Characteristics of Good Website Scraping
Website scrapers first appeared in the early 1990s, and since then they have directly or indirectly helped nearly everyone by making sense of the huge piles of unrelated data accumulated over many years on the Internet.
Today, we have access to many ready-made scrapers with varying degrees of ease of use, and we can choose based on which parser best meets our data requirements. It's no surprise that the data scraping market is huge: new scrapers appear every day, but only a few earn a worthy place in the data industry. Why?
Because, no matter how simple the theoretical task of collecting pages sounds (viewing and saving a web page, following links from one page to another, moving to the next page, etc.), building an effective website scraper is no less complex a job.
With ever-growing data volumes split across various formats, numerous codes and languages, and different categories interconnected in no particular order, developing a scraper is an ongoing process. Here are some pointers to the qualities of a good scraper that you need to know to stay ahead in the game of quality scraping solutions.
1. Architecture
Speed and efficiency are two basic requirements for any scraper before it is released to the web. The architecture of web scraper programs or bots comes into play.
Just as any full-fledged organization needs a hierarchy or smooth architecture between different job functions to run smoothly, scrapers also require a well-defined architecture for flawless operation. Thus, scrapers should follow the Gearman model with a supervisor and multiple worker scripts to collect data.
Each manager script will control worker scrapers running at different levels of the same link, thus helping to speed up the data collection process for each link. Apart from speed, a safe and reliable site traversal system is also desirable to prevent any loss of data extracted by supervisor scrapers.
Therefore, it becomes mandatory to create a backup storage support system for all manager scraper scripts to avoid dependence on a single data management point and to survey the Internet efficiently and reliably.
2. Intelligent Data Updating
Web crawling has many applications and different clients looking for relevant data. Suppose a client in the retail book business needs titles, author names, and price scraping for each book as it is published.
Since different genres and categories of books are updated on different sites and by different publishers, all these sites may update their lists at different frequencies. Continuously sending a scraper bot to these sites to collect data would waste time and resources. Therefore, it is important to develop intelligent scrapers that can analyze the update frequency of pages on target sites.
3. Efficient Algorithms
Data scrapers mostly follow the Last In First Out (LIFO) or First In First Out (FIFO) methodology to browse data presented on interconnected pages and websites. Theoretically, this works well, but a problem arises when the data to be traversed in one pass grows larger and deeper than expected.
Hence, another requirement for optimized crawling arises that must be incorporated into data scrapers.
This can be achieved by assigning a priority or priorities to pages being crawled based on popular queries and characteristics such as page rank, update frequency, reviews, etc., and estimating the time required to crawl them. This way, you can improve your scraping system by analyzing crawl times of such pages and distributing tasks among all scripts evenly to avoid idle resources and bottlenecks.
4. Scalability
Looking at the data cloud on the Internet from a more futuristic perspective, given the growing number of data files and uploaded documents, you need to check the scalability of your data collection system before launching it.
With that in mind, two key features to include in a data collection system are storage and extensibility. On average, each page contains more than 100 links and about 10–100 KB of text data. Retrieving this data from each page takes about 350 KB of space.
Therefore, ensure your scraper compresses data before retrieval or uses limited memory for storage scalability. Additionally, a modular architectural structure of the web scraper helps, so the scraper can be easily modified to accommodate any changes in client big data collection requirements.
5. Language Independence
With increasing demand for data collection, it is important that a website scraper is language-neutral and extracts data in all languages worldwide. Although English remains the most used language on the Internet, other languages and their data are gradually gaining momentum and capturing a significant portion of Internet data available for viewing to derive business insights and analytics.
It would be unfair to crawl only English texts, leaving the rest of the world in the dark. Therefore, it is important to use a multilingual approach in your scraper so that users can request data in any language or combination of languages and make smart business decisions based on the data provided by your website scraping system.
6. Politeness
Sending a scraper to collect data on the Internet at the wrong time or with a poor structure could mean launching a DoS attack. To prevent this, some web pages have bot crawling restrictions specified on their page to help regulate the collection process.
Therefore, avoid sending overly persistent requests to a website so as not to overload the server and cause the site to crash. For more polite and ethical practices, read our article on ethics in data scraping.