Elasticsearch——聚合搜索案例

创建索引及映射

PUT /tvs
PUT /tvs/_mapping
{
  "properties":{
    "price":{
      "type":"long"
    },
    "color":{
      "type":"keyword"
    },
    "brand":{
      "type":"keyword"
    },
    "sold_date":{
      "type":"date"
    }
  }
}

插入数据

POST /tvs/_bulk
{"index":{}}
{"price":1000,"color":"红色","brand":"长虹","sold_date":"2019-10-28"}
{"index":{}}
{"price":2000,"color":"红色","brand":"长虹","sold_date":"2019-11-05"}
{"index":{}}
{"price":3000,"color":"绿色","brand":"小米","sold_date":"2019-05-18"}
{"index":{}}
{"price":1500,"color":"蓝色","brand":"TCL","sold_date":"2019-07-02"}
{"index":{}}
{"price":1200,"color":"绿色","brand":"TCL","sold_date":"2019-08-19"}
{"index":{}}
{"price":2000,"color":"红色","brand":"长虹","sold_date":"2019-11-05"}
{"index":{}}
{"price":5000,"color":"红色","brand":"三星","sold_date":"2020-01-01"}
{"index":{}}
{"price":2500,"color":"蓝色","brand":"小米","sold_date":"2020-02-12"}
{"index":{}}
{"price":6000,"color":"黑色","brand":"海信","sold_date":"2020-02-18"}
{"index":{}}
{"price":4000,"color":"黑色","brand":"海尔","sold_date":"2020-02-28"}
{"index":{}}
{"price":5000,"color":"白色","brand":"海尔","sold_date":"2020-03-28"}
{"index":{}}
{"price":3500,"color":"黑色","brand":"创维","sold_date":"2020-03-18"}

1、统计哪种种电视的销量最高

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "popular_color": {
      "terms": {
        "field": "color"
      }
    }
  }
}

返回结果：

{
  "took" : 1031,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 12,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "popular_color" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4
        },
        {
          "key" : "黑色",
          "doc_count" : 3
        },
        {
          "key" : "绿色",
          "doc_count" : 2
        },
        {
          "key" : "蓝色",
          "doc_count" : 2
        },
        {
          "key" : "白色",
          "doc_count" : 1
        }
      ]
    }
  }
}

返回结果解析：

• hits.hits：指定了size为0，所以hits.hits为空
• aggregations：聚合结果
• popular_color：指定某个聚合的名称
• buckets：根据指定的field划分出bucket
• key：每个bucket对应的值
• doc_count：bucket分组内，有多少数据数量

每种颜色对应的bucket中的数据默认的排序规则：按照doc_count降序排序

2、统计每种颜色电视的平均价格

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "popular_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

3、每个颜色下的平均价格以及每个颜色下每个品牌的平均价格

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "color_avg_price": {
          "avg": {
            "field": "price"
          }
        },
        "group_by_brand":{
          "terms": {
            "field": "brand"
          },
          "aggs": {
            "brand_avg_brand": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}

4、更多的metric

count：bucket，terms，自动就会有一个doc_count，就相当于count。
avg：avg aggs，求平均值。
max：求一个bucket内，指定field值最大的那个数据。
min：求一个bucket内，指定field值最小的那个数据。
sum：求一个bucket内，指定field值的总和。

求出每个颜色的销售数量、平均价格、最大价格、最小价格、价格总和

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "color_avg": {
          "avg": {
            "field": "price"
          }
        },
        "max_price":{
          "max": {
            "field": "price"
          }
        },
        "min_price":{
          "min": {
            "field": "price"
          }
        },
        "sum_price":{
          "sum": {
            "field": "price"
          }
        }
      }
    }
  }
}

5、划分范围 histogram，求出价格每2000为一个区间，每个区间的销售总额

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "price_histogram": {
      "histogram": {
        "field": "price",
        "interval": 2000
      },
      "aggs": {
        "income": {
          "sum": {
            "field": "price"
          }
        }
      }
    }
  }
}

histogram类似于terms，也是进行bucket分组操作，接收一个field，按照这个field的值的各个范围区间，进行bucket分组操作。

"histogram": {
    "field": "price",
    "interval": 2000
}

interval：2000，划分范围，0-2000、2000-4000、4000-6000、6000-8000，buckets
bucket有了之后，对每个bucket进行avg、count、sum、max、min等各种metric操作，聚合分析。

