Why Vector Databases Like Milvus Outperform Elasticsearch in Hybrid Search

For decades, keyword matching based on Elasticsearch has been the default for enterprise search and recommendation systems.

With advances in AI‑driven search, developers are shifting to semantic search that understands query intent, using embedding models and vector databases.

Semantic search represents data as high‑dimensional vectors, offering nuanced intent understanding, while keyword matching provides precise term matches; many organizations therefore adopt a hybrid approach.

Challenges of Hybrid Search

A common implementation uses a dedicated vector database (e.g., Milvus) for efficient semantic search and a traditional engine (e.g., Elasticsearch or OpenSearch) for full‑text search.

Managing two separate systems adds infrastructure, configuration, and maintenance complexity, increasing operational burden and integration risk.

Benefits of a Unified Hybrid Search Solution

Reduces infrastructure maintenance by consolidating into a single system.

Unified schema stores both dense (vector) and sparse (keyword) data with shared metadata.

Simplifies queries: a single request can perform semantic and full‑text search without multiple API calls.

Improves security and access control by centralizing management within the vector database.

Unified Vector Approach Simplifies Hybrid Search

In semantic search, machine‑learning models embed text into dense vectors; similar meanings are close in this space, enabling fast approximate nearest‑neighbor (ANN) retrieval.

The same method can support full‑text search by encoding documents and queries into sparse vectors, where each dimension corresponds to a term and its weight.

Sparse vectors are highly zero‑filled; for example, in the MS‑MARCO dataset, over 99% of values are zero, making storage and processing efficient.

Milvus recently added native support for Sparse‑BM25, a sparse‑vector implementation of the classic BM25 algorithm, unlocking efficient full‑text search within a vector database.

Data‑pruned retrieval algorithm discards low‑value sparse vectors, reducing index size with minimal quality loss.

Further optimizations include graph‑based indexes, product quantization (PQ), and scalar quantization (SQ) to lower memory usage.

Milvus’s C++ core provides superior memory management compared to Java‑based Elasticsearch, saving several gigabytes of RAM, and supports memory‑mapped (MMap) storage for large indexes.

Why Traditional Search Stacks Lag in Vector Search

Elasticsearch, built for inverted indexes, struggles with dense vector workloads; for one million vectors, Elasticsearch takes 3770 ms versus Milvus’s 6 ms—a 600× gap that widens at scale.

Elasticsearch also lacks key vector‑search features such as disk‑based ANN, optimized metadata filtering, and range search.

Conclusion

Vector databases like Milvus are poised to surpass Elasticsearch as the unified solution for hybrid search, delivering exceptional performance, scalability, and efficiency by integrating dense vector search with optimized sparse‑vector techniques.

This unified approach simplifies infrastructure, reduces memory consumption, and enhances search capabilities, making it the future for advanced search requirements.