What is the relationship between search recall and throughput, and how can one adjust system settings to achieve the needed balance for a specific application?

The relationship between search recall and throughput involves a trade-off: higher recall typically requires more computational resources, which reduces throughput, while optimizing for throughput often involves simplifying processes that may lower recall. Balancing these requires understanding your application’s priorities and making targeted system adjustments[7][9].

1. Recall vs. Throughput Dynamics Recall measures how many relevant items a search system retrieves from the total available, while throughput refers to the number of queries processed per second. To achieve high recall, systems often need to scan larger datasets, apply complex ranking algorithms, or use broader search parameters. These steps increase computational load, directly reducing throughput. For example, a product search engine scanning 10 million items with detailed filters will have lower throughput than one scanning 1 million items with basic keyword matching[8][9].

2. Adjusting System Parameters Developers can adjust:

• Indexing granularity: Smaller, distributed indexes reduce query latency but may split related data, lowering recall. Sharding strategies like term-based partitioning can help[9].
• Query complexity: Limiting filters or ranking stages (e.g., reducing ML model layers) improves throughput. For instance, an e-commerce app might prioritize price/delivery-time filters over personalized recommendations during peak traffic.
• Caching: Storing frequent query results (e.g., “best-selling phones”) bypasses resource-heavy searches, freeing capacity for high-recall tasks like new product discovery.

3. Scenario-Specific Optimization Applications requiring high recall (e.g., legal document retrieval) might use batch processing with asynchronous queries, accepting lower throughput. Conversely, real-time systems (e.g., chat search) often limit recall depth—searching only recent messages—to maintain responsiveness. Hybrid approaches, such as precomputing recall-optimized results during off-peak hours, can balance both metrics[7][9]. Testing with A/B frameworks to measure recall-throughput curves under load is critical for tuning.