Quick links
Elasticsearch Wildcard Queries: Advanced Techniques and Best Practices
Introduction
Wildcard queries in Elasticsearch allow users to search for documents containing specific patterns in their text fields. This powerful search functionality enables users to find relevant documents even when the exact search term is not known.
In this article, we will discuss advanced techniques and best practices for using Elasticsearch wildcard queries effectively.
Using Wildcard Queries
A wildcard query in Elasticsearch uses the `*` and `?` symbols to represent any number of characters or a single character, respectively. To perform a wildcard query, you can use the `wildcard` query type in the Elasticsearch Query DSL. Here’s an example:
GET /_search
{
"query": {
"wildcard": {
"field_name": {
"value": "*search*pattern*"
}
}
}
}In this example, the query searches for documents containing the pattern “*search*pattern*” in the specified field.
Best Practices and Advanced Techniques
1. Use wildcards sparingly:
Wildcard queries can be resource-intensive, especially when using the `*` symbol at the beginning of a search pattern. To optimize performance, try to minimize the use of wildcards and use them only when necessary.
2. Combine with other query types:
To improve search accuracy and performance, consider combining wildcard queries with other query types, such as `match`, `term`, or `bool`. This allows you to narrow down the search results and reduce the impact of wildcard queries on performance.
Example:
GET /_search
{
"query": {
"bool": {
"must": [
{
"match": {
"field_name": "specific term"
}
},
{
"wildcard": {
"field_name": {
"value": "*search*pattern*"
}
}
}
]
}
}
}3. Use n-grams:
To improve the performance of wildcard queries, consider using n-grams. N-grams are smaller substrings of text that can be indexed and searched more efficiently. By indexing n-grams, you can reduce the need for wildcard queries and improve search performance.
Note: Just be aware, though, that using n-grams will make your index size grow to some extent. Make sure to test n-grams in a non-production environment first to assess the impact on your index size.
To use n-grams, you need to create a custom analyzer with an `ngram` token filter in your index settings:
PUT /my_index
{
"settings": {
"analysis": {
"analyzer": {
"my_ngram_analyzer": {
"tokenizer": "standard",
"filter": ["lowercase", "my_ngram_filter"]
}
},
"filter": {
"my_ngram_filter": {
"type": "ngram",
"min_gram": 3,
"max_gram": 5
}
}
}
},
"mappings": {
"properties": {
"field_name": {
"type": "text",
"analyzer": "my_ngram_analyzer"
}
}
}
}4. Use prefix queries for suffix wildcards:
If your search pattern ends with a wildcard, consider using a prefix query instead. Prefix queries are more efficient than wildcard queries with suffix wildcards.
Example:
GET /_search
{
"query": {
"prefix": {
"field_name": {
"value": "search"
}
}
}
}5. Optimize index settings:
To improve the performance of wildcard queries, consider optimizing your index settings. For example, you can increase the `max_expansions` parameter to limit the number of terms that a wildcard query can expand to. Additionally, you can use the `rewrite` parameter to control how the query is rewritten and executed.
Conclusion
Elasticsearch wildcard queries provide a powerful way to search for documents containing specific patterns. By following the best practices and advanced techniques discussed in this article, you can optimize the performance and accuracy of your wildcard queries and make the most of this powerful search functionality.
