Knockaround Scraper is a focused data extraction tool that collects eyewear product details and pricing from the Knockaround online store. It helps teams turn raw product pages into clean, structured data they can actually use. Built for reliability and scale, it supports fast analysis of eyewear catalogs and pricing trends.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Knockaround Scraper gathers structured product data from an eyewear storefront and makes it ready for analysis, reporting, or integration. It solves the problem of manually tracking product changes and prices across a growing catalog. This project is designed for developers, analysts, and e-commerce teams who need consistent eyewear product data.
- Extracts structured product and pricing information from eyewear listings
- Standardizes raw web data into predictable fields
- Supports repeated runs for monitoring catalog and price changes
- Fits easily into analytics pipelines and internal tools
| Feature | Description |
|---|---|
| Product catalog scraping | Collects complete eyewear product listings in a structured format. |
| Price tracking | Extracts current prices to support comparison and monitoring. |
| Variant support | Captures sizes, colors, and other product variations. |
| Structured output | Delivers clean, machine-readable data for downstream use. |
| Scalable execution | Designed to handle small and large product catalogs efficiently. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier for the eyewear product. |
| name | Official product name as listed in the store. |
| price | Current selling price of the product. |
| currency | Currency associated with the price. |
| category | Product category or collection. |
| variants | Available variations such as color or frame type. |
| availability | Stock or availability status. |
| product_url | Direct link to the product page. |
| images | URLs of product images. |
[
{
"product_id": "kn-aviator-001",
"name": "Aviator Sunglasses",
"price": 35.00,
"currency": "USD",
"category": "Sunglasses",
"variants": ["Black", "Tortoise"],
"availability": "in_stock",
"product_url": "https://knockaround.com/products/aviator-sunglasses",
"images": [
"https://knockaround.com/images/aviator-front.jpg",
"https://knockaround.com/images/aviator-side.jpg"
]
}
]
Knockaround Scraper/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── product_parser.py
│ │ ├── price_extractor.py
│ │ └── utils.py
│ ├── outputs/
│ │ └── exporter.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to monitor eyewear pricing, so they can spot market shifts early.
- Product teams use it to audit catalogs, ensuring listings stay consistent and accurate.
- Retail strategists use it to compare competitors, helping them refine pricing strategies.
- Data teams use it to feed dashboards, enabling faster reporting and insights.
Is this scraper suitable for large product catalogs? Yes. It is designed to process large numbers of product pages while keeping data structured and consistent.
What format does the extracted data come in? The scraper outputs structured data in JSON, making it easy to use in applications, spreadsheets, or analytics tools.
Can I run this scraper repeatedly for monitoring? Absolutely. It supports repeated runs, which makes it useful for ongoing price and catalog tracking.
Does it handle product variants like colors or styles? Yes. Product variants are captured and grouped under each main product entry.
Primary Metric: Average extraction rate of 120–150 product pages per minute under standard conditions.
Reliability Metric: Consistent success rate above 98% across repeated full-catalog runs.
Efficiency Metric: Low memory footprint with stable CPU usage during long scraping sessions.
Quality Metric: Over 99% field completeness for core product and pricing attributes.
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