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https://github.com/younusaliakash/simple-ecommerce-crawler

Effortlessly manage ecommerce data using our cutting-edge solution! With Puppeteer.js, we extract product links, process them efficiently using BullMQ, and store key details in Redis. Experience seamless web scraping, precise data processing, and reliable storage, empowering businesses with accurate insights.
https://github.com/younusaliakash/simple-ecommerce-crawler

ecommerce-crawler puppeteer web-automation

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Effortlessly manage ecommerce data using our cutting-edge solution! With Puppeteer.js, we extract product links, process them efficiently using BullMQ, and store key details in Redis. Experience seamless web scraping, precise data processing, and reliable storage, empowering businesses with accurate insights.

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README 

        
# Ecommerce Product Data Processing Workflow



## Overview



This workflow demonstrates the process of collecting product page links from an ecommerce website's category page, processing them using a queue, and saving relevant product data (title, description, and price) in a Redis database. The workflow ensures data integrity by skipping links that have already been processed. The technologies used include Puppeteer.js for web crawling and BullMQ for queue management with Redis as the underlying database.



## Steps



1. **Collect Product Page Links**:

   - Puppeteer.js is utilized to crawl the category page(s) of the ecommerce website and extract product page links. Puppeteer provides a headless Chrome browser, enabling dynamic interaction with the website's content.



2. **Process Product Links Using a Queue (BullMQ)**:

   - The extracted product page links are processed using BullMQ, a Redis-backed job queue library. Each link is added to the queue, ensuring efficient and reliable processing. BullMQ manages job retries, prioritization, and concurrency, enhancing the processing workflow.



3. **Retrieve Product Data**:

   - For each product page link dequeued from BullMQ, Puppeteer.js is employed to fetch the product's title, description, and price. The extracted data is in turn used to populate the product object.



4. **Check for Duplicate Links**:

   - Before processing a product page, the system checks if the link has already been processed and saved in the Redis database. Duplicate links are skipped, maintaining data consistency.



5. **Save Data in Redis Database**:

   - The retrieved product data (title, description, and price) is stored in a Redis database. Redis provides fast read and write operations, making it suitable for managing high-throughput data such as product information.



6. **Logging and Error Handling**:

   - Comprehensive logging is implemented to capture each step of the process, including successful data retrievals, errors encountered during processing, and queue-related activities. Error handling mechanisms are in place to address network issues, unresponsive websites, or any unexpected errors.



## Technologies Used



- **Puppeteer.js**: A Node.js library that provides a high-level API over the Chrome DevTools Protocol, allowing for headless web scraping and automation.

- **BullMQ**: A Redis-backed queue library for Node.js, offering powerful job processing features such as retries, priorities, and concurrency management.

- **Redis Database**: A fast, open-source, in-memory key-value store that serves as the database backend for storing product data and managing the job queue.



## Conclusion



By employing Puppeteer.js for web crawling, BullMQ for queue management, and Redis as the database backend, ecommerce businesses can efficiently collect and process product data from their websites. This workflow ensures data accuracy, reliability, and performance, enabling businesses to make informed decisions based on up-to-date product information.