motovaultpro/ETL-FIX-V2.md

ETL Fix V2: Year-Accurate Vehicle Dropdown Data

Executive Summary

This document provides a complete implementation plan for fixing the vehicle dropdown ETL to produce year-accurate data. The fix addresses impossible year/trim combinations (e.g., "1992 Corvette Z06") by using the NHTSA VPIC API for authoritative Year/Make/Model validation and automobiles.json for evidence-based trim data with year ranges.

---

Problem Statement

Current Issues

1. Year-inaccurate trims: The makes-filter/*.json files contain ALL trims ever made for a model, applied to EVERY year
2. Impossible combinations: Users can select "1992 Corvette Z06" (Z06 didn't exist until 2001)
3. Data bloat: 400 records for 2000 Corvette with 20 trims instead of ~3-4

Root Cause

The makes-filter/*.json data structure does NOT have year-specific trims. Example from chevrolet.json:

{
  "year": "2025",
  "models": [{
    "name": "corvette",
    "submodels": ["LT", "35th Anniversary Edition", "427", "Z06", "ZR1", ...]
  }]
}

The same submodels array is repeated for every year, making ALL trims appear valid for ALL years.

---

Solution Architecture

Data Sources (Priority Order)

1. NHTSA VPIC API - Authoritative Year/Make/Model validation
2. automobiles.json - Primary trim source with year-range evidence
3. makes-filter/*.json - Engine data enrichment
4. Defaults - "Base" trim, "Gas" engine, "Manual"/"Automatic" transmission

Year Range

• Minimum: 1990
• Maximum: 2026

---

Implementation Steps

Phase 1: Create NHTSA Data Fetcher

Create file: data/make-model-import/nhtsa_fetch.py

#!/usr/bin/env python3
"""
NHTSA VPIC API Data Fetcher
Fetches authoritative Year/Make/Model data from the US government database.
"""

import json
import os
import time
from pathlib import Path
from typing import Dict, List, Set
import urllib.request
import urllib.error

class NHTSAFetcher:
    BASE_URL = "https://vpic.nhtsa.dot.gov/api/vehicles"
    CACHE_DIR = Path("nhtsa_cache")
    OUTPUT_FILE = Path("nhtsa_vehicles.json")

    def __init__(self):
        self.min_year = int(os.getenv("MIN_YEAR", "1990"))
        self.max_year = int(os.getenv("MAX_YEAR", "2026"))
        self.request_delay = 0.1  # 100ms between requests

        # Makes we care about (from makes-filter)
        self.target_makes = self._load_target_makes()

    def _load_target_makes(self) -> Set[str]:
        """Load makes from makes-filter directory."""
        makes_dir = Path("makes-filter")
        makes = set()
        for f in makes_dir.glob("*.json"):
            make_name = f.stem.replace("_", " ").title()
            makes.add(make_name)
        return makes

    def fetch_url(self, url: str) -> dict:
        """Fetch JSON from URL with error handling."""
        try:
            with urllib.request.urlopen(url, timeout=30) as response:
                return json.loads(response.read().decode())
        except urllib.error.URLError as e:
            print(f"  Error fetching {url}: {e}")
            return {"Results": []}

    def get_all_makes(self) -> List[Dict]:
        """Fetch all makes for passenger cars and trucks."""
        makes = []
        for vehicle_type in ["car", "truck"]:
            url = f"{self.BASE_URL}/GetMakesForVehicleType/{vehicle_type}?format=json"
            data = self.fetch_url(url)
            makes.extend(data.get("Results", []))
        return makes

    def get_models_for_make_year(self, make: str, year: int) -> List[str]:
        """Fetch models for a specific make and year."""
        cache_file = self.CACHE_DIR / f"{make.lower().replace(' ', '_')}_{year}.json"

        # Check cache first
        if cache_file.exists():
            with open(cache_file) as f:
                return json.load(f)

        url = f"{self.BASE_URL}/GetModelsForMakeYear/make/{make}/modelyear/{year}?format=json"
        time.sleep(self.request_delay)
        data = self.fetch_url(url)

        models = list(set(r.get("Model_Name", "") for r in data.get("Results", []) if r.get("Model_Name")))

        # Cache result
        self.CACHE_DIR.mkdir(exist_ok=True)
        with open(cache_file, "w") as f:
            json.dump(models, f)

        return models

    def fetch_all_data(self) -> Dict:
        """Fetch all Year/Make/Model data."""
        print("Fetching NHTSA data...")

