How do you submit a Spark job to Dataproc?

    

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How do you submit a Spark job to Dataproc?

Submitting a Spark job to Google Cloud Dataproc involves running your job on a managed Spark cluster using either the gcloud CLI, REST API, or via the Google Cloud Console. The process typically includes specifying the cluster, main application file, and any dependencies or arguments.

Using the gcloud CLI, a Spark job can be submitted with the following command:

bash

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gcloud dataproc jobs submit spark \

    --cluster=my-cluster \

    --region=us-central1 \

    --class=org.apache.spark.examples.SparkPi \

    --jars=file:///path/to/your-jar-file.jar \

    -- 100

--cluster: Specifies the name of the Dataproc cluster.

--region: The region where the cluster is deployed.

--class: The main class containing the Spark job.

--jars: Path to the JAR file or other dependencies.

Arguments after -- are passed to the Spark job.

Alternatively, if your Spark job is written in Python (PySpark), you can use:

The cluster is active.

Your code and dependencies are stored in GCS or accessible paths.

IAM roles grant permissions for Dataproc and GCS usage.

Dataproc also integrates with workflow templates and Airflow (Cloud Composer) for scheduling Spark jobs, making it ideal for production pipelines.

You can also submit jobs programmatically via the REST API or from the Cloud Console by navigating to the Dataproc cluster and clicking on “Submit Job”, then filling in job parameters.

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