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Infrastructure Prerequisites

In order to install Elasticsearch instance Elastic Cloud on Kubernetes (ECK) can be used. Use ECK quickstart to deploy CRDs and create Elasticsearch instances: Elasticsearch instance: 
  apiVersion: elasticsearch.k8s.elastic.co/v1
  kind: Elasticsearch
  metadata:
    name: elasticsearch-flowx
    namespace: elastic-system
  spec:
    version: 7.9.3
    updateStrategy:
      changeBudget:
        maxSurge: 3
        maxUnavailable: 1
    nodeSets:
    # 3 dedicated master nodes
    - name: master
      count: 3
      config:
        node.master: true
        node.data: false
        node.ingest: false
        node.remote_cluster_client: false
        # this allows ES to run on nodes even if their vm.max_map_count has not been increased, at a performance cost
        # node.store.allow_mmap: false
      podTemplate:
        spec:
          initContainers:
          - name: sysctl
            securityContext:
              privileged: true
            command: ['sh', '-c', 'sysctl -w vm.max_map_count=262144']
          - name: install-plugins
            command:
            - sh
            - -c
            - |
              bin/elasticsearch-plugin install --batch repository-gcs
          containers:
          - name: elasticsearch
            resources:
              limits:
                memory: 6Gi
                cpu: 2
              requests:
                memory: 2Gi
                cpu: 1
            env:
            - name: ES_JAVA_OPTS
              value: "-Xms2g -Xmx2g"
            - name: READINESS_PROBE_TIMEOUT
              value: "10"
            readinessProbe:
              exec:
                command:
                - bash
                - -c
                - /mnt/elastic-internal/scripts/readiness-probe-script.sh
              failureThreshold: 3
              initialDelaySeconds: 10
              periodSeconds: 12
              successThreshold: 1
              timeoutSeconds: 12
          affinity:
            podAntiAffinity:
              preferredDuringSchedulingIgnoredDuringExecution:
              - weight: 100
                podAffinityTerm:
                  labelSelector:
                    matchLabels:
                      elasticsearch.k8s.elastic.co/cluster-name: elasticsearch-flowx
                  topologyKey: kubernetes.io/hostname
      # request 2Gi of persistent data storage for pods in this topology element
      volumeClaimTemplates:
      - metadata:
          name: elasticsearch-data
        spec:
          accessModes:
          - ReadWriteOnce
          resources:
            requests:
              storage: 5Gi
          storageClassName: standard
    # 3 ingest-data nodes
    - name: ingest-data
      count: 3
      config:
        node.master: false
        node.data: true
        node.ingest: true
        # this allows ES to run on nodes even if their vm.max_map_count has not been increased, at a performance cost
        # node.store.allow_mmap: false
      podTemplate:
        spec:
          initContainers:
          - name: sysctl
            securityContext:
              privileged: true
            command: ['sh', '-c', 'sysctl -w vm.max_map_count=262144']
          containers:
          - name: elasticsearch
            resources:
              limits:
                memory: 8Gi
                cpu: 2
              requests:
                memory: 4Gi
                cpu: 1
            env:
            - name: ES_JAVA_OPTS
              value: "-Xms2g -Xmx2g"
          affinity:
            podAntiAffinity:
              preferredDuringSchedulingIgnoredDuringExecution:
              - weight: 100
                podAffinityTerm:
                  labelSelector:
                    matchLabels:
                      elasticsearch.k8s.elastic.co/cluster-name: elasticsearch-flowx
                  topologyKey: kubernetes.io/hostname
         # nodeSelector:
         #   diskType: ssd
         #   environment: production
      # request 2Gi of persistent data storage for pods in this topology element
      volumeClaimTemplates:
      - metadata:
          name: elasticsearch-data
        spec:
          accessModes:
          - ReadWriteOnce
          resources:
            requests:
              storage: 20Gi
          storageClassName: standard
(Optional) Kibana instance: 
  apiVersion: kibana.k8s.elastic.co/v1
  kind: Kibana
  metadata:
    name: kibana-flowx
    namespace: elastic-system
  spec:
    version: 7.9.3
    count: 1
    elasticsearchRef:
      name: elasticsearch-flowx
      namespace: elastic-system
    config:
       elasticsearch.requestHeadersWhitelist:
       - authorization
    podTemplate:
      spec:
        containers:
        - name: kibana
          resources:
            requests:
              memory: 1Gi
              cpu: 0.5
            limits:
              memory: 3Gi
              cpu: 2
The index used by customer management plugin should be created.

