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LatticeDB reports much faster graph traversals than SQLite in its published synthetic social-network benchmark, but that result is specific to one workload and has not been independently verified here. For repeated multi-hop queries, the figures make LatticeDB worth evaluating; for ordinary row-based queries, concurrent readers, or a mature embedded database ecosystem, SQLite may remain the better fit.
What the published benchmark reports
LatticeDB’s documentation compares the two engines on a generated graph with 100,000 nodes and 500,000 edges. The vendor says both engines used the same machine, data, and benchmark harness, and computed the same reachable node sets. Its published results are:
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| Traversal | LatticeDB | SQLite | Reported speedup |
|---|---|---|---|
| 1 hop | 8.0 μs | 290.0 μs | 36× |
| 2 hops | 38.7 μs | 548.3 μs | 14× |
| 3 hops | 197.3 μs | 1.2 ms | 6× |
| Variable path (1–5) | 134.4 μs | 10.1 ms | 75× |
These are values reported by LatticeDB Documentation; the reviewed page gives no publication year. They are vendor-published results, not measurements independently replicated for this article. The ratios also vary by query: the largest listed speedup is for the variable path, while the fixed-hop results range from 6× to 36×.
Why the depth-limited ratios need careful reading
A separate vendor table uses a 10,000-node graph and reports these depth-limited traversal timings:
#1 Best Overall
| Depth limit | LatticeDB | SQLite | Reported speedup |
|---|---|---|---|
| 10 | 311 μs | 121 ms | 390× |
| 15 | 380 μs | 271 ms | 713× |
| 25 | 318 μs | 587 ms | 1,848× |
| 50 | 500 μs | 1.4 s | 2,819× |
LatticeDB’s own warning is the right way to interpret these figures: read them as “how much does depth cost you,” not as a claim that LatticeDB is thousands of times faster than SQLite. They show how the two implementations behaved in this particular depth-limited test, not a universal ranking of database performance. The same documentation reports point lookups at 0.13 μs for LatticeDB and roughly 0.2 μs for in-memory SQLite, a much closer comparison. LatticeDB’s benchmark documentation
What the test measures—and what it does not
The comparison describes a synthetic social-network graph with a power-law degree distribution. LatticeDB uses breadth-first search over an adjacency cache and a bitset to track visited nodes. SQLite uses a recursive common table expression; the vendor attributes additional per-level query-engine work and UNION deduplication to overhead that increases with recursion depth.
Rank #2
The documentation says the head-to-head run uses zig build sqlite-benchmark on the same machine and generated data. The repository also describes a pre-warmed adjacency cache and gives zig build graph-benchmark -- --quick as a reproduction command. The comparison text reviewed does not provide exact hardware and software environment details, and there is no third-party audit or independent replication established by these materials. Consequently, treat the timings as evidence for the stated workload and configuration, not as a promise for your own graph, hardware, cache state, or query semantics. LatticeDB repository
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Nor should this be read as a broad graph-analytics benchmark. Graph Data Council’s Graphalytics uses six core algorithms, standard datasets, and reference outputs for comparing graph-analysis platforms. LatticeDB’s tables do not claim to be Graphalytics results. Graph Data Council: Graphalytics
Rank #3
Choose by workload and deployment
Consider LatticeDB for connected-data queries
LatticeDB is positioned for connected data, traversal, and hybrid retrieval that combines graph relationships, text search, and vector similarity in one query. It is most relevant when multi-hop traversal is a recurring, central part of the application rather than an occasional operation.
That fit comes with an operational constraint: the vendor describes LatticeDB as single-writer and single-process. That model may suit an embedded application with a controlled write path, but it is important to check against how the application deploys and shares data.
Consider SQLite for row-oriented applications and broad compatibility
SQLite is a strong option when the application primarily filters or aggregates rows and uses joins only occasionally. Its broad deployment, mature ecosystem, and support for concurrent readers can matter more than a graph traversal advantage on a specialized synthetic workload. In WAL mode, SQLite can handle many concurrent readers across processes.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11If relationships are mostly incidental joins, the LatticeDB source itself recommends SQLite. Where graph, vector, or full-text retrieval is needed alongside SQLite, the alternative may involve extensions and separately composed query paths rather than a single integrated query system.
Best Value
Check the benchmark’s resemblance to your application
- Query shape: Compare repeated multi-hop traversal with row filters, aggregations, and occasional joins.
- Graph structure: Consider whether your graph size and degree distribution resemble the synthetic power-law graph in the published test.
- Traversal and results: Match depth, cache state, and the meaning of the results your application needs; similar-sounding queries may not do equivalent work.
- Concurrency and deployment: Decide whether a single-process, single-writer system fits, or whether readers across processes and other deployment needs are central.
- Operations: Weigh the maturity of available ecosystem, migration tools, GUI browsers, ORMs, and operational tooling against the capabilities of a newer, leaner system.
How to validate the choice
Use the published figures as a reason to run a workload-specific evaluation, not as a substitute for one. Reproduce comparable query semantics and use representative data, graph structure, traversal depths, cache conditions, and concurrency. Include the queries that matter to the application—especially ordinary lookups as well as traversals—so a fast specialized path does not obscure trade-offs elsewhere. The vendor provides the commands above, but the reviewed materials do not establish an independent benchmark run or a complete hardware and software setup.
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