Cloud Dataproc API Example

Sample command-line programs for interacting with the Cloud Dataproc API.

Please see the tutorial on the using the Dataproc API with the Python client library for more information.

Note that while this sample demonstrates interacting with Dataproc via the API, the functionality demonstrated here could also be accomplished using the Cloud Console or the gcloud CLI.

list_clusters.py is a simple command-line program to demonstrate connecting to the Dataproc API and listing the clusters in a region

create_cluster_and_submit_job.py demonstrates how to create a cluster, submit the pyspark_sort.py job, download the output from Google Cloud Storage, and output the result.

pyspark_sort.py_gcs is the asme as pyspark_sort.py but demonstrates reading from a GCS bucket.

Prerequisites to run locally:

• pip

Go to the Google Cloud Console.

Under API Manager, search for the Google Cloud Dataproc API and enable it.

Set Up Your Local Dev Environment

To install, run the following commands. If you want to use virtualenv (recommended), run the commands within a virtualenv.

* pip install -r requirements.txt

Authentication

Please see the Google cloud authentication guide. The recommended approach to running these samples is a Service Account with a JSON key.

Environment Variables

Set the following environment variables:

GOOGLE_CLOUD_PROJECT=your-project-id
REGION=us-central1 # or your region
CLUSTER_NAME=waprin-spark7
ZONE=us-central1-b

Running the samples

To run list_clusters.py:

python list_clusters.py $GOOGLE_CLOUD_PROJECT --region=$REGION

submit_job_to_cluster.py can create the Dataproc cluster, or use an existing one. If you'd like to create a cluster ahead of time, either use the Cloud Console or run:

gcloud dataproc clusters create your-cluster-name

To run submit_job_to_cluster.py, first create a GCS bucket for Dataproc to stage files, from the Cloud Console or with gsutil:

gsutil mb gs://<your-staging-bucket-name>

Set the environment variable's name:

BUCKET=your-staging-bucket
CLUSTER=your-cluster-name

Then, if you want to rely on an existing cluster, run:

python submit_job_to_cluster.py --project_id=$GOOGLE_CLOUD_PROJECT --zone=us-central1-b --cluster_name=$CLUSTER --gcs_bucket=$BUCKET

Otherwise, if you want the script to create a new cluster for you:

python submit_job_to_cluster.py --project_id=$GOOGLE_CLOUD_PROJECT --zone=us-central1-b --cluster_name=$CLUSTER --gcs_bucket=$BUCKET --create_new_cluster

This will setup a cluster, upload the PySpark file, submit the job, print the result, then delete the cluster.

You can optionally specify a --pyspark_file argument to change from the default pyspark_sort.py included in this script to a new script.