排序总和聚合-嵌套(ElasticSearch)
要求是按员工Id和姓名分组,然后合计总额(工资)。我已经设法使它起作用了。但是,最终聚合结果必须按总和的降序排序。我试过两种方法,但订购似乎不起作用排序总和聚合-嵌套(ElasticSearch),
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,,要求是按员工Id和姓名分组,然后合计总额(工资)。我已经设法使它起作用了。但是,最终聚合结果必须按总和的降序排序。我试过两种方法,但订购似乎不起作用 var resp = elasticClient.Search<Employee>( k => k.Size(0).Query(q => q.QueryString(s => s.Query(query))) .Aggregations(a =>
var resp =
elasticClient.Search<Employee>(
k => k.Size(0).Query(q => q.QueryString(s => s.Query(query)))
.Aggregations(a => a.Terms("EmpId", v => v.Field(f => f.EmpId)
.Aggregations(aa => aa.Terms("EmpName",vv => vv.Field(ff => ff.EmpName).OrderDescending("Salary")
.Aggregations(aaa => aaa.Sum("Salary", sa => sa.Field(fff => fff.Salary))))))));
映射:
POST /sample_employee
{
"mappings": {
"post":{
"properties": {
"empId": {
"type" : "long"
},
"empName": {
"type": "string",
"index": "not_analyzed"
},
"salary": {
"type": "float"
},
"dateId": {
"type": "long"
},
"divisionNumber":{
"type": "long"
}
}
}
}
}
聚合查询:
GET /sample_employee/_search
{
"size": 0,
"query": {
"query_string": {
"query": "dateId:(1780) AND divisionNumber:(1)"
}
},
"aggs": {
"EmpId": {
"terms": {
"field": "empId"
},
"aggs": {
"EmpName": {
"terms": {
"field": "empName",
"size": 30,
"order":{
"Amount.value": "desc"}
},
"aggs": {
"Amount": {
"sum": {
"field": "salary"
}
}
}
}
}
}
}
}
答复:
"aggregations": {
"EmpId": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 10,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Jerry Mathews",
"doc_count": 1,
"Amount": {
"value": 10000
}
}
]
}
},
{
"key": 11,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Tom Raju",
"doc_count": 1,
"Amount": {
"value": 15000
}
}
]
}
},
{
"key": 12,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Nel Gad",
"doc_count": 1,
"Amount": {
"value": 20000
}
}
]
}
},
{
"key": 13,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Jim Thomas",
"doc_count": 1,
"Amount": {
"value": 25000
}
}
]
}
},
{
"key": 14,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Amat Ahu",
"doc_count": 1,
"Amount": {
"value": 30000
}
}
]
}
}
]
}
}
}resp.OrderByDescending(c=>c.Salary)您可以分享
Employee
类,这将加快复制过程:)嗯。。。对我来说,它正在被分类。请分享您的elasticsearch响应,并指出为什么您认为它是错误的?是的,我正在使用您的代码。我正在测试ES 2.3和NEST 2.3。也许我不明白你的问题,这就是为什么我问起响应的原因。@Rob我已经分享了我用于测试的映射和查询。看起来每当我聚合时,排序似乎都不起作用。
"aggregations": {
"EmpId": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 10,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Jerry Mathews",
"doc_count": 1,
"Amount": {
"value": 10000
}
}
]
}
},
{
"key": 11,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Tom Raju",
"doc_count": 1,
"Amount": {
"value": 15000
}
}
]
}
},
{
"key": 12,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Nel Gad",
"doc_count": 1,
"Amount": {
"value": 20000
}
}
]
}
},
{
"key": 13,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Jim Thomas",
"doc_count": 1,
"Amount": {
"value": 25000
}
}
]
}
},
{
"key": 14,
"doc_count": 1,
"EmpName": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Amat Ahu",
"doc_count": 1,
"Amount": {
"value": 30000
}
}
]
}
}
]
}