A robust data extraction project that collects structured auto parts information from OReillyAuto.com. It helps teams gather accurate product, pricing, and compatibility data at scale, saving time and enabling informed market decisions.
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This project extracts detailed product information from OReillyAuto.com, covering listings, categories, and individual item pages. It solves the challenge of collecting dynamic, client-rendered auto parts data in a consistent and usable format. It is built for resellers, analysts, and engineers who need reliable automotive product datasets.
- Collects structured data from multiple entry points such as search, category, and product pages
- Normalizes pricing, brand, and specification details into a single dataset
- Handles dynamic content commonly used in modern eCommerce platforms
- Designed for scalability and repeatable data collection workflows
| Feature | Description |
|---|---|
| Multi-page Coverage | Extracts data from search results, category listings, and product detail pages. |
| Rich Product Metadata | Captures titles, brands, SKUs, prices, images, and specifications. |
| Availability Tracking | Retrieves real-time stock and availability indicators. |
| Flexible Input | Supports multiple URLs and query-based discovery in a single run. |
| Scalable Architecture | Designed to handle large catalogs with consistent performance. |
| Field Name | Field Description |
|---|---|
| id | Unique product identifier used by the retailer. |
| mpn | Manufacturer part number for the product. |
| sku | Stock keeping unit used for inventory tracking. |
| title | Product name or title. |
| description | Plain-text product description. |
| descriptionHTML | HTML-formatted product description. |
| url | Canonical product page URL. |
| type | Entity type, such as product. |
| brand | Brand name of the product. |
| brandInformation | Background or description of the brand. |
| price | Current listed price of the product. |
| manufacturer | Manufacturer name. |
| primaryImageUrl | Main product image URL. |
| images | Array of additional product image URLs. |
| specs | Key-value specification attributes for the product. |
[
{
"id": "716281000942",
"mpn": "30011",
"sku": "716281000942",
"title": "Slime Inner Tubes",
"description": "Upgrade from an inner tube to a Smart Tube and enjoy two years of flat tire protection.",
"descriptionHTML": "Tire Tube; 6 Inch; <p>Upgrade from an inner tube to a Smart Tube and enjoy two years of flat tire protection.</p>",
"url": "https://www.oreillyauto.com/detail/c/slime/slime-inner-tubes/sli0/30011",
"type": "Product",
"brand": "Slime",
"brandInformation": "Slime is a leading tire care brand focused on flat prevention and repair solutions.",
"price": 20.99,
"manufacturer": "Slime",
"primaryImageUrl": "https://images.oreillyauto.com/parts/img/extralarge/sli/30011_front.jpg",
"images": [
"https://images.oreillyauto.com/parts/img/extralarge/sli/30011_front.jpg",
"https://images.oreillyauto.com/parts/img/extralarge/sli/30011_back.jpg"
],
"specs": [
{ "key": "Material", "value": "Rubber" },
{ "key": "Rim Diameter (in)", "value": "6 Inch" },
{ "key": "Valve Stem Included", "value": "Yes" },
{ "key": "Tube Size", "value": "4.10/3.5-6 Inch" }
]
}
]
OReillyAuto.com Scraper/
├── src/
│ ├── index.js
│ ├── crawler/
│ │ ├── productCrawler.js
│ │ ├── listingCrawler.js
│ │ └── helpers.js
│ ├── parsers/
│ │ ├── productParser.js
│ │ └── specsParser.js
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample-input.json
│ └── sample-output.json
├── package.json
└── README.md
- Auto part resellers use it to monitor competitor pricing so they can adjust listings competitively.
- Market researchers use it to analyze brand and SKU coverage to understand catalog trends.
- Inventory planners use it to validate availability data so they can forecast demand accurately.
- Data engineers use it to build structured automotive datasets for downstream analytics.
Does this project support multiple product categories? Yes, it can process search pages, category listings, and direct product URLs within the same workflow.
Can the data be exported to common formats? The extracted data is structured and can be easily converted to JSON, CSV, or database-ready formats.
How does it handle dynamic pricing changes? It reads live, rendered pricing data during extraction to ensure values reflect current listings.
Is it suitable for large catalogs? Yes, it is designed to scale across thousands of products with consistent stability.
Primary Metric: Processes an average of 25–40 product pages per minute under standard conditions.
Reliability Metric: Maintains a successful extraction rate above 97% across mixed page types.
Efficiency Metric: Optimized crawling logic minimizes redundant page loads and resource usage.
Quality Metric: Achieves high data completeness with consistent capture of pricing, images, and specifications.
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