google-spark-connect 0.4.1

Google client library for Spark Connect

# Google 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

pip install google_spark_connect

## Uninstall

pip uninstall google_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 Google Spark Connect modules:

   pip install google_cloud_dataproc --force-reinstall
   pip install google_spark_connect --force-reinstall

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

   from google.cloud.spark_connect import GoogleSparkSession

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:

      spark = GoogleSparkSession.builder.getOrCreate()

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

      from google.cloud.dataproc_v1 import SparkConnectConfig
      from google.cloud.dataproc_v1 import Session
      dataproc_config = Session()
      dataproc_config.spark_connect_session = SparkConnectConfig()
      dataproc_config.environment_config.execution_config.subnetwork_uri = "<subnet>"
      dataproc_config.runtime_config.version = '3.0'
      spark = GoogleSparkSession.builder.dataprocConfig(dataproc_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.

   pip install -r requirements.txt

2. Build the code.

   python setup.py sdist bdist_wheel

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

   VERSION=<version> gsutil cp dist/google_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.

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