parsing system<\/a>.<\/p>
The Need for Efficient Web Data Parsing<\/h2>
Searching and extracting data on the Internet can be done in several ways. In fact, there are many different technologies, tools, and techniques that can be used when it comes to scraping. However, not all of them yield the same results. Although using browser automation tools to control a web browser is one of the simplest scraping methods, it is significantly slower because rendering takes a considerable amount of time.<\/p>
Few DIY scrapers and libraries can be easily included in a web scraping pipeline. Moreover, it is always possible to build most of them from scratch to ensure maximum efficiency and flexibility. Since this provides much more customization, which is vital for such a dynamic process as scraping, we have a custom-built infrastructure for collecting data from the Internet.<\/p>
Meeting Complex Scraper Customization Requirements<\/h2>
Every scraping requirement we receive is unique. The sites we regularly crawl differ from each other in backend technology, coding practices, and navigation structure. Despite all the complexities, eliminating scraping pain points and delivering ready-to-use data to clients is our priority.<\/p>
Some applications that use web data require data to be extracted with low latency. This means data must be retrieved at the moment it is updated on the target site with minimal delay. Price comparison, for example, requires low-latency data. The optimal method for configuring a scraper is selected based on the data application area. We ensure that the delivered data truly helps your application to its fullest extent.<\/p>
<\/p>
How We Configured Our Pipeline for Highly Efficient Scraping<\/h2>
We continuously improve and tune our scraping system to push boundaries and enhance its performance, including processing time and data quality. Here are some of the performance improvements we recently made.<\/p>
1. Optimized database query to improve overall system response time.<\/strong><\/p>
All collection statistics metadata is stored in a database, and together it accumulates into a significant volume of data that must be managed. Our scrapers need to query this database to get details that will guide them to the next scraping task. This typically takes a few seconds because the metadata is retrieved from the database.<\/p>
Recently, we optimized this database query, which significantly reduced data retrieval time to fractions of a second from approximately 4 seconds. This made the crawling process much faster and smoother than before.<\/p>
2. Distributed approach with servers in different geographic regions<\/strong><\/p>
Instead of using a single server to crawl millions of records, we deployed scrapers on multiple servers located in different geographic regions. Since multiple machines perform the extraction, the load on each server will be significantly lower, which in turn helps speed up the extraction process.<\/p>
Another advantage is that some sites, which are only accessible from a specific geography, can be scraped using the distributed approach. Since the speed of operation increases significantly with the distributed approach, our clients can enjoy faster order fulfillment times.<\/p>
3. Bulk indexing for faster deduplication<\/strong><\/p>
Duplicate records are never a sign of a good dataset. That is why we have a data processing system that identifies and removes duplicate records from the data before providing it to clients. A dedicated NoSQL database is used for this deduplication task.<\/p>
Recently, we updated this system to perform bulk indexing of records, which will significantly increase data processing speed, ultimately reducing the overall time spent on crawling and delivering data.<\/p>
In Conclusion<\/h2>
As web data has become an inevitable resource for businesses operating in various industries, the demand for efficient and optimized site scraping has grown.<\/p>
We make every effort to keep our scraping system up to date, experimenting, improving, and learning from every project we undertake. This helps us maintain a steady supply of clean, structured data ready for our clients to use in record time.<\/p>
