I Accidentally Built a Zero-Config Redis Alternative in Go – ScaloDB
Lock-free embedded KV store in Go, but Redis/SQLite/LevelDB already own this space.
High-performance embedded graph database for analytics and real-time transactions
50x Neo4j throughput with embedded-to-service mode switch requiring zero migration.
Backend developers building graph-powered applications
Kuzu · Neo4j · DuckDB
What's different from existing embedded graph DBs:
- Dual-mode: start embedded, flip one line to expose as a network service — same data, same queries, no migration - Built on GraphScope Flex, the engine behind the current LDBC SNB Interactive world record (80k+ QPS)
Local benchmark highlights on LDBC SNB SF1 (~3M nodes, 17M edges):
Embedded mode vs LadybugDB (Kuzu-based): NeuG wins 8/9 LSQB queries single-threaded vs LadybugDB's best multi-threaded result. 287x on triangle patterns (Q3), 91x on two-hop filtering (Q2).
Service mode vs Neo4j: 617 QPS vs Neo4j's 12 QPS on LDBC SNB Interactive — 50.6x throughput. P95 latency 20ms vs Neo4j's 1,728ms.
Currently Python only. Node.js bindings and GraphRAG/vector extensions are on the roadmap.
Would love feedback — especially from anyone who's tried Kùzu, LadybugDB, or runs Neo4j in production.
GitHub: https://github.com/alibaba/neug Blog post with full details: https://graphscope.io/blog/tech/2026/04/12/neug-one-engine-t...
Lock-free embedded KV store in Go, but Redis/SQLite/LevelDB already own this space.
Graph-vector-FTS in one database, but Weaviate and Neo4j already offer hybrid search.
SQLite for graphs—embedded Cypher DB with 93x faster COUNT than Neo4j.
Single-file mmap storage plus an HNSW vector index and explicit graph edges is an elegant, practical combo — think "SQLite for agent memory" with CRC-32 crash recovery and zero-server convenience. The C++20 core + nanobind gives zero-copy NumPy views and GIL-free searches, and the claimed FAISS-like throughput makes this genuinely interesting for local setups; main caveat is build/toolchain friction and how rich the surrounding ecosystem becomes.
Single-file DB for LightRAG is clever, but HN scraping is solved by existing tools.
Using a single-file .pardus format with CREATE/INSERT/SELECT + SIMILARITY queries gives a very familiar developer UX for embedding storage. The combination of graph-based ANN, full transactions, thread-safety, and zero external dependencies is an uncommon and useful engineering combo for local-first AI work; it would win more attention with benchmark comparisons and richer ecosystem integrations (connectors/clients).