Efficient Vector Search: Techniques and Architectures

Last updated: June 16, 2025

Efficient vector search ° is a foundational technology for applications in information retrieval, recommendation systems, LLMs, and multimodal search °. The latest research and engineering developments have pushed the boundaries of efficiency, scalability, and flexibility by introducing novel index structures, data layouts, and search algorithms tailored for modern hardware and massive datasets °. Below, we provide an overview of state-of-the-art techniques and systems that enable efficient vector search across single-node, distributed, and disaggregated memory ° settings.

1. Foundations and Challenges

Vector search systems must address several key requirements:

• Low Search Latency: Return nearest neighbors in under tens of milliseconds, even at billion-scale.
• High Recall: Maintain accuracy close to exact nearest neighbors.
• Scalability: Scale to billions of vectors and dynamic update workloads.
• Cost and Resource Efficiency: Optimize for memory, storage, compute, and operational cost.
• Flexibility: Support different query types (filtered, multi-modal, set-based, hybrid), evolving data, and new hardware paradigms.

These goals are complicated by the high dimensionality of embeddings, the need for flexible queries (e.g., with filters or attributes), and the emergence of cloud-native °/disaggregated data platforms.

2. Modern Indexing Techniques and System Design

2.1 Hierarchical Graph-based Indexes (HNSW and Variants)

HNSW ° (Hierarchical Navigable Small World) graphs have become the de facto standard for high-quality, efficient Approximate Nearest Neighbor Search ° (ANNS):

• Structure: Multi-layer, navigable graph; each layer offers a tradeoff between connectivity (for recall) and sparseness (for memory).
• Query: Greedy graph traversal ° from upper sparse layers to denser lower layers.
• Insertion/Deletion: Handled via local neighborhood updates; more challenging on disk owing to data placement °.

Performance: Near state-of-the-art recall/latency tradeoffs, especially when the entire index fits in memory.

2.2 Disaggregated Memory-Aware Indexing (d-HNSW) (Liu et al., 17 May 2025 ° )

d-HNSW adapts HNSW to RDMA °-based remote/disaggregated memory architectures:

• Representative Index Caching: Builds a small meta-index (meta-HNSW) from a sample of the data and caches it in compute nodes °. This meta-index pinpoints which partition to search, cutting network traffic.
• RDMA-Friendly Layout: Serializes index data (sub-HNSWs and overflow buffers) contiguously, enabling single-shot remote fetches.
• Batched Query-Aware Data Loading: Batches queries and merges overlapping data ° requests, further reducing bandwidth and latency via RDMA doorbell batching.

Results: Up to 117× lower latency than naive remote index implementations, with recall ≈ 0.87 on SIFT1M. Suitable for cloud-native AI serving at extreme scale.

2.3 Disk-Based Large-Scale Dynamic Indices (LSM-VEC) (Zhong et al., 22 May 2025 ° )

LSM-VEC leverages a hybrid of graph (HNSW) and LSM-tree ° (Log-Structured Merge tree) storage:

• Upper HNSW Layers in RAM: The top of the index remains in memory for fast access.
• Bottom Layer on Disk in LSM-tree: The dense bottom HNSW layer is distributed as key/value pairs in an LSM-tree database (e.g., AsterDB), which supports fast, out-of-place updates.
• Sampling-based Probabilistic Search: Instead of always visiting all neighbors, uses random-projection hashes as a filter to minimize unnecessary (expensive disk) neighbor visits.
• Connectivity-Aware Graph Reordering: Dynamically reorders disk layout so frequently traversed neighbors are co-located, reducing random disk reads.

Performance: Outperforms DiskANN and SPFresh with higher recall, lower latencies, and up to 66% lower memory usage at billion-scale.

