Elasticsearch 将 NGram 与简单查询字符串查询混合
Elasicsearch mixing NGram with Simple query string query
目前,我正在使用Ngram tokenizer to-do员工的部分匹配。
我可以匹配全名、电子邮件地址和员工编号
我当前的设置如下:
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
我面临的问题是 员工编号 可以是 1 个字符长,因为 min_gram 和max_gram,我永远配不上。我也不能使 min_gram 1 因为结果看起来不正确。
所以我尝试将 Ngram 与标准分词器混合使用,而不是在多匹配搜索中进行搜索,而是在 simple_query_string.
这似乎也部分起作用。
我的问题是如何在所有 3 个字段上部分匹配,同时记住员工编号可以是 1 或 2 个字符长。如果我在单词或数字周围使用半引号
,则完全匹配
在下面的示例中如何搜索 11 和 return 文档 4 和 5?
另外,如果我必须搜索部分匹配的 706,我希望文档 2 到 return,但是如果我必须使用“7061”进行搜索,我只会 return 文档 2
完整代码
PUT index
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"my_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "text",
"analyzer": "english",
"fields": {
"exact": {
"type": "text",
"analyzer": "english_exact"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "my_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
PUT index/_doc/1
{
"number" : 1,
"fullName": "Brenda eaton"
}
PUT index/_doc/2
{
"number" : 7061,
"fullName": "Bruce wayne"
}
PUT index/_doc/3
{
"number" : 23,
"fullName": "Bruce Banner"
}
PUT index/_doc/4
{
"number" : 111,
"fullName": "Cat woman"
}
PUT index/_doc/5
{
"number" : 1112,
"fullName": "0723568521"
}
GET index/_search
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact"],
"query": "11"
}
}
}
您需要更改number.exact
字段的分析器并减少min_gram
计数为2。修改索引映射如下图
添加一个工作示例
索引映射:
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"my_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "keyword", // note this
"fields": {
"exact": {
"type": "text",
"analyzer": "my_analyzer"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "my_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
搜索查询:
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact"],
"query": "11"
}
}
}
搜索结果:
"hits": [
{
"_index": "66311552",
"_type": "_doc",
"_id": "4",
"_score": 0.9929736,
"_source": {
"number": 111,
"fullName": "Cat woman"
}
},
{
"_index": "66311552",
"_type": "_doc",
"_id": "5",
"_score": 0.8505551,
"_source": {
"number": 1112,
"fullName": "0723568521"
}
}
]
更新 1:
如果只需要搜索1
,修改number
字段的数据类型由text
类型修改为keyword
类型,如索引所示上面的映射。
搜索查询:
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact","number"],
"query": "1"
}
}
}
搜索结果将是
"hits": [
{
"_index": "66311552",
"_type": "_doc",
"_id": "1",
"_score": 1.3862942,
"_source": {
"number": 1,
"fullName": "Brenda eaton"
}
}
]
更新二:
您可以对 fullName
字段和 number
字段使用两个带有 n-gram 分词器的单独分析器。使用以下索引映射进行修改:
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"name_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "name_tokenizer"
},
"number_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "number_tokenizer"
}
},
"tokenizer": {
"name_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
},
"number_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "keyword",
"fields": {
"exact": {
"type": "text",
"analyzer": "number_analyzer"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "name_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
目前,我正在使用Ngram tokenizer to-do员工的部分匹配。
我可以匹配全名、电子邮件地址和员工编号
我当前的设置如下:
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
我面临的问题是 员工编号 可以是 1 个字符长,因为 min_gram 和max_gram,我永远配不上。我也不能使 min_gram 1 因为结果看起来不正确。
所以我尝试将 Ngram 与标准分词器混合使用,而不是在多匹配搜索中进行搜索,而是在 simple_query_string.
这似乎也部分起作用。
我的问题是如何在所有 3 个字段上部分匹配,同时记住员工编号可以是 1 或 2 个字符长。如果我在单词或数字周围使用半引号
,则完全匹配在下面的示例中如何搜索 11 和 return 文档 4 和 5? 另外,如果我必须搜索部分匹配的 706,我希望文档 2 到 return,但是如果我必须使用“7061”进行搜索,我只会 return 文档 2
完整代码
PUT index
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"my_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "text",
"analyzer": "english",
"fields": {
"exact": {
"type": "text",
"analyzer": "english_exact"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "my_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
PUT index/_doc/1
{
"number" : 1,
"fullName": "Brenda eaton"
}
PUT index/_doc/2
{
"number" : 7061,
"fullName": "Bruce wayne"
}
PUT index/_doc/3
{
"number" : 23,
"fullName": "Bruce Banner"
}
PUT index/_doc/4
{
"number" : 111,
"fullName": "Cat woman"
}
PUT index/_doc/5
{
"number" : 1112,
"fullName": "0723568521"
}
GET index/_search
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact"],
"query": "11"
}
}
}
您需要更改number.exact
字段的分析器并减少min_gram
计数为2。修改索引映射如下图
添加一个工作示例
索引映射:
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"my_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "keyword", // note this
"fields": {
"exact": {
"type": "text",
"analyzer": "my_analyzer"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "my_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
搜索查询:
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact"],
"query": "11"
}
}
}
搜索结果:
"hits": [
{
"_index": "66311552",
"_type": "_doc",
"_id": "4",
"_score": 0.9929736,
"_source": {
"number": 111,
"fullName": "Cat woman"
}
},
{
"_index": "66311552",
"_type": "_doc",
"_id": "5",
"_score": 0.8505551,
"_source": {
"number": 1112,
"fullName": "0723568521"
}
}
]
更新 1:
如果只需要搜索1
,修改number
字段的数据类型由text
类型修改为keyword
类型,如索引所示上面的映射。
搜索查询:
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact","number"],
"query": "1"
}
}
}
搜索结果将是
"hits": [
{
"_index": "66311552",
"_type": "_doc",
"_id": "1",
"_score": 1.3862942,
"_source": {
"number": 1,
"fullName": "Brenda eaton"
}
}
]
更新二:
您可以对 fullName
字段和 number
字段使用两个带有 n-gram 分词器的单独分析器。使用以下索引映射进行修改:
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"name_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "name_tokenizer"
},
"number_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "number_tokenizer"
}
},
"tokenizer": {
"name_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
},
"number_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "keyword",
"fields": {
"exact": {
"type": "text",
"analyzer": "number_analyzer"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "name_analyzer"
}
},
"analyzer": "standard"
}
}
}
}