Yes it is possible. Briefly you will need to ensure following:

• Make sure all your applications store event logs in a specific location (filesystem, s3, hdfs etc).
• Deploy the history server in your cluster with access to above event logs location.

Now spark (by default) only read from the filesystem path so I will elaborate this case in details with spark operator:

• Create a PVC with a volume type that supports ReadWriteMany mode. For example NFS volume. The following snippet assumes you have storage class for NFS (nfs-volume) already configured:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: spark-pvc
  namespace: spark-apps
spec:
  accessModes:
    - ReadWriteMany
  volumeMode: Filesystem
  resources:
    requests:
      storage: 5Gi
  storageClassName: nfs-volume

• Make sure all your spark applications have event logging enabled and at the correct path:

  sparkConf:
    "spark.eventLog.enabled": "true"
    "spark.eventLog.dir": "file:/mnt"

• With event logs volume mounted to each application (you can also use operator mutating web hook to centralize it ) pod. An example manifest with mentioned config is show below:

---
apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: spark-java-pi
  namespace: spark-apps

spec:
  type: Java
  mode: cluster

  image: gcr.io/spark-operator/spark:v2.4.4
  mainClass: org.apache.spark.examples.SparkPi
  mainApplicationFile: "local:///opt/spark/examples/jars/spark-examples_2.11-2.4.4.jar"

  imagePullPolicy: Always
  sparkVersion: 2.4.4
  sparkConf:
    "spark.eventLog.enabled": "true"
    "spark.eventLog.dir": "file:/mnt"
  restartPolicy:
    type: Never
  volumes:
    - name: spark-data
      persistentVolumeClaim:
        claimName: spark-pvc
  driver:
    cores: 1
    coreLimit: "1200m"
    memory: "512m"
    labels:
      version: 2.4.4
    serviceAccount: spark
    volumeMounts:
      - name: spark-data
        mountPath: /mnt
  executor:
    cores: 1
    instances: 1
    memory: "512m"
    labels:
      version: 2.4.4
    volumeMounts:
      - name: spark-data
        mountPath: /mnt

• Install spark history server mounting the shared volume. Then you will have access events in history server UI:

apiVersion: apps/v1beta1
kind: Deployment

metadata:
  name: spark-history-server
  namespace: spark-apps

spec:
  replicas: 1

  template:
    metadata:
      name: spark-history-server
      labels:
        app: spark-history-server

    spec:
      containers:
        - name: spark-history-server
          image: gcr.io/spark-operator/spark:v2.4.0

          resources:
            requests:
              memory: "512Mi"
              cpu: "100m"

          command:
            -  /sbin/tini
            - -s
            - --
            - /opt/spark/bin/spark-class
            - -Dspark.history.fs.logDirectory=/data/
            - org.apache.spark.deploy.history.HistoryServer

          ports:
            - name: http
              protocol: TCP
              containerPort: 18080

          readinessProbe:
            timeoutSeconds: 4
            httpGet:
              path: /
              port: http

          livenessProbe:
            timeoutSeconds: 4
            httpGet:
              path: /
              port: http

          volumeMounts:
            - name: data
              mountPath: /data
      volumes:
      - name: data
        persistentVolumeClaim:
          claimName: spark-pvc
          readOnly: true

Feel free to configure Ingress, Service for accessing the UI.

Also you can use Google Cloud Storage, Azrue Blob Storage or AWS S3 as event log location. For this you will need to install some extra jars so I would recommend having a look at lightbend spark history server image and charts.