I’m working on a project that involves real-time web scraping using headless browsers. My algorithm opens multiple tabs per request to scrape data from different URLs at the same time. Each run needs about 1-10 tabs and a separate browser for 20-30 seconds.
I’ve tried some browser-as-a-service options, but they keep failing due to speed issues and weird browser behavior. Now I’m thinking about hosting my own headless browsers on my servers with proxies.
The main problem is memory usage. I’ve already disabled loading of images, videos, and other non-essential stuff, focusing only on text and URLs. But this doesn’t work for all websites because of how they’re built.
Does anyone have tips on how to make these browser instances use less memory? I’m trying to keep costs down as we start to scale up. Any ideas would be really helpful!
I’ve been in a similar situation with web scraping projects, and memory usage can indeed be a major hurdle. One approach that worked well for me was implementing a pool of reusable browser instances. Instead of creating new instances for each scraping task, I maintained a fixed number of browsers and recycled them after each use. This significantly reduced memory overhead.
Another effective strategy was to use a lightweight headless browser like Puppeteer-core with minimal Chrome flags. By fine-tuning the launch options, I managed to strip down the browser to bare essentials, which helped conserve memory.
Additionally, I found that breaking down the scraping process into smaller, more manageable chunks and implementing a queuing system helped distribute the load more evenly. This approach allowed me to control the number of concurrent scraping tasks and prevent memory spikes.
Lastly, regularly clearing the browser cache and closing unused tabs proved beneficial in keeping memory usage in check. It’s a bit of extra work, but the results were worth it in terms of resource efficiency.
hey bob, have u tried using a headless browser like phantomjs? it’s pretty lightweight and might help with memory issues. also, maybe look into lazy loading - only grab the data u need when u need it. that could cut down on memory usage too. just some ideas, hope they help!
Have you considered using browser extensions or userscripts to optimize memory usage? I’ve found success with extensions that automatically suspend inactive tabs, freeing up precious RAM. Additionally, implementing a robust error handling system can prevent memory leaks from crashed or hung processes. Another approach worth exploring is distributed scraping - splitting the workload across multiple lower-spec machines instead of relying on a single powerful server. This can be more cost-effective and easier to scale. Lastly, regular profiling and monitoring of your scraping processes can help identify memory bottlenecks and optimize accordingly. It’s a bit of extra work upfront, but it pays off in the long run.