数据准备：
PUT /shop
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 2
}
} PUT /shop/_mapping/goods
{
"properties": {
"brand": {
"type": "keyword"
},
"price": {
"type": "float"
},
"model": {
"type": "keyword"
}
}
} POST /shop/goods/_bulk
{"index": {}}
{"price" : 2299.00, "model" : "小米8", "brand" : "小米"}
{"index": {}}
{"price" : 4499.00, "model" : "Mate 20", "brand" : "华为"}
{"index": {}}
{"price" : 3299.00, "model" : "小米Mix3", "brand" : "小米"}
{"index": {}}
{"price" : 1199.00, "model" : "荣耀9i", "brand" : "华为"}
{"index": {}}
{"price" : 2799.00, "model" : "R17", "brand" : "OPPO"}
{"index": {}}
{"price" : 729.00, "model" : "红米6", "brand" : "小米"}
{"index": {}}
{"price" : 2799.00, "model" : "X23", "brand" : "VIVO"}
{"index": {}}
{"price" : 1799.00, "model" : "K1", "brand" : "OPPO"}
一、聚合为桶
按照手机的品牌brand划分为桶
查询指令：
GET /shop/_search
{
"size": 0,
"aggs": {
"brand_aggs": {
"terms": {
"field": "brand"
}
}
}
}
- size： 查询条数，这里设置为0，因为我们不关心搜索到的数据，只关心聚合结果，提高效率
- aggs：声明这是一个聚合查询，是aggregations的缩写
- popular_colors：给这次聚合起一个名字，任意。
- terms：划分桶的方式，这里是根据词条划分
- field：划分桶的字段
查询结果：
{
"took": 6,
"timed_out": false,
"_shards": {
"total": 3,
"successful": 3,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 8,
"max_score": 0,
"hits": []
},
"aggregations": {
"brand_aggs": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "小米",
"doc_count": 3
},
{
"key": "OPPO",
"doc_count": 2
},
{
"key": "华为",
"doc_count": 2
},
{
"key": "VIVO",
"doc_count": 1
}
]
}
}
}- hits：查询结果为空，因为我们设置了size为0
- aggregations：聚合的结果
- brand_aggs：我们定义的聚合名称
- buckets：查找到的桶，每个不同的brand字段值都会形成一个桶
- key：这个桶对应的brand字段的值
- doc_count：这个桶中的文档数量
二、桶内度量
为聚合结果添加求价格平均值的度量
查询指令：
GET /shop/_search
{
"size": 0,
"aggs": {
"brand_aggs": {
"terms": {
"field": "brand"
},
"aggs": {
"price_aggs": {
"avg": {
"field": "price"
}
}
}
}
}
}
- aggs：我们在上一个aggs(brand_aggs)中添加新的aggs。可见度量也是一个聚合
- price_aggs：聚合的名称
- avg：度量的类型，这里是求平均值
- field：度量运算的字段
查询结果：
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 3,
"successful": 3,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 8,
"max_score": 0,
"hits": []
},
"aggregations": {
"brand_aggs": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "小米",
"doc_count": 3,
"price_aggs": {
"value": 2109
}
},
{
"key": "OPPO",
"doc_count": 2,
"price_aggs": {
"value": 2299
}
},
{
"key": "华为",
"doc_count": 2,
"price_aggs": {
"value": 2849
}
},
{
"key": "VIVO",
"doc_count": 1,
"price_aggs": {
"value": 2799
}
}
]
}
}
}可以看到每个桶中都有自己的 price_aggs 字段，这是度量聚合的结果