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Elasticsearch: The Definitive Guide [2.x] » Modeling Your Data » Handling Relationships » Field Collapsing
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Field Collapsingedit

A common requirement is the need to present search results grouped by a particular field. We might want to return the most relevant blog posts grouped by the user’s name. Grouping by name implies the need for a terms aggregation. To be able to group on the user’s whole name, the name field should be available in its original not_analyzed form, as explained in Aggregations and Analysis:

PUT /my_index/_mapping/blogpost
{
  "properties": {
    "user": {
      "properties": {
        "name": { 
          "type": "string",
          "fields": {
            "raw": { 
              "type":  "string",
              "index": "not_analyzed"
            }
          }
        }
      }
    }
  }
}

The user.name field will be used for full-text search.

The user.name.raw field will be used for grouping with the terms aggregation.

Then add some data:

PUT /my_index/user/1
{
  "name": "John Smith",
  "email": "john@smith.com",
  "dob": "1970/10/24"
}

PUT /my_index/blogpost/2
{
  "title": "Relationships",
  "body": "It's complicated...",
  "user": {
    "id": 1,
    "name": "John Smith"
  }
}

PUT /my_index/user/3
{
  "name": "Alice John",
  "email": "alice@john.com",
  "dob": "1979/01/04"
}

PUT /my_index/blogpost/4
{
  "title": "Relationships are cool",
  "body": "It's not complicated at all...",
  "user": {
    "id": 3,
    "name": "Alice John"
  }
}

Now we can run a query looking for blog posts about relationships, by users called John, and group the results by user, thanks to the top_hits aggregation:

GET /my_index/blogpost/_search
{
  "size" : 0, 
  "query": { 
    "bool": {
      "must": [
        { "match": { "title":     "relationships" }},
        { "match": { "user.name": "John"          }}
      ]
    }
  },
  "aggs": {
    "users": {
      "terms": {
        "field":   "user.name.raw",      
        "order": { "top_score": "desc" } 
      },
      "aggs": {
        "top_score": { "max":      { "script":  "_score"           }}, 
        "blogposts": { "top_hits": { "_source": "title", "size": 5 }}  
      }
    }
  }
}

The blog posts that we are interested in are returned under the blogposts aggregation, so we can disable the usual search hits by setting the size to 0.

The query returns blog posts about relationships by users named John.

The terms aggregation creates a bucket for each user.name.raw value.

The top_score aggregation orders the terms in the users aggregation by the top-scoring document in each bucket.

The top_hits aggregation returns just the title field of the five most relevant blog posts for each user.

The abbreviated response is shown here:

...
"hits": {
  "total":     2,
  "max_score": 0,
  "hits":      [] 
},
"aggregations": {
  "users": {
     "buckets": [
        {
           "key":       "John Smith", 
           "doc_count": 1,
           "blogposts": {
              "hits": { 
                 "total":     1,
                 "max_score": 0.35258877,
                 "hits": [
                    {
                       "_index": "my_index",
                       "_type":  "blogpost",
                       "_id":    "2",
                       "_score": 0.35258877,
                       "_source": {
                          "title": "Relationships"
                       }
                    }
                 ]
              }
           },
           "top_score": { 
              "value": 0.3525887727737427
           }
        },
...

The hits array is empty because we set size to 0.

There is a bucket for each user who appeared in the top results.

Under each user bucket there is a blogposts.hits array containing the top results for that user.

The user buckets are sorted by the user’s most relevant blog post.

Using the top_hits aggregation is the equivalent of running a query to return the names of the users with the most relevant blog posts, and then running the same query for each user, to get their best blog posts. But it is much more efficient.

The top hits returned in each bucket are the result of running a light mini-query based on the original main query. The mini-query supports the usual features that you would expect from search such as highlighting and pagination.

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