NornicDB vs Neo4j — Northwind Benchmark Comparison

Products seeded: 48,000, Orders seeded: 48,000
Query workloads: 14 NornicDB / 14 Neo4j
Iterations/query: 30 NornicDB (5 warmup) / 30 Neo4j (5 warmup)
Seed batches / parallel sessions: 500 / 4 NornicDB; 500 / 4 Neo4j

Summary

Metric	NornicDB	Neo4j	Delta	Ratio
Overall mean latency (ms)	0.13	42.00	-99.7%	322.76×
End-to-end query-loop throughput (ops/sec)	20.22	19.57	+3.3%	1.03×
Query-latency-only aggregate throughput (ops/sec)	7,684.15	23.81	+32175.6%	322.76×
Query-loop duration (s)	20.773	21.458	-3.2%	1.03×
Seed duration (ms)	5,388.93	5,390.93	-0.0%	1.00×
Wipe duration (ms)	1.16	100.14	-98.8%	86.70×
Index setup duration (ms)	2.96	492.42	-99.4%	166.41×
Ingestion duration (ms)	5,384.81	4,798.36	+12.2%	0.89×
Ingestion nodes/sec	18,095.36	20,306.93	-10.9%	0.89×
Ingestion relationships/sec	57,950.10	65,032.61	-10.9%	0.89×
Avg CPU power (mW)	9,245.25	5,859.92	+57.8%	0.63×
Avg GPU power (mW)	205.31	1.23	+16613.5%	0.01×
Avg package power (mW)	9,450.56	5,861.15	+61.2%	0.62×
Energy during benchmark (J)	259.94	257.77	+0.8%	0.99×
Benchmark wall-clock (s)	28.48	44.37	-35.8%	1.56×
Peak memory used (bytes)	24.2 GiB	23.5 GiB	+2.7%	0.97×
Raw data files (bytes)	155,422,720	53,207,040	+192.1%	0.34×

Delta = (NornicDB − Neo4j) / Neo4j. Ratio compares Neo4j to NornicDB for metrics where lower is better (latency, energy, disk), and NornicDB to Neo4j for throughput (higher is better).End-to-end query-loop throughput divides measured operations by the full suite window, including warmups and per-query setup; the query-latency-only rate excludes both.

NornicDB Parser Modes: default vs ANTLR

Query latency and throughput of the two NORNICDB_PARSER modes over the same seeded Northwind graph and the same workload. Each mode ran serialized from a freshly wiped data directory. Seeding, storage, power and memory are not compared here.

Metric	NornicDB (default)	NornicDB (ANTLR)	Delta	ANTLR slower by
Overall mean latency (ms)	0.13	0.17	+30.1%	1.30×
End-to-end query-loop throughput (ops/sec)	20.22	21.08	+4.3%	0.96×
Query-latency-only aggregate throughput (ops/sec)	7,684.15	5,908.17	-23.1%	1.30×
Query-loop duration (s)	20.773	19.923	-4.1%	0.96×

Delta = (ANTLR − default) / default. "ANTLR slower by" is ANTLR ÷ default for latency and default ÷ ANTLR for throughput, so values above 1.00× mean ANTLR mode is slower.

