A detailed technical comparison of IVF (Inverted File Index) and HNSW (Hierarchical Navigable Small World) indexing strategies in Milvus, the open-source vector database. Covers how each algorithm works internally, performance benchmarks across datasets from 1M to 100M vectors, memory and build-time trade-offs, parameter tuning guidance, and Python code examples. HNSW delivers superior recall and lower latency for datasets under 50M vectors, while IVF variants (especially IVF_SQ8 and IVF_PQ) are better suited for massive datasets, GPU workloads, or memory-constrained environments. A decision framework and two-stage retrieval pattern (IVF_PQ → HNSW re-rank) are also provided.
Table of contents
Milvus Architecture OverviewIVF — Inverted File IndexIVF Index Variants in MilvusHow IVF_FLAT Works — Step by StepHNSW — Hierarchical Navigable Small WorldThe HNSW Graph StructureHNSW Build AlgorithmHNSW SearchHead-to-Head ComparisonPros and Cons BreakdownPerformance BenchmarksCode Examples in MilvusDecision FrameworkTL;DR SummaryGet TechLatest.Net ’s stories in your inboxConclusionThank you so much for readingSort: