ElasticSearch6.x版本聚合统计在Kibana上的实操和在SpringBoot上的实操

Bertha 。 2023-10-07 22:33 14阅读 0赞

#### 文章目录 ####

*  一、教学讲解视频
 *  二、Kibana上操作
 *   *  1.直方图聚合统计
     *   *  ①概念
         *  ②代码
     *  2.强制直方图聚合统计范围
     *   *  ①概念
         *  ②代码
     *  3.直方图聚合统计排序
     *   *  ①概念
         *  ②代码
     *  4.直方图聚合统计偏移
     *   *  ①概念
         *  ②代码
     *  5.日期直方图聚合统计
     *   *  ①概念
         *  ②代码
     *  6.日期范围聚合统计
     *   *  ①概念
         *  ②代码
     *  7.过滤、多重过滤直方图聚合统计
     *   *  ①概念
         *  ②代码
     *  8.空值聚合统计
     *   *  ①概念
         *  ②代码
     *  9.平均、最大、最小和求和的聚合统计
     *   *  ①概念
         *  ②代码
     *  10.去重聚合统计
     *   *  ①概念
         *  ②代码
     *  11.分组聚合统计
     *   *  ①概念
         *  ②代码
     *  12.分组聚合统计之取Top N
     *   *  ①概念
         *  ②代码
     *  13.百分位聚合统计
     *   *  ①概念
         *  ②代码
 *  三、SpringBoot上操作
 *   *  1.直方图聚合统计
     *  2.强制直方图聚合统计范围
     *  3.直方图聚合统计排序
     *  4.直方图聚合统计偏移
     *  5.日期直方图聚合统计
     *  6.日期范围聚合统计
     *  7.过滤、多重过滤直方图聚合统计
     *  8.空值聚合统计
     *  9.平均、最大、最小和求和的聚合统计
     *  10.去重聚合统计
     *  11.分组聚合统计
     *  12.分组聚合统计之取Top N
     *  13.百分位聚合统计

## 一、教学讲解视频 ##

教学讲解视频地址：[视频地址][Link 1]

## 二、Kibana上操作 ##

### 1.直方图聚合统计 ###

#### ①概念 ####

直方图聚合是一种基于`多桶值聚合`，可从文档中提取的数值或数值范围值来进行聚合。它可以对参与聚合的值来动态的生成固定大小的桶。

#### ②代码 ####

“size”: 0 作用是： `不显示具体的内容`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_histogram": {
        
          "histogram": {
        
            "field": "area",
            "interval": 5
          }
        }
      }
    }

### 2.强制直方图聚合统计范围 ###

#### ①概念 ####

使用`extended_bounds`可以进行强制指定直方图的统计起止范围。

#### ②代码 ####

注意：`extended_bounds`不会过滤分组，即使分组的值超过了`extended_bounds`的范围区间，也依然会显示。

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_histogram": {
        
          "histogram": {
        
            "field": "area",
            "interval": 5,
            "extended_bounds":{
        
              "min": 0,
              "max": 10
            }
          }
        }
      }
    }

### 3.直方图聚合统计排序 ###

#### ①概念 ####

在直方图聚合统计之后，我们可以对统计结果进行排序，排序分两种，一个根据统计后的key值进行排序。另一种就是根据统计后的count值进行排序。

#### ②代码 ####

`根据统计后的key值降序排序`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_histogram": {
        
          "histogram": {
        
            "field": "area",
            "interval": 5,
            "order": {
        
              "_key": "desc"
            }
          }
        }
      }
    }

`根据统计后的count值降序排序`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_histogram": {
        
          "histogram": {
        
            "field": "area",
            "interval": 5,
            "order": {
        
              "_count": "desc"
            }
          }
        }
      }
    }

### 4.直方图聚合统计偏移 ###

#### ①概念 ####

分组默认从0开始以interval为间隔步进，可以通过offset修改分组的开始位置，`这样就可以从offset开始以interval为间隔步进`。

#### ②代码 ####

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_histogram": {
        
          "histogram": {
        
            "field": "area",
            "interval": 5,
            "offset": 3
          }
        }
      }
    }

### 5.日期直方图聚合统计 ###

#### ①概念 ####

**interval(时间间隔)的可用表达式：**

*  year `1y` 年
 *  quarter `1q` 季度
 *  month `1M` 月份
 *  week `1w` 星期
 *  day `1d` 天
 *  hour `1h` 小时
 *  minute `1m` 分钟
 *  second `1s` 秒

