Hackernoon logoElasticsearch: Building AutoComplete functionality by@taranjeet

Elasticsearch: Building AutoComplete functionality

Taranjeet Singh Hacker Noon profile picture

@taranjeetTaranjeet Singh

Senior Software Engineer

What is Autocomplete ?

Let’s take a very common example. Whenever you go to google and start typing, a drop-down appears which lists the suggestions. Those suggestions are related to the query and help the user in completing his query.

Suggestions when typing on Google

Autocomplete as the wikipedia says

Autocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing

It is also known as Search as you type or Type Ahead Search. It helps in navigating or guiding a user by prompting them with likely completions and alternatives to the text as they are typing it. It reduces the amount of character a user needs to type before executing any search actions, thereby enhancing the search experience of users.

AutoCompletion can be implemented by using any database. In this post, we will use Elasticsearch to build autocomplete functionality.

Elasticsearch is an open source, distributed and JSON based search engine built on top of Lucene.


There can be various approaches to build autocomplete functionality in Elasticsearch. We will discuss the following approaches.

  • Prefix Query
  • Edge Ngram
  • Completion Suggester

Prefix Query

This approach involves using a prefix query against a custom field. The value for this field can be stored as a keyword so that multiple terms(words) are stored together as a single term. This can be accomplished by using keyword tokeniser. This approach has some disadvantages.

  • Since the matching is supported only at the beginning of the term, one cannot match the query in the middle of the text.
  • This type of query is not optimised for large dataset and may result in increased latency.
  • Since this is a query, duplicate results won’t be filtered out. One workaround to deal with this approach can be using an aggregation query to group results and then filtering out results. This involves a bit of processing though on the server side.

Edge Ngrams

This approach involves using different analysers at index and search time. When indexing the document, a custom analyser with an edge n-gram filter can be applied. At search time, standard analyser can be applied. which prevents the query from being split.

Edge N-gram tokeniser first breaks the text down into words on custom characters (space, special characters, etc..) and then keeps the n-gram from the start of the string only.

This approach works well for matching query in the middle of the text as well. This approach is generally fast for queries but may result in slower indexing and in large index storage.

Completion Suggester

Elasticsearch is shipped with an in-house solution called Completion Suggester. It uses an in-memory data structure called Finite State Transducer(FST). Elasticsearch stores FST on a per segment basis, which means suggestions scale horizontally as more new nodes are added.

Some of the things to keep in mind when implementing Completion Suggester

  • The autosuggest items should have completion types as its field type.
  • An input field can have various canonical or alias name for a single term.
  • Weights can be defined with each document to control their ranking.
  • Storing all the terms in lowercase helps in the case-insensitive match.
  • Context suggesters can be enabled to support filtering or boosting by certain criteria.

This approach is the ideal approach to implement autocomplete functionality, however, it also has certain disadvantages

  • Matching always starts at the beginning of the text. So search for america in marvels movie dataset will not yield any result. One way to overcome is tokenizing the input text on space and keep all the phrases as canonical names. This way Captain America: Civil War will be stored as

Highlighting of the matched words are not supported.

  • No sorting mechanism is available. The only way to sort suggestions is via weights. This creates a problem when any custom sorting like alphabetical sort or sort by context is required.


Let’s implement the above approaches in Elasticsearch. We will be using Marvels movie data to build our sample index. For easy reference, here is the

  • Spider-Man: Homecoming
  • Ant-man and the Wasp
  • Avengers: Infinity War Part 2
  • Captain Marvel
  • Black Panther
  • Avengers: Infinity War
  • Thor: Ragnarok
  • Guardians of the Galaxy Vol 2
  • Doctor Strange
  • Captain America: Civil War
  • Ant-Man
  • Avengers: Age of Ultron
  • Guardians of the Galaxy
  • Captain America: The Winter Soldier
  • Thor: The Dark World
  • Iron Man 3
  • Marvel’s The Avengers
  • Captain America: The First Avenger
  • Thor
  • Iron Man 2
  • The Incredible Hulk
  • Iron Man

We will be creating an index movies with type marvels.

If we see the mapping, we will observe that name is a nested field which contains several field, each analysed in a different way.

  • Fieldname.keywordstring is analysed using a Keyword tokenizer, hence it will be used for Prefix Query Approach
  • Field name.edgengram is analysed using Edge Ngram tokenizer, hence it will be used for Edge Ngram Approach.
  • Field name.completion is stored as a completion type, hence it will be used for Completion Suggester.

We will index all our movies by using

Let’s start with Prefix Query approach and try finding movie beginning with th.

Query will be

This will result in the following movie

  • Thor: The Dark World
  • Thor: Ragnarok
  • The Incredible Hulk
  • Thor

The result is fair, but some movies like Captain America: The Winter Soldier, Guardians of the Galaxy are missed because prefix query only matches at the beginning of the text and not in the middle.

Lets try finding another movie beginning with am.

Here we do not get any results, although Captain America satisfy this condition. This confirms the point that Prefix query cannot be used to match in the middle of the text.

Let's run the same search am but with Edge Ngram Approach.

Here we get the following result

  • Captain America: The First Avenger
  • Captain America: Civil War
  • Captain America: The Winter Soldier

Let’s try finding for Captain America again, but this time with a bigger phrase captain america the

Using Edge N-gram approach, we get the following movies

  • Captain America: The Winter Soldier
  • Captain America: The First Avenger
  • Captain America: Civil War
  • Thor: The Dark World
  • Captain Marvel
  • Guardians of the Galaxy
  • The Incredible Hulk
  • Guardians of the Galaxy Vol 2
  • Ant-man and the Wasp
  • Marvel’s The Avengers

If we observe our phrase, only the first two suggestion makes sense. The reason for so many terms getting matched is the functioning of match clause. match includes all the documents which contain captain OR america OR the. Since the field is analysed using ngram, more suggestions(if present) will get included as well.

Let’s try using the suggestion query for the same phrase captain america the . Suggestion query is written in a slightly different way.

We get the following movies as result

  • Captain America: The First Avenger
  • Captain America: The Winter Soldier

Let’s try the same query, but this time with a typo captain amrica the.

The above movie-suggest returns no result because no support for fuzziness is present. We can update the query to include support for fuzziness in the following way

The above query returns the following results

  • Captain America: The First Avenger
  • Captain America: The Winter Soldier


Various approaches can be used to implement autocomplete functionality in ElasticSearch. Completion Suggester covers most of the cases which are required in implementing a fully functional and fast autocomplete.


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