To prioritize query throughput over recall, focus on simplifying the search process, reducing computational overhead, and optimizing data structures. The goal is to minimize the work required per query, even if it means potentially skipping some relevant results. Here are key configuration changes:
1. Index Optimization
Reduce the index size and complexity to speed up data retrieval. Use fewer fields by indexing only essential data, and avoid storing unnecessary metadata. For text fields, limit tokenization by using simpler analyzers (e.g., omitting stemming or stop-word removal) or switch to keyword-based indexing for exact matches. Disable features like positional data (needed for phrase queries) or term vectors if they aren’t critical. For example, in Elasticsearch, setting index_options: docs skips positional data, reducing index size. Additionally, increase the index refresh interval (e.g., from 1s to 30s) to minimize segment creation overhead, which reduces merge operations and improves query consistency.
2. Search Parameter Tuning
Adjust query execution to favor speed. Limit the scope of searches by restricting the number of shards or partitions queried. Use size and from parameters to return smaller result sets, and set a low terminate_after threshold to stop searching once a sufficient number of hits are found. Replace expensive query types (e.g., fuzzy or phrase searches) with filters, which are faster because they don’t calculate relevance scores. For sorting, use precomputed values or deterministic fields (e.g., timestamps) instead of dynamic scoring. If using a vector database, opt for approximate nearest neighbor (ANN) algorithms like HNSW over exact k-NN to trade precision for speed.
3. Caching and Resource Allocation Leverage caching to avoid redundant computations. Enable filter caching for frequently used query clauses, and cache common aggregations or facets. Allocate more memory to the query cache (if supported by your search engine) to keep frequently accessed data readily available. If the system allows, increase hardware resources for query nodes (e.g., CPU cores for parallel query execution) or use load balancing to distribute traffic efficiently. For distributed systems, ensure shards are evenly sized and avoid over-sharding, as excessive shards increase coordination overhead.
By streamlining the index, simplifying queries, and leveraging caching, you can significantly improve throughput while accepting a marginal reduction in recall. For example, a product search might skip fuzzy matching to return 80% of relevant results in 50ms instead of 95% in 200ms, better aligning with high-volume traffic needs.