#### ②代码 ####

把符合记录的最早时间和最晚时间为范围，根据每月1号(`1M`)为区间对范围进行切分

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_date_histogram": {
        
          "date_histogram": {
        
            "field": "updateTime",
            "interval": "1M",
            "format": "yyyy-MM-dd HH:mm:ss"
          }
        }
      }
    }

### 6.日期范围聚合统计 ###

#### ①概念 ####

通过`range`查询某个日期范围内的数据个数统计。

#### ②代码 ####

`/M` 精确到月份  
`now` 当前时间  
`time_zone` 设置时区，默认是UTC，也就是现在时间会相差8h  
假设现在(now)是2023年1月6日，这里就是查询日期范围为\[2022-11-01, 2022-12-06\]之间的数据个数。

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_date_range": {
        
          "date_range": {
        
            "field": "updateTime",
            "format": "yyyy-MM-dd", 
            "time_zone": "+08:00",
            "ranges": [
              {
        
                "from": "now-2M/M",
                "to": "now-1M"
              }
            ]
          }
        }
      }
    }

### 7.过滤、多重过滤直方图聚合统计 ###

#### ①概念 ####

使用`filter`在聚合统计前过滤出符合条件的数据。

#### ②代码 ####

先过滤出money字段值大于2500的数据，然后对过滤出的数据进行`interval`为10的区间范围大小进行聚合统计。

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_filter": {
        
          "aggs": {
        
            "test_histogram": {
        
              "histogram": {
        
                "field": "area",
                "interval": 10
              }
            }
          },
          "filter": {
        
            "range": {
        
              "money": {
        
                "gte": 2500
              }
            }
          }
        }
      }
    }

`多重过滤`的话，我们可以使用`filters`实现。  
注意：多重过滤是分别过滤然后分别聚合统计。

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_filter": {
        
          "aggs": {
        
            "test_histogram": {
        
              "histogram": {
        
                "field": "area",
                "interval": 10
              }
            }
          },
          "filters": {
        
            "filters": {
        
              "test_range1": {
        
                "range": {
        
                  "money": {
        
                    "gte": 2800
                  }
                }
              },
              "test_range2" :{
        
                "range": {
        
                  "id": {
        
                    "lte": 5
                  }
                }
              }
            }
          }
        }
      }
    }

