feat: Add support for running Spark Connect client inside the Dataproc s8s batch

[ZhiweiLinnn]

For batch, we want initiated sessions.
Add integration test for batch environ

[gemini-code-assist]

Summary of Changes

Hello @ZhiweiLinnn, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces essential support for running the Spark Connect client within Dataproc batch environments. The core change involves modifying the session creation mechanism to intelligently detect batch workloads and, under such conditions, ensure that the DataprocSparkSession correctly initializes or connects to a standard PySpark session. This adjustment is critical for batch jobs that expect pre-initiated Spark sessions, and its proper functioning is verified through newly added integration tests.

Highlights

• Dataproc Batch Workload Detection: A new utility function, is_dataproc_batch(), has been added to environment.py to detect if the current execution environment is a Dataproc batch workload by checking the DATAPROC_WORKLOAD_TYPE environment variable.
• Spark Session Handling for Batch Workloads: The DataprocSparkSession.getOrCreate() method in session.py has been updated. When a Dataproc batch workload is detected, it now defers to creating or getting a standard pyspark.sql.SparkSession instead of its custom Spark Connect session logic, aligning with the requirement for initiated sessions in batch environments.
• Integration Test Coverage: New integration tests have been added in test_session.py to validate the behavior of DataprocSparkSession within a simulated batch workload environment. This includes fixtures for setting the batch environment and creating local Spark sessions, and a test to confirm that getOrCreate() returns a standard PySpark session in this context.

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[gemini-code-assist]

Code Review

This pull request adds support for running the Spark Connect client within a Dataproc serverless batch environment. When in a batch workload, instead of creating a new Dataproc session, it now correctly connects to the existing Spark session provided by the batch environment. The changes include a new environment detection function, a modification to the session creation logic, and new integration tests to verify this behavior.

My review has two main points:

1. In session.py, the getOrCreate method's return type hint is now incorrect for batch environments. I've suggested adding a type: ignore to acknowledge this and prevent static analysis errors.
2. In test_session.py, I've pointed out a redundant cleanup in the new integration test and suggested a simplification to align with pytest best practices for resource management.

Overall, the changes are logical and well-tested. Addressing these points will improve code correctness and maintainability.