Does Elasticsearch Use Trie Data Structure?


Heather Bennett

When it comes to searching and analyzing large volumes of data, Elasticsearch is a popular choice for many developers. It is a powerful open-source search and analytics engine that is built on top of Apache Lucene.

One question that often arises is whether Elasticsearch uses trie data structure or not. Let’s dive into this topic and explore the inner workings of Elasticsearch.

What is Trie Data Structure?

Trie, also known as prefix tree, is a data structure that provides efficient retrieval of keys. It organizes keys in a tree-like structure where each node represents a character.

The path from the root to a particular node forms a key. This makes trie particularly useful for searching words with common prefixes, such as autocompletion or spell-checking.

Elasticsearch’s Internal Data Structure

While Elasticsearch does not use trie as its primary data structure, it utilizes various other data structures to achieve efficient indexing and searching capabilities.

Inverted Index

The heart of Elasticsearch’s search engine lies in its inverted index. It is an index structure that maps terms to the documents containing those terms. In simple terms, it turns the traditional way of storing documents on its head by storing the terms and their locations within documents instead.

This inverted index allows for fast full-text searches by breaking down text into terms, eliminating common words (stop words), and providing information about which documents contain these terms.

TST (Ternary Search Tree)

Elasticsearch uses TST (Ternary Search Tree) as one of its supporting data structures. TST is similar to trie but optimizes memory usage by compressing branches with only one child into a single node.

  • Advantages:
    • Efficient key retrieval
    • Compact memory usage
  • Disadvantages:
    • Slower insertion and deletion compared to trie

FST (Finite State Transducer)

Elasticsearch also incorporates FST (Finite State Transducer) for efficient term lookups and autocomplete functionality. FST is a data structure that represents a set of strings, allowing for fast prefix matching and completion suggestions.


While Elasticsearch does not directly use trie as its primary data structure, it leverages various other data structures like inverted index, TST, and FST to provide powerful full-text search capabilities. Understanding these underlying structures can help developers optimize their Elasticsearch queries and make the most out of this versatile search engine.

In summary, Elasticsearch may not use trie directly, but it employs a combination of different data structures to achieve efficient indexing, searching, and autocomplete functionalities.

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