Override job tasks settings in Databricks Asset Bundles

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This article describes how to override the settings for Databricks job tasks in Databricks Asset Bundles. See What are Databricks Asset Bundles?

In Databricks bundle configuration files, you can use the task mapping within a job definition to join the job tasks settings in a top-level resources mapping with the job task settings in a targets mapping, for example (ellipses indicate omitted content, for brevity):

# ...
resources:
  jobs:
    <some-unique-programmatic-identifier-for-this-job>:
      # ...
      tasks:
        - task_key: <some-unique-programmatic-identifier-for-this-task>
          # Task settings.

targets:
  <some-unique-programmatic-identifier-for-this-target>:
    resources:
      jobs:
        <the-matching-programmatic-identifier-for-this-job>:
          # ...
          tasks:
            - task_key: <the-matching-programmatic-identifier-for-this-key>
              # Any more task settings to join with the settings from the
              # resources mapping for the matching top-level task_key.
          # ...

To join the top-level resources mapping and the targets mapping for the same task, the task mappings’ task_key must be set to the same value.

If any job task setting is defined both in the top-level resources mapping and the targets mapping for the same task, then the setting in the targets mapping takes precedence over the setting in the top-level resources mapping.

Example 1: Job task settings defined in multiple resource mappings and with no settings conflicts

In this example, spark_version in the top-level resources mapping is combined with node_type_id and num_workers in the resources mapping in targets to define the settings for the task_key named my-task (ellipses indicate omitted content, for brevity):

# ...
resources:
  jobs:
    my-job:
      name: my-job
      tasks:
        - task_key: my-key
          new_cluster:
            spark_version: 13.3.x-scala2.12

targets:
  development:
    resources:
      jobs:
        my-job:
          name: my-job
          tasks:
            - task_key: my-task
              new_cluster:
                node_type_id: n2-highmem-4
                num_workers: 1
          # ...

When you run databricks bundle validate for this example, the resulting graph is as follows (ellipses indicate omitted content, for brevity):

{
  "...": "...",
  "resources": {
    "jobs": {
      "my-job": {
        "tasks": [
          {
            "new_cluster": {
              "node_type_id": "n2-highmem-4",
              "num_workers": 1,
              "spark_version": "13.3.x-scala2.12"
            },
            "task-key": "my-task"
          }
        ],
        "...": "..."
      }
    }
  }
}

Example 2: Conflicting job task settings defined in multiple resource mappings

In this example, spark_version, and num_workers are defined both in the top-level resources mapping and in the resources mapping in targets. spark_version and num_workers in the resources mapping in targets take precedence over spark_version and num_workers in the top-level resources mapping. This defines the settings for the task_key named my-task (ellipses indicate omitted content, for brevity):

# ...
resources:
  jobs:
    my-job:
      name: my-job
      tasks:
        - task_key: my-task
          new_cluster:
            spark_version: 13.3.x-scala2.12
            node_type_id: n2-highmem-4
            num_workers: 1

targets:
  development:
    resources:
      jobs:
        my-job:
          name: my-job
          tasks:
            - task_key: my-task
              new_cluster:
                spark_version: 12.2.x-scala2.12
                num_workers: 2
          # ...

When you run databricks bundle validate for this example, the resulting graph is as follows (ellipses indicate omitted content, for brevity):

{
  "...": "...",
  "resources": {
    "jobs": {
      "my-job": {
        "tasks": [
          {
            "new_cluster": {
              "node_type_id": "n2-highmem-4",
              "num_workers": 2,
              "spark_version": "12.2.x-scala2.12"
            },
            "task_key": "my-task"
          }
        ],
        "...": "..."
      }
    }
  }
}