feat: Add micro-benchmarks for writes comparing standard (regional) vs rapid (zonal) buckets.

[chandra-siri]

• Add performance microbenchmarking suite for Writes
• This compares Regional (Standard Storage) with Zonal (Rapid Storage) buckets.
• Regional uses JSON wheres Zonal uses gRPC bidi

[gemini-code-assist]

Summary of Changes

Hello @chandra-siri, 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 suite of microbenchmarks to assess the performance of write operations (uploads) within the system. It provides a flexible framework to test different concurrency models, including single-process with varying coroutine counts and multi-process configurations, against both zonal (gRPC-based) and regional (JSON API-based) storage buckets. The goal is to gain deeper insights into upload efficiency under diverse conditions.

Highlights

• New Write Microbenchmarks: Introduced a comprehensive suite of microbenchmarks specifically for evaluating write (upload) operations to Google Cloud Storage.
• Flexible Workload Configuration: Added a new YAML configuration file (config.yaml) to define various write workloads, including single-process with single/multiple coroutines and multi-process with multiple coroutines.
• Dual Protocol Support: Implemented benchmark tests that support both gRPC-based uploads for zonal buckets (using AsyncGrpcClient and AsyncAppendableObjectWriter) and JSON API-based uploads for regional buckets (using google.cloud.storage.Client).
• Concurrency and Parallelism: Utilizes asyncio for asynchronous gRPC operations, ThreadPoolExecutor for multi-threaded JSON uploads, and multiprocessing.Pool for multi-process scenarios to thoroughly test different concurrency models.
• Performance Reporting: Integrates with existing benchmark utilities to publish detailed benchmark metrics and resource utilization information.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces benchmarking for write operations, adding a new benchmark test file and its configuration, along with version updates across the repository. My review of the new test files has identified a few areas for improvement to enhance maintainability and portability. Specifically, I suggest using environment variables for bucket names instead of hardcoding them, improving the file-level docstring for clarity, removing commented-out code, and refactoring duplicated logic across test functions. Overall, these benchmarks are a valuable addition for performance monitoring.

[chandra-siri]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a micro-benchmarking suite for GCS writes, comparing regional (JSON) and zonal (gRPC) buckets under various concurrency scenarios. The overall structure is well-organized, with separate files for configuration, parameters, and tests. My review focuses on improving the correctness of the benchmark measurements, enhancing code clarity and maintainability, and adhering to Python best practices. Key suggestions include optimizing the data generation process within the benchmark loop to prevent skewed results, improving docstrings for better readability, and refactoring for clearer code.