test: Add test that executes a colab notebook that uses spark connect

[jinnthehuman]

We use the Notebook service client to execute colab notebooks as a means to test spark connect e2e functionality in notebooks. The notebook used for testing installs the latest version of dataproc-spark-connect-python and runs a demo spark notebook that BQ Studio has.

Uses the google.com:hadoop-cloud-dev project
Uses the notebook uploaded to GCS gs://e2e-testing-bucket/input/notebooks/spark_connect_e2e_notebook_test.ipynb

[gemini-code-assist]

Summary of Changes

Hello @jinnthehuman, 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 a new end-to-end integration test aimed at verifying the seamless operation of Spark Connect within Colab notebooks. The test orchestrates the execution of a predefined Colab notebook using the Notebook service client, which then installs necessary Spark Connect components and runs a demo. This ensures that the entire workflow, from notebook invocation to successful Spark job completion, functions correctly in a real-world scenario.

Highlights

• New E2E Test: Introduced a new integration test (test_colab_execution.py) specifically designed to validate Spark Connect functionality within Colab notebooks.
• Notebook Execution via Service Client: The test leverages the aiplatform_v1.NotebookServiceClient to programmatically trigger and monitor the execution of a Colab notebook.
• Spark Connect Validation: The executed notebook is configured to install the dataproc-spark-connect-python library and run a demo Spark notebook, ensuring end-to-end functionality.
• Dynamic Resource Configuration: The test dynamically retrieves project, region, and service account details from environment variables, enhancing flexibility and reusability.

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[gemini-code-assist]

Code Review

This pull request adds an integration test for executing a Colab notebook using Spark Connect. My review found a critical issue where the test incorrectly checks for a failure state instead of a success state. I've also identified a significant inefficiency in how the test result is verified, which could lead to flaky tests. Additionally, I've included several recommendations to improve the code's maintainability and adherence to Python best practices, such as removing hardcoded values, using a standard logging mechanism, and fixing minor style issues.

[gemini-code-assist]

The test contains several hardcoded values like REPOSITORY_ID and TEMPLATE_ID (and GCS URIs on lines 70 and 74). This makes the test less flexible and harder to maintain or reuse in different environments. Consider moving these values to environment variables, similar to how GOOGLE_CLOUD_PROJECT is handled.

[gemini-code-assist]

The test uses print() for logging progress. While acceptable for debugging, it's better practice to use Python's logging module for tests. This provides more control over log levels and output formatting, and integrates well with testing frameworks like pytest (e.g., using the caplog fixture).

[fangyh20]

can we address the gemini comments and run the integration test?