Elasticsearch应用六：聚合分析（聚合分析简介、指标聚合、桶聚合）

￡神魔★判官ぃ 2022-02-21 05:24 300阅读 0赞

## 一、聚合分析简介 ##

###  1. ES聚合分析是什么？ ###

聚合分析是数据库中重要的功能特性，完成对一个查询的数据集中数据的聚合计算，如：找出某字段（或计算表达式的结果）的最大值、最小值，计算和、平均值等。ES作为搜索引擎兼数据库，同样提供了强大的聚合分析能力。

对一个数据集求最大、最小、和、平均值等指标的聚合，在ES中称为**指标聚合   metric**

而关系型数据库中除了有聚合函数外，还可以对查询出的数据进行分组group by，再在组上进行指标聚合。在 ES 中group by 称为**分桶**，**桶聚合 bucketing**

ES中还提供了矩阵聚合（matrix）、管道聚合（pipleline），但还在完善中。

###  2. ES聚合分析查询的写法 ###

在查询请求体中以aggregations节点按如下语法定义聚合分析：

"aggregations" : {
        "<aggregation_name>" : { 
            "<aggregation_type>" : { 
                <aggregation_body> 
            }
            [,"meta" : {  [<meta_data_body>] } ]? 
            [,"aggregations" : { [<sub_aggregation>]+ } ]? 
        }
        [,"<aggregation_name_2>" : { ... } ]*
    }

** 说明：**

**aggregations 也可简写为 aggs**

###  3. 聚合分析的值来源 ###

聚合计算的值可以取**字段的值**，也可是**脚本计算的结果**。

## 二、指标聚合 ##

### 1. max min sum avg ###

示例1：查询所有客户中余额的最大值

POST /bank/_search?
    {
      "size": 0, 
      "aggs": {
        "masssbalance": {
          "max": {
            "field": "balance"
          }
        }
      }
    }

结果1：

{
      "took": 2080,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "masssbalance": {
          "value": 49989
        }
      }
    }

示例2：查询年龄为24岁的客户中的余额最大值

POST /bank/_search?
    {
      "size": 2, 
      "query": {
        "match": {
          "age": 24
        }
      },
      "sort": [
        {
          "balance": {
            "order": "desc"
          }
        }
      ],
      "aggs": {
        "max_balance": {
          "max": {
            "field": "balance"
          }
        }
      }
    }

结果2：

{
      "took": 5,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 42,
        "max_score": null,
        "hits": [
          {
            "_index": "bank",
            "_type": "_doc",
            "_id": "697",
            "_score": null,
            "_source": {
              "account_number": 697,
              "balance": 48745,
              "firstname": "Mallory",
              "lastname": "Emerson",
              "age": 24,
              "gender": "F",
              "address": "318 Dunne Court",
              "employer": "Exoplode",
              "email": "malloryemerson@exoplode.com",
              "city": "Montura",
              "state": "LA"
            },
            "sort": [
              48745
            ]
          },
          {
            "_index": "bank",
            "_type": "_doc",
            "_id": "917",
            "_score": null,
            "_source": {
              "account_number": 917,
              "balance": 47782,
              "firstname": "Parks",
              "lastname": "Hurst",
              "age": 24,
              "gender": "M",
              "address": "933 Cozine Avenue",
              "employer": "Pyramis",
              "email": "parkshurst@pyramis.com",
              "city": "Lindcove",
              "state": "GA"
            },
            "sort": [
              47782
            ]
          }
        ]
      },
      "aggregations": {
        "max_balance": {
          "value": 48745
        }
      }
    }

示例3：值来源于脚本，查询所有客户的平均年龄是多少，并对平均年龄加10

POST /bank/_search?size=0
    {
      "aggs": {
        "avg_age": {
          "avg": {
            "script": {
              "source": "doc.age.value"
            }
          }
        },
        "avg_age10": {
          "avg": {
            "script": {
              "source": "doc.age.value + 10"
            }
          }
        }
      }
    }

结果3：

{
      "took": 86,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "avg_age": {
          "value": 30.171
        },
        "avg_age10": {
          "value": 40.171
        }
      }
    }

示例4：指定field，在脚本中用\_value 取字段的值

POST /bank/_search?size=0
    {
      "aggs": {
        "sum_balance": {
          "sum": {
            "field": "balance",
            "script": {
                "source": "_value * 1.03"
            }
          }
        }
      }
    }

