The trade-off curve between recall and query latency or throughput generally follows a downward-sloping trend. As recall increases, latency tends to rise (or throughput decreases), and vice versa. This relationship arises because higher recall often requires the system to examine more data points or use computationally expensive algorithms. For example, an exact nearest neighbor search guarantees perfect recall by comparing a query to every item in the dataset, but this approach has high latency and low throughput. In contrast, approximate nearest neighbor (ANN) methods like HNSW or IVF-PQ reduce latency by limiting the search space—say, by checking only a subset of candidate vectors—but this introduces the risk of missing relevant results, lowering recall. The curve is often non-linear: initial optimizations (e.g., switching from exact to ANN) yield significant latency improvements with minimal recall loss, but further gains in latency may require steeper recall sacrifices.
Index parameters directly influence where a system operates on this curve. For instance, in HNSW, increasing the efSearch parameter (the number of candidate nodes explored during a query) improves recall but increases latency, as more comparisons are performed. Similarly, in IVF-PQ, a larger nprobe value (number of clusters searched per query) raises recall but requires more computation. Adjusting these parameters allows developers to tune the system toward their needs. For example, a search engine prioritizing accuracy might set a high efSearch, accepting slower responses, while a real-time recommendation system might use a lower nprobe to ensure sub-millisecond latency, even if some relevant results are missed. The key is to test parameter combinations and measure their impact on both recall and latency to identify the optimal balance.
This trade-off curve informs decisions by forcing developers to align index parameters with application requirements. For instance, in a high-stakes medical imaging database, maximizing recall is critical, so parameters favoring exhaustive search are justified despite higher latency. Conversely, in a user-facing autocomplete feature, low latency is prioritized, so parameters that limit search depth (e.g., reducing efSearch) are chosen. The curve also highlights diminishing returns: beyond a certain point, increasing parameters like nprobe may yield negligible recall gains while drastically harming throughput. Developers can use this insight to avoid over-optimizing for one metric at the expense of the other. Additionally, understanding the curve helps in capacity planning—e.g., provisioning enough hardware to handle target throughput at an acceptable recall level. By quantifying the trade-offs, teams can make data-driven decisions rather than relying on guesswork.