Understanding Benchmarking in Vector Search: Pitfalls & Best Practices
Join me, Philip, as we dive into the world of benchmarking and explore common pitfalls, such as finding the right use case, dealing with inconsistent data sets, and avoiding implicit biases, to ensure your benchmarks are accurate and meaningful.
- Benchmarking is a process of comparing a system or product with a standard or benchmark to measure its performance.
- Many benchmarks for vector search claim that one product is faster than another, but these benchmarks can be misleading as they may not consider all the factors and variables involved.
- The use case, data structure, read-write ratio, and filtering techniques are important factors that can affect the performance of a vector search system.
- Benchmarks should be conducted under similar conditions to ensure a fair comparison between systems or products.
- It is essential to consider the quality of results, such as precision and recall, when conducting benchmarks for vector search.
- Benchmarks can be manipulated or biased unintentionally or intentionally, so it's crucial to verify their credibility and accuracy.
- Cheating in benchmarks can occur through various means, such as using creative ways to pass tests or manipulating parameters to produce better results.
- Automated and reproducible benchmarks are essential for avoiding the "slow boiling frog" problem where performance decreases gradually over time.
- It's crucial to conduct your own benchmarks based on your specific use case, data size, read-write ratio, query structure, latency requirements, and hardware configuration.
- Using someone else's glossy benchmarks can be misleading as they may not accurately reflect the performance of a system for your specific use case.
- A tool like "rally" can help create and optimize tracks for benchmarking and figuring out performance metrics for a specific workload.
- Even if a benchmark is flawed, it's essential to learn from it by understanding the strengths and weaknesses of the system being evaluated.
- It's better to conduct your own benchmarks rather than relying solely on someone else's benchmarks or believing in benchmarking without proper results.
- Benchmarking is an ongoing process that should be conducted regularly to ensure optimal performance over time.
- The speaker advises the audience to wave and say "benchmarking" for a photo with them before they hand over the presentation to the next speaker.
Source: AI Engineer via YouTube
❓ What do you think? What is one essential consideration or "gotcha" in benchmarks that, if overlooked, can significantly impact the reliability and usefulness of the results? Feel free to share your thoughts in the comments!