        # Filter to target makes
        all_makes = self.get_all_makes()
        target_make_names = [m["MakeName"] for m in all_makes
                           if m["MakeName"].title() in self.target_makes
                           or m["MakeName"].upper() in ["BMW", "GMC", "RAM"]]

        print(f"Found {len(target_make_names)} matching makes")

        result = {}
        for year in range(self.min_year, self.max_year + 1):
            result[str(year)] = {}
            for make in target_make_names:
                models = self.get_models_for_make_year(make, year)
                if models:
                    # Normalize make name
                    make_normalized = make.title()
                    if make.upper() in ["BMW", "GMC", "RAM"]:
                        make_normalized = make.upper()
                    result[str(year)][make_normalized] = sorted(models)
            print(f"  Year {year}: {sum(len(v) for v in result[str(year)].values())} models")

        # Save output
        with open(self.OUTPUT_FILE, "w") as f:
            json.dump(result, f, indent=2)

        print(f"Saved to {self.OUTPUT_FILE}")
        return result

if __name__ == "__main__":
    NHTSAFetcher().fetch_all_data()

Phase 2: Refactor ETL Script

Modify file: data/make-model-import/etl_generate_sql.py

Key changes:

2.1 Load NHTSA data as primary source

def load_nhtsa_data(self):
    """Load NHTSA Year/Make/Model data."""
    nhtsa_file = Path("nhtsa_vehicles.json")
    if not nhtsa_file.exists():
        raise FileNotFoundError("Run nhtsa_fetch.py first to generate nhtsa_vehicles.json")

    with open(nhtsa_file) as f:
        self.nhtsa_data = json.load(f)
    print(f"  Loaded NHTSA data for {len(self.nhtsa_data)} years")

2.2 Build trim evidence from automobiles.json

def build_trim_evidence(self):
    """
    Parse automobiles.json to build year-range evidence for trims.
    """
    self.trim_evidence: Dict[Tuple[str, str], List[Dict]] = defaultdict(list)

    brand_lookup = {b.get("id"): self.get_canonical_make_name(b.get("name", ""))
                    for b in self.brands_data}

    for auto in self.automobiles_data:
        brand_id = auto.get("brand_id")
        make = brand_lookup.get(brand_id)
        if not make:
            continue

        name = auto.get("name", "")
        year_range = self.parse_year_range_from_name(name)
        if not year_range:
            continue

        year_start, year_end = year_range

        # Extract model and trim from name
        model, trim = self.extract_model_trim_from_name(name, make)
        if not model:
            continue

        self.trim_evidence[(make, model)].append({
            "trim": trim or "Base",
            "year_start": year_start,
            "year_end": year_end,
            "source_name": name
        })

    print(f"  Built trim evidence for {len(self.trim_evidence)} make/model combinations")

def extract_model_trim_from_name(self, name: str, make: str) -> Tuple[Optional[str], Optional[str]]:
    """
    Extract model and trim from automobile name.
    Examples:
        "CHEVROLET Corvette Z06 2021-Present" -> ("Corvette", "Z06")
        "2020 Chevrolet Corvette C8 Stingray" -> ("Corvette", "Stingray")
        "FORD F-150 Raptor 2021-Present" -> ("F-150", "Raptor")
    """
    # Remove make prefix
    clean = re.sub(rf"^{re.escape(make)}\s+", "", name, flags=re.IGNORECASE)