结果4：

{
      "took": 165,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "sum_balance": {
          "value": 26486282.11
        }
      }
    }

示例5：为没有值字段指定值。如未指定，缺失该字段值的文档将被忽略。

POST /bank/_search?size=0
    {
      "aggs": {
        "avg_age": {
          "avg": {
            "field": "age",
            "missing": 18
          }
        }
      }
    }

###  2. 文档计数 count ###

示例1：统计银行索引bank下年龄为24的文档数量

POST /bank/_doc/_count
    {
      "query": {
        "match": {
          "age" : 24
        }
      }
    }

结果1：

{
      "count": 42,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      }
    }

###  3. Value count 统计某字段有值的文档数 ###

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_count": {
          "value_count": {
            "field": "age"
          }
        }
      }
    }

结果1：

{
      "took": 2022,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_count": {
          "value": 1000
        }
      }
    }

###  4. cardinality  值去重计数 ###

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_count": {
          "cardinality": {
            "field": "age"
          }
        },
        "state_count": {
          "cardinality": {
            "field": "state.keyword"
          }
        }
      }
    }

** 说明：state的使用它的keyword版**

结果1：

{
      "took": 2074,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "state_count": {
          "value": 51
        },
        "age_count": {
          "value": 21
        }
      }
    }

###  5. stats 统计 count max min avg sum 5个值 ###

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_stats": {
          "stats": {
            "field": "age"
          }
        }
      }
    }

结果1：

###  6. Extended stats ###

高级统计，比stats多4个统计结果： 平方和、方差、标准差、平均值加/减两个标准差的区间

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_stats": {
          "extended_stats": {
            "field": "age"
          }
        }
      }
    }

结果1：

{
      "took": 7,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_stats": {
          "count": 1000,
          "min": 20,
          "max": 40,
          "avg": 30.171,
          "sum": 30171,
          "sum_of_squares": 946393,
          "variance": 36.10375899999996,
          "std_deviation": 6.008640362012022,
          "std_deviation_bounds": {
            "upper": 42.18828072402404,
            "lower": 18.153719275975956
          }
        }
      }
    }

###  7. Percentiles 占比百分位对应的值统计 ###

对指定字段（脚本）的值按从小到大累计每个值对应的文档数的占比（占所有命中文档数的百分比），返回指定占比比例对应的值。默认返回\[ 1, 5, 25, 50, 75, 95, 99 \]分位上的值。如下中间的结果，可以理解为：占比为50%的文档的age值 <= 31，或反过来：age<=31的文档数占总命中文档数的50%

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_percents": {
          "percentiles": {
            "field": "age"
          }
        }
      }
    }

结果1：

{
      "took": 87,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_percents": {
          "values": {
            "1.0": 20,
            "5.0": 21,
            "25.0": 25,
            "50.0": 31,
            "75.0": 35.00000000000001,
            "95.0": 39,
            "99.0": 40
          }
        }
      }
    }

结果说明:

占比为50%的文档的age值 <= 31，或反过来：age<=31的文档数占总命中文档数的50%

示例2：指定分位值

POST /bank/_search?size=0
    {
      "aggs": {
        "age_percents": {
          "percentiles": {
            "field": "age",
            "percents" : [95, 99, 99.9] 
          }
        }
      }
    }

结果2：

###  8. Percentiles rank 统计值小于等于指定值的文档占比 ###

示例1：统计年龄小于25和30的文档的占比，和第7项相反

POST /bank/_search?size=0
    {
      "aggs": {
        "gge_perc_rank": {
          "percentile_ranks": {
            "field": "age",
            "values": [
              25,
              30
            ]
          }
        }
      }
    }

结果2：

结果说明：年龄小于25的文档占比为26.1%，年龄小于30的文档占比为49.2%，

###  9. Geo Bounds aggregation 求文档集中的地理位置坐标点的范围 ###

参考官网链接：

[https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geobounds-aggregation.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_search-aggregations-metrics-geobounds-aggregation.html]

### 10. Geo Centroid aggregation  求地理位置中心点坐标值 ###

参考官网链接：

[https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geocentroid-aggregation.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_search-aggregations-metrics-geocentroid-aggregation.html]

## 三、桶聚合 ##

![1227483-20180622223636818-342791427.png][]

