Elastic Search

Inverted index is a fundamental data structure used by Elasticsearch to quickly search for and retrieve documents that match a given query. It is an index structure that maps terms in a document to the documents that contain them.

In Elasticsearch, the inverted index is created automatically when documents are indexed. The index consists of a list of terms in a document, along with a list of the documents that contain each term. This allows Elasticsearch to quickly retrieve all documents that contain a given term, which is essential for efficient search.

For example, suppose we have a collection of documents about animals. The inverted index for this collection might look something like this:

jsonCopy code{
  "cat": [1, 3, 5],
  "dog": [2, 4, 6],
  "fish": [1, 4, 7],
  "bird": [3, 5, 8]
}

This index tells us that the term "cat" appears in documents 1, 3, and 5, the term "dog" appears in documents 2, 4, and 6, and so on.

When a user performs a search query, Elasticsearch uses the inverted index to quickly find all documents that contain the terms in the query. For example, a query for "cat" would return documents 1, 3, and 5.

Elasticsearch uses a number of optimizations to make the inverted index more efficient, including compression, caching, and shard-based distribution across a cluster of nodes. These optimizations make Elasticsearch a powerful tool for searching and analyzing large volumes of data.