I’ve just released a series of fundamental improvements to FerresDB, focused on low-level performance and native integration with AI ecosystems.
What’s new:
🔌 Embedded MCP (Model Context Protocol): Native support via STDIO. It’s now possible to connect the database directly to Claude Desktop or Cursor IDE.
⚡ SIMD-Accelerated Kernels: Implementation of distance kernels (Euclidean/Dot Product) in Rust using AVX2 and SSE4.1 instructions, with runtime detection.
🔍 Native HNSW Pre-filtering: Metadata filtering integrated directly into graph traversal, ensuring precision and returning the exact requested limit.
🏢 Logical Namespaces: Native multitenancy support, allowing data from multiple clients to be isolated within the same physical collection efficiently.
📊 Real-time Analytics: Updated dashboard with time-series charts for P95 latency and ingestion throughput, plus a hardware acceleration indicator.
📦 Storage Optimization: Added Zstd compression for the WAL and support for binary snapshots via bincode for ultra-fast loading.
🔄 Auto-Reindex & TTL: New background worker for automatic index compaction and support for Time-to-Live data expiration.
The project continues to evolve as a lightweight and resilient solution for vector search infrastructure.
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