Aerospike Graph and AI: Query your data in natural language

Based on the MCP server data, here’s your current database configuration and version information:

🔢 Version Information
	•	Aerospike Database: 8.0.0.7
	•	Aerospike Graph Service: 3.0.0
	•	Gremlin: 3.7.3

⚙️ Configuration Details

Graph Properties
	•	Namespace: test
	•	Graph Implementation: com.aerospike.firefly.structure.FireflyGraph
	•	Host: aerospike-db

Gremlin Server Configuration
	•	Host: 0.0.0.0
	•	Port: 8182
	•	Evaluation Timeout: 10,000ms (10 seconds)
	•	Channelizer: WebSocket
	•	Thread Pool Worker: 10
	•	Gremlin Pool: 80
	•	Result Iteration Batch Size: 64

Performance and Memory Settings
	•	Max Content Length: 10,485,760 bytes (~10MB)
	•	Write Buffer Low Water Mark: 32,768 bytes
	•	Write Buffer High Water Mark: 65,536 bytes
	•	Max Accumulation Buffer Components: 1,024

Security and SSL
	•	SSL: Disabled
	•	Strict Transaction Management: Disabled

Monitoring and Metrics
	•	Console Reporter: Enabled (180-second intervals)
	•	CSV Reporter: Disabled
	•	JMX Reporter: Disabled
	•	SLF4J Reporter: Disabled

Serializers
	•	GraphSON v3 with TinkerGraph IO Registry
	•	GraphBinary v1.0 (standard and string variants)

📑 Database Schema

Vertex Labels (with counts)
	•	airport: 3,504 vertices
	•	country: 237 vertices
	•	continent: 7 vertices
	•	version: 1 vertex

Edge Labels (with counts)
	•	contains: 7,008 edges
	•	route: 50,637 edges

Vertex Properties
	•	country – Country information
	•	code – Airport/country codes
	•	longest – Longest runway length
	•	city – City name
	•	elev – Elevation
	•	icao – ICAO airport code
	•	lon – Longitude coordinate
	•	type – Type classification
	•	region – Regional information
	•	runways – Number of runways
	•	lat – Latitude coordinate
	•	desc – Description
	•	date – Date information
	•	author – Author/creator information

Edge Properties
	•	dist – Distance (likely for routes between airports)

Summary
	•	🌍 Geographic hierarchy: Continents → Countries → Airports
	•	✈️ Airport details: coordinates, elevation, runways, ICAO codes
	•	🛫 Route network: 50,637 flight routes with distance information
	•	🔗 Containment: 7,008 “contains” edges (continent-to-country, country-to-airport)

Based on the aviation database, here are the 3 best routes from Victoria BC (YYJ) to Japan:

🥇 Victoria → Vancouver → Tokyo Narita (BEST)
	•	YYJ → YVR: 39 nautical miles
	•	YVR → NRT: 4,659 nautical miles
	•	Total Distance: 4,698 nautical miles
	•	Why it’s best: shortest total distance, Vancouver is close to Victoria, Narita is Tokyo’s main hub

🥈 Victoria → Vancouver → Tokyo Haneda
	•	YYJ → YVR: 39 nautical miles
	•	YVR → HND: 4,693 nautical miles
	•	Total Distance: 4,732 nautical miles
	•	Why it’s good: Haneda is closer to downtown Tokyo than Narita

🥉 Victoria → Seattle → Tokyo Narita
	•	YYJ → SEA: 97 nautical miles
	•	SEA → NRT: 4,754 nautical miles
	•	Total Distance: 4,851 nautical miles
	•	Why it’s third: slightly longer, but Seattle has strong international connections

Alternative routes to consider:
	•	YYJ → YVR → KIX (Osaka): 4,965 nm
	•	YYJ → YVR → NGO (Nagoya): 4,883 nm
	•	YYJ → YYC → NRT (Calgary): 5,385 nm

✅ Recommendation:
The Vancouver → Tokyo Narita route is your best option: shortest distance, simple connection, and plenty of flights.s best: shortest total distance, Vancouver is close to Victoria, Narita is Tokyo’s main hub

🌍 Top 10 Busiest Airports by Route Connectivity
	1.	FRA — Frankfurt, Germany (620 routes)
	2.	IST — Istanbul, Turkey (618 routes)
	3.	CDG — Paris, France (587 routes)
	4.	AMS — Amsterdam, Netherlands (568 routes)
	5.	MUC — Munich, Germany (541 routes)
	6.	ORD — Chicago, USA (529 routes)
	7.	DFW — Dallas/Fort Worth, USA (506 routes)
	8.	PEK — Beijing, China (497 routes)
	9.	DXB — Dubai, UAE (496 routes)
	10.	ATL — Atlanta, USA (484 routes)

Key insights:
	•	🇪🇺 Europe dominates with 4 airports, including 2 in Germany.
	•	🌍 Airports span 6 continents.
	•	✈️ All are major hubs used for transfers.
	•	💡 Frankfurt leads with 620 routes, showing its global importance.

