The open-source community has shown interest in Hann, a new high-performance approximate nearest neighbor search (ANN) library for Go, while simultaneously highlighting a critical gap: the absence of comparative benchmarks against established vector search solutions.
Community Demands Performance Comparisons
Users examining Hann's capabilities have consistently pointed to the need for benchmarks comparing it to established vector search solutions. Multiple commenters specifically mentioned the absence of performance data comparing Hann to popular alternatives like Redis, Vertex, Elasticsearch, and FAISS. This feedback underscores a common requirement in the technical community: concrete performance metrics that allow developers to make informed decisions when selecting tools for their projects.
Great lib... now is there a Go-native way to generate vectors from text?
The above comment highlights another interesting aspect of the discussion - the ecosystem around vector search in Go. While Hann provides search capabilities, users are also looking for complementary tools to generate embeddings from text, with suggestions like Ollama being offered as potential solutions.
Community Concerns
- Lack of benchmarks against established solutions (Redis, Vertex, Elastic, FAISS)
- C compiler dependency rather than pure Go implementation
- Unknown comparative performance of HNSW implementation
- Integration with vector generation tools for text
Implementation Approach Raises Questions
Hann's implementation strategy has sparked technical debate among Go developers. The library requires a C or C++ compiler due to its use of SIMD (AVX) instructions for fast distance computation, implemented through FFI (Foreign Function Interface) into C. This design choice has prompted some developers to question whether a pure Go implementation might be preferable, with one commenter suggesting that the C portions could potentially be rewritten in assembly using tools like the avo library to eliminate the external compiler dependency.
Hann Features Overview
- Supported Indexes: HNSW (Hierarchical Navigable Small World), PQIVF (Product Quantization Inverted File), RPT (Random Projection Tree)
- Distance Metrics: Euclidean, squared Euclidean, Manhattan, and cosine distances (HNSW); Euclidean only (PQIVF, RPT)
- Implementation: Go with C code for SIMD (AVX) optimized distance calculations
- Requirements: Go 1.21+, C/C++ compiler, CPU with AVX support
- Key Capabilities: Bulk operations, disk persistence, arbitrary dimension vectors
Comparative Performance of HNSW Implementation
Several community members with experience in vector search technologies noted that not all implementations of the Hierarchical Navigable Small World (HNSW) algorithm - one of Hann's supported indexes - perform equally well. References to the ann-benchmarks.com site were shared, with suggestions that Hann should benchmark its HNSW implementation against others and potentially adopt techniques from the best-performing versions. This highlights the nuanced reality of algorithm implementation, where theoretical approaches can yield significantly different real-world performance based on implementation details.
Hann represents an interesting addition to the Go ecosystem for developers working with vector search capabilities. While its feature set appears comprehensive - supporting multiple index types including HNSW, PQIVF, and RPT with various distance metrics - the community discussion makes it clear that empirical performance data will be crucial for its adoption. As vector search becomes increasingly important for applications involving AI, machine learning, and semantic search, Go developers will be watching closely to see how Hann measures up against established alternatives.
Reference: Hann