### 1. Terms Aggregation  根据字段值项分组聚合  ###

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age"
          }
        }
      }
    }

结果1：

{
      "took": 2000,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_terms": {
          "doc_count_error_upper_bound": 0,
          "sum_other_doc_count": 463,
          "buckets": [
            {
              "key": 31,
              "doc_count": 61
            },
            {
              "key": 39,
              "doc_count": 60
            },
            {
              "key": 26,
              "doc_count": 59
            },
            {
              "key": 32,
              "doc_count": 52
            },
            {
              "key": 35,
              "doc_count": 52
            },
            {
              "key": 36,
              "doc_count": 52
            },
            {
              "key": 22,
              "doc_count": 51
            },
            {
              "key": 28,
              "doc_count": 51
            },
            {
              "key": 33,
              "doc_count": 50
            },
            {
              "key": 34,
              "doc_count": 49
            }
          ]
        }
      }
    }

结果说明：

"doc\_count\_error\_upper\_bound": 0：文档计数的最大偏差值

"sum\_other\_doc\_count": 463：未返回的其他项的文档数

默认情况下返回按文档计数从高到低的前10个分组：

"buckets": [
            {
              "key": 31,
              "doc_count": 61
            },
            {
              "key": 39,
              "doc_count": 60
            },
        .............
    ]

年龄为31的文档有61个，年龄为39的文档有60个

** size 指定返回多少个分组：**

示例2：指定返回20个分组

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age",
            "size": 20
          }
        }
      }
    }

结果2：

![ContractedBlock.gif][]

示例3：每个分组上显示偏差值

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age",
            "size": 5,
            "shard_size": 20,
            "show_term_doc_count_error": true
          }
        }
      }
    }

结果3：

{
      "took": 8,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_terms": {
          "doc_count_error_upper_bound": 25,
          "sum_other_doc_count": 716,
          "buckets": [
            {
              "key": 31,
              "doc_count": 61,
              "doc_count_error_upper_bound": 0
            },
            {
              "key": 39,
              "doc_count": 60,
              "doc_count_error_upper_bound": 0
            },
            {
              "key": 26,
              "doc_count": 59,
              "doc_count_error_upper_bound": 0
            },
            {
              "key": 32,
              "doc_count": 52,
              "doc_count_error_upper_bound": 0
            },
            {
              "key": 36,
              "doc_count": 52,
              "doc_count_error_upper_bound": 0
            }
          ]
        }
      }
    }

示例4：shard\_size 指定每个分片上返回多少个分组

shard\_size 的默认值为：  
索引只有一个分片：= size  
多分片：= size \* 1.5 + 10

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age",
            "size": 5,
            "shard_size": 20
          }
        }
      }
    }

结果4：

{
      "took": 8,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_terms": {
          "doc_count_error_upper_bound": 25,
          "sum_other_doc_count": 716,
          "buckets": [
            {
              "key": 31,
              "doc_count": 61
            },
            {
              "key": 39,
              "doc_count": 60
            },
            {
              "key": 26,
              "doc_count": 59
            },
            {
              "key": 32,
              "doc_count": 52
            },
            {
              "key": 36,
              "doc_count": 52
            }
          ]
        }
      }
    }

** order  指定分组的排序**

示例5：根据文档计数排序

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age",
            "order" : { "_count" : "asc" }
          }
        }
      }
    }

结果5：

{
      "took": 3,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_terms": {
          "doc_count_error_upper_bound": 0,
          "sum_other_doc_count": 584,
          "buckets": [
            {
              "key": 29,
              "doc_count": 35
            },
            {
              "key": 27,
              "doc_count": 39
            },
            {
              "key": 38,
              "doc_count": 39
            },
            {
              "key": 23,
              "doc_count": 42
            },
            {
              "key": 24,
              "doc_count": 42
            },
            {
              "key": 25,
              "doc_count": 42
            },
            {
              "key": 37,
              "doc_count": 42
            },
            {
              "key": 20,
              "doc_count": 44
            },
            {
              "key": 40,
              "doc_count": 45
            },
            {
              "key": 21,
              "doc_count": 46
            }
          ]
        }
      }
    }

示例6：根据分组值排序

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age",
            "order" : { "_key" : "asc" }
          }
        }
      }
    }

