我在弹性搜索中有下一个字段映射(YML中的定义):
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:
"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"
}]
我试图获取每个过滤器的唯一过滤器名称列表和唯一过滤器值列表.
我的意思是,我希望获得如下结构:Rahmengröße:
39,5 cm 45,5
cm
33,5
cm
颜色:
gelb
为了得到它,我尝试了几种聚合变体,例如:
{
"aggs": {
"bla": {
"terms": {
"field": "products_filter.filter_name"
},
"aggs": {
"bla2": {
"terms": {
"field": "products_filter.filter_value"
}
}
}
}
}
}
这个要求是错误的.
它将返回我的唯一过滤器名称列表,每个过滤器名称都包含所有filter_values列表.
"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Reverse嵌套聚合,但它对我没有帮助.
所以我认为我的尝试有一些逻辑错误？
正如我所说的那样.您的问题是您的文本被分析,而弹性搜索总是在令牌级别聚合.因此,为了解决这个问题,您的字段值必须编入索引为单个标记.有两种选择:
不要分析它们
使用关键字分析器+小写(不区分大小写的aggs)索引它们
因此,这将是创建自定义关键字分析器的设置,其中包含小写过滤器和已删除的重音字符(ö => o以及ß => ss字段的其他字段,因此它们可用于聚合(raw和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"
}
}
}
}
}
}
}
}
}
你给我们的测试文件:
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"
}
]
}
那将是使用raw字段聚合的查询:
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(带有过滤器名称的桶和带有值的子桶):
{
"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
}
]
}
}
]
}
}
}
}
另外,您可以使用带有关键字分析器的字段(以及一些规范化)来获得更多通用和不区分大小写的结果:
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
}
]
}
}
]
}
}
}
}
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