### 8.空值聚合统计 ###

#### ①概念 ####

使用`missing`可以把指定字段为null的数据统计出来。

#### ②代码 ####

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
      "aggs": {
        
        "test_missing": {
        
          "missing": {
        
            "field": "user.password"
          }
        }
      }
    }

### 9.平均、最大、最小和求和的聚合统计 ###

#### ①概念 ####

使用`avg` `max` `min` `sum`可以对某字段分别进行求平均、求最大、求最小和求和的操作。

#### ②代码 ####

`平均`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
       "aggs" : {
        
          "avg_grade" : {
         "avg" : {
         "field" : "area" } }
       }
    }

`最大`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
       "aggs" : {
        
          "max_grade" : {
         "max" : {
         "field" : "area" } }
       }
    }

`最小`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
       "aggs" : {
        
          "min_grade" : {
         "min" : {
         "field" : "area" } }
       }
    }

`求和`

GET /online_house_achieve/house/_search
    {
        
      "size": 0, 
       "aggs" : {
        
          "sum_grade" : {
         "sum" : {
         "field" : "area" } }
       }
    }

`一次性查询最大、最小、平均、求和`  
`写法一`

GET /online_house_achieve/house/_search
    {
        
      "size": 0,
      "aggs":{
        
        "max_area":{
        
          "max":{
        
            "field":"area"
          }
        },
        "min_area":{
        
          "min":{
        
            "field":"area"
          }
        },
        "avg_area":{
        
          "avg":{
        
            "field":"area"
          }
        },
        "sum_area":{
        
          "sum":{
        
            "field":"area"
          }
        }
      }
    }

`写法二`

GET /online_house_achieve/house/_search
    {
        
      "size":0,
      "aggs":{
        
        "area_stats":{
        
          "stats":{
        
            "field":"area"
          }
        }
      }
    }

### 10.去重聚合统计 ###

#### ①概念 ####

`area_count` 取名，随便定义即可。  
使用`cardinality`即可实现去重。

#### ②代码 ####

GET /online_house_achieve/house/_search
    {
        
      "size":0,
      "aggs":{
        
        "area_count":{
        
          "cardinality": {
        
            "field": "area"
          }
        }
      }
    }

### 11.分组聚合统计 ###

#### ①概念 ####

`根据category字段分组，然后求每个分组的area字段的平均值`

#### ②代码 ####

GET /online_house_achieve/house/_search
    {
        
      "size": 0,
      "aggs":{
        
        "group_search":{
        
          "terms":{
        
            "field":"category"
          },
          "aggs":{
        
            "avg_area":{
        
              "avg":{
        
                "field":"area"
              }
            }
          }
        }
      }
    }

### 12.分组聚合统计之取Top N ###

#### ①概念 ####

使用`top_hits`可以对分组后的结果，取前N条数据。

#### ②代码 ####

GET /online_house_achieve/house/_search
    {
        
      "size": 0,
      "aggs":{
        
        "group_search":{
        
          "terms":{
        
            "field":"category"
          },
          "aggs":{
        
            "top_area_hits":{
        
              "top_hits":{
        
                "sort":[
                  {
        
                    "area":{
        
                      "order":"desc"
                    }
                  }
                ],
                "_source":{
        
                  "includes":["area","id","category"]
                },
                "size": 3
              }
            }
          }
        }
      }
    }

### 13.百分位聚合统计 ###

#### ①概念 ####

`百分位数（percentiles）`：统计学术语，如果将一组数据从小到大排序，并计算相应的累计百分位，则某一百分位所对应数据的值就称为这一百分位的百分位数。可表示为：一组n个观测值按数值大小排列。如，处于p%位置的值称第p百分位数。

`举个栗子~`

今年是单位体检的日子，所有人到体检中心来测身高。
    单位一共100个人，所有人的身高都在 163cm-190cm 之间。同事 A 的身高值是180cm处于百分位70%。也就是说第70百分位数为180cm。
    那么我们就可以理解，同事 A 的身高比公司中70%的人都高，比其他30%的人要矮。

#### ②代码 ####

`这里默认展示1%、5%、25%、50%、75%、95%、99%对应的具体数值`

GET /online_house_achieve/house/_search
    {
        
      "size":0,
      "aggs":{
        
        "area_percentiles":{
        
          "percentiles":{
        
            "field": "area",
            "keyed": false
          }
        }
      }
    }

`当然，如果不想用系统默认的百分位，也可以自己指定~`  
`指定值去查百分位占比`

GET /online_house_achieve/house/_search
    {
        
      "size": 0,
      "aggs":{
        
        "area_percentiles":{
        
          "percentile_ranks":{
        
            "field": "area",
            "values": [22, 50, 73],
            "keyed": false
          }
        }
      }
    }

`指定百分位占比去查对应值`  
`这里也相当于是在查中位数`

GET /online_house_achieve/house/_search
    {
        
      "size":0,
      "aggs":{
        
        "area_percentiles":{
        
          "percentiles":{
        
            "field":"area",
            "percents":[50],
            "keyed":false
          }
        }
      }
    }

## 三、SpringBoot上操作 ##

`以下在SpringBoot操作的代码都是根据上面的Kibana操作的代码转换而来`

### 1.直方图聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     HistogramAggregationBuilder aggregationBuilder = AggregationBuilders
              .histogram("test_histogram")
              .field("area")
              .interval(5);
     searchSourceBuilder.aggregation(aggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedHistogram parsedHistogram = aggregations.get("test_histogram");
     parsedHistogram.getBuckets().forEach(bucket -> {
        
         logger.info("key: {}", bucket.getKey()); //阶梯值
         logger.info("count: {}", bucket.getDocCount()); //获取总数
     });

### 2.强制直方图聚合统计范围 ###

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     HistogramAggregationBuilder aggregationBuilder = AggregationBuilders
              .histogram("test_histogram")
              .field("area")
              .interval(5)
              .extendedBounds(0, 10);
     searchSourceBuilder.aggregation(aggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedHistogram parsedHistogram = aggregations.get("test_histogram");
     parsedHistogram.getBuckets().forEach(bucket -> {
        
         logger.info("key: {}", bucket.getKey()); //阶梯值
         logger.info("count: {}", bucket.getDocCount()); //获取总数
     });