2.4 GPU-Accelerated Filtered Search Systems (VecFlow) (Xi et al., 1 Jun 2025 ° )

VecFlow is optimized for filtered-ANNS at web/VLDB scale on GPUs:

• Label-Centric Index Partitioning: Splits the dataset into high-specificity (common) and low-specificity (rare) label groups, using IVF °-Graph on the former and brute-force on the latter.
• Redundancy-Bypassing Data Layout: Stores vectors once with label-index indirection, avoiding duplicate memory use.
• Interleaved Layout and Persistent Kernel: Memory layout is tailored for GPU ° coalesced access; persistent CUDA kernels ° support both streaming and batched scenarios.
• Multi-label Query Support: Efficiently processes queries with multiple label predicates (AND/OR), with early stopping ° and GPU parallel search °.

Performance: Achieves up to 5 million QPS ° at 90% recall (A100), 135× faster than state-of-the-art CPU baselines, and uniquely sustains high recall (up to 99%) in strict filter settings.

2.5 Disk-Based, Cloud-Native ANN (Cosmos DB + DiskANN) (Upreti et al., 9 May 2025 ° )

Cosmos ° DB integrates DiskANN inside its NoSQL ° cloud database:

• One Index per Partition: Graph index terms stored within the Bw-Tree index layer, reusing its transactional, concurrent, and durable features.
• Quantized Compression: Product Quantization ° reduces memory/IO needs (12KB floats → 128B codes).
• Hybrid & Filtered Search °: Support for metadata-based pruning, paginated search, and bitmap intersection at the storage layer.
• Operational Benefits: No data replication, up-to-date indices, transactional semantics, low cost.

Empirical Results: Sub-20ms latency at 10M scale, 41× lower query cost versus Pinecone/Zilliz, scales to billions of vectors with automatic sharding.

3. Dynamic and Update-Aware Index Maintenance

3.1 Incremental Maintenance of IVF and Graph Indices

SPFresh (Xu et al., 18 Oct 2024 ° ) and Ada-IVF ° (Mohoney et al., 1 Nov 2024 ° ) reduce maintenance overhead with:

• Incremental, Local Partition Rebalancing: Only reassess partition boundaries and move vectors at local boundaries (not a global rebuild), often reassigning less than 0.5% of vectors per update.
• Adaptive Triggers: Ada-IVF triggers re-clustering based on partition access patterns and health metrics, spending effort where queries are focused.
• Local Re-clustering: Uses local, batch k-means for partition splits/merges, balancing partition size, centroid drift, and read temperature for optimal query QPS and update throughput.

Results: Up to 5× higher update throughput with similar or higher search throughput and recall, supporting high-throughput streaming insert/delete workloads.

4. Hybrid, Filtered, and Multi-modal Search

4.1 Dense-Sparse Hybrid Search with Graph Extensions (Zhang et al., 27 Oct 2024 ° )

• Distribution Alignment: Pre-samples similarities and rescales the sparse vector distance to match the dense component, achieving 1–9% recall improvement.
• Adaptive Two-Stage Computation: Computes dense-only distances early, then applies expensive hybrid (dense+sparse) distance refinement to prune.
• Sparse Vector Pruning: Drops low-magnitude elements, reducing computation with negligible accuracy loss.

Throughput: 8.9–11.7× speedup over prior hybrid methods.

4.2 Dynamic Hybrid Vector Search (DEG) (Yin et al., 11 Feb 2025 ° )

DEG efficiently supports hybrid queries ° with a dynamic parameter $\alpha$ (weighting per modality):

• Pareto Frontier ° Neighbor Search: Ensures each node's neighbor set contains the nearest for any $\alpha$ value.
• Dynamic Edge Pruning: Each edge maintains an 'active range'—the set of $\alpha$ for which it is valid—enabling on-the-fly, query-adaptive graph traversal and fewer expansions.
• Edge Seed Method: Selects diverse graph entry points per query, accelerating adaptive search °.

Guarantee: Single index serves all $\alpha$, matching or exceeding recall/latency of per-$\alpha$ indexes.

