Web Crawlers came into existence in the early 90’s and since then they have been helping us make sense of the massive piles of unrelated data accumulated over the years on the internet. There are numerous web crawlers available to us today with varying degrees of usability and we can pick and choose according to whichever crawler matches our criteria for data requirement the best. No wonder, there is a huge market of data crawling with newer web crawlers popping up every day but only a few make a mark in the data industry.
Reason? Because however easy a web crawler might sound in theory (crawling a page, following page links from one page to another and crawling the next page and so forth), creating an efficient web crawler is an equally difficult job. With ever expanding data divided in different formats, multiple codes and languages and various categories, interconnected in no particular order, web crawler development is an ever-evolving process. Here are a few pointers to know in order to get ahead in the game of qualitative web crawling solutions.
Speed and efficiency are two basic requirements in any data crawler before it is let out on the internet. The architectural design of the web crawler programs or auto bots comes into the picture. Like any fully functional organization to work smoothly, a hierarchy or smooth architecture is required between different job roles, similarly web crawlers require a well-defined architecture to function flawlessly. Web crawlers should thus follow the gearman model with supervisor sub crawlers and multiple worker crawlers.
Each supervisor crawler would manage the worker crawlers working on different levels of the same link and thus help speed up the data crawling process per link. Apart from the speed, a safe and reliable web crawling system is also desirable to prevent any loss of data retrieved by the supervisor crawlers. Therefore, it becomes mandatory to create a backup storage support system for all supervisor crawlers and not depend on a single point of data management and crawl the web efficiently in a reliable manner.
2. Intelligent recrawling
Web crawling has multiple uses and various clients who are looking for relevant data. Let’s say a book retail client needs the titles, author names and prices of each book as and when it gets published. Since different genres and book categories get updated on different websites and by different publishers, all these websites might have a different frequency of updating their lists. Sending a crawler to these sites relentlessly for data scraping will prove to be a wastage of time and resources. Thus it is important to develop intelligent crawlers that can analyze the frequency with which the pages get updated on the targeted websites.
3. Thorough/efficient algorithms
Mostly data crawlers follow a Last in First out (LIFO) or First in First out (FIFO) methodology to traverse the data present on the interconnected pages and websites. It works well theoretically but a problem arises when the data to be traversed per crawl sprawls larger and deeper than anticipated. Hence arises another requirement for optimized crawling to be incorporated in the data crawlers. This can be accomplished by assigning priority or to the crawled pages on the basis of popular demands and features such as page rank, a frequency of updates, reviews, etc. and identifying the time it takes to crawl them all. You can thus enhance your web crawling system by analyzing the crawling time of such pages and dividing the tasks among all data crawlers equally so there are no vacant resources (read data crawlers) and no bottlenecks are created either.
Taking a more futuristic view of data cloud present on the web, based on the escalating number of data files and document uploads, it is necessary to test out the scalability of your data crawling system before you launch it. Keeping that in mind, the two key features you need to incorporate in your data crawling system are Storage and Extensibility. On an average, each page has over 100 links and about 10-100 kb of textual data. Fetching that data from each page takes just about 350kb of space. Now multiply it with over 400 billion pages (as of 2010) it comes down to 140 petabytes of data per crawl!! So make sure that your crawler compresses the data before fetching it or uses a bounded amount of storage for storage-related scalability. Also, a modular architectural design of the web crawler helps, so the crawler can be modified easily to accommodate any changes in the big data crawling requirements of the client.
5. Language independent
With the rising demand for data acquisition, it is important for a web crawler to be language neutral and extract data in all languages as requested across the globe. Though English is still the most used language over the web, but other languages and their data is slowly catching up and capture a large chunk of the Internet data available to crawl for business insights and analytical conclusions. It would be unfair to crawl only English text and leave the rest of the world in dark. Thus it is important to adopt a more multilingual approach in your crawler so the users can request for data in any language or a combination of languages and make intelligent business decisions derived from the insights provided by your data crawling system.
Sending a badly timed or poorly structured data crawler to crawl the web could mean unleashing a DoS attack on the internet. To help prevent this, some web pages have crawling restrictions mentioned on their page, to help regulate their crawling process. So avoid sending over eager crawlers to a website to avoid overcrowding its server resulting in a site crash. For more polite and ethical practices refer to Data Crawling & Extraction Ethics
Do consider these points while building the next crawling system for improved performance and better results. Till then, Crawl Away!