google-spark-connect 0.5.6
Google client library for Spark Connect
License: Apache 2.0

# 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

.. code-block:: console

      pip install google_spark_connect

## Uninstall

.. code-block:: console

      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:

      .. code-block:: console

            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:

      .. code-block:: python

            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`:

      .. code-block:: python

            spark = GoogleSparkSession.builder.getOrCreate()

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

      .. code-block:: python

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

      .. code-block:: console

            pip install -r requirements.txt

2. Build the code.

      .. code-block:: 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.

      .. code-block:: console

            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.

      .. code-block:: console

            %%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