elasticsearch聚合查询

1，terms 分组，利用field分组

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

2，avg 求平均值 ,下面的这个是在分组的情况下进行的求平均数

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

3，下钻

GET /tvs/sales/_search
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "avg_price": {
          "avg": {
            "field": "price"
          }
        },
        "group_by_brand" : {
          "terms": {
            "field": "brand"
          },
          "aggs": {
            "avg_brand_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}
------------------------
aggs 是不能并列的，aggs是可以下钻（嵌套的）
aggs下面的name可以是多个

4，min max就最大值最小值

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

5，数字区间分组 histogram

GET /tvs/sales/_search
{
  "size": 0,
  "aggs": {
    "group_by_price": {
      "histogram": {
        "field": "price",
        "interval": 50
      },
      "aggs": {
        "sum_price": {
          "sum": {
            "field": "price"
          }
        }
      }
    }
  }
}

6，日期区间分组 date_histogram

GET /tvs/sales/_search
{
  "size": 0,
  "aggs": {
    "group_by_date": {
      "date_histogram": {
        "field": "sold_date",
        "interval": "month",
        "format": "yyyy-MM-dd",
        "min_doc_count": 0,
        "extended_bounds" : {
          "min" : "2016-01-01",
          "max" : "2017-01-01"
        }
      }
    }
  }
}

7，查询到基础上分组

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

局部分组中，取全部数据进行分析

GET /tvs/sales/_search
{
  "query": {
    "match": {
      "brand": "小米"
    }
  },
  "size": 0, 
  "aggs": {
    "sing_brand_avg_price": {
      "avg": {
        "field": "price"
      }
    },
    "all" : {
      "global": {},
      "aggs": {           //这里要记住，不能直接跟aggs的name，必须要继续进行下钻
        "all_avg_price" : {
          "avg" : {
            "field" : "price"
          }
        }
      }
    }
  }
}

范围搜索之后进行分组

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

对各个分组中的数据进行过滤

GET /tvs/sales/_search
{
  "query": {
    "match": {
      "brand": "小米"
    }
  },
  "aggs": {
    "avg_brand": {
      "filter" : {     //aggs_name下面接filter，aggs(filter 和 aggs)
        "range": {
          "price": {
            "gte": 1200
          }
        }
      },
      "aggs": {
        "avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

按每种颜色的平均销售额降序排序

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

深层排序、

GET /tvs/sales/_search
{
  "size": 0, 
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "group_by_brand": {
          "terms": {
            "field": "brand",
            "order": {
              "avg_price": "asc"   //在这一层进行排序
            }
          },
          "aggs": {
            "avg_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}

cardinality去重算法，每月销售的品牌数量

GET /tvs/sales/_search
{
  "size": 0,
  "aggs": {
    "group_by_sold_data": {
      "date_histogram": {     //先用月份进行分组
        "field": "sold_date",
        "interval": "month"
      },
      "aggs": {
        "distinct_brand_cnt": {
          "cardinality": {    //cardinality相当于count(distinct)
            "field": "brand"
          }
        }
      }
    }
  }
}

网站tp访问量

GET /website/logs/_search
{
  "size": 0,
  "aggs": {
    "latency_percentiles": {
      "percentiles": {    //这个对于网站的平均访问时间统计很有用
        "field": "timestamp",
        "percents": [
          50,
          95,
          99
        ]
      }
    },
    "latency_avg" : {
      "avg": {
        "field": "latency"
      }
    }
  }
}

先用省份进行分组，再分析每个省份内的tp量

GET /website/logs/_search
{
  "size": 0,
  "aggs": {
    "group_by_province": {
      "terms": {
        "field": "province"
      },
      "aggs": {
        "latency_percentiles": {
          "percentiles": {
            "field": "timestamp",
            "percents": [
              50,
              95,
              99
            ]
          }
        },
        "latency_avg" : {
          "avg": {
            "field": "latency"
          }
        }
      }
    }
  }
}

percentiles rank以及网站访问时延SLA统计

GET /website/logs/_search
{
  "size": 0,
  "aggs": {
    "group_by_province": {
      "terms": {
        "field": "province"
      },
      "aggs": {
        "load_time": {
          "percentile_ranks": {
            "field": "latency",
            "values": [
              200,
              1000
            ]
          }
        }
      }
    }
  }
}