFrom:
                fieldRef:
                  fieldPath: status.podIP
            - name: "KUBERNETES_NAMESPACE"
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
            - name: "IPF_JAVA_ARGS"
              value: "-Dma.glasnost.orika.writeClassFiles=false -Dma.glasnost.orika.writeSourceFiles=false"
          resources:
            limits:
              memory: "2Gi"
            requests:
              memory: "2Gi"
              cpu: "1000m"
          volumeMounts:
            - mountPath: /ipf-tutorial-app/conf/logback.xml
              name: config-volume
              subPath: logback.xml
            - mountPath: /ipf-tutorial-app/conf/application.conf
              name: config-volume
              subPath: application.conf
      volumes:
        - name: config-volume
          configMap:
            name: ipf-tutorial-service-cm
---
apiVersion: v1
kind: Service
metadata:
  name: ipf-tutorial-service
  namespace: ipf-tutorial
  labels:
    name: ipf-tutorial-service
spec:
  type: NodePort
  ports:
    - protocol: TCP
      port: 8080
      targetPort: 8080
      nodePort: 30100
      name: ipf-tutorial-service
  selector:
    app: ipf-tutorial-service
the affinity rules (commented out), if used are a way of specifying where/on which nodes pods should be scheduled. This can be useful for ensuring that pods are scheduled on different nodes for example.

In this IPF Kubernetes deployment we have used the following:

  1. securityContext for IPF pod

  2. Liveness and readiness probe to monitor the health of the IPF pod.

  3. Application and logging configuration files stored as Kubernetes ConfigMap items.

Create the deployment using kubectl.

kubectl apply -f deployment.yaml

Check the deployment status.

kubectl get deployments -n ipf-tutorial

Now, you can get the deployment details using the following command.

kubectl describe deployments --namespace=ipf-tutorial

Send a payment using the following command.

curl -X POST http://<cluster-node-ip-address>:30100/submit | jq

Check the payment in the developer app at the following URL.

http://<cluster-node-ip-address>:30200/explorer.html
Consult the documentation of your chosen cluster option on how to get the node IP address. Some possible options are: 'kubectl get nodes -o wide', 'minikube ip'.