Databricks Jobs

Databricks Lakeflow Jobs

Overview

Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles.

Reference Files

Quick Start

Python SDK

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.jobs import Task, NotebookTask, Source

w = WorkspaceClient()

job = w.jobs.create(
    name="my-etl-job",
    tasks=[
        Task(
            task_key="extract",
            notebook_task=NotebookTask(
                notebook_path="/Workspace/Users/user@example.com/extract",
                source=Source.WORKSPACE
            )
        )
    ]
)
print(f"Created job: {job.job_id}")

CLI

databricks jobs create --json '{
  "name": "my-etl-job",
  "tasks": [{
    "task_key": "extract",
    "notebook_task": {
      "notebook_path": "/Workspace/Users/user@example.com/extract",
      "source": "WORKSPACE"
    }
  }]
}'

Asset Bundles (DABs)

# resources/jobs.yml
resources:
  jobs:
    my_etl_job:
      name: "[${bundle.target}] My ETL Job"
      tasks:
        - task_key: extract
          notebook_task:
            notebook_path: ../src/notebooks/extract.py

Core Concepts

Multi-Task Workflows

Jobs support DAG-based task dependencies:

tasks:
  - task_key: extract
    notebook_task:
      notebook_path: ../src/extract.py

  - task_key: transform
    depends_on:
      - task_key: extract
    notebook_task:
      notebook_path: ../src/transform.py

  - task_key: load
    depends_on:
      - task_key: transform
    run_if: ALL_SUCCESS  # Only run if all dependencies succeed
    notebook_task:
      notebook_path: ../src/load.py

run_if conditions:

• ALL_SUCCESS (default) - Run when all dependencies succeed
• ALL_DONE - Run when all dependencies complete (success or failure)
• AT_LEAST_ONE_SUCCESS - Run when at least one dependency succeeds
• NONE_FAILED - Run when no dependencies failed
• ALL_FAILED - Run when all dependencies failed
• AT_LEAST_ONE_FAILED - Run when at least one dependency failed

Task Types Summary

Trigger Types Summary

Compute Configuration

Job Clusters (Recommended)

Define reusable cluster configurations:

job_clusters:
  - job_cluster_key: shared_cluster
    new_cluster:
      spark_version: "15.4.x-scala2.12"
      node_type_id: "i3.xlarge"
      num_workers: 2
      spark_conf:
        spark.speculation: "true"

tasks:
  - task_key: my_task
    job_cluster_key: shared_cluster
    notebook_task:
      notebook_path: ../src/notebook.py

Autoscaling Clusters

new_cluster:
  spark_version: "15.4.x-scala2.12"
  node_type_id: "i3.xlarge"
  autoscale:
    min_workers: 2
    max_workers: 8

Existing Cluster

tasks:
  - task_key: my_task
    existing_cluster_id: "0123-456789-abcdef12"
    notebook_task:
      notebook_path: ../src/notebook.py

Serverless Compute

For notebook and Python tasks, omit cluster configuration to use serverless:

tasks:
  - task_key: serverless_task
    notebook_task:
      notebook_path: ../src/notebook.py
    # No cluster config = serverless

Job Parameters

Define Parameters

parameters:
  - name: env
    default: "dev"
  - name: date
    default: "{{start_date}}"  # Dynamic value reference

Access in Notebook

# In notebook
dbutils.widgets.get("env")
dbutils.widgets.get("date")

Pass to Tasks

tasks:
  - task_key: my_task
    notebook_task:
      notebook_path: ../src/notebook.py
      base_parameters:
        env: "{{job.parameters.env}}"
        custom_param: "value"

Common Operations

Python SDK Operations

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# List jobs
jobs = w.jobs.list()

# Get job details
job = w.jobs.get(job_id=12345)

# Run job now
run = w.jobs.run_now(job_id=12345)

# Run with parameters
run = w.jobs.run_now(
    job_id=12345,
    job_parameters={"env": "prod", "date": "2024-01-15"}
)

# Cancel run
w.jobs.cancel_run(run_id=run.run_id)

# Delete job
w.jobs.delete(job_id=12345)

CLI Operations

# List jobs
databricks jobs list

# Get job details
databricks jobs get 12345

# Run job
databricks jobs run-now 12345

# Run with parameters
databricks jobs run-now 12345 --job-params '{"env": "prod"}'

# Cancel run
databricks jobs cancel-run 67890

# Delete job
databricks jobs delete 12345

Asset Bundle Operations

# Validate configuration
databricks bundle validate

# Deploy job
databricks bundle deploy

# Run job
databricks bundle run my_job_resource_key

# Deploy to specific target
databricks bundle deploy -t prod

# Destroy resources
databricks bundle destroy

Permissions (DABs)

resources:
  jobs:
    my_job:
      name: "My Job"
      permissions:
        - level: CAN_VIEW
          group_name: "data-analysts"
        - level: CAN_MANAGE_RUN
          group_name: "data-engineers"
        - level: CAN_MANAGE
          user_name: "admin@example.com"

Permission levels:

• CAN_VIEW - View job and run history
• CAN_MANAGE_RUN - View, trigger, and cancel runs
• CAN_MANAGE - Full control including edit and delete

Common Issues

Related Skills

• asset-bundles - Deploy jobs via Databricks Asset Bundles
• spark-declarative-pipelines - Configure pipelines triggered by jobs

Resources

• Jobs API Reference
• Jobs Documentation
• DABs Job Task Types
• Bundle Examples Repository