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Introduction
Search-as-you-type is a popular feature in modern applications, providing users with real-time search results as they type their query. Elasticsearch, a powerful and scalable search engine, can be configured to provide this functionality. In this article, we will discuss how to implement search-as-you-type in Elasticsearch using edge n-grams and the completion suggester.
1. Edge N-Grams
Edge n-grams are a technique used to break down text into smaller tokens, starting from the beginning of the text. This allows Elasticsearch to match partial words, providing search results as the user types.
Step-by-step implementation:
1.1. Create an index with a custom analyzer
First, create an index with a custom analyzer that uses the edge_ngram tokenizer. This tokenizer will generate edge n-grams for the indexed text.
PUT /search_as_you_type
{
"settings": {
"analysis": {
"analyzer": {
"autocomplete": {
"tokenizer": "autocomplete",
"filter": [
"lowercase"
]
},
"search_autocomplete": {
"tokenizer": "lowercase"
}
},
"tokenizer": {
"autocomplete": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 10,
"token_chars": [
"letter"
]
}
}
}
},
"mappings": {
"properties": {
"title": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "search_autocomplete"
}
}
}
}1.2. Index sample documents
Index some sample documents to test the search-as-you-type functionality.
POST /search_as_you_type/_doc
{
"title": "Elasticsearch Guide"
}
POST /search_as_you_type/_doc
{
"title": "Elasticsearch Advanced Techniques"
}1.3. Test the search-as-you-type functionality
Now, test the search-as-you-type functionality by running a search query.
GET /search_as_you_type/_search
{
"query": {
"match": {
"title": "elas"
}
}
}The search results should include both sample documents, as the query “elas” matches the beginning of the word “Elasticsearch” in both titles.
2. Completion Suggester
The completion suggester is another approach to implementing search-as-you-type in Elasticsearch. It provides fast and efficient auto-complete suggestions based on user input.
Step-by-step implementation:
2.1. Create an index with a completion field
Create an index with a completion field to store the suggestions.
PUT /suggestions
{
"mappings": {
"properties": {
"title": {
"type": "text"
},
"suggest": {
"type": "completion"
}
}
}
}2.2. Index sample documents with suggestions
Index sample documents with the “suggest” field containing the input for auto-complete suggestions.
POST /suggestions/_doc
{
"title": "Elasticsearch Guide",
"suggest": {
"input": ["Elasticsearch", "Guide"]
}
}
POST /suggestions/_doc
{
"title": "Elasticsearch Advanced Techniques",
"suggest": {
"input": ["Elasticsearch", "Advanced", "Techniques"]
}
}2.3. Test the search-as-you-type functionality
Test the search-as-you-type functionality using the _search endpoint with the “suggest” field.
GET /suggestions/_search
{
"suggest": {
"title-suggestion": {
"prefix": "elas",
"completion": {
"field": "suggest"
}
}
}
}The search results should include both sample documents as suggestions, as the query “elas” matches the beginning of the word “Elasticsearch” in both titles.
Conclusion
In conclusion, Elasticsearch provides multiple ways to implement search-as-you-type functionality, such as edge n-grams and the completion suggester. Choose the approach that best fits your application’s requirements and performance needs.
