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This blog breaks down how to build a basic search engine backend and covers key components like a web crawler, an inverted index, and a query processing module. You’ll also learn how search engines scale to handle millions of pages and queries efficiently, using techniques like indexing and distribution.

Ever wonder how Google finds answers in a blink?

Designing a search engine is all about smart architecture and data handling.

In simple terms, a search engine crawls the web to collect pages, indexes that information for quick lookup, and then processes your queries to find the most relevant results.

In this guide, we’ll explore how to build a basic search engine backend from scratch – from a humble web crawler that gathers data, to an inverted index that stores it, to a query processor that retrieves answers.

We’ll also discuss how to scale your search engine so it doesn’t crumble under billions of pages or queries.

Let’s dive in!

Web Crawler: Collecting the Web’s Data

The first piece of a search engine is the web crawler (also known as a spider).

This is the bot that goes out and scours the internet for content. It starts with a list of known URLs (seed sites) and systematically visits them to fetch their pages.

As it downloads a page, it also extracts all the hyperlinks on that page.

Each discovered link gets added to the crawler’s to-do list (often called the URL frontier or queue).

By continually following links from one page to the next, the crawler can discover new and updated pages across the web.

A web crawler’s job isn’t just clicking links randomly. It performs a few key functions methodically:

• Discovery: Start with seed URLs and follow hyperlinks to find new pages. This way, the crawler gradually explores the web, much like a person browsing from link to link.

• Retrieval: For each page found, download the content (HTML, text, images, etc.) so it can be processed for the index. Think of this as the crawler “reading” the page and saving a copy.

• Parsing: After fetching a page, the crawler parses the HTML to extract useful information – primarily the text and links on the page. It might also note metadata like the title or description.

• Politeness & Filtering: A good crawler respects rules and limits. It checks for a site’s robots.txt file and skips disallowed paths (so it doesn’t crawl private or forbidden sections). It also avoids hammering any single website too hard or too fast (politeness), and may skip duplicate content or non-HTML files that aren’t useful for indexing.

By adhering to these rules, the crawler operates ethically and efficiently – it avoids overloading websites and focuses on relevant content.

Modern search engines even distribute this crawling work across many machines.

For example, Google uses a distributed web crawler running in data centers around the world, which helps crawl pages faster and reduces bandwidth usage.

After all, the web has billions of pages, so crawling at scale requires spreading the effort out.

Indexing and the Inverted Index: Organizing Information

Once the crawler has fetched pages, the next step is indexing – organizing all that data to make it searchable.

Raw web pages are full of HTML, scripts, and text; the indexer’s job is to distill this into a structured format that can be queried quickly.

The cornerstone of a search engine’s index is the inverted index.

What’s an Inverted Index?

It’s a data structure that maps each term (word) to the list of documents (pages) that contain that term.

In other words, it’s like a giant hash map where the keys are words and the values are lists of page IDs or URLs.

This is “inverted” because it flips the usual relationship – instead of storing the words per document (like a book’s glossary), it stores documents per word.

An inverted index allows the search engine to find relevant pages without scanning every document every time.

If you search for “database systems,” the engine can jump directly to the index entries for “database” and “systems” to get lists of pages, rather than reading through all pages end-to-end.

Building the inverted index involves a few steps:

1. Parsing & Tokenization: The indexer takes the raw text from each crawled page and breaks it into individual words (tokens). It typically lowercases them, removes punctuation, and might drop very common “stop words” like “the” or “and” that don’t add meaning. It may also apply stemming (e.g., treating “cats” and “cat” as the same root word) to normalize variations.

2. Recording Document IDs: Each page processed by the indexer gets a unique ID (or uses its URL as an identifier). As words are extracted from a page, the indexer notes the word -> doc ID relationship. For example, it might record that the word “search” appears in document #123, #456, and #789, etc.

3. Building the Index Structure: The indexer organizes these mappings into the inverted index data structure, effectively creating a dictionary of terms pointing to posting lists (lists of documents). According to one source, “the indexing component creates an inverted index, which maps terms or keywords to the documents that contain them,” enabling fast full-text search. The index is often stored on disk (and/or memory) in a compressed form for efficiency, since it can be enormous.

Alongside the main inverted index, the search engine might store some metadata for each page – like the title, URL, a summary, and maybe some ranking signals (more on that shortly).

But the inverted index is the star of the show because it lets us answer the fundamental question: “Which pages contain this word?” quickly.

With our index built, we’re ready to actually answer user queries.

Query Processing and Ranking: Answering User Queries

Now for the part that users actually see – the query processing.

When you type a query into a search engine, a lot happens in a split second.

The query processing module is responsible for interpreting your words, finding matching documents via the index, and ranking those results by relevance.

Here’s a breakdown of what happens when a search query comes in:

Query Parsing

The search engine first parses your query.

Much like the indexer did with documents, the query is tokenized into words, and possibly normalized (lowercased, spelling-corrected, etc.).

The engine might also guess if you meant a specific phrase by quotes or do basic query expansion (for instance, treating “runs” and “running” similarly).

Modern engines can even apply some NLP to understand intent, but for a basic engine we stick to keywords.

Lookup in the Inverted Index

Next, the engine uses the inverted index to fetch candidate results.

For a single-word query, it’s straightforward – retrieve the list of all documents containing that word.

For multi-word queries, the engine might retrieve the list for each word and then find the intersection (documents that have all the terms) or do a union with a ranking algorithm to handle “any of these words.”

Either way, thanks to the index, this lookup is very fast – the engine is essentially doing a few dictionary lookups and set operations instead of scanning every page.

Ranking the Results

When the engine has a set of candidate pages, it needs to sort them by how relevant they are to your query.

Not all pages that contain the word “pizza” are equally useful if you search "pizza recipe". This is where the ranking module comes in.

A basic ranking approach might use textual relevance – for example, count how frequently the query terms appear in the page (more = likely more relevant), how close together the terms are, and if they appear in important places like the title or headings.

There are well-known formulas like TF-IDF or BM25 that score documents based on term frequency and how rare the terms are across all documents.

On top of that, search engines incorporate other signals: “Common relevance signals include keyword frequency, document freshness, link popularity, user engagement metrics, and contextual relevance,” to name a few.

In practice, Google’s early secret sauce was the PageRank algorithm, which judges a page’s importance by how many other pages link to it (and how important those linker pages are).

Modern search ranking is very sophisticated, potentially using hundreds of signals (including AI models), but the fundamental goal is the same – deliver the most helpful results first.

After ranking, the top N results (say, the top 10 for page one) are packaged into a response.

The search engine will format these results with the page title, URL, and a snippet of text (usually pulled from the part of the page that contained your query words).

This is then sent back to the user’s browser to display on the Search Engine Results Page (SERP).

From the user’s perspective, all of this felt instantaneous – and a well-designed search engine does return results in a fraction of a second.

(For completeness, a search engine also has a front-end component: the user interface that handles the query input and results display. In our discussion, we’re focusing on the backend components. The UI is crucial for user experience, but it doesn’t affect how the search itself works behind the scenes.)