Remove dataproc samples (#4479)

dataproc/README.md

@@ -1,84 +1,3 @@
-# Cloud Dataproc API Examples
+These samples have been moved.
 
-[![Open in Cloud Shell][shell_img]][shell_link]
-
-[shell_img]: http://gstatic.com/cloudssh/images/open-btn.png
-[shell_link]: https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/GoogleCloudPlatform/python-docs-samples&page=editor&open_in_editor=dataproc/README.md
-
-Sample command-line programs for interacting with the Cloud Dataproc API.
-
-See [the tutorial on the using the Dataproc API with the Python client
-library](https://cloud.google.com/dataproc/docs/tutorials/python-library-example)
-for information on a walkthrough you can run to try out the Cloud Dataproc API sample code.
-
-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 Cloud Dataproc API and listing the clusters in a region.
-
-`submit_job_to_cluster.py` demonstrates how to create a cluster, submit the
-`pyspark_sort.py` job, download the output from Google Cloud Storage, and output the result.
-
-`single_job_workflow.py` uses the Cloud Dataproc InstantiateInlineWorkflowTemplate API to create an ephemeral cluster, run a job, then delete the cluster with one API request.
-
-`pyspark_sort.py_gcs` is the same as `pyspark_sort.py` but demonstrates
- reading from a GCS bucket.
-
-## Prerequisites to run locally:
-
-* [pip](https://pypi.python.org/pypi/pip)
-
-Go to the [Google Cloud Console](https://console.cloud.google.com).
-
-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](https://virtualenv.readthedocs.org/en/latest/)
-(recommended), run the commands within a virtualenv.
-
-    * pip install -r requirements.txt
-
-## Authentication
-
-Please see the [Google cloud authentication guide](https://cloud.google.com/docs/authentication/).
-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 cluster. To create a cluster before running the code, you can use the [Cloud Console](console.cloud.google.com) or run:
-
-    gcloud dataproc clusters create your-cluster-name
-
-To run submit_job_to_cluster.py, first create a GCS bucket (used by Cloud Dataproc to stage files) from the Cloud Console or with gsutil:
-
-    gsutil mb gs://<your-staging-bucket-name>
-
-Next, set the following environment variables:
-
-    BUCKET=your-staging-bucket
-    CLUSTER=your-cluster-name
-
-Then, if you want to use an existing cluster, run:
-
-    python submit_job_to_cluster.py --project_id=$GOOGLE_CLOUD_PROJECT --zone=us-central1-b --cluster_name=$CLUSTER --gcs_bucket=$BUCKET
-
-Alternatively, to create a new cluster, which will be deleted at the end of the job, run:
-
-    python submit_job_to_cluster.py --project_id=$GOOGLE_CLOUD_PROJECT --zone=us-central1-b --cluster_name=$CLUSTER --gcs_bucket=$BUCKET --create_new_cluster
-
-The script will setup a cluster, upload the PySpark file, submit the job, print the result, then, if it created the cluster, delete the cluster.
-
-Optionally, you can add the `--pyspark_file` argument to change from the default `pyspark_sort.py` included in this script to a new script.
+https://github.com/googleapis/python-dataproc/tree/master/samples