Per-query latency

Query	Mean default (ms)	Mean ANTLR (ms)	Mean ×	Median default (ms)	Median ANTLR (ms)	P95 default (ms)	P95 ANTLR (ms)	P99 default (ms)	P99 ANTLR (ms)	Ops/sec default	Ops/sec ANTLR	Same result
`products_per_category`	0.15	0.18	1.18×	0.15	0.18	0.18	0.21	0.18	0.23	5,874.77	5,020.33	Yes
`customer_category_distinct_orders`	0.11	0.18	1.59×	0.11	0.17	0.13	0.22	0.14	0.29	8,668.22	5,488.64	Yes
`optional_match_orders_count`	0.15	0.19	1.30×	0.14	0.18	0.18	0.23	0.18	0.25	6,072.82	4,807.92	Yes
`revenue_by_product`	0.09	0.16	1.69×	0.09	0.15	0.12	0.18	0.18	0.20	10,394.26	6,224.71	Yes
`products_by_supplier`	0.11	0.14	1.28×	0.11	0.14	0.12	0.16	0.14	0.17	8,585.94	6,700.67	Yes
`orders_by_customer`	0.12	0.13	1.10×	0.12	0.13	0.14	0.15	0.15	0.15	7,890.93	7,268.54	Yes
`revenue_by_category`	0.14	0.20	1.44×	0.14	0.20	0.16	0.24	0.17	0.25	6,100.71	4,367.55	Yes
`revenue_by_supplier`	0.12	0.16	1.32×	0.12	0.16	0.15	0.19	0.16	0.20	7,782.44	5,985.98	Yes
`revenue_by_customer`	0.12	0.16	1.29×	0.12	0.15	0.15	0.18	0.16	0.18	7,810.47	6,132.30	Yes
`order_line_sales_by_country`	0.11	0.14	1.27×	0.10	0.14	0.17	0.17	0.18	0.18	8,599.68	6,835.79	Yes
`low_stock_products`	0.18	0.20	1.10×	0.17	0.19	0.21	0.22	0.32	0.24	4,864.08	4,488.30	Yes
`products_in_category`	0.16	0.19	1.19×	0.16	0.19	0.17	0.20	0.18	0.21	5,420.75	4,635.35	Yes
`order_line_quantity_distribution`	0.10	0.14	1.43×	0.09	0.13	0.12	0.17	0.13	0.17	9,835.53	7,048.87	Yes
`customer_order_details`	0.16	0.21	1.30×	0.16	0.21	0.17	0.22	0.19	0.23	5,211.80	4,188.51	Yes

Mean × is ANTLR mean ÷ default mean. "Same result" compares each query's row count and SHA-256 result fingerprint across the two modes.

Both modes returned identical results for all 14 queries.

Full Query Suite

Each workload is reported independently with all recorded latency percentiles, range, sample count, and per-query rate.

`products_per_category`

Product counts grouped by category, with a full result sort.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.15	0.15	0.18	0.18	0.14	0.18	0.01	5,874.77	96
Neo4j	30	6.33	6.25	7.35	8.65	5.62	9.10	0.68	157.49	96
NornicDB (ANTLR)	30	0.18	0.18	0.21	0.23	0.14	0.23	0.02	5,020.33	96

Mean-latency ratio (Neo4j / NornicDB): 41.62×.

Mean-latency ratio (NornicDB ANTLR / default): 1.18×.

Cypher

cypher

MATCH (c:Category)<-[:PART_OF]-(p:Product)
			RETURN c.categoryName AS categoryName, count(p) AS productCount
			ORDER BY productCount DESC

`customer_category_distinct_orders`

Four-hop customer-to-category traversal with distinct-order aggregation.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.11	0.11	0.13	0.14	0.10	0.14	0.01	8,668.22	10
Neo4j	30	172.48	171.01	182.50	192.73	165.32	196.69	7.12	5.80	10
NornicDB (ANTLR)	30	0.18	0.17	0.22	0.29	0.15	0.32	0.03	5,488.64	10

Mean-latency ratio (Neo4j / NornicDB): 1546.46×.

Mean-latency ratio (NornicDB ANTLR / default): 1.59×.

Cypher

cypher

MATCH (c:Customer)-[:PURCHASED]->(o:Order)-[:ORDERS]->(p:Product)-[:PART_OF]->(cat:Category)
			RETURN c.companyName AS companyName, cat.categoryName AS categoryName, count(DISTINCT o) AS orders
			ORDER BY orders DESC, companyName ASC, categoryName ASC
			LIMIT 10

`optional_match_orders_count`

Optional product-to-order traversal with zero-match preservation and top-100 sorting.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.15	0.14	0.18	0.18	0.13	0.18	0.01	6,072.82	100
Neo4j	30	53.05	52.90	54.88	55.75	51.66	56.09	0.99	18.84	100
NornicDB (ANTLR)	30	0.19	0.18	0.23	0.25	0.16	0.26	0.03	4,807.92	100

Mean-latency ratio (Neo4j / NornicDB): 364.67×.