6、按照日期聚合分组，求出每个月销售个数

date_histogram：按照我们指定的某个date类型的日期field，以及日期interval，按照一定的日期间隔，去划分bucket。
min_doc_count：即使某个日期interval，如2019-01-01~2019-01-31中一条数据也没有，那么这个区间也是要返回的，不然默认是会过滤掉这个区间。
extended_bounds、min、max：划分bucket的时候，会限定在这个起始日期和结束日期内。

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "date_sales": {
      "date_histogram": {
        "field": "sold_date",
        "calendar_interval": "month",
        "format": "yyyy-MM-dd",
        "min_doc_count": 0,
        "extended_bounds": {
          "min": "2019-01-01",
          "max": "2019-12-31"
        }
      }
    }
  }
}

7、统计每个季度每个品牌的销售额，及每个季度销售总额

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "date_sales": {
      "date_histogram": {
        "field": "sold_date",
        "calendar_interval": "quarter",
        "format": "yyyy-MM-dd",
        "min_doc_count": 0,
        "extended_bounds": {
          "min": "2019-01-01",
          "max": "2020-12-31"
        }
      },
      "aggs": {
        "group_by_brand": {
          "terms": {
            "field": "brand"
          },
          "aggs": {
            "sum_price": {
              "sum": {
                "field": "price"
              }
            }
          }
        },
        "total_sum_price":{
          "sum":{
            "field": "price"
          }
        }
      }
    }
  }
}

8、搜索与聚合结合，查询某个品牌按颜色销量

aggregation，scope，任何的聚合，都必须在搜索出来的结果数据之中，搜索结果就是聚合分析操作的scope

GET /tvs/_search
{
  "size": 0,
  "query": {
    "term": {
      "brand": {
        "value": "小米"
      }
    }
  },
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      }
    }
  }
}

9、global bucket、单个品牌与所有品牌均价对比

aggregation，scope，一个聚合操作，必须在query的搜索结果范围内执行。
出来两个结果，一个结果是基于query搜索结果来聚合的，一个结果是对所有数据执行聚合的。

GET /tvs/_search
{
  "size": 0,
  "query": {
    "term": {
      "brand": {
        "value": "小米"
      }
    }
  },
  "aggs": {
    "single_brand_avg_price": {
      "avg": {
        "field": "price"
      }
    },
    "all":{
      "global": {},
      "aggs":{
        "all_brand_avg_price":{
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

10、过滤+聚合：统计价格大于1200的电视平均价格

搜索+聚合
过滤+聚合

GET /tvs/_search
{
  "size": 0,
  "query": {
    "constant_score": {
      "filter": {
        "range": {
          "price": {
            "gte": 1200
          }
        }
      },
      "boost": 1.2
    }
  },
  "aggs": {
    "avg_price": {
      "avg": {
        "field": "price"
      }
    }
  }
}

11、bucket filter：统计品牌最近一个月的平均价格

GET /tvs/_search
{
  "size": 0,
  "query": {
    "term": {
      "brand": {
        "value": "小米"
      }
    }
  },
  "aggs": {
    "recent_150d": {
      "filter": {
        "range": {
          "sold_date": {
            "gte": "now-150d"
          }
        }
      },
      "aggs": {
        "recent_150d_avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    },
    "recent_60d": {
      "filter": {
        "range": {
          "sold_date": {
            "gte": "now-60d"
          }
        }
      },
      "aggs": {
        "recent_60d_avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    },
    "recent_30d": {
      "filter": {
        "range": {
          "sold_date": {
            "gte": "now-30d"
          }
        }
      },
      "aggs": {
        "recent_30d_avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

aggs.filter、针对的是聚合去做的。
如果放query里面的filter是全局的，会对所有的数据都有影响。
但是如果要统计海信最近30天，60天，最近3个月，最近6个月的平均值。
bucket filter：对不同的bucket下的aggs，进行filter。

12、排序、按每种颜色的平均销售额降序排序

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color",
        "order": {
          "avg_price": "desc"
        }
      },
      "aggs": {
        "avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

13、排序、按每种颜色的每种品牌平均销售额降序排序

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "group_by_brand": {
          "terms": {
            "field": "brand",
            "order": {
              "avg_price": "desc"
            }
          }, 
          "aggs": {
            "avg_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}