### 3.直方图聚合统计排序 ###

`根据统计后的key值降序排序`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    HistogramAggregationBuilder aggregationBuilder = AggregationBuilders
            .histogram("test_histogram")
            .field("area")
            .interval(5)
            .order(BucketOrder.aggregation("_key", false));
    searchSourceBuilder.aggregation(aggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedHistogram parsedHistogram = aggregations.get("test_histogram");
    parsedHistogram.getBuckets().forEach(bucket -> {
        
        logger.info("key: {}", bucket.getKey()); //阶梯值
        logger.info("count: {}", bucket.getDocCount()); //获取总数
    });

`根据统计后的count值降序排序`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    HistogramAggregationBuilder aggregationBuilder = AggregationBuilders
            .histogram("test_histogram")
            .field("area")
            .interval(5)
            .order(BucketOrder.aggregation("_count", false));
    searchSourceBuilder.aggregation(aggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedHistogram parsedHistogram = aggregations.get("test_histogram");
    parsedHistogram.getBuckets().forEach(bucket -> {
        
        logger.info("key: {}", bucket.getKey()); //阶梯值
        logger.info("count: {}", bucket.getDocCount()); //获取总数
    });

### 4.直方图聚合统计偏移 ###

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    HistogramAggregationBuilder aggregationBuilder = AggregationBuilders
            .histogram("test_histogram")
            .field("area")
            .interval(5)
            .offset(3);
    searchSourceBuilder.aggregation(aggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedHistogram parsedHistogram = aggregations.get("test_histogram");
    parsedHistogram.getBuckets().forEach(bucket -> {
        
        logger.info("key: {}", bucket.getKey()); //阶梯值
        logger.info("count: {}", bucket.getDocCount()); //获取总数
    });

### 5.日期直方图聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    DateHistogramAggregationBuilder dateHistogramAggregationBuilder = AggregationBuilders
            .dateHistogram("test_date_histogram")
            .field("updateTime")
            .dateHistogramInterval(DateHistogramInterval.MONTH)
            .format("yyyy-MM-dd HH:mm:ss");
    searchSourceBuilder.aggregation(dateHistogramAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedDateHistogram parsedDateHistogram = aggregations.get("test_date_histogram");
    parsedDateHistogram.getBuckets().forEach(bucket -> {
        
        logger.info("keyString: {}", bucket.getKeyAsString()); //字符串阶梯值
        logger.info("key: {}", bucket.getKey()); //阶梯值
        logger.info("count: {}", bucket.getDocCount()); //获取总数
    });

### 6.日期范围聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     DateRangeAggregationBuilder dateRangeAggregationBuilder = AggregationBuilders
             .dateRange("test_date_range")
             .field("updateTime")
             .format("yyyy-MM-dd")
             .timeZone(DateTimeZone.forOffsetHours(8))
             .addRange("now-2M/M", "now-1M");
     searchSourceBuilder.aggregation(dateRangeAggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedRange parsedRange = aggregations.get("test_date_range");
     parsedRange.getBuckets().forEach(bucket -> {
        
         logger.info("key: {}", bucket.getKey());
         logger.info("fromString: {}", bucket.getFromAsString());
         logger.info("toString: {}", bucket.getToAsString());
         logger.info("count: {}", bucket.getDocCount());
     });

### 7.过滤、多重过滤直方图聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    FilterAggregationBuilder filterAggregationBuilder = AggregationBuilders
            .filter("test_filter", QueryBuilders.rangeQuery("money").gte(2500));
    HistogramAggregationBuilder histogramAggregationBuilder = AggregationBuilders
            .histogram("test_histogram")
            .field("area")
            .interval(10);
    filterAggregationBuilder.subAggregation(histogramAggregationBuilder);
    searchSourceBuilder.aggregation(filterAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedFilter parsedFilter = aggregations.get("test_filter");
    logger.info("过滤总数：{}", parsedFilter.getDocCount());
    ParsedHistogram parsedHistogram = parsedFilter.getAggregations().get("test_histogram");
    parsedHistogram.getBuckets().forEach(bucket -> {
        
        logger.info("key: {}", bucket.getKey()); //阶梯值
        logger.info("count: {}", bucket.getDocCount()); //获取总数
    });

