I have successfully completed similar projects in the past where I implemented distributed web crawlers for data extraction tasks.
1.) Technical Approach:
- Develop a distributed web crawler in Python using frameworks like Scrapy for efficient data extraction.
- Implement a scheduled task to search Bilibili (B站) for specified keywords (e.g., "电影解说") every 5 minutes to retrieve video information (bvid, title, link, duration) and save it to a database.
- Utilize Bilibili API to fetch real-time metrics (views, likes, favorites) for videos using their bvid. Fetch data every 5 minutes and store it in the database.
- Track and crawl data for newly released videos within the past week, continuously for a week.
2.) Technologies:
- Python for web crawling and data processing.
- Scrapy framework for building the distributed web crawler.
- Bilibili API for fetching video metrics.
- Database (e.g., MySQL, MongoDB) for storing extracted data.
3.) Testing and Integration Plan:
- Conduct unit tests to validate the functionality of the web crawler, data extraction, and API interactions.
- Perform integration testing to ensure seamless communication between components and data flow.
- Implement error handling mechanisms for API interactions to prevent data loss or corruption.
4.) Performance and Scalability Optimizations:
- Implement parallel processing to enhance the crawling speed and efficiency.
- Optimize database queries and indexing for faster data retrieval and storage.
- Utilize asynchronous programming for non-blocking API calls to improve performance.
By following this technical approach, leveraging relevant technologies, ensuring testing and integration, and incorporating performance optimizations, the solution will be reliable, scalable, and ready for use.
I am confident in my ability to deliver a high-quality solution for the "分布式爬虫爬取B站数据 -- 2" project, meeting all specified requirements within the set timeline.