概述

继续跟中华石杉老师学习ES，第34篇

课程地址： https://www.roncoo.com/view/55

案例

原始数据：

需求

在白话Elasticsearch33-深入聚合数据分析之案例实战Terms Aggregation + Avg Aggregation （ bucket + metrics ） 中，我们演示了 对颜色进行bucket操作以后，再计算每种颜色的平均价格的metrics操作。

假设 又来了个新需求： 从颜色到品牌进行下钻分析，每种颜色的平均价格，以及找到每种颜色每个品牌的平均价格

那就需要进行多层次的下钻分析了

举个例子：比如说，我们现在的索引中红色的电视有4台，同时这4台电视中，有3台是属于长虹的，1台是属于小米的

那如何计算出 ： 红色电视中的3台长虹的平均价格是多少？ 红色电视中的1台小米的平均价格是多少？

解决

Step1.对每种颜色进行bucket分组

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

返回

Step2.对每种颜色进行bucket分组 ， 然后对每个分组再次计算平均价格

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

返回：

Step3.对每种颜色进行bucket分组 ， 然后对每个分组再次计算平均价格 ， 紧接再对每种颜色按照brand分组，直接写到和 color_avg_price 并列的地方就可以了

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

返回

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_color": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "红色",
          "doc_count": 4,
          "color_avg_price": {
            "value": 3250
          },
          "group_by_brand": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "长虹",
                "doc_count": 3
              },
              {
                "key": "三星",
                "doc_count": 1
              }
            ]
          }
        },
        {
          "key": "绿色",
          "doc_count": 2,
          "color_avg_price": {
            "value": 2100
          },
          "group_by_brand": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "TCL",
                "doc_count": 1
              },
              {
                "key": "小米",
                "doc_count": 1
              }
            ]
          }
        },
        {
          "key": "蓝色",
          "doc_count": 2,
          "color_avg_price": {
            "value": 2000
          },
          "group_by_brand": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "TCL",
                "doc_count": 1
              },
              {
                "key": "小米",
                "doc_count": 1
              }
            ]
          }
        }
      ]
    }
  }
}

Step4.对每种颜色进行bucket分组 ， 然后对每个分组再次计算平均价格 ， 紧接再对每种颜色按照brand分组，直接写到和 color_avg_price 并列的地方就可以了。 最后对品牌进行metrics操作，即求每种品牌的平均价格，所以要在 brand 这个bucket中，再次aggs

GET /tvs/sales/_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_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}

到这里，就一步步完成了该需求,来看下返回结果吧

返回：

{
  "took": 4,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_color": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "红色",
          "doc_count": 4,
          "color_avg_price": {
            "value": 3250
          },
          "group_by_brand": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "长虹",
                "doc_count": 3,
                "brand_avg_price": {
                  "value": 1666.6666666666667
                }
              },
              {
                "key": "三星",
                "doc_count": 1,
                "brand_avg_price": {
                  "value": 8000
                }
              }
            ]
          }
        },
        {
          "key": "绿色",
          "doc_count": 2,
          "color_avg_price": {
            "value": 2100
          },
          "group_by_brand": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "TCL",
                "doc_count": 1,
                "brand_avg_price": {
                  "value": 1200
                }
              },
              {
                "key": "小米",
                "doc_count": 1,
                "brand_avg_price": {
                  "value": 3000
                }
              }
            ]
          }
        },
        {
          "key": "蓝色",
          "doc_count": 2,
          "color_avg_price": {
            "value": 2000
          },
          "group_by_brand": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "TCL",
                "doc_count": 1,
                "brand_avg_price": {
                  "value": 1500
                }
              },
              {
                "key": "小米",
                "doc_count": 1,
                "brand_avg_price": {
                  "value": 2500
                }
              }
            ]
          }
        }
      ]
    }
  }
}

校验下

原始数据：

我们通过ES算出来的数据：

对比下原始数据，符合预期，至此实现了该需求的DSL 。