feat: add time based benchmarks

[chandra-siri]

feat: add time based benchmarks

[gemini-code-assist]

Summary of Changes

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This pull request establishes a foundational framework for time-based performance microbenchmarks within the project. The primary goal is to enable more accurate and continuous measurement of Google Cloud Storage read throughput under various conditions, moving beyond operation-count-based benchmarks to duration-based evaluations. This enhancement will provide deeper insights into the system's sustained performance characteristics.

Highlights

• New Time-Based Benchmarking Framework: Introduced a new set of microbenchmarks designed to measure performance over a specified duration, including a warmup period, for Google Cloud Storage read operations.
• Configurable Benchmark Parameters: Implemented a flexible configuration system using YAML to define various benchmark parameters such as bucket types, file sizes, chunk sizes, duration, and workload patterns (sequential and random reads).
• Multi-Process and Asynchronous Read Operations: Developed a pytest-based test suite that leverages multiprocessing and asyncio to simulate concurrent read operations, allowing for realistic performance evaluation under various concurrency levels.
• CPU Affinity for Worker Processes: Configured worker processes to set CPU affinity, pinning them to specific cores to potentially reduce context switching overhead and improve benchmark consistency.

Changelog

• tests/perf/microbenchmarks/time_based/conftest.py
  • Added a pytest fixture to provide workload parameters and dynamically generated filenames for time-based benchmarks.
• tests/perf/microbenchmarks/time_based/reads/config.py
  • Added a module to parse YAML configuration and generate a comprehensive list of benchmark parameters for time-based read operations.
• tests/perf/microbenchmarks/time_based/reads/config.yaml
  • Added a YAML configuration file defining common and workload-specific parameters for time-based sequential and random read benchmarks.
• tests/perf/microbenchmarks/time_based/reads/parameters.py
  • Added a dataclass to encapsulate parameters specific to time-based read benchmarks, including pattern, duration, and warmup duration.
• tests/perf/microbenchmarks/time_based/reads/test_reads.py
  • Added the main pytest file implementing time-based microbenchmarks for Google Cloud Storage read operations, featuring multi-process, asynchronous execution, and throughput calculation.

Activity

• No human activity (comments, reviews, etc.) has been recorded on this pull request yet.

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[gemini-code-assist]

Code Review

This pull request introduces time-based benchmarks for read operations. The changes include new configuration files (yaml and .py), a conftest.py for test setup, and the benchmark test logic itself. The implementation uses multiprocessing to run downloads in parallel.

My review focuses on improving the maintainability, portability, and performance of the benchmark code. Key suggestions include removing commented-out code, making benchmark names more descriptive, avoiding hardcoded values for CPU affinity to improve portability, and optimizing the download loop to reduce memory allocations and redundant calls, which is crucial for accurate performance measurement.