    # Remove year/range patterns
    clean = re.sub(r"\d{4}(-\d{4}|-Present)?", "", clean)

    # Remove common suffixes
    clean = re.sub(r"\s*(Photos|engines|full specs|&).*$", "", clean, flags=re.IGNORECASE)

    # Clean up extra spaces
    clean = " ".join(clean.split())

    # Try to match against known models
    known_models = self.known_models_by_make.get(make, set())

    for model in sorted(known_models, key=len, reverse=True):
        pattern = re.compile(rf"^{re.escape(model)}\b\s*(.*)", re.IGNORECASE)
        match = pattern.match(clean)
        if match:
            trim = match.group(1).strip()
            # Remove generation codes like C5, C6, C7, C8
            trim = re.sub(r"^C\d+\s*", "", trim)
            return (model, trim if trim else None)

    return (None, None)

2.3 New trim resolution logic

def get_trims_for_vehicle(self, year: int, make: str, model: str) -> List[str]:
    """
    Get valid trims for a year/make/model combination.
    Uses evidence from automobiles.json, falls back to "Base".
    """
    evidence = self.trim_evidence.get((make, model), [])
    valid_trims = set()

    for entry in evidence:
        if entry['year_start'] <= year <= entry['year_end']:
            valid_trims.add(entry['trim'])

    # Always include "Base" as an option
    valid_trims.add("Base")

    return sorted(valid_trims)

2.4 Updated vehicle record building

def build_vehicle_records(self):
    """Build vehicle records using NHTSA for Y/M/M, evidence for trims."""
    print("\n Building vehicle option records...")
    records = []

    for year_str, makes in self.nhtsa_data.items():
        year = int(year_str)
        if year < self.min_year or year > self.max_year:
            continue

        for make, models in makes.items():
            for model in models:
                # Get valid trims from evidence
                trims = self.get_trims_for_vehicle(year, make, model)

                # Get engines from makes-filter (or default)
                engines = self.get_engines_for_vehicle(year, make, model)

                # Default transmissions
                transmissions = ["Manual", "Automatic"]

                for trim in trims:
                    for engine in engines:
                        for trans in transmissions:
                            records.append({
                                "year": year,
                                "make": make,
                                "model": model,
                                "trim": trim,
                                "engine_name": engine,
                                "trans_name": trans
                            })

    # Deduplicate
    unique_set = set()
    deduped = []
    for r in records:
        key = (r["year"], r["make"].lower(), r["model"].lower(),
               r["trim"].lower(), r["engine_name"].lower(), r["trans_name"].lower())
        if key not in unique_set:
            unique_set.add(key)
            deduped.append(r)

    self.vehicle_records = deduped
    print(f"  Vehicle records: {len(self.vehicle_records):,}")

def get_engines_for_vehicle(self, year: int, make: str, model: str) -> List[str]:
    """Get engines from makes-filter or use defaults."""
    # Try to find in makes-filter data
    for baseline in self.baseline_records:
        if (baseline['year'] == year and
            baseline['make'].lower() == make.lower() and
            baseline['model'].lower() == model.lower()):
            engines = []
            for trim_data in baseline.get('trims', []):
                engines.extend(trim_data.get('engines', []))
            if engines:
                return list(set(engines))

    # Default based on make/model patterns
    model_lower = model.lower()
    if 'electric' in model_lower or 'ev' in model_lower or 'lightning' in model_lower:
        return ["Electric"]

    return ["Gas"]

Phase 3: Update Import Script

Modify file: data/make-model-import/import_data.sh

Add NHTSA cache check:

#!/bin/bash
# Import generated SQL files into PostgreSQL database

set -e

echo "=========================================="
echo " Automotive Database Import"
echo "=========================================="

# Check NHTSA cache freshness
NHTSA_FILE="nhtsa_vehicles.json"
CACHE_AGE_DAYS=30

if [ ! -f "$NHTSA_FILE" ]; then
    echo "NHTSA data not found. Fetching..."
    python3 nhtsa_fetch.py
elif [ $(find "$NHTSA_FILE" -mtime +$CACHE_AGE_DAYS 2>/dev/null | wc -l) -gt 0 ]; then
    echo "NHTSA cache is stale (>$CACHE_AGE_DAYS days). Refreshing..."
    python3 nhtsa_fetch.py
else
    echo "Using cached NHTSA data"
fi

