Neo4j — Northwind Benchmark Report

Run: 2026-10-09T09:31:27.408652-07:00 → 2026-10-09T09:31:54.389442-07:00Endpoint: bolt://localhost:7687 (database neo4j)

Workload

Categories: 96 | Suppliers: 144 | Customers: 1,200
Products seeded: 48,000
Orders seeded: 48,000 (1..6 lines each)
Random seed: 42 (deterministic dataset)
Seed nodes: 97,440
Seed relationships: 312,050
Approx. seed payload (JSON-serialized): 35.4 MiB
Seed duration: 5,390.93 ms
Wipe duration: 100.14 ms
Index setup duration: 492.42 ms
Ingestion duration (row generation and writes): 4,798.36 ms
Ingestion nodes/sec: 20,306.93
Ingestion relationships/sec: 65,032.61
Seed batch size: 500 rows
Seed parallelism: 4 sessions per phase
Query workloads: 14
Iterations per query: 30
Warmup iterations per query: 5

Query Latency

Query	Description	Samples	Mean (ms)	Median (ms)	P95 (ms)	P99 (ms)	Min (ms)	Max (ms)	StdDev (ms)	Ops/sec
`products_per_category`	Product counts grouped by category, with a full result sort.	30	6.33	6.25	7.35	8.65	5.62	9.10	0.68	157.49
`customer_category_distinct_orders`	Four-hop customer-to-category traversal with distinct-order aggregation.	30	172.48	171.01	182.50	192.73	165.32	196.69	7.12	5.80
`optional_match_orders_count`	Optional product-to-order traversal with zero-match preservation and top-100 sorting.	30	53.05	52.90	54.88	55.75	51.66	56.09	0.99	18.84
`revenue_by_product`	Relationship-property arithmetic and revenue aggregation grouped by product.	30	67.65	67.10	70.47	71.28	65.36	71.49	1.51	14.78
`products_by_supplier`	Supplier-to-product traversal with top-N aggregation and deterministic ties.	30	6.87	6.52	8.75	9.82	5.87	9.99	1.03	145.33
`orders_by_customer`	Customer-to-order traversal grouped into a top-25 order-count ranking.	30	6.74	6.66	6.89	8.39	6.46	8.99	0.44	148.13
`revenue_by_category`	Three-hop category revenue aggregation from order-line quantities and product prices.	30	63.18	62.36	68.46	69.24	60.74	69.48	2.37	15.82
`revenue_by_supplier`	Supplier-to-product-to-order traversal with revenue aggregation and top-25 sorting.	30	60.92	60.68	62.69	63.42	59.30	63.68	0.96	16.41
`revenue_by_customer`	Customer-order-product traversal with relationship-property revenue aggregation.	30	69.97	69.60	72.59	73.82	68.21	74.29	1.48	14.29
`order_line_sales_by_country`	Order-line scan grouped by shipping country with line-count and unit aggregation.	30	48.79	48.51	50.78	51.67	48.05	51.69	0.85	20.49
`low_stock_products`	Selective numeric property filter followed by a stable top-100 product sort.	30	7.57	7.47	7.79	8.97	7.32	9.45	0.37	131.51
`products_in_category`	Selective category lookup and adjacent product traversal with a top-100 result.	30	0.95	0.84	1.07	2.58	0.79	3.19	0.43	1,015.91
`order_line_quantity_distribution`	Full relationship-property scan grouped by line quantity.	30	22.58	22.05	25.98	26.36	21.55	26.40	1.29	44.26
`customer_order_details`	Selective customer lookup followed by order-line expansion and computed row projection.	30	0.95	0.83	1.22	2.85	0.67	3.50	0.51	1,018.17

Overall mean latency: 42.00 ms
Measured query operations: 420
End-to-end query-loop throughput: 19.57 ops/sec
Query-latency-only aggregate throughput: 23.81 ops/sec
Query-loop duration: 21.458 s
Query-loop duration includes warmups and per-query setup; only measured iterations count toward the end-to-end rate.
Full lifecycle wall-clock (sampled): 44.367 s

Correctness

Seed counts (from the database's own count(...) queries):