Mean-latency ratio (NornicDB ANTLR / default): 1.30×.

Cypher

cypher

MATCH (p:Product)
			OPTIONAL MATCH (p)<-[r:ORDERS]-(o:Order)
			RETURN p.productName AS productName, count(o) AS orderCount
			ORDER BY orderCount DESC, productName ASC
			LIMIT 100

`revenue_by_product`

Relationship-property arithmetic and revenue aggregation grouped by product.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.09	0.09	0.12	0.18	0.07	0.20	0.03	10,394.26	10
Neo4j	30	67.65	67.10	70.47	71.28	65.36	71.49	1.51	14.78	10
NornicDB (ANTLR)	30	0.16	0.15	0.18	0.20	0.13	0.20	0.02	6,224.71	10

Mean-latency ratio (Neo4j / NornicDB): 728.49×.

Mean-latency ratio (NornicDB ANTLR / default): 1.69×.

Cypher

cypher

MATCH (p:Product)<-[r:ORDERS]-(:Order)
			WITH p, sum(p.unitPrice * r.quantity) AS revenue
			RETURN p.productName AS productName, revenue
			ORDER BY revenue DESC, productName ASC
			LIMIT 10

`products_by_supplier`

Supplier-to-product traversal with top-N aggregation and deterministic ties.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.11	0.11	0.12	0.14	0.09	0.15	0.01	8,585.94	25
Neo4j	30	6.87	6.52	8.75	9.82	5.87	9.99	1.03	145.33	25
NornicDB (ANTLR)	30	0.14	0.14	0.16	0.17	0.13	0.18	0.01	6,700.67	25

Mean-latency ratio (Neo4j / NornicDB): 61.93×.

Mean-latency ratio (NornicDB ANTLR / default): 1.28×.

Cypher

cypher

MATCH (s:Supplier)-[:SUPPLIES]->(p:Product)
			RETURN s.companyName AS supplier, count(p) AS products
			ORDER BY products DESC, supplier ASC
			LIMIT 25

`orders_by_customer`

Customer-to-order traversal grouped into a top-25 order-count ranking.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.12	0.12	0.14	0.15	0.10	0.16	0.01	7,890.93	25
Neo4j	30	6.74	6.66	6.89	8.39	6.46	8.99	0.44	148.13	25
NornicDB (ANTLR)	30	0.13	0.13	0.15	0.15	0.12	0.16	0.01	7,268.54	25

Mean-latency ratio (Neo4j / NornicDB): 55.85×.

Mean-latency ratio (NornicDB ANTLR / default): 1.10×.

Cypher

cypher

MATCH (c:Customer)-[:PURCHASED]->(o:Order)
			RETURN c.companyName AS customer, count(o) AS orders
			ORDER BY orders DESC, customer ASC
			LIMIT 25

`revenue_by_category`

Three-hop category revenue aggregation from order-line quantities and product prices.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.14	0.14	0.16	0.17	0.12	0.17	0.01	6,100.71	96
Neo4j	30	63.18	62.36	68.46	69.24	60.74	69.48	2.37	15.82	96
NornicDB (ANTLR)	30	0.20	0.20	0.24	0.25	0.18	0.25	0.02	4,367.55	96

Mean-latency ratio (Neo4j / NornicDB): 444.62×.

Mean-latency ratio (NornicDB ANTLR / default): 1.44×.

Cypher

cypher

MATCH (c:Category)<-[:PART_OF]-(p:Product)<-[r:ORDERS]-(:Order)
			RETURN c.categoryName AS category, sum(p.unitPrice * r.quantity) AS revenue
			ORDER BY revenue DESC, category ASC

`revenue_by_supplier`

Supplier-to-product-to-order traversal with revenue aggregation and top-25 sorting.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.12	0.12	0.15	0.16	0.10	0.16	0.02	7,782.44	25
Neo4j	30	60.92	60.68	62.69	63.42	59.30	63.68	0.96	16.41	25
NornicDB (ANTLR)	30	0.16	0.16	0.19	0.20	0.14	0.21	0.02	5,985.98	25

Mean-latency ratio (Neo4j / NornicDB): 501.64×.