Here’s what’s happening with Beijing:

Beijing Airport Situation
	•	PEK — Beijing Capital: 497 routes (#8 on the list)
	•	PKX — Beijing Daxing: 113 routes
	•	NAY — Beijing Nanyuan: 84 routes

Why it feels low:
	•	📊 Passenger volume vs. routes: Beijing is one of the busiest in reality, but this dataset counts routes.
	•	📅 Data age: Daxing opened in 2019, and growth may not be fully reflected.
	•	🛫 Domestic vs. international: Beijing handles massive domestic traffic that may not show here.

🎉 AerospikeVI International Airport Successfully Created!

Airport Details
	•	Code: AVI
	•	ICAO: CAVI
	•	Name: AerospikeVI International Airport
	•	Location: Duncan, BC, Canada
	•	Coordinates: 48.7787°N, 123.7067°W
	•	Elevation: 200 feet
	•	Runways: 2 (longest: 8,000 feet)
	•	Region: CA-BC

Direct Routes Added
	•	AVI ↔ YCD (Nanaimo): 25 nm
	•	AVI ↔ YYJ (Victoria): 35 nm
	•	AVI ↔ YVR (Vancouver): 45 nm
	•	AVI ↔ HND (Tokyo Haneda): 4,720 nm

Route Summary
	•	Total: 8 routes (4 outbound + 4 inbound)
	•	Domestic: 3
	•	International: 1 (Japan)

✨ Fun fact: Your new airport now has a direct Duncan → Tokyo Haneda route, just like Vancouver International.

🥇 Quickest route
	•	AVI → YVR: 45 nm
	•	YVR → DEL: 6,916 nm
	•	DEL → BOM: 707 nm
	•	Total: 7,668 nm
	•	Connections: Vancouver, Delhi

🥈 Alternative route 1
	•	AVI → YVR → LHR → BOM
	•	Total: 9,230 nm

🥉 Alternative route 2
	•	AVI → YVR → FRA → BOM
	•	Total: 9,131 nm

🌏 Via Asia route
	•	AVI → HND → DXB → BOM
	•	Total: 10,843 nm

✅ Recommendation:
The AVI → Vancouver → Delhi → Mumbai path is the fastest at 7,668 nm and uses strong existing connections.

🚀 Schema Optimization Recommendations
	1.	Indexing Strategy
    	•	Add indexes on: airport.code, airport.country, airport.city, route.dist
	2.	Data Normalization
    	•	Current: redundant strings, missing country vertices, inconsistent coordinates
    	•	Fix: normalize countries into vertices, standardize coordinates
	3.	Property Optimization
    	•	Drop unused (author, date) and redundant (type) fields
    	•	Fix malformed ICAO codes
	4.	Route Bidirectionality
    	•	Current: ~50k routes stored twice
    	•	Fix: undirected edges or direction logic
	5.	Geographic Hierarchy
    	•	Add explicit continent → country → airport structure
	6.	Distance Optimization
    	•	Add distance categories for faster filtering
	7.	Query Performance
    	•	Code lookups: ~8ms (good)
    	•	City lookups: ~9ms (needs index)
    	•	Route traversals: ~2ms (acceptable)

🎯 Priorities
      •	High: add indexes, remove duplicates, drop redundant type
      •	Medium: normalize countries, add distance categories
      •	Low: build hierarchy, add temporal and capacity data

📊 Expected Gains
      •	50% storage reduction
      •	3–5× faster country queries
      •	2–3× faster city searches

⚡ Query Performance Analysis
	•	Total execution time: 321.09 ms
	•	Total processing time: 642.19 ms (profiling overhead included)

📊 Performance Breakdown
	•	FireflyBatchVertexReadStep — 188.84 ms (59%)
	•	PropertyMapStep — 102.41 ms (32%)
	•	FireflyGraphStep — 25.54 ms (8%)
	•	FireflyScanProfileStep — 4.30 ms (1%)

🔍 Why expensive?
	•	Traverses all outgoing edges from all airports
	•	No edge filter: includes both route and contains
	•	Loads all properties for every result

📈 Scale impact
	•	3,505 airports × multiple edges = massive result set

✅ Optimization Suggestions
	1.	Limit scope: limit(10)
	2.	Filter edges: outE("route")
	3.	Select fewer props: valueMap("code","city","country")
	4.	Add filters: has("country","CA")