ElasticSearch 嵌套聚合 下钻分析 聚合分析

1、第一个分析需求

计算每个tag下的商品数量

GET /ecommerce/product/_search
{
  "aggs": {
    "group_by_tags": {
      "terms": { "field": "tags" }
    }
  }
}

将文本field的fielddata属性设置为true

PUT /ecommerce/_mapping/product
{
  "properties": {
    "tags": {
      "type": "text",
      "fielddata": true
    }
  }
}

GET /ecommerce/product/_search
{
  "size": 0,
  "aggs": {
    "all_tags": {
      "terms": { "field": "tags" }
    }
  }
}

2、第二个聚合分析的需求

对名称中包含yagao的商品，计算每个tag下的商品数量

GET /ecommerce/product/_search
{
  "size": 0,
  "query": {
    "match": {
      "name": "yagao"
    }
  },
  "aggs": {
    "all_tags": {
      "terms": {
        "field": "tags"
      }
    }
  }
}

3、第三个聚合分析的需求

先分组，再算每组的平均值，计算每个tag下的商品的平均价格

GET /ecommerce/product/_search
{
    "size": 0,
    "aggs" : {
        "group_by_tags" : {
            "terms" : { "field" : "tags" },
            "aggs" : {
                "avg_price" : {
                    "avg" : { "field" : "price" }
                }
            }
        }
    }
}

4、第四个数据分析需求

计算每个tag下的商品的平均价格，并且按照平均价格降序排序

GET /ecommerce/product/_search
{
    "size": 0,
    "aggs" : {
        "all_tags" : {
            "terms" : { "field" : "tags", "order": { "avg_price": "desc" } },
            "aggs" : {
                "avg_price" : {
                    "avg" : { "field" : "price" }
                }
            }
        }
    }
}

5、第五个数据分析需求

按照指定的价格范围区间进行分组，然后在每组内再按照tag进行分组，最后再计算每组的平均价格

GET /ecommerce/product/_search
{
  "size": 0,
  "aggs": {
    "group_by_price": {
      "range": {
        "field": "price",
        "ranges": [
          {
            "from": 0,
            "to": 20
          },
          {
            "from": 20,
            "to": 40
          },
          {
            "from": 40,
            "to": 50
          }
        ]
      },
      "aggs": {
        "group_by_tags": {
          "terms": {
            "field": "tags"
          },
          "aggs": {
            "average_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}