LatticeDB vs SQLite: I Ran the Graph Traversal Benchmarks. The Gap Is Real but the Fine Print Matters

说明与用途

LatticeDB 在单文件嵌入式数据库中提供更快的深层图遍历和统一的向量/全文搜索，而 SQLite 在表格数据、多进程访问和成熟生态系统方面更具优势。

问题

Choosing an embedded database that can efficiently handle graph traversal, vector search, and full-text search for AI agent memory and other graph-heavy workloads.

结果

LatticeDB delivers dramatically faster deep graph traversal and unified vector/FTS queries in a single embedded file, while SQLite remains superior for tabular workloads, multi‑process access, and ecosystem maturity.

工作流

• 1. Build a 100k-node, 500k-edge graph with power-law degree distribution in SQLite using a single adjacency table and indexes.
• 2. Run recursive CTE queries for 1‑hop, 2‑hop, and variable depth (1‑5) traversals from both a random low‑degree node and the max‑degree hub.
• 3. Measure execution times and compare with LatticeDB benchmark results.
• 4. Analyze performance gaps: SQLite’s per‑level overhead and UNION deduplication explode on deep or high‑degree traversals.
• 5. Apply decision framework: choose SQLite for tabular data, multi‑process access, and ecosystem needs; choose LatticeDB for deep graph traversal, combined vector/FTS queries, and agent memory workloads.
• 6. Consider future scaling: use client‑server DBs if multiple writers or networked access are required.
• 7. Leverage LatticeDB’s changefeed for real‑time embedding pipelines if needed.

证据

LatticeDB 8.0 microseconds vs SQLite 290.0 microseconds, 36x for 1‑hop traversal

来源

From the max‑degree hub, the CTE cost explodes with depth: 3‑hop recursive CTE 705.9 ms

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LatticeDB is 2,819× faster at depth 50 compared to SQLite

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