两个核心概念：bucket和metric

city name
北京 小李
北京 小王
上海 小张
上海 小丽
上海 小陈

基于city划分buckets
划分出来两个bucket，一个是北京bucket，一个是上海bucket

北京bucket：包含了2个人，小李，小王
上海bucket：包含了3个人，小张，小丽，小陈

按照某个字段进行bucket划分，那个字段的值相同的那些数据，就会被划分到一个bucket中
有一些mysql的sql知识的话，聚合，首先第一步就是分组，对每个组内的数据进行聚合分析，分组，就是我们的bucket
metric：对一个数据分组执行的统计
当我们有了一堆bucket之后，就可以对每个bucket中的数据进行聚合分词了，比如说计算一个bucket内所有数据的数量，或者计算一个bucket内所有数据的平均值，最大值，最小值

bucket：group by user_id --> 那些user_id相同的数据，就会被划分到一个bucket中
metric，就是对一个bucket执行的某种聚合分析的操作，比如说求平均值，求最大值，求最小值

计算一个数量计算每个tag下的商品数量

GET /ecommerce/product/_search
{"size" : 0,  "aggs": {"group_by_tags": {"terms": { "field": "tags" }}}
}

size：只获取聚合结果，而不要执行聚合的原始数据
aggs：固定语法，要对一份数据执行分组聚合操作
gourp_by_tags：就是对每个aggs，都要起一个名字，这个名字是随机的，你随便取什么都ok
terms：根据字段的值进行分组
field：根据指定的字段的值进行分组将文本

field的fielddata属性设置为true （正排索引 用于嵌套聚合查询， 后面会详细描述）

PUT /ecommerce/_mapping/product{"properties": {"tags": {"type": "text","fielddata": true}}
}

GET /ecommerce/product/_search
{"size": 0,"aggs": {"all_tags": {"terms": { "field": "tags" }}}
}{"took": 20,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 4,"max_score": 0,"hits": []},"aggregations": {"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2},{"key": "meibai","doc_count": 2},{"key": "qingxin","doc_count": 1}]}}
}

hits.hits：我们指定了size是0，所以hits.hits就是空的，否则会把执行聚合的那些原始数据给你返回回来
aggregations：聚合结果
gourp_by_tags：我们指定的某个聚合的名称
buckets：根据我们指定的field划分出的buckets
key：每个bucket对应的那个值
doc_count：这个bucket分组内，有多少个数据
每种tag对应的bucket中的数据的
默认的排序规则：按照doc_count降序排序

对名称中包含yagao的商品，计算每个tag下的商品数量

GET /ecommerce/product/_search
{"size": 0,"query": {"match": {"name": "yagao"}},"aggs": {"all_tags": {"terms": {"field": "tags"}}}
}

{"took": 35,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 3,"max_score": 0,"hits": []},"aggregations": {"all_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2},{"key": "meibai","doc_count": 1},{"key": "qingxin","doc_count": 1}]}}
}

top_hits 获取前几个doc_

source 返回指定field

GET /ecommerce/product/_search
{"size": 0,"aggs" : {"group_by_tags" : {"terms" : { "field" : "tags" },"aggs" : {"top_tags": {"top_hits": { "_source": {"include": "name"}, "size": 1}} }}}
}

计算每个tag下的商品的平均价格/最小价格/最大价格/总价

count：bucket，terms，自动就会有一个doc_count，就相当于是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值的总和先分组，再算每组的平均值

GET /ecommerce/product/_search
{"size": 0,"aggs" : {"group_by_tags" : {"terms" : { "field" : "tags" },"aggs" : {"avg_price": { "avg": { "field": "price" } },"min_price" : { "min": { "field": "price"} }, "max_price" : { "max": { "field": "price"} },"sum_price" : { "sum": { "field": "price" } } }}}

avg_price：我们自己取的metric aggs的名字
value：我们的metric计算的结果，每个bucket中的数据的price字段求平均值后的结果

{"took": 3,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 3,"max_score": 0,"hits": []},"aggregations": {"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2,"max_price": {"value": 30},"min_price": {"value": 25},"avg_price": {"value": 27.5},"sum_price": {"value": 55}},{"key": "meibai","doc_count": 1,"max_price": {"value": 30},"min_price": {"value": 30},"avg_price": {"value": 30},"sum_price": {"value": 30}},{"key": "qingxin","doc_count": 1,"max_price": {"value": 40},"min_price": {"value": 40},"avg_price": {"value": 40},"sum_price": {"value": 40}}]}}
}

collect_mode

"order": { "avg_price": "desc" }

计算每个tag下的商品的平均价格，并且按照平均价格降序排序

GET /ecommerce/product/_search
{"size": 0,"aggs" : {"all_tags" : {"terms" : { "field" : "tags", "collect_mode" : "breadth_first", "order": { "avg_price": "desc" } },"aggs" : {"avg_price" : {"avg" : { "field" : "price" }}}}}
}

{"took": 2,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 3,"max_score": 0,"hits": []},"aggregations": {"all_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "qingxin","doc_count": 1,"avg_price": {"value": 40}},{"key": "meibai","doc_count": 1,"avg_price": {"value": 30}},{"key": "fangzhu","doc_count": 2,"avg_price": {"value": 27.5}}]}}
}

按照指定的价格范围区间进行分组，然后在每组内再按照tag进行分组，最后再计算每组的平均价格

GET /ecommerce/product/_search
{"size": 0,"aggs": {"group_by_price": {"range": {"field": "price","ranges": [{"from": 0,"to": 20},{"from": 20,"to": 40},{"from": 40,"to": 50}]},"aggs": {"group_by_tags": {"terms": {"field": "tags"},"aggs": {"average_price": {"avg": {"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

GET /ecommerce/product/_search
{"size" : 0,"aggs":{"price":{"histogram":{ "field": "price","interval": 10},"aggs":{"revenue": {"sum": { "field" : "price"}}}}}
}

{"took": 1,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 3,"max_score": 0,"hits": []},"aggregations": {"price": {"buckets": [{"key": 20,"doc_count": 1,"revenue": {"value": 25}},{"key": 30,"doc_count": 1,"revenue": {"value": 30}},{"key": 40,"doc_count": 1,"revenue": {"value": 40}}]}}
}

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

GET /tvs/sales/_search
{"size" : 0,"aggs": {"sales": {"date_histogram": {"field": "sold_date","interval": "month", "format": "yyyy-MM-dd","min_doc_count" : 0, "extended_bounds" : { "min" : "2016-01-01","max" : "2017-12-31"}}}}
}

{"took": 11,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"sales": {"buckets": [{"key_as_string": "2016-01-01","key": 1451606400000,"doc_count": 0},{"key_as_string": "2016-02-01","key": 1454284800000,"doc_count": 0},{"key_as_string": "2016-03-01","key": 1456790400000,"doc_count": 0},{"key_as_string": "2016-04-01","key": 1459468800000,"doc_count": 0},{"key_as_string": "2016-05-01","key": 1462060800000,"doc_count": 1},.....]}}
}

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

{"took": 4,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 3,"max_score": 0,"hits": []},"aggregations": {"all": {"doc_count": 8,"all_brand_avg_price": {"value": 2650}},"single_brand_avg_price": {"value": 1666.6666666666667}}
}