Entity	Count
Category	96
Supplier	144
Customer	1,200
Product	48,000
Order	48,000
PART_OF edges	48,000
SUPPLIES edges	48,000
PURCHASED edges	48,000
ORDERS edges	168,050

Per-query result fingerprints (SHA-256 over canonicalised rows):

Query	Rows	Hash	Stable across iterations
`products_per_category`	96	`91e9f1f063680a6d…`	Yes
`customer_category_distinct_orders`	10	`5da36214d5163220…`	Yes
`optional_match_orders_count`	100	`8950fcdaab16eaeb…`	Yes
`revenue_by_product`	10	`60b64c678f4c01fd…`	Yes
`products_by_supplier`	25	`af1e9b5d1d663a02…`	Yes
`orders_by_customer`	25	`ecff10cfcfa9cc34…`	Yes
`revenue_by_category`	96	`23ba39858bf74ace…`	Yes
`revenue_by_supplier`	25	`41900ee05a8f994a…`	Yes
`revenue_by_customer`	25	`639a286559282c97…`	Yes
`order_line_sales_by_country`	15	`a86030b2ba5eede9…`	Yes
`low_stock_products`	100	`a8f2f994d920e6d8…`	Yes
`products_in_category`	100	`e207262bf51ed857…`	Yes
`order_line_quantity_distribution`	25	`e239e5f47878c862…`	Yes
`customer_order_details`	100	`9d6ef178ee445057…`	Yes

No intra-run correctness errors.

Power Consumption

Samples collected: 43 (~1s each)
Sampled duration: 43.98 s
Avg CPU power: 5,859.9 mW
Avg GPU power: 1.2 mW
Avg package power: 5,861.1 mW
Estimated energy (benchmark window): 257.77 J

Memory Pressure

Samples collected: 47 (~1s each)
Avg used (active + wired + compressor): 23.3 GiB
Peak used: 23.5 GiB
Avg free: 7.2 GiB
Min free: 6.6 GiB
Avg compressed (logical): 7.2 GiB
Peak compressed: 7.2 GiB

Storage

Raw data files: 50.7 MiB (53,207,040 bytes)
Indexes/stats: 7.5 MiB (7,913,472 bytes)
Write-ahead logs: 144.4 MiB (151,400,448 bytes)
Metadata/bookkeeping: 1.1 MiB (1,191,936 bytes)
Preallocated scratch (excluded): 4.0 KiB (4,096 bytes)
Unclassified (other): 0 B (0 bytes)
Full data directory du: 203.8 MiB (213,716,992 bytes)
Classified sum: 203.8 MiB (213,716,992 bytes, Δ vs du = +0 bytes)

Raw-data size is the comparison headline. Preallocated memtable/WAL scratch files (8 MiB memtable on Badger, 1 MiB GC discard log, etc.) are excluded because they hold the same bytes regardless of dataset size.

Top raw-data files

File	Size
`databases/neo4j/neostore.propertystore.db`	18.1 MiB
`databases/neo4j/neostore.propertystore.db.strings`	14.9 MiB
`databases/neo4j/neostore.relationshipstore.db`	10.2 MiB
`databases/neo4j/neostore.propertystore.db.arrays`	5.9 MiB
`databases/neo4j/neostore.nodestore.db`	1.4 MiB
`databases/neo4j/neostore.relationshipgroupstore.degrees.db`	48.0 KiB
`databases/system/neostore.relationshipgroupstore.degrees.db`	40.0 KiB
`databases/neo4j/neostore`	8.0 KiB
`databases/neo4j/neostore.labeltokenstore.db.names`	8.0 KiB
`databases/neo4j/neostore.relationshiptypestore.db.names`	8.0 KiB

Queries

`products_per_category`

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`

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`

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`

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`

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`

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`

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`

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`

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`

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`

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`

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`

cypher

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

`customer_order_details`

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

Neo4j — Northwind Benchmark Report ​

Workload ​

Query Latency ​

Correctness ​

Power Consumption ​

Memory Pressure ​

Storage ​

Queries ​

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 ​

Neo4j — Northwind Benchmark Report

Workload

Query Latency

Correctness

Power Consumption

Memory Pressure

Storage

Queries

`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`