What Data Structure Is Used for Autocomplete?


Heather Bennett

What Data Structure Is Used for Autocomplete?

Autocomplete is a feature commonly found in search bars or text input fields that suggests possible completions or predictions as the user types. It helps users save time by providing relevant suggestions based on the input they have entered so far.

To efficiently provide these suggestions, data structures play a crucial role. In this article, we will explore the data structures commonly used for autocomplete functionality.

1. Trie

The trie, also known as a prefix tree, is a popular data structure for implementing autocomplete. It is particularly efficient when dealing with large sets of words or phrases.

A trie organizes the words in a tree-like structure, where each node represents a character and each path from the root to a leaf node forms a word.

Advantages of using a trie:

  • Autocomplete suggestions can be generated quickly by traversing the tree based on the user’s input.
  • Tries are space-efficient compared to other data structures like hash tables.
  • Tries can handle dynamic sets of words efficiently.

Disadvantages of using a trie:

  • Trie operations like insertion and deletion can be slower compared to other data structures.
  • Tries may consume more memory than other data structures for small sets of words.

2. Ternary Search Tree (TST)

The ternary search tree is another widely used data structure for autocomplete functionality. It combines characteristics of both tries and binary search trees.

In a TST, each node has three children: left child, middle child, and right child.

Advantages of using a TST:

  • TSTs are faster than tries for searching and retrieving suggestions.
  • TSTs can efficiently handle autocomplete with partial prefix matches.

Disadvantages of using a TST:

  • TSTs may require more memory than tries for the same set of words.
  • Insertion and deletion operations in TSTs can be more complex.

3. Hash Table

Hash tables are commonly used data structures in many applications, including autocomplete. They map keys to values using a hash function.

In the context of autocomplete, the keys can be prefixes or substrings of words, and the values can be the corresponding suggestions.

Advantages of using a hash table:

  • Hash tables provide constant time complexity for retrieval and insertion operations on average.
  • They are efficient for small to medium-sized sets of words.

Disadvantages of using a hash table:

  • Hash tables may have collisions, which can affect performance.
  • They may require additional techniques like chaining or open addressing to handle collisions efficiently.


In conclusion, several data structures can be used for implementing autocomplete functionality. The choice depends on factors such as the size of the word set, performance requirements, memory constraints, and more.

Trie and Ternary Search Tree (TST) are often preferred for large datasets due to their efficiency in generating suggestions based on user input. On the other hand, hash tables are useful when dealing with smaller word sets or when constant time complexity is desired.

Remember to consider the specific requirements of your application and choose the data structure that best suits your needs.

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