NornicDB (ANTLR parser) — Northwind Benchmark Report

Run: 2026-10-09T09:30:52.442158-07:00 → 2026-10-09T09:31:17.72512-07:00Endpoint: bolt://localhost:17687 (database nornic)

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: 5,354.84 ms
• Wipe duration: 22.65 ms
• Index setup duration: 10.05 ms
• Ingestion duration (row generation and writes): 5,322.12 ms
• Ingestion nodes/sec: 18,308.49
• Ingestion relationships/sec: 58,632.64
• 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	0.18	0.18	0.21	0.23	0.14	0.23	0.02	5,020.33
customer_category_distinct_orders	Four-hop customer-to-category traversal with distinct-order aggregation.	30	0.18	0.17	0.22	0.29	0.15	0.32	0.03	5,488.64
optional_match_orders_count	Optional product-to-order traversal with zero-match preservation and top-100 sorting.	30	0.19	0.18	0.23	0.25	0.16	0.26	0.03	4,807.92
revenue_by_product	Relationship-property arithmetic and revenue aggregation grouped by product.	30	0.16	0.15	0.18	0.20	0.13	0.20	0.02	6,224.71
products_by_supplier	Supplier-to-product traversal with top-N aggregation and deterministic ties.	30	0.14	0.14	0.16	0.17	0.13	0.18	0.01	6,700.67
orders_by_customer	Customer-to-order traversal grouped into a top-25 order-count ranking.	30	0.13	0.13	0.15	0.15	0.12	0.16	0.01	7,268.54
revenue_by_category	Three-hop category revenue aggregation from order-line quantities and product prices.	30	0.20	0.20	0.24	0.25	0.18	0.25	0.02	4,367.55
revenue_by_supplier	Supplier-to-product-to-order traversal with revenue aggregation and top-25 sorting.	30	0.16	0.16	0.19	0.20	0.14	0.21	0.02	5,985.98
revenue_by_customer	Customer-order-product traversal with relationship-property revenue aggregation.	30	0.16	0.15	0.18	0.18	0.14	0.18	0.01	6,132.30
order_line_sales_by_country	Order-line scan grouped by shipping country with line-count and unit aggregation.	30	0.14	0.14	0.17	0.18	0.12	0.19	0.02	6,835.79
low_stock_products	Selective numeric property filter followed by a stable top-100 product sort.	30	0.20	0.19	0.22	0.24	0.18	0.24	0.01	4,488.30
products_in_category	Selective category lookup and adjacent product traversal with a top-100 result.	30	0.19	0.19	0.20	0.21	0.17	0.21	0.01	4,635.35
order_line_quantity_distribution	Full relationship-property scan grouped by line quantity.	30	0.14	0.13	0.17	0.17	0.11	0.17	0.02	7,048.87
customer_order_details	Selective customer lookup followed by order-line expansion and computed row projection.	30	0.21	0.21	0.22	0.23	0.19	0.23	0.01	4,188.51

• Overall mean latency: 0.17 ms
• Measured query operations: 420
• End-to-end query-loop throughput: 21.08 ops/sec
• Query-latency-only aggregate throughput: 5,908.17 ops/sec
• Query-loop duration: 19.923 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): 27.372 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: 26 (~1s each)
• Sampled duration: 26.48 s
• Avg CPU power: 9,073.6 mW
• Avg GPU power: 172.5 mW
• Avg package power: 9,246.1 mW
• Estimated energy (benchmark window): 244.88 J

Memory Pressure

• Samples collected: 30 (~1s each)
• Avg used (active + wired + compressor): 23.4 GiB
• Peak used: 23.9 GiB
• Avg free: 7.3 GiB
• Min free: 6.8 GiB
• Avg compressed (logical): 7.2 GiB
• Peak compressed: 7.2 GiB

Storage

• Raw data files: 148.2 MiB (155,422,720 bytes)
• Indexes/stats: 0 B (0 bytes)
• Write-ahead logs: 260.0 KiB (266,240 bytes)
• Metadata/bookkeeping: 8.0 KiB (8,192 bytes)
• Preallocated scratch (excluded): 1.0 MiB (1,048,576 bytes)
• Unclassified (other): 0 B (0 bytes)
• Full data directory du: 149.5 MiB (156,745,728 bytes)
• Classified sum: 149.5 MiB (156,745,728 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
000002.sst	49.9 MiB
000003.sst	49.4 MiB
000004.sst	48.9 MiB
000001.sst	4.0 KiB
000001.vlog	4.0 KiB
000002.vlog	4.0 KiB

Queries

products_per_category

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

customer_category_distinct_orders

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

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

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

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

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

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

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

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

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

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

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

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

customer_order_details

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