dataproc-spark-connect 1.0.0

Dataproc client library for Spark Connect

Dataproc Spark Connect Client

A wrapper of the Apache 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

pip install dataproc_spark_connect

Uninstall

pip uninstall dataproc_spark_connect

Setup

This client requires permissions to manage Dataproc Sessions and Session Templates.

If you are running the client outside of Google Cloud, you need to provide authentication credentials. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to point to your Application Credentials file.

You can specify the project and region either via environment variables or directly in your code using the builder API:

• Environment variables: GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION
• Builder API: .projectId() and .location() methods (recommended)

Usage

1. Install the latest version of Dataproc Spark Connect:

   pip install -U dataproc-spark-connect
   

2. Add the required imports into your PySpark application or notebook and start a Spark session using the fluent API:

   from google.cloud.dataproc_spark_connect import DataprocSparkSession
   spark = DataprocSparkSession.builder.getOrCreate()
   

3. You can configure Spark properties using the .config() method:

   from google.cloud.dataproc_spark_connect import DataprocSparkSession
   spark = DataprocSparkSession.builder.config('spark.executor.memory', '4g').config('spark.executor.cores', '2').getOrCreate()
   

4. For advanced configuration, you can use the Session class to customize settings like subnetwork or other environment configurations:

   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 = '3.0'
   spark = DataprocSparkSession.builder.projectId('my-project').location('us-central1').dataprocSessionConfig(session_config).getOrCreate()
   

Reusing Named Sessions Across Notebooks

Named sessions allow you to share a single Spark session across multiple notebooks, improving efficiency by avoiding repeated session startup times and reducing costs.

To create or connect to a named session:

1. Create a session with a custom ID in your first notebook:

   from google.cloud.dataproc_spark_connect import DataprocSparkSession
   session_id = 'my-ml-pipeline-session'
   spark = DataprocSparkSession.builder.dataprocSessionId(session_id).getOrCreate()
   df = spark.createDataFrame([(1, 'data')], ['id', 'value'])
   df.show()
   

2. Reuse the same session in another notebook by specifying the same session ID:

   from google.cloud.dataproc_spark_connect import DataprocSparkSession
   session_id = 'my-ml-pipeline-session'
   spark = DataprocSparkSession.builder.dataprocSessionId(session_id).getOrCreate()
   df = spark.createDataFrame([(2, 'more-data')], ['id', 'value'])
   df.show()
   

3. Session IDs must be 4-63 characters long, start with a lowercase letter, contain only lowercase letters, numbers, and hyphens, and not end with a hyphen.

4. Named sessions persist until explicitly terminated or reach their configured TTL.

5. A session with a given ID that is in a TERMINATED state cannot be reused. It must be deleted before a new session with the same ID can be created.

Using Spark SQL Magic Commands (Jupyter Notebooks)

The package supports the sparksql-magic library for executing Spark SQL queries directly in Jupyter notebooks.

Installation: To use magic commands, install the required dependencies manually:

pip install dataproc-spark-connect
pip install IPython sparksql-magic

1. Load the magic extension:

   %load_ext sparksql_magic
   

2. Configure default settings (optional):

   %config SparkSql.limit=20
   

3. Execute SQL queries:

   %%sparksql
   SELECT * FROM your_table
   

4. Advanced usage with options:

   # Cache results and create a view
   %%sparksql --cache --view result_view df
   SELECT * FROM your_table WHERE condition = true
   

Available options:

• --cache / -c: Cache the DataFrame
• --eager / -e: Cache with eager loading
• --view VIEW / -v VIEW: Create a temporary view
• --limit N / -l N: Override default row display limit
• variable_name: Store result in a variable

See sparksql-magic for more examples.

Note: Magic commands are optional. If you only need basic DataprocSparkSession functionality without Jupyter magic support, install only the base package:

pip install dataproc-spark-connect

Developing

For development instructions see guide.

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 for more information on using pull requests.