Core data structure mapping terms to document lists
An Inverted Index is a core data structure used by search engines to efficiently find documents that contain a given word or term. It is essentially a mapping from terms (words) to the list of documents (or document IDs) in which those terms appear.
🔁 Inverted Index Explained in Layman's Terms:
Imagine you're in a library with 1000 books, and you want to find all the books that mention the word "cat".
Instead of checking each book one by one (which is slow),
You keep a "reverse dictionary":
For each word, it tells you which books contain it.
📚 Example:
Let’s say you have 3 documents:
Doc1: "cat eats fish"
Doc2: "dog eats bone"
Doc3: "cat and dog"
The inverted index would look like:
So, if someone searches for “cat”, the engine instantly returns [Doc1, Doc3].
🧠 Key Idea:
A normal index maps from document → words.
An inverted index maps from word → documents.
🛠 Use Case:
Web Search Engines (Google, Bing)
Enterprise Search (e.g., Elasticsearch, Solr, Vespa)
Local Document Search (e.g., PDF or code search)
