Search engines have two major functions - crawling and indexing, and providing answers by calculating relevancy.
1- Crawling and indexing
Imagine the World Wide Web as a network of stops in a big city subway system. Each stop is its own unique web page. The search engines need a way to “crawl” the entire city and find all the stops along the way, so they use the best path available, Links.
Through links, search engines’ automated robots, called “crawlers,” or “spiders” can reach billions of interconnected web pages.
Once the engines find these pages, they next decipher the code from them and store selected pieces in massive hard drives, to be recalled later when needed for a search query. To accomplish the monumental task of holding billions of pages that can be accessed in a fraction of a second, the search engines have constructed datacenters all over the world.
These monstrous storage facilities hold thousands of machines processing large quantities of information. After all, when a person performs a search at any of the major engines, they demand results instantaneously, even a 1 or 2 second delay can cause dissatisfaction, so the engines work hard to provide answers as fast as possible.
2 - Providing Answers
Search engines are answer machines. When a person looks for something online, it requires the search engines to search billions of web pages and do two things- first, return only those results that are relevant or useful to the searcher’s query, and second, rank those results in order of perceived usefulness. It is both “relevance” and “importance” that the process of SEO is meant to influence.
To a search engine, relevance means more than simply finding a page with the right words. In the early days of the web, search engines didn’t go much further than this simplistic step, and their results suffered as a consequence. Thus, through evolution, smart engineers at the engines devised better ways to find valuable results that searchers would appreciate and enjoy. Today, 100s of factors influence relevance.
Currently, the major engines typically interpret importance as popularity – the more popular a site, page or document, the more valuable the information contained therein must be. This assumption has proven fairly successful in practice, as the engines have continued to increase users’ satisfaction by using metrics that interpret popularity.
Popularity and relevance aren’t determined manually. Instead, the engines craft careful, mathematical equations – algorithms – to sort the wheat from the chaff and to then rank the wheat in order of tastiness.