5. Large-Scale Dynamic and Multi-Tenant Index Structures

5.1 LSM-VEC: Hierarchical LSM-tree/Graph Hybrid (Zhong et al., 22 May 2025 ° )

• Supports arbitrary insert/delete: LSM-tree structure makes updates and compactions fast and out-of-place.
• Sampling-based Search and Connectivity-aware Placement: Lowers I/O per query by only visiting likely close neighbors and reordering disk placement dynamically.
• Scalability: Validated at 100M–1B vectors, supporting dynamic AI search needs (RAG, recommendations, etc.).

5.2 Multi-Tenant Indexing (Curator) (Jin et al., 13 Jan 2024 ° )

• Layered Clustering Trees: Global clustering tree plus compact per-tenant subtrees (using Bloom filters and shortlists) enables permission-filtered search without per-tenant index duplication.
• Efficiency: Achieves per-tenant search performance ° close to dedicated-per-tenant indexing at memory use similar to a single shared index.

6. Algorithmic Optimizations

6.1 Quantization and Dimensionality Reduction

• LeanVec/GleanVec (Tepper et al., 2023 ° , Tepper et al., 14 Oct 2024 ° ): Dimensionality reduction with query/database-aware projections (including nonlinear schemes), achieving 2–4× higher throughput and dramatic memory savings in high-dimensional settings ° (D > 512), with optimal or near-optimal recall for both in- and out-of-distribution queries °.
• Locally-Adaptive Vector Quantization ° (LVQ °) (Aguerrebere et al., 3 Feb 2024 ° ): Extremely fast, memory-efficient quantization, including Turbo ° LVQ (SIMD/data layout optimized) and Multi-Means LVQ (local codebooks), supporting streaming/dynamic index use-cases.

6.2 GPU Architectural Integration (VecFlow) (Xi et al., 1 Jun 2025 ° )

• Label-centric dual index: Divides by specificity to optimally use IVF-Graph and IVF-BFS routines per label type.
• Interleaved and redundancy-bypassing layouts: Exploit GPU’s warp structure and memory bandwidth °.

7. Practical Implementation and Scaling Considerations

• Hardware Awareness: Advanced systems like VecFlow and d-HNSW explicitly target GPU or RDMA hardware. Tensor-based batching (e.g., via BLAS, GEMM) enables new optimal scan vs probe tradeoffs on CPUs ° (Sanca et al., 23 Mar 2024 ° ).
• Resource Usage: Recent systems (e.g., LSM-VEC, Cosmos DB) achieve >66% lower memory than prior state-of-the-art, with orders-of-magnitude less compute required for updates.
• Hybrid and Filtered Queries: Attribute/pattern–aware routing (as in TigerVector (Liu et al., 20 Jan 2025 ° ) and Cosmos DB) enables integration with relational and property graph models °.
• Open Source and Integration: Many systems (VecFlow, SVS, Curator, Cosmos DB, TigerVector) release libraries or code, enabling integration and further research/deployment.

8. Summary Table (Key Systems)

9. Example: Efficient Vector Search Pipeline (DiskANN in Azure Cosmos DB)

def vector_search(query_vector, k):
    # Step 1: Greedy search over quantized graph in storage engine (DiskANN)
    candidates = diskann_greedy_search(query_vector, quantized_vector_index)
    # Step 2: Return candidates above a threshold
    candidate_ids = select_top_candidates(candidates, k_prime)  # k_prime > k
    # Step 3: Fetch raw vectors for candidate_ids from partition
    full_vectors = fetch_vectors_from_partition(candidate_ids)
    # Step 4: Final reranking by exact distance (e.g., L2)
    results = rerank_by_full_precision(query_vector, full_vectors, k)
    return results

10. Conclusion

Modern efficient vector search systems combine graph-based ANN algorithms ° with hardware-aware optimizations, dynamic data structures ° (e.g., LSM-trees, disaggregated caches), and flexible filters/hybrid query support. They deliver high recall and throughput while maintaining low latency and operational cost at billion-scale—and are ready for web-scale, interactive, and dynamic AI-powered applications. Tools like VecFlow, LSM-VEC, Curator, and Cosmos DB provide practical, robust, and open solutions that can be directly deployed or extended to meet the demands of next-generation vector search workloads.