dataproc-spark-connect 0.6.0

Dataproc client library for Spark Connect

# Dataproc Spark Connect Client

A wrapper of the Apache [Spark Connect](https://spark.apache.org/spark-connect/) client with additional functionalities that allow applications to communicate with a remote Dataproc Spark cluster using the Spark Connect protocol without requiring additional steps.

## Install

`console pip install dataproc_spark_connect `

## Uninstall

`console pip uninstall dataproc_spark_connect `

## Setup This client requires permissions to manage [Dataproc sessions and session templates](https://cloud.google.com/dataproc-serverless/docs/concepts/iam). If you are running the client outside of Google Cloud, you must set following environment variables:

• GOOGLE_CLOUD_PROJECT - The Google Cloud project you use to run Spark workloads

• GOOGLE_CLOUD_REGION - The Compute Engine [region](https://cloud.google.com/compute/docs/regions-zones#available) where you run the Spark workload.

• GOOGLE_APPLICATION_CREDENTIALS - Your [Application Credentials](https://cloud.google.com/docs/authentication/provide-credentials-adc)

• DATAPROC_SPARK_CONNECT_SESSION_DEFAULT_CONFIG (Optional) - The config location, such as tests/integration/resources/session.textproto

## Usage

1. Install the latest version of Dataproc Python client and Dataproc Spark Connect modules:

   `console pip install google_cloud_dataproc --force-reinstall pip install dataproc_spark_connect --force-reinstall `

2. Add the required import into your PySpark application or notebook:

   `python from google.cloud.dataproc_spark_connect import DataprocSparkSession `

3. There are two ways to create a spark session,

   1. Start a Spark session using properties defined in DATAPROC_SPARK_CONNECT_SESSION_DEFAULT_CONFIG:

      `python spark = DataprocSparkSession.builder.getOrCreate() `

   2. Start a Spark session with the following code instead of using a config file:

      `python from google.cloud.dataproc_v1 import SparkConnectConfig from google.cloud.dataproc_v1 import Session dataproc_session_config = Session() dataproc_session_config.spark_connect_session = SparkConnectConfig() dataproc_session_config.environment_config.execution_config.subnetwork_uri = "<subnet>" dataproc_session_config.runtime_config.version = '3.0' spark = DataprocSparkSession.builder.dataprocSessionConfig(dataproc_session_config).getOrCreate() `

## Billing As this client runs the spark workload on Dataproc, your project will be billed as per [Dataproc Serverless Pricing](https://cloud.google.com/dataproc-serverless/pricing). This will happen even if you are running the client from a non-GCE instance.

## Contributing ### Building and Deploying SDK

1. Install the requirements in virtual environment.

   `console pip install -r requirements-dev.txt `

2. Build the code.

   `console python setup.py sdist bdist_wheel `

3. Copy the generated .whl file to Cloud Storage. Use the version specified in the setup.py file.

   `sh VERSION=<version> gsutil cp dist/dataproc_spark_connect-${VERSION}-py2.py3-none-any.whl gs://<your_bucket_name> `

4. Download the new SDK on Vertex, then uninstall the old version and install the new one.

   `sh %%bash export VERSION=<version> gsutil cp gs://<your_bucket_name>/dataproc_spark_connect-${VERSION}-py2.py3-none-any.whl . yes | pip uninstall dataproc_spark_connect pip install dataproc_spark_connect-${VERSION}-py2.py3-none-any.whl `