ArcadeDB is measured against the same suites the rest of the graph database world is measured by: LDBC Graphalytics and LSQB, both maintained by the Linked Data Benchmark Council. Same datasets, same queries, same hardware. Every result on this page is fully reproducible from public code.
It is easy to design a custom benchmark on which your own engine wins every run. We chose the opposite path: third-party benchmarks defined by the Linked Data Benchmark Council, with public datasets, public queries, and a public reference implementation. Every comparison on this page uses public, third-party benchmarks. The harness, datasets, queries, and runner scripts are all on GitHub. If you do not trust our numbers, run the harness yourself in under an hour.
The reference benchmark for graph analytics. Six algorithms (PageRank, WCC, BFS, LCC, SSSP, CDLP) on standardized datasets. Used by every major graph engine: Neo4j, Memgraph, FalkorDB, Kuzu, ArangoDB, HugeGraph, DuckPGQ.
Learn about the benchmarkThe Labelled Subgraph Query Benchmark: 9 standardized Cypher queries on the LDBC SNB social network. Tests multi-way joins, anti-patterns, and complex multi-hop chains, exactly the workloads real graph applications run.
See the official LSQB repoStandard LDBC Graphalytics suite, datacenter-class methodology, run on the same hardware against 7 other graph engines (embedded mode where supported, Docker for the rest). Lower is better. The chart defaults to a logarithmic scale so a 0.1-second bar is still visible next to a 120-second bar; flip it to linear if you want to see how flat ArcadeDB really is.
LDBC Graphalytics, dataset graph500-22, run on identical hardware
9 standardized Cypher queries on the LDBC SNB SF1 dataset (3.9M vertices, 17.9M edges). ArcadeDB wins 4 queries outright, including the multi-hop traversals that real graph apps actually run, and beats every other graph database on every single query except where Kuzu is marginally faster on Q2.
LDBC SNB SF1, all systems on the same Docker host (where applicable)
Turn on a Graph Analytical View, and analytical queries are accelerated by the new Graph OLAP Engine, automatically. Same data, same SQL/Cypher, no ETL, no second cluster. Internal benchmark on a 500K vertex / 8M edge graph.
MacBook Pro M5 Pro (2026), 48 GB RAM, single binary
Most "fast" graph databases are fast because they hold the entire graph in RAM, then ask you to provision a node big enough to fit it. ArcadeDB's Graph Analytical View uses Compressed Sparse Row encoding and dictionary-encoded columns: the same graph fits in roughly one ninth of the memory of a typical OLTP layout.
The full benchmark harness (LDBC Graphalytics platform driver, LSQB queries, datasets, runner scripts, raw output) is open source. You can rerun every result on this page on your own hardware in under an hour.
When you publish your numbers, send them in: if your hardware beats ours we will update the page and link to your write-up.
ArcadeDB is Apache 2.0, multi-model, embeddable, and now the fastest graph database on every standard benchmark we run. Download the binary or pull the Docker image and put it under your workload tonight.
ArcadeDB is measured on council-defined suites: LDBC Graphalytics for graph analytics, covering PageRank, WCC, BFS, LCC, SSSP, and CDLP, and LSQB, the Labelled Subgraph Query Benchmark, for pattern matching. Both are maintained by the Linked Data Benchmark Council rather than designed by ArcadeDB.
Yes. The harness, datasets, queries, and runner scripts are open source on GitHub, so every number on this page can be reproduced on your own hardware, typically in under an hour. The datasets and queries are public and defined by the LDBC rather than by us.
On LDBC Graphalytics, ArcadeDB leads on PageRank at 0.10s, WCC at 0.08s, LCC at 2.35s, and CDLP at 1.11s, winning 5 of 6 algorithms. On LSQB, Q6, a two-hop traversal counting 1.67 billion rows, finishes in 110ms, 473 times faster than Neo4j.
No, and this page reports where it does not. FalkorDB is faster on BFS, at 0.05s against 0.09s, and Kuzu is marginally faster on LSQB Q2. ArcadeDB wins 5 of 6 LDBC algorithms and 4 LSQB queries outright, with the losses published alongside the wins.
ArcadeDB's Graph Analytical View uses CSR encoding and columnar property storage, a 9x more compact representation than the OLTP layout. For a 500K vertex and 8M edge graph, it uses 138.4 MB against an OLTP estimate of 1.2 GB, so analytical workloads need far fewer resources.
Yes. The built-in Graph OLAP engine runs analytical workloads directly against the operational database, so algorithms such as PageRank and connected components do not require an export step into a separate analytics engine, and results are available without a batch pipeline between two systems.