Auto.ru Parser Quick Start

Get up and running with the Auto.ru parser in minutes. Extract comprehensive automotive data from Russia's leading marketplace with just a few simple steps.

Step-by-step guide to get started with Auto.ru automotive data extraction - from API setup to first data extraction in minutes

🚀 Prerequisites

Before you begin, ensure you have:

• CarAPIS Account: Sign up at dashboard.carapis.com
• API Key: Get your API key from the dashboard
• Basic Programming Knowledge: Familiarity with HTTP requests or our SDKs

Free Tier Available

Start with our free tier that includes 1,000 requests per day. No credit card required!

📋 Step 1: Get Your API Key

Sign Up for CarAPIS

1. Visit dashboard.carapis.com
2. Click "Sign Up" and create your account
3. Verify your email address
4. Navigate to the API Keys section

Generate API Key

# Your API key will look like this
auto_ru_parser_sk_1234567890abcdef1234567890abcdef

:::

info API Key Security Keep your API key secure and never share it publicly. You can regenerate it anytime from the dashboard. :::

🔧 Step 2: Choose Your Integration Method

Option A: Python SDK (Recommended)

# Install the CarAPIS Python SDK
pip install carapis

# Initialize the client
import carapis

client = carapis.Client(api_key="YOUR_API_KEY")

# Test the connection
try:
    # Get available brands
    brands = client.auto_ru.get_brands()
    print(f"Connected! Found {len(brands)} brands")
except Exception as e:
    print(f"Connection failed: {e}")

Option B: JavaScript/Node.js SDK

// Install the CarAPIS JavaScript SDK
npm install carapis

// Initialize the client
const CarAPIS = require('carapis');

const client = new CarAPIS('YOUR_API_KEY');

// Test the connection
async function testConnection() {
    try {
        const brands = await client.auto_ru.getBrands();
        console.log(`Connected! Found ${brands.length} brands`);
    } catch (error) {
        console.error('Connection failed:', error);
    }
}

testConnection();

Option C: Direct HTTP API

# Test the API with curl
curl -X GET "https://api.carapis.com/v1/auto-ru/brands" \
  -H "Authorization: Bearer YOUR_API_KEY"

🔍 Step 3: Your First Data Extraction

Search for Vehicles

# Search for Lada Vesta vehicles from 2020 onwards
search_params = {
    "brand": "Lada",
    "model": "Vesta",
    "year_from": 2020,
    "price_max": 1500000,
    "region": "Московская область"
}

# Execute the search
listings = client.auto_ru.search(**search_params)

print(f"Found {len(listings)} Lada Vesta vehicles")
for listing in listings[:3]:  # Show first 3 results
    print(f"- {listing['title']}: {listing['price']} ₽")

Get Detailed Listing Information

# Get detailed information about a specific listing
listing_id = "auto_ru_123456789"
detailed_listing = client.auto_ru.get_listing(listing_id)

print(f"Vehicle: {detailed_listing['title']}")
print(f"Price: {detailed_listing['price']} ₽")
print(f"Mileage: {detailed_listing['mileage']} km")
print(f"Location: {detailed_listing['location']['city']}")

Extract Market Data

# Get market analysis for Toyota vehicles
market_data = client.auto_ru.get_market_data(
    brand="Toyota",
    period="30d"
)

print(f"Market Overview:")
print(f"- Total Listings: {market_data['market_overview']['total_listings']}")
print(f"- Average Price: {market_data['market_overview']['average_price']} ₽")
print(f"- Price Trend: {market_data['market_overview']['price_trend']}")

📊 Step 4: Advanced Usage Examples

Batch Processing

# Process multiple searches efficiently
brands_to_search = ["Lada", "Toyota", "Volkswagen"]
results = {}

for brand in brands_to_search:
    listings = client.auto_ru.search(
        brand=brand,
        year_from=2020,
        limit=50
    )
    results[brand] = {
        "count": len(listings),
        "avg_price": sum(l['price'] for l in listings) / len(listings) if listings else 0
    }

for brand, data in results.items():
    print(f"{brand}: {data['count']} listings, avg price: {data['avg_price']:.0f} ₽")

Price Monitoring

# Monitor price changes for specific vehicles
def monitor_prices(listing_ids, check_interval=3600):  # Check every hour
    import time

    while True:
        for listing_id in listing_ids:
            listing = client.auto_ru.get_listing(listing_id)
            price_history = client.auto_ru.get_price_history(listing_id)

            current_price = listing['price']
            previous_price = price_history['price_history'][-2]['price'] if len(price_history['price_history']) > 1 else current_price

            if current_price != previous_price:
                change = current_price - previous_price
                print(f"Price change for {listing['title']}: {change:+,} ₽")

        time.sleep(check_interval)

