motovaultpro/data/make-model-import/QUICK_START.md

Quick Start Guide - Automotive Vehicle Database

Database Status: ✅ OPERATIONAL

• 30,066 engines
• 1,213,401 vehicle configurations
• 93 years (1918-2026)
• 53 makes
• 1,937 models

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Access the Database

docker exec -it mvp-postgres psql -U postgres -d motovaultpro

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Essential Queries

1. Get All Available Years

SELECT * FROM available_years;

2. Get Makes for a Specific Year

SELECT * FROM get_makes_for_year(2024);

3. Get Models for Year + Make

SELECT * FROM get_models_for_year_make(2024, 'Ford');

4. Get Trims for Year + Make + Model

SELECT * FROM get_trims_for_year_make_model(2024, 'Ford', 'f-150');

5. Get Complete Vehicle Details

SELECT * FROM complete_vehicle_configs
WHERE year = 2024
  AND make = 'Ford'
  AND model = 'f-150'
LIMIT 10;

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Refresh the Database

# Re-generate SQL files from JSON source data
python3 etl_generate_sql.py

# Re-import into database
./import_data.sh

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Files Overview

File	Purpose
etl_generate_sql.py	Generate SQL import files from JSON
import_data.sh	Import SQL files into database
migrations/001_create_vehicle_database.sql	Database schema
output/*.sql	Generated SQL import files (90MB total)

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Database Schema

engines
├── id (PK)
├── name
├── displacement
├── configuration (I4, V6, V8, etc.)
├── horsepower
├── torque
├── fuel_type
└── specs_json (full specifications)

vehicle_options
├── id (PK)
├── year
├── make
├── model
├── trim
├── engine_id (FK → engines)
└── transmission_id (FK → transmissions)

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Performance

• Query Time: < 50ms (indexed)
• Database Size: 219MB
• Index Size: 117MB

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Support

• Full Documentation: See ETL_README.md
• Implementation Details: See IMPLEMENTATION_SUMMARY.md