Competitive Analysis

Point Lookups

System	Latency	Type	Source
LatticeDB	0.13 us	Embedded	`zig build benchmark`
RocksDB (in-memory)	0.14 us	Embedded	RocksDB wiki
SQLite (in-memory)	~0.2 us	Embedded	Turso blog
SQLite (WAL, disk)	3 us (p90)	Embedded	marending.dev
Neo4j	28 ms (p99)	Server	Memgraph comparison

LatticeDB's B+Tree achieves sub-microsecond cached lookups, matching RocksDB in-memory and outperforming SQLite on disk by 23x.

Vector Search

System	Latency (10-NN)	Scale	Type	Source
LatticeDB	0.83 ms mean, 100% recall	1M	Embedded	`zig build vector-benchmark`
FAISS HNSW (single-thread)	0.5-3 ms	1M	Library	FAISS wiki
Weaviate	1.4 ms mean, 3.1 ms P99	1M	Server	Weaviate benchmarks
Qdrant	~1-2 ms	1M	Server	Qdrant benchmarks
Milvus + SQ8	2.2 ms P99	1M	Server	VectorDBBench
pgvector HNSW	~5 ms @ 99% recall	1M	Extension	Jonathan Katz
LanceDB	3-5 ms	1M	Embedded	LanceDB blog
Chroma	4-5 ms mean	1M	Embedded	Chroma docs
Pinecone P2	~15 ms (incl. network)	1M	Cloud	Pinecone blog
sqlite-vec (brute force)	17 ms	1M	Extension	Alex Garcia

LatticeDB at 1M achieves 0.83 ms mean with 100% recall@10 — faster than FAISS single-threaded HNSW and competitive with Weaviate and Qdrant server-based systems (which add network overhead in practice).

Graph Traversal

System	2-hop (100K nodes)	Type	Source
LatticeDB	39 us	Embedded	`zig build sqlite-benchmark`
SQLite (recursive CTE)	548 us	Embedded	`zig build sqlite-benchmark`
Kuzu	19 ms	Embedded	The Data Quarry
Neo4j	10 ms (1M nodes)	Server	Neo4j blog

LatticeDB vs SQLite

Social network graph with power-law degree distribution, adjacency cache pre-warmed.

Small Scale (10K nodes, 50K edges)

Workload	LatticeDB	SQLite	Speedup
1-hop traversal	560 ns	13.0 us	23x
2-hop traversal	3.0 us	37.5 us	13x
3-hop traversal	19.1 us	178.5 us	9x
Variable path (1..5)	82.4 us	4.3 ms	52x

Medium Scale (100K nodes, 500K edges)

Workload	LatticeDB	SQLite	Speedup
1-hop traversal	8.0 us	290.0 us	36x
2-hop traversal	38.7 us	548.3 us	14x
3-hop traversal	197.3 us	1.2 ms	6x
Variable path (1..5)	134.4 us	10.1 ms	75x

Depth-Limited Traversal (10K nodes, 50K edges)

Depth	LatticeDB	SQLite	Speedup
10	311 us	121 ms	390x
15	380 us	271 ms	713x
25	318 us	587 ms	1,848x
50	500 us	1.4 s	2,819x

LatticeDB uses BFS with adjacency cache and bitset visited tracking. SQLite uses a recursive CTE with UNION deduplication. Both compute identical reachable node sets (~8K nodes). The gap widens at deeper depths as SQLite's CTE overhead grows with each recursion level.

Full-Text Search (BM25)

System	Search Latency	Type	Source
LatticeDB	19 us	Embedded	`zig build benchmark`
SQLite FTS5	< 6 ms	Embedded	SQLite Cloud
Elasticsearch	1-10 ms	Server	Various
Tantivy	10-100 us	Library	Various

LatticeDB's inverted index with BM25 scoring is ~300x faster than SQLite FTS5 and competitive with Tantivy (a dedicated Rust search library).