Search recall and throughput are inversely related in most search systems. Recall measures the percentage of relevant results a system retrieves compared to all possible relevant results in a dataset. Higher recall often requires deeper data scans or more complex algorithms, which slow down processing and reduce throughput—the number of queries handled per second. For example, a vector search using exhaustive nearest-neighbor methods achieves high recall by comparing a query to every item in the dataset, but this approach becomes computationally expensive as data grows. Conversely, approximate nearest neighbor (ANN) algorithms prioritize speed by narrowing the search scope, improving throughput at the cost of potentially missing some relevant results.
To balance these factors, developers adjust system parameters and architecture. One key lever is indexing strategy. For text-based search, using inverted indexes with tiered segmentation (e.g., separating frequently accessed data from less active data) allows faster lookups for common queries while reserving deeper scans for edge cases. In vector databases, tuning ANN parameters like the number of probe points or graph connections directly impacts the trade-off: more probes improve recall but increase latency. Another approach is query optimization—applying filters early in the pipeline to reduce the dataset size before running resource-intensive similarity calculations. For instance, a product search might first filter by price or category, then apply semantic matching, reducing computational load without significantly affecting recall for most users.
The optimal balance depends on the application’s requirements. A medical research tool might prioritize high recall to avoid missing critical studies, accepting slower throughput by using exhaustive search and parallel processing. In contrast, a real-time recommendation engine might limit recall by using aggressive ANN settings and caching frequently accessed items to maintain high throughput. Developers should use A/B testing and monitoring tools (e.g., tracking recall@k and queries per second) to evaluate trade-offs. Dynamic adjustments, such as scaling replica instances during peak traffic or relaxing ANN parameters for low-priority queries, can help maintain balance under varying loads.