结果6：

{
      "took": 10,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_terms": {
          "doc_count_error_upper_bound": 0,
          "sum_other_doc_count": 549,
          "buckets": [
            {
              "key": 20,
              "doc_count": 44
            },
            {
              "key": 21,
              "doc_count": 46
            },
            {
              "key": 22,
              "doc_count": 51
            },
            {
              "key": 23,
              "doc_count": 42
            },
            {
              "key": 24,
              "doc_count": 42
            },
            {
              "key": 25,
              "doc_count": 42
            },
            {
              "key": 26,
              "doc_count": 59
            },
            {
              "key": 27,
              "doc_count": 39
            },
            {
              "key": 28,
              "doc_count": 51
            },
            {
              "key": 29,
              "doc_count": 35
            }
          ]
        }
      }
    }

示例7：取分组指标值排序

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "terms": {
            "field": "age",
            "order": {
              "max_balance": "asc"
            }
          },
          "aggs": {
            "max_balance": {
              "max": {
                "field": "balance"
              }
            },
            "min_balance": {
              "min": {
                "field": "balance"
              }
            }
          }
        }
      }
    }

结果7：

![ContractedBlock.gif][]

示例8：筛选分组-正则表达式匹配值

GET /_search
    {
        "aggs" : {
            "tags" : {
                "terms" : {
                    "field" : "tags",
                    "include" : ".*sport.*",
                    "exclude" : "water_.*"
                }
            }
        }
    }

示例9：筛选分组-指定值列表

GET /_search
    {
        "aggs" : {
            "JapaneseCars" : {
                 "terms" : {
                     "field" : "make",
                     "include" : ["mazda", "honda"]
                 }
             },
            "ActiveCarManufacturers" : {
                 "terms" : {
                     "field" : "make",
                     "exclude" : ["rover", "jensen"]
                 }
             }
        }
    }

示例10：根据脚本计算值分组

GET /_search
    {
        "aggs" : {
            "genres" : {
                "terms" : {
                    "script" : {
                        "source": "doc['genre'].value",
                        "lang": "painless"
                    }
                }
            }
        }
    }

示例1：缺失值处理

GET /_search
    {
        "aggs" : {
            "tags" : {
                 "terms" : {
                     "field" : "tags",
                     "missing": "N/A" 
                 }
             }
        }
    }

结果10：

![ContractedBlock.gif][]

### 2.  filter Aggregation  对满足过滤查询的文档进行聚合计算 ###

在查询命中的文档中选取符合过滤条件的文档进行聚合，先过滤再聚合

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_terms": {
          "filter": {"match":{"gender":"F"}},
          "aggs": {
            "avg_age": {
              "avg": {
                "field": "age"
              }
            }
          }
        }
      }
    }

结果1：

{
      "took": 163,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_terms": {
          "doc_count": 493,
          "avg_age": {
            "value": 30.3184584178499
          }
        }
      }
    }

###  3. Filters Aggregation  多个过滤组聚合计算 ###

示例1：

准备数据：

PUT /logs/_doc/_bulk?refresh
    {"index":{"_id":1}}
    {"body":"warning: page could not be rendered"}
    {"index":{"_id":2}}
    {"body":"authentication error"}
    {"index":{"_id":3}}
    {"body":"warning: connection timed out"}

获取组合过滤后聚合的结果：

GET logs/_search
    {
      "size": 0,
      "aggs": {
        "messages": {
          "filters": {
            "filters": {
              "errors": {
                "match": {
                  "body": "error"
                }
              },
              "warnings": {
                "match": {
                  "body": "warning"
                }
              }
            }
          }
        }
      }
    }

上面的结果：

{
      "took": 18,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 3,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "messages": {
          "buckets": {
            "errors": {
              "doc_count": 1
            },
            "warnings": {
              "doc_count": 2
            }
          }
        }
      }
    }

示例2：为其他值组指定key

GET logs/_search
    {
      "size": 0,
      "aggs": {
        "messages": {
          "filters": {
            "other_bucket_key": "other_messages",
            "filters": {
              "errors": {
                "match": {
                  "body": "error"
                }
              },
              "warnings": {
                "match": {
                  "body": "warning"
                }
              }
            }
          }
        }
      }
    }

结果2：

{
      "took": 5,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 3,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "messages": {
          "buckets": {
            "errors": {
              "doc_count": 1
            },
            "warnings": {
              "doc_count": 2
            },
            "other_messages": {
              "doc_count": 0
            }
          }
        }
      }
    }