Mean-latency ratio (NornicDB ANTLR / default): 1.32×.

Cypher

cypher

MATCH (s:Supplier)-[:SUPPLIES]->(p:Product)<-[r:ORDERS]-(:Order)
			RETURN s.companyName AS supplier, sum(p.unitPrice * r.quantity) AS revenue
			ORDER BY revenue DESC, supplier ASC
			LIMIT 25

`revenue_by_customer`

Customer-order-product traversal with relationship-property revenue aggregation.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.12	0.12	0.15	0.16	0.09	0.16	0.02	7,810.47	25
Neo4j	30	69.97	69.60	72.59	73.82	68.21	74.29	1.48	14.29	25
NornicDB (ANTLR)	30	0.16	0.15	0.18	0.18	0.14	0.18	0.01	6,132.30	25

Mean-latency ratio (Neo4j / NornicDB): 573.54×.

Mean-latency ratio (NornicDB ANTLR / default): 1.29×.

Cypher

cypher

MATCH (c:Customer)-[:PURCHASED]->(:Order)-[r:ORDERS]->(p:Product)
			RETURN c.companyName AS customer, sum(p.unitPrice * r.quantity) AS revenue
			ORDER BY revenue DESC, customer ASC
			LIMIT 25

`order_line_sales_by_country`

Order-line scan grouped by shipping country with line-count and unit aggregation.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.11	0.10	0.17	0.18	0.09	0.19	0.03	8,599.68	15
Neo4j	30	48.79	48.51	50.78	51.67	48.05	51.69	0.85	20.49	15
NornicDB (ANTLR)	30	0.14	0.14	0.17	0.18	0.12	0.19	0.02	6,835.79	15

Mean-latency ratio (Neo4j / NornicDB): 437.32×.

Mean-latency ratio (NornicDB ANTLR / default): 1.27×.

Cypher

cypher

MATCH (o:Order)-[r:ORDERS]->(:Product)
			RETURN o.shipCountry AS country, count(r) AS orderLines, sum(r.quantity) AS units
			ORDER BY orderLines DESC, country ASC

`low_stock_products`

Selective numeric property filter followed by a stable top-100 product sort.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.18	0.17	0.21	0.32	0.15	0.36	0.04	4,864.08	100
Neo4j	30	7.57	7.47	7.79	8.97	7.32	9.45	0.37	131.51	100
NornicDB (ANTLR)	30	0.20	0.19	0.22	0.24	0.18	0.24	0.01	4,488.30	100

Mean-latency ratio (Neo4j / NornicDB): 42.49×.

Mean-latency ratio (NornicDB ANTLR / default): 1.10×.

Cypher

cypher

MATCH (p:Product)
			WHERE p.unitsInStock < 25
			RETURN p.productName AS product, p.unitsInStock AS unitsInStock, p.unitPrice AS unitPrice
			ORDER BY unitsInStock ASC, product ASC
			LIMIT 100

`products_in_category`

Selective category lookup and adjacent product traversal with a top-100 result.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.16	0.16	0.17	0.18	0.14	0.18	0.01	5,420.75	100
Neo4j	30	0.95	0.84	1.07	2.58	0.79	3.19	0.43	1,015.91	100
NornicDB (ANTLR)	30	0.19	0.19	0.20	0.21	0.17	0.21	0.01	4,635.35	100

Mean-latency ratio (Neo4j / NornicDB): 6.06×.

Mean-latency ratio (NornicDB ANTLR / default): 1.19×.

