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Elasticsearch Autocomplete and Autocorrect 

Elasticsearch is a distributed search and analytics engine. It offers two functionalities, namely, Elasticsearch Autocomplete and Autocorrect.

They help enhance user experience and improve search result accuracy in search applications.

Autocomplete can be described as type-ahead or auto-suggest. It offers real-time suggestions to users when they type in a search box or input field. This helps users find the results they are looking for quickly, even if they don’t know the exact search terms.

We can implement Elasticsearch Autocomplete by indexing our data in Elasticsearch and using its built-in features for “suggesters.”

Furthermore, Elasticsearch offers two main types of suggesters as seen below:

• Completion Suggester:

  The completion suggester provides autocomplete suggestions. It works well for fields that represent partial or full text, like product names or user names.

  It returns suggestions according to the input provided by the user, even if the input is only a partial match.
• Phrase Suggester:

  The phrase suggester is useful for autocorrect functionality rather than pure autocomplete. It detects and corrects spelling mistakes in user input.

  When users enter a search query with typos, the phrase suggester suggests correct versions of the query, making sure the search results are more accurate.

On the other hand, the Elasticsearch Autocorrect feature helps users overcome typographical errors and spelling mistakes in their search queries.

By automatically suggesting correct versions of search terms, the search results become more accurate and relevant, even if the original search terms were misspelled.

We can implement Elasticsearch Autocorrect, by using the phrase suggester. When a user submits a query with typos, the phrase suggester will analyze the query and suggest alternative, correctly spelled terms that are more likely to match indexed data.

When users enter their search query, they get real-time suggestions based on indexed data (autocomplete), and if they make any mistakes in their input, Elasticsearch can provide helpful corrections (autocorrect) to refine their search and get better results.

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Conclusion

In summary, our Support Techs demonstrated how combining Autocomplete and Autocorrect in Elasticsearch lets us create a powerful search experience for users.