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Postgres database

This plugin can work without this database, it will not store the audit data. Basic Postgres configuration 
  crmdb:
    existingSecret: {{secretName}}
    metrics:
      enabled: true
      service:
        annotations:
          prometheus.io/port: {{phrometeus port}}
          prometheus.io/scrape: "true"
        type: ClusterIP
      serviceMonitor:
        additionalLabels:
          release: prometheus-operator
        enabled: true
        interval: 30s
        scrapeTimeout: 10s
    persistence:
      enabled: true
      size: 4Gi
    postgresqlDatabase: {{postgres databaseName}}
    postgresqlUsername: {{postgres user}}
    resources:
      limits:
        cpu: 500m
        memory: 512Mi
      requests:
        cpu: 200m
        memory: 256Mi
    service:
      annotations:
        fabric8.io/expose: "false"

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Configuration

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Authorization configuration

The following variables need to be set in order to connect to the identity management platform:
  • SECURITY_OAUTH2_BASE_SERVER_URL
  • SECURITY_OAUTH2_CLIENT_CLIENT_ID
  • SECURITY_OAUTH2_REALM

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Datasource configuration

To store audit for searches this plugins use a postgres database. The following configuration details need to be added using environment variables:
  • SPRING_DATASOURCE_URL
  • SPRING_DATASOURCE_USERNAME
  • SPRING_DATASOURCE_PASSWORD
You will need to make sure that the user, password, connection link and db name are configured correctly, otherwise you will receive errors at start time. If you are going to use a database to store the audit, you can use the built-in script to maintain the database schema.

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Elastic search configuration

The connection to the Elasticsearch cluster is done over https using the Elasticsearch api. To connect to the it you will need to configure the connection details and index use to store customers. 
elasticsearch:
  ssl: false
  nodes:
    -
      hostname: ${ELASTICSEARCH_HOST}
      port: ${ELASTICSEARCH_PORT}
      scheme: ${ELASTICSEARCH_HTTP_SCHEME}
  user: ${ELASTICSEARCH_USER}
  password: ${ELASTICSEARCH_PASSWORD}
  customer-index: ${ELASTICSEARCH_CUSTOMER_INDEX}
  size: 10

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Kafka configuration

The following Kafka related configurations can be set by using environment variables:
  • SPRING_KAFKA_BOOTSTRAP_SERVERS - address of the Kafka server
  • SPRING_KAFKA_CONSUMER_GROUP_ID - group of consumers
  • KAFKA_CONSUMER_THREADS - the number of Kafka consumer threads
  • KAFKA_AUTH_EXCEPTION_RETRY_INTERVAL - the interval between retries after AuthorizationException is thrown by KafkaConsumer
  • KAFKA_MESSAGE_MAX_BYTES - this is the largest size of the message that can be received by the broker from a producer.
Each action available in the service corresponds to a Kafka event. A separate Kafka topic must be configured for each use-case.
The Engine is listening for messages on topics with names of a certain pattern, make sure to use correct outgoing topic names when configuring the documents plugin.
Needed topics:
  • KAFKA_TOPIC_CUSTOMER_SEARCH_IN
  • KAFKA_TOPIC_CUSTOMER_SEARCH_OUT
In order to match a request made to the customer management plugin, the engine will have to send the process id on a Kafka header.

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Logging

The following environment variables could be set in order to control log levels:
  • LOGGING_LEVEL_ROOT - root Spring Boot microservice logs
  • LOGGING_LEVEL_APP - app level logs