Cypher

cypher

MATCH (c:Category {categoryID: 7})<-[:PART_OF]-(p:Product)
			RETURN p.productName AS product, p.unitPrice AS unitPrice, p.unitsInStock AS unitsInStock
			ORDER BY product ASC
			LIMIT 100

`order_line_quantity_distribution`

Full relationship-property scan grouped by line quantity.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.10	0.09	0.12	0.13	0.09	0.13	0.01	9,835.53	25
Neo4j	30	22.58	22.05	25.98	26.36	21.55	26.40	1.29	44.26	25
NornicDB (ANTLR)	30	0.14	0.13	0.17	0.17	0.11	0.17	0.02	7,048.87	25

Mean-latency ratio (Neo4j / NornicDB): 235.33×.

Mean-latency ratio (NornicDB ANTLR / default): 1.43×.

Cypher

cypher

MATCH ()-[r:ORDERS]->()
			RETURN r.quantity AS quantity, count(r) AS lineCount
			ORDER BY quantity ASC

`customer_order_details`

Selective customer lookup followed by order-line expansion and computed row projection.

Engine	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec	Rows
NornicDB	30	0.16	0.16	0.17	0.19	0.14	0.20	0.01	5,211.80	100
Neo4j	30	0.95	0.83	1.22	2.85	0.67	3.50	0.51	1,018.17	100
NornicDB (ANTLR)	30	0.21	0.21	0.22	0.23	0.19	0.23	0.01	4,188.51	100

Mean-latency ratio (Neo4j / NornicDB): 5.94×.

Mean-latency ratio (NornicDB ANTLR / default): 1.30×.

Cypher

cypher

MATCH (c:Customer {customerID: 42})-[:PURCHASED]->(o:Order)-[r:ORDERS]->(p:Product)
			RETURN o.orderID AS orderID, p.productName AS product, r.quantity AS quantity,
			       p.unitPrice * r.quantity AS extendedPrice
			ORDER BY orderID ASC, product ASC
			LIMIT 100

Correctness

Seed verification. Post-seed counts reported by each database (via MATCH (n:Label) RETURN count(n) and equivalent edge queries).

Entity	NornicDB	Neo4j	Match
Category	96	96	Yes
Supplier	144	144	Yes
Customer	1,200	1,200	Yes
Product	48,000	48,000	Yes
Order	48,000	48,000	Yes
PART_OF	48,000	48,000	Yes
SUPPLIES	48,000	48,000	Yes
PURCHASED	48,000	48,000	Yes
ORDERS	168,050	168,050	Yes

Per-query result fingerprints. Each engine runs the query on the first (warmup) iteration, canonicalises the full result set, and hashes it with SHA-256. A matching row_count + matching hash means the engines returned the same data.

Query	NornicDB rows	Neo4j rows	NornicDB hash	Neo4j hash	Match
`products_per_category`	96	96	`91e9f1f06368…`	`91e9f1f06368…`	Yes
`customer_category_distinct_orders`	10	10	`5da36214d516…`	`5da36214d516…`	Yes
`optional_match_orders_count`	100	100	`8950fcdaab16…`	`8950fcdaab16…`	Yes
`revenue_by_product`	10	10	`60b64c678f4c…`	`60b64c678f4c…`	Yes
`products_by_supplier`	25	25	`af1e9b5d1d66…`	`af1e9b5d1d66…`	Yes
`orders_by_customer`	25	25	`ecff10cfcfa9…`	`ecff10cfcfa9…`	Yes
`revenue_by_category`	96	96	`23ba39858bf7…`	`23ba39858bf7…`	Yes
`revenue_by_supplier`	25	25	`41900ee05a8f…`	`41900ee05a8f…`	Yes
`revenue_by_customer`	25	25	`639a28655928…`	`639a28655928…`	Yes
`order_line_sales_by_country`	15	15	`a86030b2ba5e…`	`a86030b2ba5e…`	Yes
`low_stock_products`	100	100	`a8f2f994d920…`	`a8f2f994d920…`	Yes
`products_in_category`	100	100	`e207262bf51e…`	`e207262bf51e…`	Yes
`order_line_quantity_distribution`	25	25	`e239e5f47878…`	`e239e5f47878…`	Yes
`customer_order_details`	100	100	`9d6ef178ee44…`	`9d6ef178ee44…`	Yes

Intra-run stability. Every iteration of each query re-fingerprints its result set; a mismatch within a single engine's run is flagged below.

