I’m working on a web scraping project using Puppeteer. My goal is to scrape about 500,000 pages daily but the jobs come in at random times. I’m not sure how to handle this for best performance and memory usage in a production environment.
I’ve thought of two approaches:
Open a new browser for each job, scrape the page, then close it. This might be slower but could be better for memory.
Keep one browser open all the time, just opening and closing pages as needed. This seems faster but might use more memory over time.
Has anyone dealt with this kind of setup before? What’s the best way to manage Puppeteer for large-scale scraping jobs? Are there any potential issues I should watch out for?
I’m new to using Puppeteer in production so any advice would be really helpful. Thanks!
I’ve tackled similar challenges in my web scraping projects. One approach that worked well for me was implementing a dynamic browser pool. Instead of a fixed number, I adjusted the pool size based on current load and system resources. This way, during peak times, more browsers were available, while during lulls, resources were conserved.
Another crucial aspect was intelligent job scheduling. I developed a queue system that prioritized jobs based on various factors like urgency, site complexity, and historical performance. This helped maintain a steady flow and prevented overwhelming the system.
Don’t forget about data storage and processing. With that volume, you’ll need a robust database setup and possibly some distributed processing to handle the scraped data efficiently. I found combining MongoDB for raw data storage with Apache Spark for processing worked wonders for my large-scale operations.
Lastly, invest time in creating comprehensive logs and alerts. They’re lifesavers when diagnosing issues in a production environment.
Having worked on large-scale web scraping projects, I recommend a hybrid approach that maintains a small pool of open browser instances rather than opening a new one for every job. This method helps balance performance with memory management. It is important to distribute the workload on multiple servers, control the scrape rate to prevent IP bans, and employ a robust proxy rotation system. You should also optimize your code by waiting for specific elements to load instead of using fixed delays while implementing strong error handling and retry mechanisms.
In my experience, continuous monitoring and iterative adjustments are crucial for a stable production environment.
hey emma, i’ve done similar stuff before. try using a browser pool - keep like 5-10 browsers open and reuse em. it’s faster than opening new ones all the time but doesnt eat up too much memory. also, make sure ur using a good proxy setup and maybe spread the load across a few servers. good luck with ur project!