Running Spark Applications on a Schedule¶

The Spark Operator supports running Spark applications on a standard cron schedule using the ScheduledSparkApplication custom resource. A ScheduledSparkApplication object defines a cron schedule and a SparkApplication template from which the operator creates a new SparkApplication object for each scheduled run.

Example ScheduledSparkApplication¶

The following example shows a ScheduledSparkApplication that runs the Spark Pi example every 5 minutes:

apiVersion: sparkoperator.k8s.io/v1beta2
kind: ScheduledSparkApplication
metadata:
  name: spark-pi-scheduled
  namespace: default
spec:
  schedule: "@every 5m"
  concurrencyPolicy: Allow
  successfulRunHistoryLimit: 3
  failedRunHistoryLimit: 3
  template:
    type: Scala
    mode: cluster
    sparkVersion: 4.0.0
    image: docker.io/library/spark:4.0.0
    imagePullPolicy: Always
    mainClass: org.apache.spark.examples.SparkPi
    mainApplicationFile: local:///opt/spark/examples/jars/spark-examples.jar
    arguments:
    - "5000"
    restartPolicy:
      type: Never
    driver:
      cores: 1
      memory: 512m
      template:
        metadata:
          labels:
            spark.apache.org/version: 4.0.0
          annotations:
            spark.apache.org/version: 4.0.0
        spec:
          containers:
          - name: spark-kubernetes-driver
            securityContext:
              allowPrivilegeEscalation: false
              capabilities:
                drop:
                - ALL
              runAsGroup: 185
              runAsNonRoot: true
              runAsUser: 185
              seccompProfile:
                type: RuntimeDefault
          serviceAccount: spark-operator-spark
    executor:
      instances: 2
      cores: 1
      memory: 512m
      template:
        metadata:
          labels:
            spark.apache.org/version: 4.0.0
          annotations:
            spark.apache.org/version: 4.0.0
        spec:
          containers:
          - name: spark-kubernetes-executor
            securityContext:
              allowPrivilegeEscalation: false
              capabilities:
                drop:
                - ALL
              runAsGroup: 185
              runAsNonRoot: true
              runAsUser: 185
              seccompProfile:
                type: RuntimeDefault

Cron Schedule¶

The cron schedule can be configured with .spec.schedule. The Spark Operator parses the schedule with github.com/robfig/cron/v3 ParseStandard, which accepts:

Standard 5-field crontab specs (minute hour day-of-month month day-of-week), e.g. "*/5 * * * *". Note that the Quartz-style ? placeholder is not supported.
Descriptors, e.g. "@midnight", "@every 1h30m".

Concurrency Policy¶

The concurrency of runs is controlled by .spec.concurrencyPolicy, which has the following valid values (with Allow being the default):

Allow: More than one run of an application is allowed. For example, the next run can start even if the previous run has not completed yet.
Forbid: No more than one run of an application is allowed. The next run can only start after the previous run has completed.
Replace: No more than one run of an application is allowed. When the next run is due, the previous run is killed and the next run starts as a replacement.

Suspending Schedules¶

A scheduled ScheduledSparkApplication can be temporarily suspended (no future runs will be triggered) by setting .spec.suspend to true. To resume the schedule, remove .spec.suspend or set it to false.

Specifying Time Zone¶

The timezone for the schedule is specified by .spec.timezone. If not specified, the default timezone is Local. If you want to use a different timezone, you can specify a timezone using the IANA timezone name (e.g. America/New_York).

Status and Run History¶

The Status section of a ScheduledSparkApplication object shows:

The time of the last run through .status.lastRun.
The proposed time of the next run through .status.nextRun.

Additional status information includes:

.status.lastRunName: The name of the SparkApplication object for the most recent run.
.status.pastSuccessfulRunNames: The names of SparkApplication objects of past successful runs.
.status.pastFailedRunNames: The names of SparkApplication objects of past failed runs.

A ScheduledSparkApplication can track the names of SparkApplication objects for past runs in the Status section. The numbers of past successful and failed runs to keep track of are controlled by .spec.successfulRunHistoryLimit and .spec.failedRunHistoryLimit, respectively. In the example above, 3 past successful runs and 3 past failed runs are tracked.

Restart Policy¶

Certain restart policies specified in .spec.template.restartPolicy may not work well with the schedule and concurrency policy. For example:

A restart policy of Always should never be used with a ScheduledSparkApplication.
A restart policy of OnFailure may not be a good choice since the next scheduled run usually continues where the previous run left off.

For these reasons, it’s often best to use a restart policy of Never, as shown in the example above.

Updating a ScheduledSparkApplication¶

When the spec of a ScheduledSparkApplication is updated, existing SparkApplications that have already been created will not be updated or deleted. The changes only affect subsequently scheduled SparkApplications.

This behavior ensures that running applications are not disrupted by spec changes. If you need to update a currently running SparkApplication, you must manually delete and recreate it or update it directly.

Deleting a ScheduledSparkApplication¶

Deleting a ScheduledSparkApplication will cascade delete all the SparkApplication objects that were created by that schedule. This means all historical and currently running Spark applications associated with the schedule will be deleted. If you want to preserve the Spark applications that have already been scheduled, make sure to update the ScheduledSparkApplication with .spec.suspend=true instead of deleting it.