No intra-run mismatches on either engine.

All correctness checks passed — both engines seeded identically and returned identical result sets (by row count and canonical SHA-256 fingerprint) for every benchmark query.

Storage

Raw data files only (preallocated scratch, WAL, and indexes excluded from the headline):

Bucket	NornicDB	Neo4j
Raw data	148.2 MiB (155,422,720 B)	50.7 MiB (53,207,040 B)
Indexes / stats	0 B (0 B)	7.5 MiB (7,913,472 B)
Write-ahead logs	260.0 KiB (266,240 B)	144.4 MiB (151,400,448 B)
Metadata	8.0 KiB (8,192 B)	1.1 MiB (1,191,936 B)
Scratch (excluded)	1.0 MiB (1,048,576 B)	4.0 KiB (4,096 B)
Unclassified	0 B (0 B)	0 B (0 B)
Total `du`	149.6 MiB	203.8 MiB

Raw data ratio: 2.92× Neo4j (larger)
Full-dir ratio (includes scratch/WAL): 0.73× Neo4j

Power

	NornicDB	Neo4j
Samples	27	43
Duration (s)	27.51	43.98
CPU avg (mW)	9,245.2	5,859.9
GPU avg (mW)	205.3	1.2
Package avg (mW)	9,450.6	5,861.1
Energy (J)	259.94	257.77

Memory Pressure

System-wide memory during each engine's full lifecycle (startup → benchmark → shutdown).

	NornicDB	Neo4j
Samples	31	47
Avg used (active+wired+compressor)	23.4 GiB	23.3 GiB
Peak used	24.2 GiB	23.5 GiB
Avg free	7.3 GiB	7.2 GiB
Min free	6.4 GiB	6.6 GiB
Avg compressed (logical)	7.2 GiB	7.2 GiB
Peak compressed	7.2 GiB	7.2 GiB

Notes

Power figures are Apple powermetrics estimates; treat as directional, not absolute. Apple's own docs note that reported averages are approximate.
Both databases were freshly initialized before each run; Neo4j was stopped during the NornicDB run, and vice versa, to isolate measurements.
Benchmarks ran over the Bolt protocol using the neo4j-go-driver.
Storage classification: NornicDB raw data = *.sst + *.vlog (LSM records + value log). Neo4j raw data = neostore*store.db* (record stores). Preallocated scratch files — Badger's 8 MiB memtable (*.mem) and 1 MiB discard log (DISCARD), and Neo4j empty *.id allocation files — are excluded because their size is fixed/preallocated and does not scale with the dataset.

NornicDB vs Neo4j — Northwind Benchmark Comparison ​

Summary ​

NornicDB Parser Modes: default vs ANTLR ​

Per-query latency ​

Full Query Suite ​

products_per_category ​

customer_category_distinct_orders ​

optional_match_orders_count ​

revenue_by_product ​

products_by_supplier ​

orders_by_customer ​

revenue_by_category ​

revenue_by_supplier ​

revenue_by_customer ​

order_line_sales_by_country ​

low_stock_products ​

products_in_category ​

order_line_quantity_distribution ​

customer_order_details ​

Correctness ​

Storage ​

Power ​

Memory Pressure ​

Notes ​

NornicDB vs Neo4j — Northwind Benchmark Comparison

Summary

NornicDB Parser Modes: default vs ANTLR

Per-query latency

Full Query Suite

`products_per_category`

`customer_category_distinct_orders`

`optional_match_orders_count`

`revenue_by_product`

`products_by_supplier`

`orders_by_customer`

`revenue_by_category`

`revenue_by_supplier`

`revenue_by_customer`

`order_line_sales_by_country`

`low_stock_products`

`products_in_category`

`order_line_quantity_distribution`

`customer_order_details`

Correctness

Storage

Power

Memory Pressure

Notes