计算emp每个sex下的数量

将文本field的fielddata属性设置为true

PUT /company/_mapping/emp
{
  "properties": {
    "sex": {
      "type": "text",
      "fielddata": true
    }
  }
}

执行结果

{
  "acknowledged": true
}

统计男女人数

一般统计方式

GET /company/emp/_search
{
  "aggs": {
    "group_by_tags": {
      "terms": { "field": "sex" }
    }
  }
}

添加size=0，优化统计结果

GET /company/emp/_search
{
  "size": 0,
  "aggs": {
    "all_tags": {
      "terms": { "field": "sex" }
    }
  }
}

执行结果

{
  "took": 21,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 9,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "all_tags": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "man",
          "doc_count": 5
        },
        {
          "key": "woman",
          "doc_count": 4
        }
      ]
    }
  }
}

对hobby中包含trivel的emp，计算每个sex下的数量

GET /company/emp/_search
{
  "size": 0,
  "query": {
    "match": {
      "hobby": "trivel"
    }
  },
  "aggs": {
    "all_tags": {
      "terms": {
        "field": "sex"
      }
    }
  }
}

执行结果

{
  "took": 33,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 3,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "all_tags": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "man",
          "doc_count": 2
        },
        {
          "key": "woman",
          "doc_count": 1
        }
      ]
    }
  }
}

先按sex分组，算出每组的数量，再计算每个sex下的商品的平均年龄

GET /company/emp/_search
{
    "size": 0,
    "aggs" : {
        "group_by_tags" : {
            "terms" : { "field" : "sex" },
            "aggs" : {
                "avg_age" : {
                    "avg" : { "field" : "age" }
                }
            }
        }
    }
}

执行结果

{
  "took": 13,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 9,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_tags": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "man",
          "doc_count": 5,
          "avg_age": {
            "value": 24.2
          }
        },
        {
          "key": "woman",
          "doc_count": 4,
          "avg_age": {
            "value": 22
          }
        }
      ]
    }
  }
}

计算每个sex下的平均年龄，并且按照平均年龄降序排序

GET /company/emp/_search
{
"size": 0,
"aggs" : {
    "all_tags" : {
        "terms" : { "field" : "sex", "order": { "avg_age": "desc" } },
        "aggs" : {
            "avg_age" : {
                "avg" : { "field" : "age" }
                }
            }
        }
    }
}

执行结果

{
  "took": 7,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 9,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "all_tags": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "man",
          "doc_count": 5,
          "avg_age": {
            "value": 24.2
          }
        },
        {
          "key": "woman",
          "doc_count": 4,
          "avg_age": {
            "value": 22
          }
        }
      ]
    }
  }
}

按照指定的年龄范围区间进行分组，然后在每组内再按照sex进行分组，最后再计算每组的平均年龄

GET /company/emp/_search
  {
  "size": 0,
  "aggs": {
    "group_by_age": {
      "range": {
        "field": "age",
        "ranges": [
          {
            "from": 0,
            "to": 10
          },
          {
            "from": 10,
            "to": 20
          },
          {
            "from": 20,
            "to": 30
          },
          {
            "from": 30,
            "to": 100
          }
        ]
      },
      "aggs": {
        "group_by_sex": {
          "terms": {
            "field": "sex"
          },
          "aggs": {
            "average_age": {
              "avg": {
                "field": "age"
              }
            }
          }
        }
      }
    }
  }
}

执行结果

{
  "took": 16,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 9,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_age": {
      "buckets": [
        {
          "key": "0.0-10.0",
          "from": 0,
          "to": 10,
          "doc_count": 0,
          "group_by_sex": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": []
          }
        },
        {
          "key": "10.0-20.0",
          "from": 10,
          "to": 20,
          "doc_count": 2,
          "group_by_sex": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "man",
                "doc_count": 1,
                "average_age": {
                  "value": 17
                }
              },
              {
                "key": "woman",
                "doc_count": 1,
                "average_age": {
                  "value": 18
                }
              }
            ]
          }
        },
        {
          "key": "20.0-30.0",
          "from": 20,
          "to": 30,
          "doc_count": 5,
          "group_by_sex": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "woman",
                "doc_count": 3,
                "average_age": {
                  "value": 23.333333333333332
                }
              },
              {
                "key": "man",
                "doc_count": 2,
                "average_age": {
                  "value": 21
                }
              }
            ]
          }
        },
        {
          "key": "30.0-100.0",
          "from": 30,
          "to": 100,
          "doc_count": 2,
          "group_by_sex": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "man",
                "doc_count": 2,
                "average_age": {
                  "value": 31
                }
              }
            ]
          }
        }
      ]
    }
  }
}