Even though search engines are relatively recent (early 1990s), the concepts underpinning them are much older. Theoretical concepts about structuring information go back to the post-war era (thanks to Vannevar Bush and other thinkers), graph theory tools date from the 1930s, and information retrieval systems emerged in the 1960s and 70s. Despite their age, these concepts are not all well-known among professional SEOs.
Today, I’ll start with the basics: defining what a search engine is and introducing the first technical building block—collecting information. I won’t dive into overly technical details since they aren’t necessary for a general understanding. For those interested in deeper exploration, I recommend two reference books: An Introduction to Information Retrieval and Recherche d’information: applications, modèles et algorithmes (the latter is in French, as many of our readers ;)).
What is a Search Engine?
Before defining what is a search engine, it’s relevant to define its subject of analysis: the web. Literally speaking, the web is a navigation system for exploring information, built on top of the internet. Information is contained in web pages (organized within websites), and users can navigate from one page to another by following hyperlinks.
From an algorithmic point-of-view, the web is a directed graph where nodes represent web pages, and edges represent hyperlinks connecting these pages. This graph-based structure is crucial as it forms the basis for ranking algorithms such as Google’s PageRank, which revolutionized web search (I’ll discuss this in detail in a future article).
Now that we’ve defined the web, let’s turn to search engines. A search engine is simply a website (or a smartphone app or an API) designed to return relevant results for a user’s query. For example, if someone searches for apartments, a search engine does its job well if it shows available listings.
Here’s where things get complicated: what defines a “relevant” result? And what exactly is a “query”?
Understanding Queries and Information Needs
A query is how users express their information needs—their real intention—though often clumsily or ambiguously. For example, a user might want to see what a jaguar (the animal) looks like but types “jaguar” into the search bar, leaving the search engine to determine if they meant the animal or the luxury car brand. This ambiguity arises because the user’s information need is locked in their mind, making it hard to discern, especially since users search for what they don’t know and may struggle to describe. In SEO it’s all about search intent right now, you can think of “information need” as the academic way of saying “search intent” 😉
In essence, the search engine’s goal is to understand each user’s information need and return one or more web pages to satisfy it. But is this straightforward once the information need is clear? Absolutely not, for several reasons:
- An abundance of content: For almost every query, countless relevant pages exist, but the search engine must select just a few (e.g., 10 results on Google’s first page, which is often the only page users check). Finding the best 10 pages out of hundreds of billions pages is hard. SEO is here to help (or not) 😉
- Understanding page content: Interpreting content is hard; words can convey different ideas, and subtleties like double meanings, irony, and homonyms are particularly challenging.
- Speed requirements: Users won’t wait several minutes for the best page to be selected. Search engines must balance quality with response time, ensuring results are returned quickly.
- Financial constraints: Search engines need to generate profits, meaning they can’t afford enormous indexes or computationally expensive algorithms. A lack of resources often explains why newer search engines struggle to match the quality of giants like Google or Bing.
- Manipulation attempts: Some individuals try to game search engine rankings for personal or client benefits. This creates an “adversarial information retrieval” scenario, where engines must filter manipulative tactics to ensure quality results. Yes, I’m speaking about the SEO community here.
A Search Engine’s Framework
The structure of a search engine can be broken down into several key functional components, each playing a crucial role in addressing a user’s query. While this explanation simplifies the process, it provides enough detail to understand the fundamental principles at work.
Step One: Crawling
The search engine’s first task is crawling the web, or exploring all the pages it can find. To deliver relevant results, it must analyze an enormous number of pages. This job is performed by an indexing robot (also called a bot or spider). Intuitively, the process is simple: starting from a set of seed URLs, the bot follows hyperlinks recursively to discover new pages.
While the idea is straightforward, execution is challenging due to the sheer volume of data and the web’s dynamic nature. A “brute-force” crawler would be inefficient, wasting resources revisiting the same pages. Effective crawling requires sophisticated strategies and a focus on efficiency—something Google, for instance, prioritizes by promoting fast-loading websites. Not only do fast sites improve user experience, but they also reduce the cost of crawling.
Step Two: Indexing
Indexing involves storing data from crawled pages in a global structure: the search engine’s index. This index is its most valuable asset, containing two main types of information:
- Structural information: Describing relationships between pages (the web graph).
- Content information: Focusing primarily on textual data, although advances in image and video understanding are improving non-textual indexing.
Step Three: Evaluating Importance
Search engines differentiate structural and content information because the former is preprocessed to speed up query responses. Importance evaluation involves ranking pages based on their perceived popularity, independent of content—this is where algorithms like PageRank come into play. More recently we all have seen that real popularity (e.g. user signals) is used by Google to help understand what are the most important websites and webpages.
Step Four: Query Analysis
Query analysis, or “query expansion,” helps search engines better understand user intent. Google’s focus on this aspect became particularly evident with the introduction of Hummingbird in 2013, highlighting its efforts to align results with user intent. With the advent of language models (BERT and followers), search engines are now really good at this task.
Step Five: Relevance Analysis
At this stage, the engine identifies which pages discuss similar topics, forming thematic clusters. This process isn’t about true “understanding” but rather about finding patterns in the data to match user queries with relevant pages. Algorithms such as QBST are used for this task (QBST, that our tool yourtextguru is reverse-engineering).
Step Six: Spam Filtering
Manipulative practices—like link farming, content generation, or dubious redirects—are countered through spam filters applied at every stage, from crawling to final ranking. This ongoing battle between manipulation and defense keeps SEOs busy and fuels discussions in all SEO venues.
Final Step: Final Ranking and Reranking
After pages are crawled, indexed, and analyzed for relevance and importance, the engine returns results. The ranking is adjusted for personalization (e.g., showing shopping sites for purchase intent) and localization (e.g., surfacing local businesses).
Finally, user behavior metrics like click-through rates (CTR), click-skip patterns (e.g., skipping result 2 but clicking 1 and 3) and many others help engines fine-tune rankings for individual queries.
In the Google inner architecture, the NavBoost system is in charge of this task.
In this article, I explained the fundamental components of a search engine and how they work together to process and deliver relevant results. From crawling to algorithms that rank and filter pages, every piece of the system is here to provide the most relevant results possible.
Stay tuned for upcoming articles that will be more detailed about each of the tasks a search engine has to complete.
