我正在尝试通过弹性搜索查询中的聚合计算定价分数属性的第 75 个百分位值。我的最后一个条件似乎返回 0 docCount 返回 0 百分位值。
这是我索引中属性的映射：
"pricingbytrade" : {
"type" : "nested",
"properties" : {
"name" : {
"type" : "text",
"fields" : {
"analyzed" : {
"type" : "text",
"analyzer" : "standard",
"search_analyzer" : "synonym_analyzer"
},
"keyword" : {
"type" : "keyword",
"ignore_above" : 256,
"normalizer" : "lowercase"
}
}
},
"pricingbycbsa" : {
"type" : "nested",
"properties" : {
"cbsaid" : {
"type" : "integer"
},
"cbsaname" : {
"type" : "text"
},
"pricingscore" : {
"type" : "float"
}
}
}
}
}
这是我的 AggregationContainerDescriptor 中的代码。 在这里，pricingbytrade.name 必须与给定的搜索词匹配，pricingbytrade.pricingbycbsa.cbsaid 必须与给定的 cbsaid 匹配才能匹配文档并计算pricingbytrade.pricingbycbsa.pricingscore 的第 75 个百分点：
.Nested("pricingbytrade", n => n
.Path("pricingbytrade")
.Aggregations(nestedAggs => nestedAggs
.Nested("pricingbycbsa", nn => nn
.Path("pricingbytrade.pricingbycbsa")
.Aggregations(nestedNestedAggs => nestedNestedAggs
.Filter("filter_bycbsa", ff => ff
.Filter(filter => filter
.Bool(b => b
.Should(filterList => filterList
.Term(t => t
.Field("pricingbytrade.pricingbycbsa.cbsaid")
.Value(_cbsaId)
),
filterList => filterList
.Term(t => t
.Field("pricingbytrade.name")
.Value(_searchTerm.ToLower())
)
)
)
)
.Aggregations(subAggs => subAggs
.Percentiles("price_score_percentiles", p => p
.Field("pricingbytrade.pricingbycbsa.pricingscore")
.Percents(75)
)
)
)
)
)
)
);
我在 bool 中尝试了“必须”而不是“应该”，但没有区别。
我按如下方式访问我的百分位值，该值始终为 0 :
var priceScorePercentiles = response.Aggregations
.Nested("pricingbytrade")
.Nested("pricingbycbsa")
.Nested("filter_bycbsa")
.PercentilesBucket("price_score_percentiles")
.Items[0]
.Value;
虽然有匹配的文档，但我得到的价格_分数_百分位数存储桶的 docCount 为 0。代码中是否有任何错误，我必须正确计算百分位值，仅当两个条件匹配时才选择文档？
我想给你写评论，但我想我不能，因为 Stack Overflow 规则 -_-。无论如何，我做了和你一样的事情，但我得到了结果。我将分享我所做的；也许它对你有用。
首先使用映射创建索引：
PUT /new_stack_over_flow_question
{
"mappings": {
"properties": {
"pricingbytrade": {
"type": "nested",
"properties": {
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"pricingbycbsa": {
"type": "nested",
"properties": {
"cbsaid": {
"type": "long"
},
"cbsaname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"pricingscore": {
"type": "float"
}
}
}
}
}
}
}
}
第二次插入虚拟数据
POST /new_stack_over_flow_question/_doc
{
"pricingbytrade": {
"name": "dummy_name1",
"pricingbycbsa": [
{
"cbsaid": 123,
"cbsaname": "dummy_cbsa_name1",
"pricingscore": 10.5
},
{
"cbsaid": 456,
"cbsaname": "dummy_cbsa_name2",
"pricingscore": 15.2
}
]
}
}
POST /new_stack_over_flow_question/_doc
{
"pricingbytrade": {
"name": "dummy_name2",
"pricingbycbsa": [
{
"cbsaid": 789,
"cbsaname": "dummy_cbsa_name3",
"pricingscore": 20.1
},
{
"cbsaid": 101,
"cbsaname": "dummy_cbsa_name4",
"pricingscore": 25.7
}
]
}
}
这里可以看到所有数据
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 3,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "new_stack_over_flow_question",
"_id": "wFMHqY4BH07gFgTpzL-g",
"_score": 1,
"_source": {
"pricingbytrade": {
"name": "dummy_name1",
"pricingbycbsa": [
{
"cbsaid": 123,
"cbsaname": "dummy_cbsa_name1",
"pricingscore": 10.5
},
{
"cbsaid": 456,
"cbsaname": "dummy_cbsa_name2",
"pricingscore": 15.2
}
]
}
}
},
{
"_index": "new_stack_over_flow_question",
"_id": "wVMIqY4BH07gFgTpar81",
"_score": 1,
"_source": {
"pricingbytrade": {
"name": "dummy_name2",
"pricingbycbsa": [
{
"cbsaid": 789,
"cbsaname": "dummy_cbsa_name3",
"pricingscore": 20.1
},
{
"cbsaid": 101,
"cbsaname": "dummy_cbsa_name4",
"pricingscore": 25.7
}
]
}
}
},
{
"_index": "new_stack_over_flow_question",
"_id": "wlMLqY4BH07gFgTpA78p",
"_score": 1,
"_source": {
"pricingbytrade": {
"name": "dummy_name2",
"pricingbycbsa": [
{
"cbsaid": 789,
"cbsaname": "dummy_cbsa_name3",
"pricingscore": 20.1
},
{
"cbsaid": 101,
"cbsaname": "dummy_cbsa_name4",
"pricingscore": 25.7
}
]
}
}
}
]
}
}
POST /new_stack_over_flow_question/_doc
{
"pricingbytrade": {
"name": "dummy_name2",
"pricingbycbsa": [
{
"cbsaid": 789,
"cbsaname": "dummy_cbsa_name3",
"pricingscore": 20.1
},
{
"cbsaid": 101,
"cbsaname": "dummy_cbsa_name4",
"pricingscore": 25.7
}
]
}
}
然后进行搜索
POST /new_stack_over_flow_question/_search
{
"size": 0,
"aggs": {
"pricingbytrade_nested": {
"nested": { "path": "pricingbytrade"},
"aggs": {
"pricingbycbsa_nested": {
"nested": {"path": "pricingbytrade.pricingbycbsa"},
"aggs": {
"filter_bycbsa": {
"filter": {
"bool": {
"should": [
{
"term": {
"pricingbytrade.pricingbycbsa.cbsaid": {
"value": 123
}
}
},
{
"term": {
"pricingbytrade.name": {
"value": "Product 1"
}
}
}
]
}
},
"aggs": {
"price_score_percentiles": {
"percentiles": {
"field": "pricingbytrade.pricingbycbsa.pricingscore",
"percents": [75]
}
}
}
}
}
}
}
}
}
}
这是回复：
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 3,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"pricingbytrade_nested": {
"doc_count": 3,
"pricingbycbsa_nested": {
"doc_count": 6,
"filter_bycbsa": {
"doc_count": 1,
"price_score_percentiles": {
"values": {
"75.0": 10.5
}
}
}
}
}
}
}
您可以将我与您分享的内容与（响应）进行比较，或者将调用（sourceBuilder）与搜索查询请求进行比较。使用调试工具打开C#（sourceBuilder）数据；它应该看起来像我使用的相同搜索查询。
如果您无法修复它，只需共享调试器中的（响应）数据和（sourceBuilder）数据即可。