Elasticsearch Collapse: Efficiently Handling Duplicate Results
Quick links
- Introduction
- Use Cases for Elasticsearch Collapse
- Implementing Elasticsearch Collapse
- Optimizing Performance with Inner Hits
- Best Practices for Using Elasticsearch Collapse
- Some shortcomings when Using Elasticsearch Collapse
- Conclusion
Introduction
When working with Elasticsearch, duplicate results can sometimes be an issue, especially when dealing with large datasets. The collapse feature in Elasticsearch is designed to address this problem by grouping similar results together and returning only the most relevant result from each group. This article will discuss the Elasticsearch collapse feature in detail, including its use cases, how to implement it, and some best practices for optimizing its performance.
Use Cases for Elasticsearch Collapse
1. Removing duplicate results: In scenarios where the same document appears multiple times in the search results, the collapse feature can be used to remove duplicates and present a cleaner result set to the user.
2. Grouping results by a specific field: When you want to group search results by a particular field, such as a category or a tag, the collapse feature can be used to achieve this.
3. Aggregating data: The collapse feature can be used in conjunction with aggregations to provide a more comprehensive view of the data, such as calculating the average rating for each group of products.
Implementing Elasticsearch Collapse
To implement the collapse feature in Elasticsearch, you need to use the `collapse` parameter in your search query. The `collapse` parameter accepts an object with a single field called `field`, which specifies the field to use for collapsing the results. Here’s an example of how to use the collapse feature in a search query:
GET /my-index/_search
{
"query": {
"match_all": {}
},
"collapse": {
"field": "category"
}
}In this example, the search results will be collapsed based on the `category` field, meaning that only one document per category will be returned in the results.
Optimizing Performance with Inner Hits
When using the collapse feature, you may want to retrieve additional information about the collapsed documents, such as the total number of documents in each group or the highest-rated document in each group. To achieve this, you can use the `inner_hits` parameter, which allows you to retrieve additional documents for each collapsed group.
Here’s an example of how to use the `inner_hits` parameter in conjunction with the collapse feature:
GET /my-index/_search
{
"query": {
"match_all": {}
},
"collapse": {
"field": "category",
"inner_hits": {
"name": "highest_rating",
"size": 1,
"sort": [
{
"rating": "desc"
}
]
}
}
}In this example, the search results will be collapsed based on the `category` field, and for each collapsed group, the highest-rated document will be returned as an inner hit.
It is also possible to retrieve several `inner_hits` for each collapsed hit, which can be useful when multiple representations of the collapsed hits need to be returned.
Furthermore, within the `inner_hits` section, it is allowed to specify a second level of collapsing in case a sub-grouping of the documents is desired.
GET /my-index/_search
{
"query": {
"match_all": {}
},
"collapse": {
"field": "category",
"inner_hits": {
"name": "highest_rating",
"size": 1,
"collapse": { "field": "subcategory" },
"sort": [
{
"rating": "desc"
}
]
}
}
}In this example building upon the previous one, the search results will be collapsed based on the `category` field. Each collapsed group will contain the highest-rated document for the category as well another sub-grouping for the `subcategory` field.
Best Practices for Using Elasticsearch Collapse
1. Use the collapse feature sparingly: Collapsing results can be resource-intensive, especially when dealing with large datasets. Therefore, it’s best to use the collapse feature only when necessary and avoid using it for every search query.
2. Use filters and queries wisely: When using the collapse feature, it’s essential to use filters and queries that accurately target the documents you want to collapse. This will help improve the performance of the collapse feature and ensure that you get the desired results.
Some shortcomings when Using Elasticsearch Collapse
Probably the biggest shortcoming would be that it is impossible to know in advance how many collapsed groups exist. For that, a high precision `cardinality` aggregation would be in order, or a `terms` aggregation with a large size, but this latter solution could impact the performance of your cluster.
The next constraint is that when collapsing and sorting in the same query, both must be applied on the same field.
Finally, the collapse feature does not work with the scroll API or rescoring.
Conclusion
The Elasticsearch collapse feature is a powerful tool for handling duplicate results and grouping search results based on a specific field. By implementing the collapse feature in your search queries and following the best practices outlined in this article, you can efficiently manage duplicate results and provide a better search experience for your users.
