# 排序，在下钻的聚合操作上排序
考虑这样一个场景：按颜色分组，并统计每个品牌的平均价格，且按平均价格排序
GET /tvs/sales/_search
{
"size": 0,
"aggs": {
"group_by_color": {
"terms": {
"field": "color"
},
"aggs": {
"group_by_brand": {
"terms": {
"field": "brand",
"order": {
"avg_price": "desc"
}
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
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响应结果
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 8,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_color": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "红色",
"doc_count": 4,
"group_by_brand": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "三星",
"doc_count": 1,
"avg_price": {
"value": 8000
}
},
{
"key": "长虹",
"doc_count": 3,
"avg_price": {
"value": 1666.6666666666667
}
}
]
}
},
{
"key": "绿色",
"doc_count": 2,
"group_by_brand": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "小米",
"doc_count": 1,
"avg_price": {
"value": 3000
}
},
{
"key": "TCL",
"doc_count": 1,
"avg_price": {
"value": 1200
}
}
]
}
},
{
"key": "蓝色",
"doc_count": 2,
"group_by_brand": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "小米",
"doc_count": 1,
"avg_price": {
"value": 2500
}
},
{
"key": "TCL",
"doc_count": 1,
"avg_price": {
"value": 1500
}
}
]
}
}
]
}
}
}
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在第一个 bucket 的时候，是按照 doc_count 降序排列的， 对于第二个 bucket（下钻这个），看默认的顺序是升序排列的。 那么可以改变第二个 bucket 的排序方式。
再看看下面这个语法，把第一个 bucket 的排序也更改了，响应结果表现是按 doc_count 升序排列的
GET /tvs/sales/_search
{
"size": 0,
"aggs": {
"group_by_color": {
"terms": {
"field": "color",
"order": {
"_term": "desc"
}
},
"aggs": {
"group_by_brand": {
"terms": {
"field": "brand",
"order": {
"avg_price": "desc"
}
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
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由这里可以看出来，对于每个聚合操作的结果都可以进行定制排序