v0.2 Performance Snapshot

This page is the short, product-facing performance snapshot for v0.2.

It is intentionally narrower than a full benchmark dashboard:

it uses named local benchmark artifacts from this repository;
it keeps graph-vs-Neo4j separate from the broader SurrealDB/pgstack matrix;
it does not collapse every workload into one winner number.

If you want the full benchmark machinery, use:

Snapshots used here

This page is based on two concrete local benchmark snapshots:

Graph vs Neo4j

harness: benchmarks/neo4j-graph-compare/run.py
run id: 20260519-150230
dataset: rows=5000, degree=4, edges=20000
warmup: 2
iterations: 6
artifact: target/benchmarks/neo4j-graph-compare/20260519-150230/report.json

CRUD refresh vs SurrealDB and pgstack

harness: benchmarks/run.sh surreal-suite
run id: 20260519T135910Z
engines: aiondb, surrealdb, pgstack
storage: all three engines on durable local storage
tests: [C]reate, [R]ead, [U]pdate
artifacts:
benchmarks/.state/surreal-suite/20260519T135910Z/summary.tsv
benchmarks/.state/surreal-suite/20260519T135910Z/metadata.json

Broader matrix vs SurrealDB and pgstack

harness: benchmarks/run.sh surreal-suite
run id: full-all-20260512T192959Z
engines: aiondb, surrealdb, pgstack
artifacts:
benchmarks/.state/surreal-suite/full-all-20260512T192959Z/summary.tsv
Benchmark Results

The repository also contains ultra-compare, but this page is based on the two snapshots above because they are the current inspectable artifacts with actual results.

Graph vs Neo4j

The current v0.2 graph snapshot is strong on core traversals and graph scans.

All representative queries below kept result parity = true.

Query	AionDB p50	Neo4j p50	Relative result
`out_depth1`	`0.546 ms`	`9.595 ms`	AionDB faster
`out_depth2`	`0.512 ms`	`7.457 ms`	AionDB faster
`out_depth3`	`0.407 ms`	`4.698 ms`	AionDB faster
`in_depth1`	`0.653 ms`	`4.476 ms`	AionDB faster
`edge_filter`	`12.810 ms`	`30.640 ms`	AionDB faster
`multi_out_where`	`15.845 ms`	`34.657 ms`	AionDB faster
`variable_len_4`	`0.549 ms`	`6.024 ms`	AionDB faster
`shortest_path`	`0.927 ms`	`3.022 ms`	AionDB faster

Read this correctly

This is not a claim that AionDB beats Neo4j on every graph workload.

It is a precise claim:

on this pinned local graph workload,
with this dataset shape,
with this exact harness,
AionDB is ahead on the measured traversal and shortest-path shapes in this snapshot.

Broader matrix vs SurrealDB and pgstack

The broader matrix is mixed, which is exactly why this page keeps it separate.

Fresh CRUD snapshot from surreal-suite:

Workload	AionDB	SurrealDB	pgstack	Read
`[C]reate`	`203.8 ops/s`	`197.0 ops/s`	`486.4 ops/s`	near tie with SurrealDB; pgstack ahead
`[R]ead`	`1409.1 ops/s`	`1840.3 ops/s`	`4545.6 ops/s`	AionDB behind both, but same order of magnitude as SurrealDB
`[U]pdate`	`556.1 ops/s`	`520.1 ops/s`	`1128.1 ops/s`	near tie with SurrealDB; pgstack ahead

Representative broader graph/hybrid results from the full surreal-suite snapshot:

Workload	AionDB	SurrealDB	pgstack	Read
`[S]can::count_all (2000)`	`7819.5 ops/s`	`168.85 ops/s`	`1073.5 ops/s`	AionDB far ahead
`[S]can::graph_edge_filter (2000)`	`1056.55 ops/s`	`219.0 ops/s`	`446.55 ops/s`	AionDB ahead
`[S]can::graph_bidirectional (2000)`	`2874.1 ops/s`	`1322.1 ops/s`	`8.66 ops/s`	AionDB ahead
`[S]can::graph_multi_count (2000)`	`2597.4 ops/s`	`22.82 ops/s`	`532.8 ops/s`	AionDB ahead
`[S]can::graph_multi_out (2000)`	`328.9 ops/s`	`341.9 ops/s`	`UNSUPPORTED`	near tie, slight SurrealDB lead
`[Complex]::graph_two_hop_filter_aggregate (5000)`	`35.20 ops/s`	`UNSUPPORTED`	`0.35 ops/s`	AionDB ahead where supported
`[Complex]::vector_join_graph_filter_rank (5000)`	`61.15 ops/s`	`UNSUPPORTED`	`418.05 ops/s`	pgstack ahead

What this means

The broader v0.2 picture is:

graph scans and graph-shaped filters are already a strong area for AionDB;
simple CRUD throughput is now competitive with SurrealDB on this all-durable CRUD refresh, while pgstack remains ahead;
hybrid graph/vector is real and runnable, but not yet a universal win;
some comparison cells are still UNSUPPORTED on one side or another, so fairness depends on the exact workload family.

That is why v0.2 should be described as:

strong on a growing set of graph and hybrid graph/query shapes, but still mixed as a general benchmark matrix.

Not:

faster than Neo4j, PostgreSQL, and SurrealDB overall.

Current v0.2 performance read

If you want the short version:

against Neo4j on the current pinned graph snapshot, AionDB looks strong;
against the broader SurrealDB / pgstack matrix, AionDB is strong on several graph-heavy scans but clearly not the winner on every workload family;
v0.2 has a credible performance story for graph evaluation, not a universal cross-engine win story.

Reproduce the snapshots

Current Neo4j graph snapshot:

python3 benchmarks/neo4j-graph-compare/run.py \
  --rows 5000 \
  --degree 4 \
  --warmup 2 \
  --iterations 6

Broader SurrealDB / pgstack matrix:

SURREAL_SUITE_ROWS=2000 \
SURREAL_SUITE_WARMUP_SECONDS=3 \
SURREAL_SUITE_ITERATIONS=1 \
SURREAL_SUITE_DURATION_SECONDS=20 \
benchmarks/run.sh surreal-suite

For claim discipline, keep:

the exact command;
the run id;
the commit hash;
the hardware;
the raw output artifacts.