在 Elasticsearch 中使用嵌套文档聚合多个存储桶

Question

I'm currently working on an Elasticsearch project. I want to aggregate data from our existing documents.

The (simplified) structure is as follows:

{
  "products" : {
    "mappings" : {
      "product" : {
        "properties" : {
          "created" : {
            "type" : "date",
            "format" : "yyyy-MM-dd HH:mm:ss"
          },
          "description" : {
            "type" : "text"
          },
          "facets" : {
            "type" : "nested",
            "properties" : {
              "facet_id" : {
                "type" : "long"
              }
              "name_slug" : {
                "type" : "keyword"
              },
              "value_slug" : {
                "type" : "keyword"
              }
            }
          },
       }
      }
    }
   }
}

Want I want to achieve with one query:

1. Select the unique facet_name values

2. Under the facet_names I want all corresponding facet_values

Something like this:

- facet_name
-- facet_sub_value (counter?)
-- facet_sub_value (counter?)
-- facet_sub_value (counter?)
- facet_name
-- facet_sub_value (counter?)
-- facet_sub_value (counter?)
-- facet_sub_value (counter?)

Can you guys point me in the right direction? I've looked at the aggs query, but the documentation is not clearly enough in order to realise this.

Answer 1

You'll be using nested terms aggregations . Since the facet names & values are under the same path, you can try this:

GET products/_search
{
  "size": 0,
  "aggs": {
    "by_facet_names_parent": {
      "nested": {
        "path": "facets"
      },
      "aggs": {
        "by_facet_names_nested": {
          "terms": {
            "field": "facets.name_slug",
            "size": 10
          },
          "aggs": {
            "by_facet_subvalues": {
              "terms": {
                "field": "facets.value_slug",
                "size": 10
              }
            }
          }
        }
      }
    }
  }
}

And your response should look like something along these lines:

{
  "took": 26,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 30,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "by_facet_names_parent": {
      "doc_count": 90,
      "by_facet_names_nested": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 80,
        "buckets": [
          {
            "key": "0JDcya7Y7Y",     <-------- your facet name keyword
            "doc_count": 4,
            "by_facet_subvalues": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 0,
              "buckets": [
                {
                  "key": "3q4E9R6h5k",    <-------- one of the facet values + its count
                  "doc_count": 3
                },
                {
                  "key": "1q4E9R6h5k",   <-------- another facet value & count
                  "doc_count": 1
                }
              ]
            }
          },
          {
            "key": "0RyRKWugU1",
            "doc_count": 1,
            "by_facet_subvalues": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 0,
              "buckets": [
                {
                  "key": "Af7qeCsXz6",
                  "doc_count": 1
                }
              ]
            }
          }
          .....
        ]
      }
    }
  }
}

Notice how the number of nested buckets might be >= the number of your actual products docs. This is because the nested aggregations treat the nested subdocuments as separate documents within the parent documents . This takes some time to digest but it'll make sense when you play around with them long enough.