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<https://blog.csdn.net/l1028386804/article/details/79721254>


本文是基于博文《ElasticSearch之——文档增删改查
<https://blog.csdn.net/l1028386804/article/details/79720625>
》一文中,创建的索引文档进行的,请先阅读博文《ElasticSearch之——文档增删改查
<https://blog.csdn.net/l1028386804/article/details/79720625>》。


1、第一个分析需求

计算每个tag下的商品数量

GET /ecommerce/product/_search { "aggs": { "group_by_tags": { "terms": {
"field": "tags" } } } }将文本field的fielddata属性设置为true
PUT /ecommerce/_mapping/product { "properties": { "tags": { "type": "text",
"fielddata": true } } }GET /ecommerce/product/_search { "size": 0, "aggs": {
"all_tags": { "terms": { "field": "tags" } } } }
2、第二个聚合分析的需求

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" } } } }
3、第三个聚合分析的需求

先分组,再算每组的平均值,计算每个tag下的商品的平均价格
GET /ecommerce/product/_search { "size": 0, "aggs" : { "group_by_tags" : {
"terms" : { "field" : "tags" }, "aggs" : { "avg_price" : { "avg" : { "field" :
"price" } } } } } }
4、第四个数据分析需求

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", "order": { "avg_price": "desc" } }, "aggs" : {
"avg_price" : { "avg" : { "field" : "price" } } } } } }
5、第五个数据分析需求

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" } }
} } } } } }