Crawling and Indexing:
World wide web is like a network of stations in a huge railway system.Each stop is its own unique document (usually a web page, but sometimes a PDF, JPG or other
file). 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 the
many billions of interconnected documents.
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.

How they solve queries: 
Search engines are answer machines. When a person looks for something online, it requires the
search engines to scour their corpus of billions of documents 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, many of which we’ll discuss throughout this guide.

How do they work:
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 (or however it is that farmers determine wheat’s value).
These algorithms are often comprised of hundreds of components. In the search marketing field,
we often refer to them as “ranking factors” 


(…) (…)