design-search-index-infra

Configure Elasticsearch mappings by defining robust analyzers for full-text BM25 retrieval alongside dedicated vector index parameters for semantic search. This approach ensures high-recall retrieval and efficient memory utilization across millions of documents.

Tune HNSW vector parameters by adjusting index configurations to balance graph connectivity and search radius, achieving an optimal recall-latency trade-off. This ensures efficient memory utilization during high-performance vector search operations.

Hybrid search using Reciprocal Rank Fusion (RRF) in OpenSearch combines BM25 full-text relevance scores with vector search similarity scores. This ranking strategy merges independent result sets to deliver high-recall retrieval without requiring manual score normalization.

Perform zero-downtime reindexing in Elasticsearch by establishing an atomic alias-based reindexing strategy. This index lifecycle management technique routes queries through aliases, allowing background index rebuilding without interrupting continuous search availability.

Yes, this search infrastructure design applies directly when scaling a search cluster to millions of documents. It optimizes shard topology and HNSW vector parameters to maintain high-recall retrieval and efficient memory utilization at massive scale.

Slow query performance and poor relevance in vector search often stem from unoptimized HNSW parameters and inadequate shard topology. Designing proper vector index parameters and hybrid RRF ranking strategies resolves these backend architecture pitfalls.