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Conditions Aggregation for a nested field in elastic search

I have the following mapping for a field in Elastic Search (definition in YML):

              my_analyzer:
                  type: custom
                  tokenizer:  keyword
                  filter: lowercase

               products_filter:
                    type: "nested"
                    properties:
                        filter_name: {"type" : "string", analyzer: "my_analyzer"}
                        filter_value: {"type" : "string" , analyzer: "my_analyzer"}

Each document has many filters, and it looks like this:

"products_filter": [
{
"filter_name": "Rahmengröße",
"filter_value": "33,5 cm"
}
,
{
"filter_name": "color",
"filter_value": "gelb"
}
,
{
"filter_name": "Rahmengröße",
"filter_value": "39,5 cm"
}
,
{
"filter_name": "Rahmengröße",
"filter_value": "45,5 cm"
}]

I am trying to get a list of unique filter names and a list of unique filter values ​​for each filter.

I mean, I want to get a structure like: Rahmengröße:
39.5 cm
45.5 cm
33.5 cm
Color:
yellow

To get this, I tried several aggregation options, for example:

{
  "aggs": {
    "bla": {
      "terms": {
        "field": "products_filter.filter_name"
      },
      "aggs": {
        "bla2": {
          "terms": {
            "field": "products_filter.filter_value"
          }
        }
      }
    }
  }
}

And this request is incorrect.

It will return me a list of unique filter names, and each will contain a list of ALL filter_values.

"bla": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 103,
"buckets": [
{
"key": "color",
"doc_count": 9,
"bla2": {
"doc_count_error_upper_bound": 4,
"sum_other_doc_count": 366,
"buckets": [
{
"key": "100",
"doc_count": 5
}
,
{
"key": "cm",
"doc_count": 5
}
,
{
"key": "unisex",
"doc_count": 5
}
,
{
"key": "11",
"doc_count": 4
}
,
{
"key": "160",
"doc_count": 4
}
,
{
"key": "22",
"doc_count": 4
}
,
{
"key": "a",
"doc_count": 4
}
,
{
"key": "alu",
"doc_count": 4
}
,
{
"key": "aluminium",
"doc_count": 4
}
,
{
"key": "aus",
"doc_count": 4
}
]
}
}
,

In addition, I tried using reverse nested aggregation, but that doesn't help me.

, , ?

+4
1

, . , , elasticsearch . , , . :

  • + ( )

, (ö => o ß => ss) , (raw keyword):

PUT /test
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer_keyword": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "asciifolding",
            "lowercase"
          ]
        }
      }
    }
  },
  "mappings": {
    "data": {
      "properties": {
        "products_filter": {
          "type": "nested",
          "properties": {
            "filter_name": {
              "type": "string",
              "analyzer": "standard",
              "fields": {
                "raw": {
                  "type": "string",
                  "index": "not_analyzed"
                },
                "keyword": {
                  "type": "string",
                  "analyzer": "my_analyzer_keyword"
                }
              }
            },
            "filter_value": {
              "type": "string",
              "analyzer": "standard",
              "fields": {
                "raw": {
                  "type": "string",
                  "index": "not_analyzed"
                },
                "keyword": {
                  "type": "string",
                  "analyzer": "my_analyzer_keyword"
                }
              }
            }
          }
        }
      }
    }
  }
}

, :

PUT /test/data/1
{
  "products_filter": [
    {
      "filter_name": "Rahmengröße",
      "filter_value": "33,5 cm"
    },
    {
      "filter_name": "color",
      "filter_value": "gelb"
    },
    {
      "filter_name": "Rahmengröße",
      "filter_value": "39,5 cm"
    },
    {
      "filter_name": "Rahmengröße",
      "filter_value": "45,5 cm"
    }
  ]
}

raw:

GET /test/_search
{
  "size": 0,
  "aggs": {
    "Nesting": {
      "nested": {
        "path": "products_filter"
      },
      "aggs": {
        "raw_names": {
          "terms": {
            "field": "products_filter.filter_name.raw",
            "size": 0
          },
          "aggs": {
            "raw_values": {
              "terms": {
                "field": "products_filter.filter_value.raw",
                "size": 0
              }
            }
          }
        }
      }
    }
  }
}

( ):

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "Nesting": {
      "doc_count": 4,
      "raw_names": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 0,
        "buckets": [
          {
            "key": "Rahmengröße",
            "doc_count": 3,
            "raw_values": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 0,
              "buckets": [
                {
                  "key": "33,5 cm",
                  "doc_count": 1
                },
                {
                  "key": "39,5 cm",
                  "doc_count": 1
                },
                {
                  "key": "45,5 cm",
                  "doc_count": 1
                }
              ]
            }
          },
          {
            "key": "color",
            "doc_count": 1,
            "raw_values": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 0,
              "buckets": [
                {
                  "key": "gelb",
                  "doc_count": 1
                }
              ]
            }
          }
        ]
      }
    }
  }
}

, ( ), :

GET /test/_search
{
  "size": 0,
  "aggs": {
    "Nesting": {
      "nested": {
        "path": "products_filter"
      },
      "aggs": {
        "keyword_names": {
          "terms": {
            "field": "products_filter.filter_name.keyword",
            "size": 0
          },
          "aggs": {
            "keyword_values": {
              "terms": {
                "field": "products_filter.filter_value.keyword",
                "size": 0
              }
            }
          }
        }
      }
    }
  }
}

:

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "Nesting": {
      "doc_count": 4,
      "keyword_names": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 0,
        "buckets": [
          {
            "key": "rahmengrosse",
            "doc_count": 3,
            "keyword_values": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 0,
              "buckets": [
                {
                  "key": "33,5 cm",
                  "doc_count": 1
                },
                {
                  "key": "39,5 cm",
                  "doc_count": 1
                },
                {
                  "key": "45,5 cm",
                  "doc_count": 1
                }
              ]
            }
          },
          {
            "key": "color",
            "doc_count": 1,
            "keyword_values": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 0,
              "buckets": [
                {
                  "key": "gelb",
                  "doc_count": 1
                }
              ]
            }
          }
        ]
      }
    }
  }
}
+3

Source: https://habr.com/ru/post/1618287/

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