0. Reasons why web crawling is considered important
As the digital environment becomes more data-centric, web crawling (web scraping) is becoming increasingly important. By extracting large amounts of data from the web, companies can gain insights, improve decision-making, and maintain competitiveness.
However, to obtain accurate insights, a large amount of data is required, leading to the challenging task of needing distributed web crawling methods to acquire large amounts of data. This comprehensive guide explores the world of distributed web crawling and provides in-depth strategies for successful implementation.
Delve deep into the world of distributed web crawling (web scraping) in this comprehensive guide. Learn about its principles, implementation strategies, related tools and technologies, as well as legal and ethical considerations to keep in mind.
1. Understanding web crawling and the need for distributed processing
Web scraping (web crawling) or spidering is the systematic traversal of the World Wide Web (WWW) to extract information from websites. This is typically done by automated scripts known as web crawlers or crawling bots.
As the scale of data to be extracted increases, a single computer (or single IP) or crawler may struggle to effectively handle the load. Distributed web crawling technology is then utilized, allowing multiple crawlers or computers to work simultaneously to collect data more efficiently and quickly.
As a result, the productivity efficiency accumulated through such distributed processing can bring over 10 times more revenue to web crawling developers.
2. Principles of distributed web crawling
Scalability
Scalability refers to the system's ability to effectively handle increased workloads. This means that as data demands increase, more web crawlers or servers should be automatically added to the network. By utilizing AWS's AutoScaling technology, the number of servers can be increased as the amount of data collected grows, and then reduced to a minimum once all data has been collected.Load Balancing
Load balancing ensures that tasks are evenly distributed among all scrapers in the network. It prevents overload of specific scrapers and ensures efficient utilization of resources.Fault Tolerance
Fault tolerance refers to the system's ability to continue operating even if one or more crawlers fail in data processing. It includes technologies such as preventing data duplication to avoid loss.
3. Implementation of distributed web crawling: Step-by-step guide
Step-1. Designing crawling architecture
The first step is to design an architecture that facilitates distributed web crawling. This could be a master-worker architecture where the master assigns tasks to crawlers, or an independent peer-to-peer architecture for each crawler.
Hashscraper processes data by extracting links from the list in a typical "list-detail page" structure, then requesting all detail page tasks at once to collect data from multiple servers simultaneously for faster data collection.
Step-2. Implementing scalability and load balancing
The next step is to implement scalability and load balancing in the architecture. This can be done using various tools and technologies such as cloud computing platforms and load balancing algorithms.
Hashscraper collects data based on a multi-cloud (AWS, GCP, domestic IDC, self-owned servers, etc.) infrastructure.
Step-3. Ensuring fault tolerance
The final step is to ensure fault tolerance. This includes creating duplicate copies of data, setting up automatic system checks, and implementing recovery procedures.
Hashscraper inherits the HsDynamicWorker class to create crawlers, which raises an Exception if data collection fails in the web crawler, and the task is placed in the Retry Queue for several retries.
4. Tools and technologies for distributed web crawling
There are various tools and technologies that can help accelerate distributed web crawling, including:
- Cloud computing platforms: Platforms like AWS, Google Cloud, and Azure provide scalable computing resources for distributed web crawling.
- Web crawling frameworks: Frameworks like Scrapy and BeautifulSoup can be used to build distributed web crawling systems.
- Big data technologies: Technologies like Hadoop and Spark are useful for processing and analyzing large-scale data extracted through distributed web crawling.
5. Legal and ethical considerations
While distributed web crawling can significantly enhance data extraction capabilities, it is important to comply with relevant laws and website terms of use. Ignoring these can lead to legal issues (such as business interference).
High traffic spikes can inconvenience users of the target site due to the load placed on the site when extracting data. Therefore, it is recommended to introduce appropriate delays when collecting data.
6. Conclusion: Utilizing distributed web crawling for data-centric success
Mastering distributed web crawling technology can provide companies with vast data resources, leading to improved insights and decision-making foundations. This can enhance my competitiveness and lead to significant efficiency gains.
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