`多重过滤`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    
    FiltersAggregationBuilder filtersAggregationBuilder = AggregationBuilders
            .filters("test_filter",
                    QueryBuilders.rangeQuery("money").gte(2800),
                    QueryBuilders.rangeQuery("id").lte(5));
    HistogramAggregationBuilder histogramAggregationBuilder = AggregationBuilders
            .histogram("test_histogram")
            .field("area")
            .interval(10);
    filtersAggregationBuilder.subAggregation(histogramAggregationBuilder);
    searchSourceBuilder.aggregation(filtersAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedFilters parsedFilters = aggregations.get("test_filter");
    parsedFilters.getBuckets().forEach(bucket -> {
        if("0".equals(bucket.getKey())) {
            logger.info("第一个过滤聚合后的结果...");
            ParsedHistogram parsedHistogram = bucket.getAggregations().get("test_histogram");
            parsedHistogram.getBuckets().forEach(e -> {
                logger.info("key：{}", e.getKey()); //阶梯值
                logger.info("count：{}", e.getDocCount()); //获取总数
            });
        } else if ("1".equals(bucket.getKey())) {
            logger.info("第二个过滤聚合后的结果...");
            ParsedHistogram parsedHistogram = bucket.getAggregations().get("test_histogram");
            parsedHistogram.getBuckets().forEach(e -> {
                logger.info("key：{}", e.getKey()); //阶梯值
                logger.info("count：{}", e.getDocCount()); //获取总数
            });
        }
    });

### 8.空值聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    MissingAggregationBuilder missingAggregationBuilder = AggregationBuilders
            .missing("test_missing")
            .field("user.password");
    searchSourceBuilder.aggregation(missingAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedMissing parsedMissing = aggregations.get("test_missing");
    logger.info("count: {}", parsedMissing.getDocCount()); // 获取总数

### 9.平均、最大、最小和求和的聚合统计 ###

`平均`

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders.avg("avg_area").field("area");
     searchSourceBuilder.aggregation(avgAggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedAvg parsedAvg = aggregations.get("avg_area");
     logger.info("平均值:{}", parsedAvg.getValue());

`最大`

`最小`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    MinAggregationBuilder minAggregationBuilder = AggregationBuilders.min("min_area").field("area");
    searchSourceBuilder.aggregation(minAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedMin parsedMin = aggregations.get("min_area");
    logger.info("最小值:{}", parsedMin.getValue());

`求和`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    SumAggregationBuilder sumAggregationBuilder = AggregationBuilders.sum("sum_area").field("area");
    searchSourceBuilder.aggregation(sumAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedSum parsedSum = aggregations.get("sum_area");
    logger.info("求和:{}", parsedSum.getValue());

`一次性查询最大、最小、平均、求和`  
`写法一`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    MaxAggregationBuilder maxAggregationBuilder = AggregationBuilders
            .max("max_area").field("area");
    MinAggregationBuilder minAggregationBuilder = AggregationBuilders
            .min("min_area").field("area");
    AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders
            .avg("avg_area").field("area");
    SumAggregationBuilder sumAggregationBuilder = AggregationBuilders
            .sum("sum_area").field("area");
    searchSourceBuilder
            .aggregation(avgAggregationBuilder)
            .aggregation(maxAggregationBuilder)
            .aggregation(minAggregationBuilder)
            .aggregation(sumAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedMax parsedMax = aggregations.get("max_area");
    ParsedMin parsedMin = aggregations.get("min_area");
    ParsedAvg parsedAvg = aggregations.get("avg_area");
    ParsedSum parsedSum = aggregations.get("sum_area");
    logger.info("最大值:{}", parsedMax.getValue());
    logger.info("最小值:{}", parsedMin.getValue());
    logger.info("平均值:{}", parsedAvg.getValue());
    logger.info("求和值:{}", parsedSum.getValue());

`写法二`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    StatsAggregationBuilder statsAggregationBuilder = AggregationBuilders
            .stats("area_status").field("area");
    searchSourceBuilder.aggregation(statsAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedStats parsedStats = aggregations.get("area_status");
    logger.info("最大值:{}", parsedStats.getMax());
    logger.info("最小值:{}", parsedStats.getMin());
    logger.info("平均值:{}", parsedStats.getAvg());
    logger.info("求和值:{}", parsedStats.getSum());

