Static workload benchmarks are insufficient for evaluating ANN indexes in vector databases because they focus only on recall and query performance, overlooking crucial aspects like indexing performance and memory usage. The author advocates for streaming workload benchmarks, showcasing new insights into recall stability and performance by comparing HNSWLIB and DiskANN under a streaming workload. The post calls for updated benchmarking methods to reflect real-world vector database use.
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Why Traditional Static Workload Benchmarks Fall Short
Vector databases are essential for retrieving high-dimensional data like text, images, and audio. They use Approximate Nearest Neighbor (ANN) indexes for quick retrieval. However, the common practice of using static workload benchmarks to evaluate these indexes is no longer sufficient.
Limitations of Static Workload Benchmark
Static benchmarks don’t account for indexing performance and memory usage, which are crucial for real-world applications. They also fail to represent data distribution changes and do not measure the Delete API, which is vital for dynamic data management.
Streaming Workload: A More Comprehensive Approach
Streaming workload benchmarks provide a more realistic evaluation by considering data insertion, querying, and deletion as an ongoing process. This approach offers a more accurate measure of an ANN index’s performance in real scenarios.
Benefits of Streaming Workload Benchmark
- Flexibility: Reflects real-world data shifts and workload patterns.
- Realism: Captures the continuous nature of data indexing and querying.
- Simple Analysis: Offers a clear view of the trade-offs between recall accuracy and performance.
- Completeness: Includes evaluation of insert and delete operations.
Insights from Streaming Workload Benchmark
By using a streaming workload benchmark, I discovered new insights into the performance of different ANN indexes, particularly comparing HNSW and Vamana. This led to a better understanding of how different algorithms handle deletions and their impact on recall stability.
Conclusion: A Call for Modern Benchmarks
It’s time to adopt streaming workload benchmarks for vector databases, similar to the evolution of benchmarks in traditional database systems. This will ensure more accurate and relevant performance evaluations.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Please Use Streaming Workload to Benchmark Vector Databases
- Towards Data Science – Medium
- Twitter – @itinaicom
