主要内容：nested 类型查询以及聚合操作

1、nested object

冗余数据方式的来建模，其实用的就是object类型，我们这里又要引入一种新的object类型，nested object类型

修改mapping，将comments的类型从object设置为nested

PUT /blogs
{
  "mappings": {
    "properties": {
      "comments": {
        "type": "nested",
        "properties": {
          "name": {
            "type": "keyword"
          },
          "comment": {
            "type": "keyword"
          },
          "age": {
            "type": "short"
          },
          "stars": {
            "type": "short"
          },
          "date": {
            "type": "date"
          }
        }
      }
    }
  }
}

插入数据：

PUT blogs/_doc/6
{
  "title": "花无缺发表的一篇帖子",
  "content":  "我是花无缺，大家要不要考虑一下投资房产和买股票的事情啊。。。",
  "tags":  [ "投资", "理财" ],
  "comments": [ 
    {
      "name":    "小鱼儿",
      "comment": "什么股票啊？推荐一下呗",
      "age":     28,
      "stars":   4,
      "date":    "2016-09-01"
    },
    {
      "name":    "黄药师",
      "comment": "我喜欢投资房产，风，险大收益也大",
      "age":     31,
      "stars":   5,
      "date":    "2016-10-22"
    }
  ]
}

针对nested类型进行搜索

GET blogs/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "title": "花无缺"
          }
        },
        {
          "nested": {
            "path": "comments",
            "score_mode": "avg", ##  max，min，avg，none，默认是avg
            "query": {
              "bool": {
                "must": [
                  {
                    "match": {
                      "comments.name": "黄药师"
                    }
                  },
                  {
                    "match": {
                      "comments.age": 31
                    }
                  }
                ]
              }
            }
          }
        }
      ]
    }
  }
}

score_mode：如果搜索命中了多个nested document，如何将多个nested document的分数合并为一个分数

2、针对nested 类型进行数据分析

我们讲解一下基于nested object中的数据进行聚合分析

聚合数据分析的需求1：按照评论日期进行bucket划分，然后拿到每个月的评论的评分的平均值

GET /blogs/_search 
{
  "size": 0, 
  "aggs": {
    "comments_path": {
      "nested": {
        "path": "comments"
      }, 
      "aggs": {
        "group_by_comments_date": {
          "date_histogram": {
            "field": "comments.date",
            "calendar_interval": "month",
            "format": "yyyy-MM"
          },
          "aggs": {
            "avg_stars": {
              "avg": {
                "field": "comments.stars"
              }
            }
          }
        }
      }
    }
  }
}

GET /blogs/_search 
{
  "size": 0,
  "aggs": {
    "comments_path": {
      "nested": {
        "path": "comments"
      },
      "aggs": {
        "group_by_comments_age": {
          "histogram": {
            "field": "comments.age",
            "interval": 10
          },
          "aggs": {
            "reverse_path": {
              "reverse_nested": {},  ## 根据外层的tag进行划分
              "aggs": {
                "group_by_tags": {
                  "terms": {
                    "field": "tags.keyword"
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}