###  4. Range Aggregation 范围分组聚合 ###

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "age_range": {
          "range": {
            "field": "age",
            "ranges": [
              {
                "to": 25
              },
              {
                "from": 25,
                "to": 35
              },
              {
                "from": 35
              }
            ]
          },
          "aggs": {
            "bmax": {
              "max": {
                "field": "balance"
              }
            }
          }
        }
      }
    }

结果1：

{
      "took": 7,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_range": {
          "buckets": [
            {
              "key": "*-25.0",
              "to": 25,
              "doc_count": 225,
              "bmax": {
                "value": 49587
              }
            },
            {
              "key": "25.0-35.0",
              "from": 25,
              "to": 35,
              "doc_count": 485,
              "bmax": {
                "value": 49795
              }
            },
            {
              "key": "35.0-*",
              "from": 35,
              "doc_count": 290,
              "bmax": {
                "value": 49989
              }
            }
          ]
        }
      }
    }

示例2：为组指定key

POST /bank/_search?size=0
    {
      "aggs": {
        "age_range": {
          "range": {
            "field": "age",
            "keyed": true,
            "ranges": [
              {
                "to": 25,
                "key": "Ld"
              },
              {
                "from": 25,
                "to": 35,
                "key": "Md"
              },
              {
                "from": 35,
                "key": "Od"
              }
            ]
          }
        }
      }
    }

结果2：

{
      "took": 2,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "age_range": {
          "buckets": {
            "Ld": {
              "to": 25,
              "doc_count": 225
            },
            "Md": {
              "from": 25,
              "to": 35,
              "doc_count": 485
            },
            "Od": {
              "from": 35,
              "doc_count": 290
            }
          }
        }
      }
    }

### 5. Date Range Aggregation  时间范围分组聚合 ###

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "range": {
          "date_range": {
            "field": "date",
            "format": "MM-yyy",
            "ranges": [
              {
                "to": "now-10M/M"
              },
              {
                "from": "now-10M/M"
              }
            ]
          }
        }
      }
    }

结果1：

{
      "took": 115,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "range": {
          "buckets": [
            {
              "key": "*-2017-08-01T00:00:00.000Z",
              "to": 1501545600000,
              "to_as_string": "2017-08-01T00:00:00.000Z",
              "doc_count": 0
            },
            {
              "key": "2017-08-01T00:00:00.000Z-*",
              "from": 1501545600000,
              "from_as_string": "2017-08-01T00:00:00.000Z",
              "doc_count": 0
            }
          ]
        }
      }
    }

### 6. Date Histogram Aggregation  时间直方图（柱状）聚合 ###

就是按天、月、年等进行聚合统计。可按 year (1y), quarter (1q), month (1M), week (1w), day (1d), hour (1h), minute (1m), second (1s) 间隔聚合或指定的时间间隔聚合。

示例1：

POST /bank/_search?size=0
    {
      "aggs": {
        "sales_over_time": {
          "date_histogram": {
            "field": "date",
            "interval": "month"
          }
        }
      }
    }

结果1：

{
      "took": 9,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
      },
      "hits": {
        "total": 1000,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "sales_over_time": {
          "buckets": []
        }
      }
    }

### 7. Missing Aggregation  缺失值的桶聚合 ###

POST /bank/_search?size=0
    {
        "aggs" : {
            "account_without_a_age" : {
                "missing" : { "field" : "age" }
            }
        }
    }

### 8. Geo Distance Aggregation  地理距离分区聚合 ###

参考官网链接：

[https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-geodistance-aggregation.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_search-aggregations-bucket-geodistance-aggregation.html]

[https_www.elastic.co_guide_en_elasticsearch_reference_current_search-aggregations-metrics-geobounds-aggregation.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geobounds-aggregation.html
[https_www.elastic.co_guide_en_elasticsearch_reference_current_search-aggregations-metrics-geocentroid-aggregation.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geocentroid-aggregation.html
[1227483-20180622223636818-342791427.png]: /images/20220221/79a59c153f2c4c84b88dcba198ff0701.png
[ContractedBlock.gif]: /images/20220221/b596433d26bc4d38abb1cd69f617f21f.png
[https_www.elastic.co_guide_en_elasticsearch_reference_current_search-aggregations-bucket-geodistance-aggregation.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-geodistance-aggregation.html