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For the graph-traversal workloads in LatticeDB’s published benchmark, LatticeDB reports substantially lower query times than SQLite. The figures come from LatticeDB’s own test—not an independent replication—and apply to a particular synthetic social-network graph and benchmark setup. They are useful evidence if your application repeatedly follows relationships across multiple hops, but they do not establish that LatticeDB is faster for databases in general.
What LatticeDB’s published benchmark reports
LatticeDB’s documentation compares traversal times on a generated social-network graph with 100,000 nodes and 500,000 edges. It reports the following results:
| Traversal workload | 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 hops | 134.4 μs | 10.1 ms | 75× |
These are vendor-reported measurements; the documentation page reviewed does not state a publication year. The ratios vary considerably by query shape. In this table, the reported advantage is largest for the variable-length path, not for the three-hop traversal.
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A separate LatticeDB table tests depth-limited traversal on a 10,000-node graph:
#1 Best Overall
| Traversal depth | LatticeDB | SQLite | Reported ratio |
|---|---|---|---|
| 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× |
The unusually large ratios are easy to misread as a universal database ranking. LatticeDB’s own warning is more precise: read these figures as “how much does depth cost you,” not as a claim that LatticeDB is thousands of times faster than SQLite. This table describes how the two implementations behave as traversal depth increases in that particular test. It is not evidence that every deep query, graph, or application will see the same ratio.
The same documentation reports point lookups at 0.13 μs for LatticeDB and roughly 0.2 μs for in-memory SQLite—a much smaller difference. That contrast reinforces why benchmark results should be matched to the operation you actually need, rather than reduced to a single speed score.
Rank #2
How the benchmark was set up—and what it establishes
LatticeDB says both engines ran on the same machine, against the same generated data, using the zig build sqlite-benchmark harness. The workload models a social network with a power-law degree distribution, and the vendor says both engines compute the same reachable node sets.
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- LatticeDB’s approach: breadth-first search over an adjacency cache, with a bitset to track visited nodes.
- SQLite’s approach: a recursive common table expression. LatticeDB attributes increasing overhead with recursion depth to per-level query-engine work and UNION deduplication.
The repository also describes a pre-warmed adjacency cache and provides zig build graph-benchmark -- --quick as a reproduction command. These methodological details help explain the result, but they do not make it an independent comparison. The reviewed materials do not provide a third-party audit or replication, exact hardware and software environment details in the comparison text, or evidence of a separate author-run test.
Rank #3
Accordingly, treat the numbers as evidence for the workload and configuration LatticeDB describes. They do not establish performance across all graph analytics, relational queries, data distributions, cache conditions, or deployment environments.
How to decide which database fits your workload
Start with the shape of your application’s queries and deployment, not the benchmark’s largest multiplier.
Choose around 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. Its reported traversal results are most relevant if following relationships across several hops is a recurring, important part of your workload.
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SQLite is positioned for tabular data, broad deployment, and a mature ecosystem. If relationships are mostly incidental joins and your work is dominated by row filters or aggregations, LatticeDB’s own guidance recommends SQLite. SQLite’s WAL mode supports many concurrent readers across processes, which matters when an application has more than one process accessing the database.
Best Value
Check concurrency and deployment requirements
LatticeDB is described as single-writer and single-process. That may suit an embedded application with an acceptable single-writer model, but it is a constraint to weigh against your deployment needs. If you need client-server or distributed operation, or many readers across processes, verify the required architecture before choosing; the benchmark does not answer that question.
Account for retrieval design and operational maturity
With LatticeDB, graph traversal and hybrid retrieval are presented as integrated query capabilities. With SQLite, extensions and separately composed query paths may be needed for comparable retrieval combinations. Also consider the surrounding ecosystem: migration tools, GUI browsers, ORMs, and operational tooling may matter as much as query speed, particularly for a newer, leaner system.
Match the benchmark to your data
Before relying on these figures for a decision, compare your own situation with the published test:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Is your graph’s size and degree distribution similar to the generated social-network graph?
- Do you need repeated multi-hop traversal, or mostly filters, aggregates, and occasional joins?
- Will your queries run with a pre-warmed adjacency cache, and are your traversal depths comparable?
- Do both systems return the same result set under the semantics your application requires?
- Does the single-process, single-writer description fit your concurrency and deployment requirements?
Why this is not a broad graph-database benchmark
The LatticeDB-versus-SQLite figures concern a particular traversal workload; they are not Graphalytics results. The Graph Data Council describes Graphalytics as an “industrial-grade benchmark” using six core algorithms, standard datasets, and reference outputs for comparing graph-analysis platforms. That broader benchmark context is distinct from the narrower comparison presented by LatticeDB.
Use the LatticeDB tables to understand a specific claim about traversal behavior, not as a substitute for testing the queries, data, and deployment model your application will actually use.
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