### 10.去重聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     CardinalityAggregationBuilder cardinalityAggregationBuilder = AggregationBuilders
             .cardinality("area_count").field("area");
     searchSourceBuilder.aggregation(cardinalityAggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedCardinality parsedCardinality = aggregations.get("area_count");
     logger.info("统计个数:{}", parsedCardinality.getValue());

### 11.分组聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    TermsAggregationBuilder termsAggregationBuilder = AggregationBuilders
            .terms("group_search")
            .field("category");
    AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders.avg("avg_area").field("area");
    termsAggregationBuilder.subAggregation(avgAggregationBuilder);
    searchSourceBuilder.aggregation(termsAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedTerms parsedTerms = aggregations.get("group_search");
    parsedTerms.getBuckets().forEach(bucket -> {
        
        logger.info("分组值：{}", bucket.getKey());
        logger.info("分组包含个数：{}", bucket.getDocCount());
        ParsedAvg parsedAvg = bucket.getAggregations().get("avg_area");
        logger.info("分组后的平均值：{}", parsedAvg.getValue());
    });

### 12.分组聚合统计之取Top N ###

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     TermsAggregationBuilder termsAggregationBuilder = AggregationBuilders
             .terms("group_search")
             .field("category");
     String[] include = {
        "area", "id", "category"};
     String[] exclude = {
        };
     TopHitsAggregationBuilder topHitsAggregationBuilder = AggregationBuilders
             .topHits("top_area_hits")
             .sort("area", SortOrder.DESC)
             .fetchSource(include, exclude);
     termsAggregationBuilder.subAggregation(topHitsAggregationBuilder);
     searchSourceBuilder.aggregation(termsAggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedTerms parsedTerms = aggregations.get("group_search");
     parsedTerms.getBuckets().forEach(bucket -> {
        
         logger.info("分组值：{}", bucket.getKey());
         logger.info("分组包含个数：{}", bucket.getDocCount());
         ParsedTopHits parsedTopHits = bucket.getAggregations().get("top_area_hits");
         SearchHit[] hits = parsedTopHits.getHits().getHits();
         for (SearchHit hit : hits) {
        
             logger.info("数据：{}", hit.getSourceAsString());
        }
     });

### 13.百分位聚合统计 ###

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    PercentilesAggregationBuilder percentilesAggregationBuilder = AggregationBuilders
            .percentiles("area_percentiles")
            .field("area")
            .keyed(false);
    searchSourceBuilder.aggregation(percentilesAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedPercentiles parsedPercentiles = aggregations.get("area_percentiles");
    parsedPercentiles.forEach(e -> {
        
        logger.info("百分位数：{}", e.getPercent());
        logger.info("对应数值：{}", e.getValue());
    });

`指定值去查百分位占比`

SearchRequest searchRequest = new SearchRequest();
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    double[] values = {
        22, 50, 73};
    PercentileRanksAggregationBuilder percentileRanksAggregationBuilder = AggregationBuilders
            .percentileRanks("area_percentiles", values)
            .field("area")
            .keyed(false);
    searchSourceBuilder.aggregation(percentileRanksAggregationBuilder);
    searchSourceBuilder.size(0);
    searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
    SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
    Aggregations aggregations = search.getAggregations();
    ParsedPercentileRanks parsedPercentileRanks = aggregations.get("area_percentiles");
    parsedPercentileRanks.forEach(e -> {
        
        logger.info("百分位数：{}", e.getPercent());
        logger.info("对应数值：{}", e.getValue());
    });

`指定百分位占比去查对应值`

SearchRequest searchRequest = new SearchRequest();
     SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
     double[] values = {
        50};
     PercentilesAggregationBuilder percentilesAggregationBuilder = AggregationBuilders
             .percentiles("area_percentiles")
             .percentiles(values)
             .field("area")
             .keyed(false);
     searchSourceBuilder.aggregation(percentilesAggregationBuilder);
     searchSourceBuilder.size(0);
     searchRequest.indices("online_house_achieve").types("house").source(searchSourceBuilder);
     SearchResponse search = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
     Aggregations aggregations = search.getAggregations();
     ParsedPercentiles parsedPercentiles = aggregations.get("area_percentiles");
     parsedPercentiles.forEach(e -> {
        
         logger.info("百分位数：{}", e.getPercent());
         logger.info("对应数值：{}", e.getValue());
     });

[Link 1]: https://www.bilibili.com/video/BV1QD4y1V7R7