Elasticsearch 聚合操作

数据准备：

PUT /shop
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 2
  }
}

PUT /shop/_mapping/goods
{
  "properties": {
    "brand": {
      "type": "keyword"
    },
    "price": {
      "type": "float"
    },
    "model": {
      "type": "keyword"
    }
  }
}

POST /shop/goods/_bulk
{"index": {}}
{"price" : 2299.00, "model" : "小米8", "brand" : "小米"}
{"index": {}}
{"price" : 4499.00, "model" : "Mate 20", "brand" : "华为"}
{"index": {}}
{"price" : 3299.00, "model" : "小米Mix3", "brand" : "小米"}
{"index": {}}
{"price" : 1199.00, "model" : "荣耀9i", "brand" : "华为"}
{"index": {}}
{"price" : 2799.00, "model" : "R17", "brand" : "OPPO"}
{"index": {}}
{"price" : 729.00, "model" : "红米6", "brand" : "小米"}
{"index": {}}
{"price" : 2799.00, "model" : "X23", "brand" : "VIVO"}
{"index": {}}
{"price" : 1799.00, "model" : "K1", "brand" : "OPPO"}

 

一、聚合为桶

按照手机的品牌brand划分为桶

查询指令：

GET /shop/_search
{
  "size": 0, 
  "aggs": {
    "brand_aggs": {
      "terms": {
        "field": "brand"
      }
    }
  }
}

- size： 查询条数，这里设置为0，因为我们不关心搜索到的数据，只关心聚合结果，提高效率
- aggs：声明这是一个聚合查询，是aggregations的缩写
　　- popular_colors：给这次聚合起一个名字，任意。
　　　　- terms：划分桶的方式，这里是根据词条划分
　　　　　　- field：划分桶的字段

 

查询结果：

{
  "took": 6,
  "timed_out": false,
  "_shards": {
    "total": 3,
    "successful": 3,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "brand_aggs": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "小米",
          "doc_count": 3
        },
        {
          "key": "OPPO",
          "doc_count": 2
        },
        {
          "key": "华为",
          "doc_count": 2
        },
        {
          "key": "VIVO",
          "doc_count": 1
        }
      ]
    }
  }
}

- hits：查询结果为空，因为我们设置了size为0
- aggregations：聚合的结果
　　- brand_aggs：我们定义的聚合名称
　　　　- buckets：查找到的桶，每个不同的brand字段值都会形成一个桶
　　　　　　- key：这个桶对应的brand字段的值
　　　　　　- doc_count：这个桶中的文档数量

 

二、桶内度量

为聚合结果添加求价格平均值的度量

查询指令：

GET /shop/_search
{
  "size": 0, 
  "aggs": {
    "brand_aggs": {
      "terms": {
        "field": "brand"
      },
      "aggs": {
        "price_aggs": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

- aggs：我们在上一个aggs(brand_aggs)中添加新的aggs。可见度量也是一个聚合
　　- price_aggs：聚合的名称
　　　　- avg：度量的类型，这里是求平均值
　　　　　　- field：度量运算的字段

 

查询结果：

{
  "took": 5,
  "timed_out": false,
  "_shards": {
    "total": 3,
    "successful": 3,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "brand_aggs": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "小米",
          "doc_count": 3,
          "price_aggs": {
            "value": 2109
          }
        },
        {
          "key": "OPPO",
          "doc_count": 2,
          "price_aggs": {
            "value": 2299
          }
        },
        {
          "key": "华为",
          "doc_count": 2,
          "price_aggs": {
            "value": 2849
          }
        },
        {
          "key": "VIVO",
          "doc_count": 1,
          "price_aggs": {
            "value": 2799
          }
        }
      ]
    }
  }
}

可以看到每个桶中都有自己的 price_aggs 字段，这是度量聚合的结果

 

标签：count,聚合,key,doc,brand,Elasticsearch,aggs,操作,price
来源： https://www.cnblogs.com/heqiuyong/p/10353472.html