# Continue with existing import logic...

Phase 4: Update QA Validation

Modify file: data/make-model-import/qa_validate.py

Add invalid combination checks:

def check_invalid_combinations():
    """Verify known invalid combinations do not exist."""
    invalid_combos = [
        # (year, make, model, trim) - known to be invalid
        (1992, 'Chevrolet', 'Corvette', 'Z06'),  # Z06 started 2001
        (2000, 'Chevrolet', 'Corvette', '35th Anniversary Edition'),  # Was 1988
        (2000, 'Chevrolet', 'Corvette', 'Stingray'),  # Stingray started 2014
        (1995, 'Ford', 'Mustang', 'Mach-E'),  # Mach-E is 2021+
    ]

    issues = []
    for year, make, model, trim in invalid_combos:
        query = f"""
            SELECT COUNT(*) FROM vehicle_options
            WHERE year = {year}
            AND make = '{make}'
            AND model = '{model}'
            AND trim = '{trim}'
        """
        count = int(run_psql(query).strip())
        if count > 0:
            issues.append(f"Invalid combo found: {year} {make} {model} {trim}")

    return issues

def check_trim_coverage():
    """Report on trim coverage statistics."""
    query = """
        SELECT
            COUNT(DISTINCT (year, make, model)) as total_models,
            COUNT(DISTINCT (year, make, model)) FILTER (WHERE trim = 'Base') as base_only,
            COUNT(DISTINCT (year, make, model)) FILTER (WHERE trim != 'Base') as has_specific_trims
        FROM vehicle_options
    """
    result = run_psql(query).strip()
    print(f"Trim coverage: {result}")

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Files Summary

File	Action	Purpose
data/make-model-import/nhtsa_fetch.py	CREATE	Fetch Year/Make/Model from NHTSA API
data/make-model-import/etl_generate_sql.py	MODIFY	Use NHTSA data, evidence-based trims
data/make-model-import/import_data.sh	MODIFY	Add NHTSA cache refresh
data/make-model-import/qa_validate.py	MODIFY	Add invalid combo checks

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Execution Order

# 1. Navigate to ETL directory
cd data/make-model-import

# 2. Fetch NHTSA data (creates nhtsa_vehicles.json)
python3 nhtsa_fetch.py

# 3. Generate SQL files
python3 etl_generate_sql.py

# 4. Import to database
./import_data.sh

# 5. Validate results
python3 qa_validate.py

---

Expected Results

Before

• 2000 Corvette: 400 records, 20 trims (most invalid)
• Total records: ~1,675,335
• Many impossible year/trim combinations

After

• 2000 Corvette: ~8 records (Base, Coupe, Convertible)
• 2015 Corvette: ~20 records (Stingray, Z06, Grand Sport, Base)
• Total records: ~400,000-600,000
• No invalid year/trim combinations

Validation Checks

1. No 1992 Corvette Z06
2. No 2000 Corvette Stingray
3. No 1995 Mustang Mach-E
4. Year range: 1990-2026

---

Data Source Coverage

automobiles.json trim coverage (samples):

Model	Entries	Trims Found
Civic	67	Si, Type R, eHEV, Sedan, Hatchback
Mustang	38	GT, Dark Horse, Mach-E GT, GTD
Accord	35	Sedan, Coupe (various years)
Corvette	31	Z06, ZR1, Stingray, Grand Sport
Camaro	29	ZL1, Convertible, Coupe
F-150	19	Lightning, Raptor, Tremor

---

Environment Variables

Variable	Default	Description
MIN_YEAR	1990	Minimum year to include
MAX_YEAR	2026	Maximum year to include

---

Troubleshooting

NHTSA API Rate Limiting

The script includes 100ms delays between requests. If you encounter rate limiting:

• Increase request_delay in nhtsa_fetch.py
• Use cached data in nhtsa_cache/ directory

Missing Models

If NHTSA returns fewer models than expected:

• Check if the make name matches exactly
• Some brands (BMW, GMC) need uppercase handling
• Verify the year range is supported (NHTSA has data back to ~1995)

Cache Refresh

To force refresh NHTSA data:

rm nhtsa_vehicles.json
rm -rf nhtsa_cache/
python3 nhtsa_fetch.py