1.两个核心概念
bucket：一个数据分组，类似数据库分组group bymetric：对一个数据分组执行的统计，常见的数据分析的metric操作有count，avg，max，min，sum
2.有分词的字段需要field的fielddata属性设置为true
PUT /ecommerce/_mapping/product{
"properties": {
"tags": {
"type": "text",
"fielddata": true
}
}
}
3.常用统计示例
例1.计算每个tag下的商品数量GET /ecommerce/product/_search
{
"aggs": {
"group_by_tags": {
"terms": { "field": "tags" }
}
}
}
例2.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"
}
}
}
}
例3.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" }
}
}
}
}
}
例4.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" }
}
}
}
}
}
例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"
}
}
}
}
}
}
}
}
4.count、avg、max、min、sum
GET /tvs/sales/_search{
"size" : 0,
"aggs": {
"colors": {
"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.使用内置field不分词，对string field进行聚合
GET /test_index/test_type/_search{
"size": 0,
"aggs": {
"group_by_test_field": {
"terms": {
"field": "test_field.keyword"
}
}
}
}
6.下钻：多层次的下钻，即分组再分组，多层嵌套聚合
例：每个颜色的电视中的品牌的数量和平均价格是多少GET /tvs/sales/_search
{
"size": 0,
"aggs": {
"group_by_color": {
"terms": {
"field": "color"
},
"aggs": {
"color_avg_price": {
"avg": {
"field": "price"
}
},
"group_by_brand": {
"terms": {
"field": "brand"
},
"aggs": {
"brand_avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
7.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
例：GET /tvs/sales/_search{
"size" : 0,
"aggs":{
"price":{
"histogram":{
"field": "price",
"interval": 2000
},
"aggs":{
"revenue": {
"sum": {
"field" : "price"
}
}
}
}
}
}
8.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
min_doc_count：区间无值补0extended_bounds，min，max：划分bucket的时候，会限定在这个起始日期，和截止日期内
例：GET /tvs/sales/_search
{
"size" : 0,
"aggs": {
"sales": {
"date_histogram": {
"field": "sold_date",
"interval": "month",
"format": "yyyy-MM",
"min_doc_count": 0,
"extended_bounds" : {
"min" : "2016-01",
"max" : "2017-12"
}
}
}
}
}
9.搜索结合聚合aggregation，scope
出来两个结果，一个结果，是基于query搜索结果来聚合的; 一个结果，是对所有数据执行聚合的global：就是global bucket，就是将所有数据纳入聚合的scope，而不管之前的query
例：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"
}
}
}
}
}
}
global：就是global bucket，就是将所有数据纳入聚合的scope，而不管之前的query