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Web Crawling at Large Scale

Web crawling at scale is essential for aggregating data, monitoring websites, and enabling downstream applications like machine learning models and analytics. This project involved developing a scalable web crawling and re-crawling pipeline using Scrapy, storing the data in MongoDB, and deploying a machine learning model accessible through a Flask API. The entire pipeline was Dockerized and deployed on AWS EC2 for robust, high-availability operations.

Challenges

  • Scalability of Crawling:

    Crawling a vast number of URLs efficiently while minimizing resource usage and ensuring data accuracy.

  • Dynamic and Evolving Content:

    Re-crawling updated or changed content without redundantly processing unchanged data.

  • Data Storage and Access:

    Storing large-scale crawled data in a structured format and making it easily retrievable.

  • Deployment and Automation:

    Ensuring that the entire pipeline, including crawling, machine learning, and APIs, was deployable, maintainable, and scalable.

  • High Availability:

    Deploying the system in a high-availability environment with web servers and workers to manage API load.

Our Solutions

The SQGS addresses these challenges with an AI-driven, cloud-based solution powered by AWS services and advanced data pipelines.

  • Web Crawling Pipeline:

    Built a robust pipeline using Scrapy to crawl and re-crawl large sets of URLs. Implemented logic to identify and skip unchanged pages during re-crawling to save resources.

  • URL Discovery:

    Ticket URL Discovery: Identified ticket purchase links using the similarity model. Image Source Discovery: Extracted image source URLs for event pages to ensure complete page rendering.

  • Data Storage with MongoDB:

    Stored crawled data in MongoDB for fast, queryable access. Maintained metadata like last crawled date and content hash for efficient re-crawling.

  • Machine Learning Integration:

    Deployed a machine learning model for analyzing crawled data (e.g., content classification or sentiment analysis). Exposed the model via a Flask API for easy integration with other systems.

  • Containerization and Deployment:

    Dockerized the entire pipeline using Ubuntu as the base image. Deployed using Nginx as the webserver and Gunicorn as the worker, ensuring high performance and reliability. Hosted the system on AWS EC2 for scalability and cost efficiency.

  • API Development:

    Developed APIs to: Access crawled data. Trigger new crawls or re-crawls. Interact with the machine learning model.

Technology Slack

Nginx

Gunicorn

Scrapy

Python

Mongo DB

Docker

Impacts

  • Crawling and Re-Crawling Logic:

    Designed Scrapy spiders to scrape data from a wide range of web pages. Used MongoDB to track page changes by storing content hashes and timestamps.

  • Data Processing and Storage:

    Parsed crawled data and transformed it into structured formats. Stored processed data and metadata in MongoDB for efficient querying.

  • Machine Learning Model Deployment:

    Trained a machine learning model for specific tasks (e.g., content classification). Exposed the model as a Flask API, allowing real-time predictions on newly crawled data.

  • Containerization and Deployment:

    Dockerized Scrapy, MongoDB, and Flask services for consistent environments across development and production. Configured Nginx and Gunicorn to handle API requests efficiently.

  • AWS Deployment:

    Hosted the system on AWS EC2 for robust, scalable infrastructure. Monitored performance and scaled resources as needed.

Benefits

  • Scalable Crawling:

    Efficiently crawled and re-crawled large numbers of URLs without overloading resources.

  • Actionable Insights:

    Integrated machine learning models enabled real-time analysis of crawled data.

  • High Performance and Reliability:

    Nginx and Gunicorn ensured API responsiveness under high loads.

  • Ease of Deployment:

    Dockerization allowed seamless deployment and scaling across environments.

  • Cost Efficiency:

    Used AWS EC2 to optimize infrastructure costs while maintaining high availability.

Future Scope

  • Real-Time Crawling:

    Implement real-time crawling for monitoring dynamic websites.

  • Advanced ML Models:

    Replace basic models with transformer-based architectures for improved analysis.

  • Dashboard Integration:

    Build dashboards for monitoring crawling progress and data quality metrics.

  • Distributed Crawling:

    Scale the crawling framework to a distributed system using tools like Apache Kafka and Spark.

  • Cloud-Native Architecture:

    Migrate to serverless frameworks (e.g., AWS Lambda) for event-driven crawling.

Conclusion

The large-scale web crawling pipeline delivered efficient, scalable, and reliable crawling and re-crawling capabilities. By integrating Scrapy, MongoDB, and Flask APIs with machine learning, the solution provided actionable insights and streamlined data access. Dockerization and AWS EC2 deployment ensured high performance and adaptability to growing demands, making this pipeline a critical tool for large-scale web data aggregation and analysis.