Skip to main content

Dataproc client library for Spark Connect

Project description

# 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 Session using the Spark Connect protocol without requiring additional steps.

## Install

`sh pip install dataproc_spark_connect `

## Uninstall

`sh 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:

## Usage

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

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

  2. Add the required imports into your PySpark application or notebook and start a Spark session with the following code instead of using environment variables:

    `python from google.cloud.dataproc_spark_connect import DataprocSparkSession from google.cloud.dataproc_v1 import Session session_config = Session() session_config.environment_config.execution_config.subnetwork_uri = '<subnet>' session_config.runtime_config.version = '2.2' spark = DataprocSparkSession.builder.dataprocSessionConfig(session_config).getOrCreate() `

## Developing

For development instructions see [guide](DEVELOPING.md).

## Contributing

We’d love to accept your patches and contributions to this project. There are just a few small guidelines you need to follow.

### Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution; this simply gives us permission to use and redistribute your contributions as part of the project. Head over to <https://cla.developers.google.com> to see your current agreements on file or to sign a new one.

You generally only need to submit a CLA once, so if you’ve already submitted one (even if it was for a different project), you probably don’t need to do it again.

### Code reviews

All submissions, including submissions by project members, require review. We use GitHub pull requests for this purpose. Consult [GitHub Help](https://help.github.com/articles/about-pull-requests/) for more information on using pull requests.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dataproc_spark_connect-1.0.0rc4.tar.gz (23.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dataproc_spark_connect-1.0.0rc4-py2.py3-none-any.whl (28.1 kB view details)

Uploaded Python 2Python 3

File details

Details for the file dataproc_spark_connect-1.0.0rc4.tar.gz.

File metadata

File hashes

Hashes for dataproc_spark_connect-1.0.0rc4.tar.gz
Algorithm Hash digest
SHA256 985275aa2c864bdf687f0739bc19913074afc0e6fccc723b81b3ca52004769cc
MD5 73187254eed10071cbaf38af2657c65b
BLAKE2b-256 3a1f8bffbefe777a100a6efb725e579522d30939b1500a214c1b64b9e0c11db3

See more details on using hashes here.

File details

Details for the file dataproc_spark_connect-1.0.0rc4-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for dataproc_spark_connect-1.0.0rc4-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 98b24f3e8ca4853c652dcd15d638fe2feddb2d5c45b2574bae145972a2bd723b
MD5 a2656759ad7d5b262f1cc09e84f0a8df
BLAKE2b-256 7831e0a22bef43a8a3ee32bac66d4cd908967dbf7294869b5fc6f04feae3db94

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page