Create and Run Spark Job in Databricks

Category : Microsoft Azure Data Engineering | Sub Category : Databricks | By Prasad Bonam Last updated: 2023-09-23 06:12:29 Viewed : 275

Creating and running a Spark job in Databricks typically involves the following steps:

1. Create a Databricks Notebook:

   • Log in to your Databricks workspace.
   • Click on "Workspace" in the left sidebar.
   • Select the folder where you want to create a notebook.
   • Click "Create" and choose "Notebook."
   • Give your notebook a name and select the default language (e.g., Python, Scala, R).
   • Click "Create."

2. Write Your Spark Code:

   • In your Databricks notebook, you can write Spark code using the selected language.
   • Start by initializing Spark and writing your data processing, analysis, or machine learning code.
   • Ensure your code is functional and error-free within the notebook before proceeding to the next steps.

3. Convert the Notebook to a Spark Job:

   • To convert the notebook into a Spark job, you need to use the Databricks Job feature. Jobs allow you to schedule and run notebooks as batch processes.
   • Click on "Jobs" in the left sidebar of your Databricks workspace.
   • Click the "Create Job" button.

4. Configure the Spark Job:

   • Fill in the job configuration details:
     • Name: Give your job a meaningful name.
     • Existing Cluster: Choose an existing Databricks cluster on which to run the job or create a new one.
     • Notebook Path: Select the notebook you want to run as a job.
     • Base Parameters: Define any parameters or variables you want to pass to the notebook.
     • Schedule (Optional): You can set up a one-time job or schedule it to run at specific intervals.

5. Submit the Job:

   • After configuring the job, click the "Run Now" button to submit the job for execution.
   • You can also use the "Submit" button if you are scheduling the job for a later time.

6. Monitor the Job:

   • You can monitor the progress and status of your job in the Databricks Jobs UI.
   • Databricks will show the jobs execution status, logs, and any errors encountered during execution.

7. View Results (Optional):

   • Depending on the nature of your Spark job, you may want to view the results.
   • If your job generates output data or visualizations, you can access them through the notebook or by exporting results to external storage.

8. Job History and Logs:

   • Databricks retains a history of job executions, which you can access to review past runs and logs.
   • Job logs provide valuable information for debugging and troubleshooting.

9. Manage and Schedule Jobs:

   • You can manage and schedule Spark jobs through the Databricks Jobs UI.
   • You can edit, clone, or delete jobs as needed.

By following these steps, you can create, configure, and run Spark jobs in Databricks. This allows you to automate and schedule your data processing and analytics tasks, making it easier to maintain and manage your workflows.