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SURREALDB 3.0

A new foundation for performance

Graph queries up to 24x faster. Query planning 4,600x smarter. Vector search ~8x faster. Built on a rearchitected execution engine designed for production-grade workloads.

SurrealDB 3.0 introduces a new streaming execution engine with a standard AST → LogicalPlan → ExecutionPlan pipeline, delivering dramatic performance improvements across every workload.

Performance at a glance

Key improvements in SurrealDB 3.0 vs. SurrealDB 2.0 across all storage engines.

4–24x

Faster graph queries

Across all traversal depths and patterns

4,600x+

Smarter query planner

WHERE id = queries now sub-millisecond

3–7x

Faster table scans

With LIMIT, START, and combined operations

~3x

Faster ORDER BY

Across all storage engines

~8x

Faster vector search

HNSW indexed queries: ~36s → ~4.5s

~36%

Faster GROUP BY

Multi-aggregation and dedup workloads

Why these benchmarks matter

Enterprises like Tencent and NVIDIA run SurrealDB in production across high-throughput, business-critical workloads. As adoption grows, demonstrating predictable, production-grade performance matters more than ever. SurrealDB 3.0 is the next major step - sharing both the results and the architectural changes behind them.

Benchmarking a multi-model database

SurrealDB unifies relational, document, graph, vector, geospatial, and full-text workloads in one engine. Fair benchmarking is complex - databases vary in durability guarantees and configuration trade-offs. We've kept comparisons balanced and transparent.

Customers like Tencent reduced nine separate tools down to one with SurrealDB.

The new execution engine

SurrealDB 3.0 rearchitects query execution around a standard AST → LogicalPlan → ExecutionPlan pipeline with optimisations at each stage. The engine is fully streaming internally - end-to-end client streaming is coming in a future minor release.

Our open-source benchmarking tool

We built crud-bench in Rust to evaluate SurrealDB across its full workload spectrum - CRUD, graph traversals, vector search, and more - consistently across embedded, networked, and remote deployments.

Benchmark hardware

All benchmarks were run on the same hardware for consistency.

ComponentSpecification
CPUAMD Ryzen Threadripper 9970X
RAMSamsung DDR5 4800MHz 64GB RDIMM
SSDLexar EQ790 4TB NVMe
MotherboardASUS PRO WS TRX50-SAGE WIFI A
CPU CoolerArctic Freezer 4U-M
Power SupplyCORSAIR RM1000x
GPUPowerColor AMD Radeon RX 7600 Fighter 8GB
CaseIn-Win IW-R400-01N 4U

Benchmark results

SurrealDB 3.0 vs. SurrealDB 2.0 across three storage engines. Lower latency and higher throughput is better.

Create

2.0 Mean

1.94 ms

3.0 Mean

0.72 ms

Latency

+62.9%

Throughput

+168.7%

Update

2.0 Mean

2.58 ms

3.0 Mean

0.73 ms

Latency

+71.7%

Throughput

+249.6%

WHERE id = record:42

2.0 Mean

3,936 ms

3.0 Mean

0.68 ms

Latency

+100.0%

Throughput

+436,303%

LIMIT (all fields)

2.0 Mean

17.45 ms

3.0 Mean

2.46 ms

Latency

+85.9%

Throughput

+550.9%

ORDER BY

2.0 Mean

17,271 ms

3.0 Mean

5,275 ms

Latency

+69.5%

Throughput

+232.3%

ORDER BY (multi)

2.0 Mean

17,388 ms

3.0 Mean

5,193 ms

Latency

+70.1%

Throughput

+235.6%

Graph out depth 3

2.0 Mean

18.02 ms

3.0 Mean

3.02 ms

Latency

+83.2%

Throughput

+449.0%

Graph multi out

2.0 Mean

148.68 ms

3.0 Mean

23.32 ms

Latency

+84.3%

Throughput

+504.6%

Graph depth 2 + LIMIT

2.0 Mean

78.83 ms

3.0 Mean

9.41 ms

Latency

+88.1%

Throughput

+696.2%

GROUP BY (multi agg)

2.0 Mean

9,738 ms

3.0 Mean

6,203 ms

Latency

+36.3%

Throughput

+53.7%

GROUP BY (dedup agg)

2.0 Mean

9,735 ms

3.0 Mean

6,278 ms

Latency

+35.5%

Throughput

+54.0%

HNSW vector search

2.0 Mean

38,582 ms

3.0 Mean

4,847 ms

Latency

+87.4%

Throughput

+671.2%

SELECT FETCH

2.0 Mean

39.07 ms

3.0 Mean

7.58 ms

Latency

+80.6%

Throughput

+401.8%

Explore the data

Filter by engine and category. Hover for values and drag to zoom.

What's next

Performance improvements are rolling out across coming releases, with broader cross-database benchmarks to follow.

Write support

Expanding the new executor to all statement types.

E2E streaming

From internal execution all the way to the client.

Indexing

Better planning, reduced scan overhead, new index types.

Pipeline

Continued optimisation of planning and execution.

Related resources

Dive deeper into SurrealDB 3.0 and our performance journey.

Introducing SurrealDB 3.0

The future of AI agent memory - our full 3.0 release announcement.

Our benchmarking journey

How we approach benchmarking a multi-model database - methodology and learnings.

crud-bench on GitHub

Our open-source benchmarking tool - run your own benchmarks and contribute.

LET'S GOOOO

Ready to experience the performance?

Try SurrealDB 3.0 today. Join the community on Discord to chat directly with the team.