134 points by webcrawlerguy 5 months ago flag hide 16 comments
crawlerbuilder1 5 months ago next
Great job on building a real-time web crawler from scratch! How did you handle the challenges of high traffic and data processing? Would love to know more about the tech stack you used.
crawlerbuilder1 5 months ago next
Thanks for the kind words! To handle high traffic and data processing, I used a distributed system with load balancing and a NoSQL database for fast writes. The tech stack consists of Python for the backend and React for the frontend.
opensourceenthusiast3 5 months ago prev next
May I know if you open-sourced the tool? Would love to contribute to the codebase!
crawlerbuilder1 5 months ago next
I plan to open-source the tool in the near future! I’ll post updates on HN. Stay tuned!
techlover2 5 months ago prev next
Impressive work! I have a few questions about the specifics of the distributed system and database you used. Could you provide more details?
crawlerbuilder1 5 months ago next
Sure! I used Redis for caching and RabbitMQ for message queuing. For the distributed system, I used a combination of Docker and Kubernetes for containerization and orchestration. The database used is MongoDB, which can handle large amounts of data and provides high read/write performance.
bigdataguru4 5 months ago prev next
Really inspiring project! Have you considered using Spark or Flink for processing the massive crawled data? It can help to process the data in real-time and efficiently.
crawlerbuilder1 5 months ago next
That’s a good suggestion! I haven’t considered implementing a real-time data processing system like Spark or Flink. I will definitely look into these options and test their abilities. Thank you for the recommendation!
webdeveloper5 5 months ago prev next
Amazing work, I can’t wait to try this out! Do you have any specific use cases or examples you recommend to get started with?
crawlerbuilder1 5 months ago next
Absolutely! I suggest trying it out for websites with constantly updated content, such as news or e-commerce websites. It’s also worth mentioning that the crawler can be adjusted to crawl data with specific keywords or from specific domains. Have fun exploring!
devopsmaster6 5 months ago prev next
The architecture sounds fantastic! I’m curious about the scaling potential of this tool. How easy would it be to scale the distributed system and databases horizontally?
crawlerbuilder1 5 months ago next
Horizontal scaling is one of the key benefits of using Docker and Kubernetes for containerization and orchestration. The distributed system and databases can be easily scaled out by adding new nodes to the clusters, and this process can be automated using autoscaling rules based on specific resource utilization metrics.
codingninja7 5 months ago prev next
I’m still amazed by this real-time web crawler! I have a question regarding the user-agent settings, as I noticed that some websites block the crawler due to incorrect user-agent settings. How did you tackle this issue?
crawlerbuilder1 5 months ago next
That’s a valid concern. To tackle this issue, the web crawler uses a rotating set of user-agents, which includes major search engine bots to help bypass limitations from websites. The user-agent info can be fetched from the scraped website’s metadata or from popular user-agent repository.
machinelearningexpert8 5 months ago prev next
Fantastic project! Have you considered integrating Machine Learning techniques such as Named Entity Recognition models on top of the extracted data to identify specific entities?
crawlerbuilder1 5 months ago next
Integrating ML techniques is an excellent idea! Applying NER models on the crawled data can extract more valuable metadata and insights. I have some experience implementing ML pipelines and will definitely consider incorporating ML to the web crawler in future iterations. Thanks for the suggestion!