What factors should be controlled to make fair performance comparisons between two vector database systems (e.g., ensuring the same hardware, similar index build configurations, and using the same dataset)?

To ensure fair performance comparisons between two vector database systems, you must control variables that directly impact results. These include hardware specifications, index configuration parameters, dataset characteristics, and testing methodology. Below is a structured explanation of the key factors:

1. Hardware Consistency

Both systems must be tested on identical hardware to eliminate performance variations caused by differences in processing power or memory. This includes:

• CPU: Same model, core count, and clock speed
• RAM: Same capacity and speed (e.g., DDR4 vs. DDR5)
• Storage: Use comparable storage types (SSD vs. HDD) and configurations (e.g., NVMe PCIe 4.0)
• Network: Ensure identical network latency and bandwidth if testing distributed setups

For example, testing one system on a high-end server with 128GB RAM and another on a mid-tier machine with 64GB RAM would skew results[8].

2. Index Build and Query Parameters

Vector databases rely heavily on index structures (e.g., HNSW, IVF), and their performance depends on configuration settings. Control:

• Index Type: Use the same algorithm (e.g., HNSW for both systems).
• Build Parameters: Match settings like ef_construction (HNSW) or nlist (IVF) to ensure similar trade-offs between build time and accuracy.
• Query Parameters: Standardize search scope (e.g., ef_search in HNSW) and top-k results retrieved.

For instance, if System A uses ef_construction=200 while System B uses ef_construction=100, their build times and query accuracy will differ significantly[8].

3. Dataset and Testing Methodology

• Dataset: Use the same dataset with identical dimensions, size, and distribution (e.g., 1M vectors of 768 dimensions). Preprocess data uniformly (normalization, quantization).
• Query Load: Replicate real-world scenarios with consistent batch sizes and concurrency levels.
• Warm-up Runs: Perform multiple warm-up queries to account for caching effects.
• Measurement: Report averages from multiple runs and exclude outliers.

Testing with datasets of varying sizes (e.g., 100K vs. 1M vectors) or different distributions (random vs. clustered) invalidates comparisons[8].

By rigorously controlling these factors, developers can isolate the impact of database design choices rather than external variables. This approach ensures meaningful, apples-to-apples comparisons for decision-making.

References: [8] multiple_comparisons