问题描述
目前,我正在使用 Ngram 标记器来进行员工的部分匹配。
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 Employee Number 可以是 1 个字符长,并且由于 min_gram 和 max_gram,我永远不能匹配。我也无法将 min_gram 设为 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 Multimatch 搜索中进行,而是使用 simple_query_string。
这似乎也部分起作用。
我的问题是如何部分匹配所有 3 个字段,请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 并返回文档 4 和 5? 另外,如果我必须搜索部分匹配的 706,我希望文档 2 返回,但是如果我必须使用“7061”进行搜索,我将只返回文档 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","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"
}
}
}
解决方法
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","_id": "5","_score": 0.8505551,"_source": {
"number": 1112,"fullName": "0723568521"
}
}
]
更新 1:
如果只需要搜索1
,将number
字段的数据类型由text
类型修改为keyword
类型,如上图索引映射.
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","_id": "1","_score": 1.3862942,"_source": {
"number": 1,"fullName": "Brenda eaton"
}
}
]
更新 2:
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","name_analyzer": {
"filter": [
"lowercase","tokenizer": "name_tokenizer"
},"number_analyzer": {
"filter": [
"lowercase","tokenizer": "number_tokenizer"
}
},"tokenizer": {
"name_tokenizer": {
"type": "ngram","min_gram": 3,"digit"
]
},"number_tokenizer": {
"type": "ngram","fields": {
"exact": {
"type": "text","analyzer": "number_analyzer"
}
}
},"analyzer": "name_analyzer"
}
},"analyzer": "standard"
}
}
}
}