Geographic Analysis

# Analyze market by region
regions = ["Московская область", "Санкт-Петербург", "Новосибирск"]
regional_data = {}

for region in regions:
    listings = client.auto_ru.search(
        brand="Lada",
        region=region,
        limit=100
    )

    if listings:
        avg_price = sum(l['price'] for l in listings) / len(listings)
        regional_data[region] = {
            "listings_count": len(listings),
            "average_price": avg_price
        }

# Display results
for region, data in regional_data.items():
    print(f"{region}: {data['listings_count']} listings, avg: {data['average_price']:.0f} ₽")

🔧 Step 5: Error Handling

Robust Error Handling

import time
from carapis.exceptions import RateLimitError, APIError

def safe_api_call(func, *args, max_retries=3, **kwargs):
    """Execute API call with retry logic"""
    for attempt in range(max_retries):
        try:
            return func(*args, **kwargs)
        except RateLimitError as e:
            if attempt < max_retries - 1:
                wait_time = (2 ** attempt) * 60  # Exponential backoff
                print(f"Rate limited. Waiting {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                raise e
        except APIError as e:
            print(f"API Error: {e}")
            raise e
        except Exception as e:
            print(f"Unexpected error: {e}")
            raise e

# Use the safe wrapper
try:
    listings = safe_api_call(client.auto_ru.search, brand="Lada", limit=50)
    print(f"Successfully retrieved {len(listings)} listings")
except Exception as e:
    print(f"Failed to retrieve listings: {e}")

📈 Step 6: Data Export and Analysis

Export to CSV

import pandas as pd

# Collect data
listings = client.auto_ru.search(brand="Lada", limit=1000)

# Convert to DataFrame
df = pd.DataFrame(listings)

# Export to CSV
df.to_csv('lada_listings.csv', index=False, encoding='utf-8')
print(f"Exported {len(df)} listings to lada_listings.csv")

Basic Market Analysis

import pandas as pd
import matplotlib.pyplot as plt

# Get market data
listings = client.auto_ru.search(brand="Toyota", limit=500)
df = pd.DataFrame(listings)

# Analyze price distribution
plt.figure(figsize=(10, 6))
df['price'].hist(bins=30, edgecolor='black')
plt.title('Toyota Price Distribution')
plt.xlabel('Price (₽)')
plt.ylabel('Frequency')
plt.show()

# Analyze by year
year_analysis = df.groupby('year')['price'].agg(['count', 'mean', 'std'])
print(year_analysis)

🚀 Step 7: Production Deployment

Environment Variables

# Set your API key as an environment variable
export CARAPIS_API_KEY="your_api_key_here"

# Use environment variable in your code
import os
import carapis

api_key = os.getenv('CARAPIS_API_KEY')
client = carapis.Client(api_key=api_key)

Rate Limiting Best Practices

# Implement rate limiting for production use
import time

class RateLimitedClient:
    def __init__(self, client, requests_per_minute=60):
        self.client = client
        self.requests_per_minute = requests_per_minute
        self.request_times = []

    def _wait_if_needed(self):
        now = time.time()
        # Remove requests older than 1 minute
        self.request_times = [t for t in self.request_times if now - t < 60]

        if len(self.request_times) >= self.requests_per_minute:
            sleep_time = 60 - (now - self.request_times[0])
            if sleep_time > 0:
                time.sleep(sleep_time)

        self.request_times.append(time.time())

    def search(self, *args, **kwargs):
        self._wait_if_needed()
        return self.client.auto_ru.search(*args, **kwargs)

# Use the rate-limited client
rate_limited_client = RateLimitedClient(client)

🔍 Troubleshooting

Common Issues

Rate Limiting

If you encounter rate limit errors, implement exponential backoff:

• Wait 1 minute, then retry
• If still limited, wait 2 minutes
• Continue doubling wait time

Authentication Errors

• Verify your API key is correct
• Check that your account is active
• Ensure you're using the correct endpoint

Performance Optimization

• Use batch operations when possible
• Implement caching for frequently accessed data
• Monitor your request usage in the dashboard

📚 Next Steps

Explore Advanced Features

1. API Reference - Complete endpoint documentation
2. Market Analysis - Understand market trends
3. Features - Discover all available capabilities
4. FAQ - Common questions and solutions

Integration Examples

• Web Application: Build a car listing website
• Mobile App: Create a vehicle search app
• Analytics Dashboard: Monitor market trends
• Price Alert System: Track price changes

Support Resources

• Documentation: docs.carapis.com
• API Status: status.carapis.com
• Community: Discord
• Email Support: api-support@carapis.com

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Ready to start extracting Auto.ru data? Get your API key now and begin building powerful automotive applications with real-time Russian market data.