What is “Inverted Search” or “Inverted Index”?

What is “Inverted Search” or “Inverted Index”?

Elasticsearch (like most search engines) uses an inverted index to make full-text search fast and efficient.

🧠 Think of it like this:

Instead of storing documents and searching each one linearly (which is slow), Elasticsearch:

1. Breaks down documents into terms (words).
2. Creates a map from each term to the list of documents (and positions) where the term appears.

This mapping is the inverted index — it’s “inverted” because it maps from terms → documents (instead of documents → terms).

📘 Example

Let’s say you index the following 3 documents:

json

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1: “Elasticsearch is fast”

2: “Elasticsearch is powerful”

3: “Search engines use inverted indexes”

Elasticsearch creates an inverted index like:

Term	Document IDs
elasticsearch	1, 2
is	1, 2
fast	1
powerful	2
search	3
engines	3
use	3
inverted	3
indexes	3

So now, when you search for elasticsearch, it instantly knows documents 1 and 2 are matches.

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✅ Inverted Search in Action

Suppose you run this query:

json

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GET /my_index/_search

{

  “query”: {

    “match”: {

      “text”: “elasticsearch”

    }

  }

}

🔎 Elasticsearch looks up the term elasticsearch in the inverted index and returns document IDs 1 and 2.

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🛠 Why Is This Powerful?

• Fast lookup — even for large datasets.
• Enables full-text search, filtering, scoring, highlighting, etc.
• Scales well across distributed nodes.

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📝 Summary

Traditional Index	Inverted Index
Document → Terms	Term → Documents

Elasticsearch uses inverted index for efficient full-text search, letting it quickly retrieve all documents that contain a specific term (or combination of terms).