Elastic Search中嵌套字段的术语聚合 [英] Terms Aggregation for nested field in Elastic Search
问题描述
我在弹性搜索（YML中的定义）中具有字段的下一个映射：
I have next mapping for field in Elastic Search (definition in YML):
my_analyzer:
type: custom
tokenizer: keyword
filter: lowercase
products_filter:
type: "nested"
properties:
filter_name: {"type" : "string", analyzer: "my_analyzer"}
filter_value: {"type" : "string" , analyzer: "my_analyzer"}
每个文档都有很多过滤器，看起来像：
Each document has a lot of filters and it looks like:
"products_filter": [
{
"filter_name": "Rahmengröße",
"filter_value": "33,5 cm"
}
,
{
"filter_name": "color",
"filter_value": "gelb"
}
,
{
"filter_name": "Rahmengröße",
"filter_value": "39,5 cm"
}
,
{
"filter_name": "Rahmengröße",
"filter_value": "45,5 cm"
}]
我试图获取唯一过滤器名称列表和每个滤镜的唯一滤镜值。
I trying to get a list of unique filter names and list of unique filter values for each filter.
我的意思是，我想获得如下结构：
Rahmengröße：
39,5 cm
45.5厘米
33.5厘米
颜色：
gelb
I mean, I want to get structure like:
Rahmengröße:
39,5 cm
45,5 cm
33,5 cm
Color:
gelb
要获得它，我尝试了几种聚合方式，例如：
To get it I tried few variants of aggregation, for example:
{
"aggs": {
"bla": {
"terms": {
"field": "products_filter.filter_name"
},
"aggs": {
"bla2": {
"terms": {
"field": "products_filter.filter_value"
}
}
}
}
}
}
此请求是错误的。
它将返回我唯一的过滤器名称列表，每个列表都包含所有filter_values的列表。
It will return me list of unique filter names, and each will contain list of ALL filter_values.
"bla": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 103,
"buckets": [
{
"key": "color",
"doc_count": 9,
"bla2": {
"doc_count_error_upper_bound": 4,
"sum_other_doc_count": 366,
"buckets": [
{
"key": "100",
"doc_count": 5
}
,
{
"key": "cm",
"doc_count": 5
}
,
{
"key": "unisex",
"doc_count": 5
}
,
{
"key": "11",
"doc_count": 4
}
,
{
"key": "160",
"doc_count": 4
}
,
{
"key": "22",
"doc_count": 4
}
,
{
"key": "a",
"doc_count": 4
}
,
{
"key": "alu",
"doc_count": 4
}
,
{
"key": "aluminium",
"doc_count": 4
}
,
{
"key": "aus",
"doc_count": 4
}
]
}
}
,
另外，我尝试使用反向嵌套聚集，但对我没有帮助。
Additionally I tried to use Reverse nested aggregation, but it doesnt help me.
所以我认为我的尝试存在逻辑错误？
So I think there some logical fault in my attempts?
推荐答案
所以我已经说过了。您的问题是对您的文本进行了分析，elasticsearch始终在令牌级别聚合。因此，为了解决该问题，必须将字段值索引为单个标记。有两种选择：
So as I've said. Your issue is that your text is analyzed and elasticsearch always aggregates at token level. So in order to fix that, your field values have to be indexed as single tokens. There are two options:
- 不对其进行分析
- 使用关键字分析器+小写字母对其进行索引（不区分大小写的aggs）
因此这是使用小写过滤器并删除重音符创建自定义关键字分析器的设置（ö=> o 和ß=> ss 以及您的字段的其他字段，因此它们可用于聚合（原始和关键字）：
So that would be settings to create custom keyword analyzer with lowercase filter and removed accent characters (ö => o and ß => ss and additional fields for your fields, so they can be used for aggregation (raw and keyword):
PUT /test
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer_keyword": {
"type": "custom",
"tokenizer": "keyword",
"filter": [
"asciifolding",
"lowercase"
]
}
}
}
},
"mappings": {
"data": {
"properties": {
"products_filter": {
"type": "nested",
"properties": {
"filter_name": {
"type": "string",
"analyzer": "standard",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
},
"keyword": {
"type": "string",
"analyzer": "my_analyzer_keyword"
}
}
},
"filter_value": {
"type": "string",
"analyzer": "standard",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
},
"keyword": {
"type": "string",
"analyzer": "my_analyzer_keyword"
}
}
}
}
}
}
}
}
}
您给了我们一份测试文档：
A test document, you've given us:
PUT /test/data/1
{
"products_filter": [
{
"filter_name": "Rahmengröße",
"filter_value": "33,5 cm"
},
{
"filter_name": "color",
"filter_value": "gelb"
},
{
"filter_name": "Rahmengröße",
"filter_value": "39,5 cm"
},
{
"filter_name": "Rahmengröße",
"filter_value": "45,5 cm"
}
]
}
该查询将使用
GET /test/_search
{
"size": 0,
"aggs": {
"Nesting": {
"nested": {
"path": "products_filter"
},
"aggs": {
"raw_names": {
"terms": {
"field": "products_filter.filter_name.raw",
"size": 0
},
"aggs": {
"raw_values": {
"terms": {
"field": "products_filter.filter_value.raw",
"size": 0
}
}
}
}
}
}
}
}
它确实带来了预期的结果（存储桶以及过滤器名称和子桶及其值）：
It does bring expected result (buckets with filter names and subbuckets with their values):
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0,
"hits": []
},
"aggregations": {
"Nesting": {
"doc_count": 4,
"raw_names": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Rahmengröße",
"doc_count": 3,
"raw_values": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "33,5 cm",
"doc_count": 1
},
{
"key": "39,5 cm",
"doc_count": 1
},
{
"key": "45,5 cm",
"doc_count": 1
}
]
}
},
{
"key": "color",
"doc_count": 1,
"raw_values": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "gelb",
"doc_count": 1
}
]
}
}
]
}
}
}
}
或者，您可以使用带有关键字分析器的字段（以及一些规范化）来获得更通用且不区分大小写的结果：
Alternitavely, you could use field with keyword analyzer (and some normalization) to get a bit more generic and case insensitive results:
GET /test/_search
{
"size": 0,
"aggs": {
"Nesting": {
"nested": {
"path": "products_filter"
},
"aggs": {
"keyword_names": {
"terms": {
"field": "products_filter.filter_name.keyword",
"size": 0
},
"aggs": {
"keyword_values": {
"terms": {
"field": "products_filter.filter_value.keyword",
"size": 0
}
}
}
}
}
}
}
}
这就是结果：
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0,
"hits": []
},
"aggregations": {
"Nesting": {
"doc_count": 4,
"keyword_names": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "rahmengrosse",
"doc_count": 3,
"keyword_values": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "33,5 cm",
"doc_count": 1
},
{
"key": "39,5 cm",
"doc_count": 1
},
{
"key": "45,5 cm",
"doc_count": 1
}
]
}
},
{
"key": "color",
"doc_count": 1,
"keyword_values": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "gelb",
"doc_count": 1
}
]
}
}
]
}
}
}
}
这篇关于Elastic Search中嵌套字段的术语聚合的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
