拼音分词器
当用户在搜索框输入字符时，我们应该提示出与该字符有关的搜索项，如图：
要实现根据字母做补全，就必须对文档按照拼音分词。在GitHub上恰好有elasticsearch的拼音分词插件。地址：
GitHub - medcl/elasticsearch-analysis-pinyin: This Pinyin Analysis plugin is used to do conversion between Chinese characters and Pinyin.This Pinyin Analysis plugin is used to do conversion between Chinese characters and Pinyin. - GitHub - medcl/elasticsearch-analysis-pinyin: This Pinyin Analysis plugin is used to do conversion between Chinese characters and Pinyin.
https://github.com/medcl/elasticsearch-analysis-pinyin 安装方式与IK分词器一样，分三步：
解压 上传到虚拟机中，elasticsearch的plugin目录,一般就是"/var/lib/docker/volumes/es-plugins/_data"
重启
elasticsearch 测试
POST /_analyze
{
"text": ["如家酒店真不错"],
"analyzer": "pinyin"
}
自定义分词器
elasticsearch中分词器（analyzer）的组成包含三部分：
character filters：在tokenizer之前对文本进行处理。例如删除字符、替换字符
tokenizer：将文本按照一定的规则切割成词条（term）。例如keyword，就是不分词；还有ik_smart
tokenizer filter：将tokenizer输出的词条做进一步处理。例如大小写转换、同义词处理、拼音处理等
PUT /test
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "ik_max_word",
"filter": "py"
}
},
"filter": {
"py": {
"type": "pinyin",
"keep_full_pinyin": false,
"keep_joined_full_pinyin": true,
"keep_original": true,
"limit_first_letter_length": 16,
"remove_duplicated_term": true,
"none_chinese_pinyin_tokenize": false
}
}
}
}
}
拼音分词器适合在创建倒排索引的时候使用，但不能在搜索的时候使用。为了避免搜索到同音字，搜索时不要使用拼音分词器
因此字段在创建倒排索引时应该用my_analyzer分词器；字段在搜索时应该使用ik_smart分词器：
PUT /test
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "ik_max_word",
"filter": "py"
}
},
"filter": {
"py": {
"type": "pinyin",
"keep_full_pinyin": false,
"keep_joined_full_pinyin": true,
"keep_original": true,
"limit_first_letter_length": 16,
"remove_duplicated_term": true,
"none_chinese_pinyin_tokenize": false
}
}
}
}
, "mappings": {
"properties": {
"name":{
"type": "text",
"analyzer": "my_analyzer",
"search_analyzer": "ik_smart"
}
}
}
}
自动补全查询
elasticsearch提供了Completion Suggester查询来实现自动补全功能。这个查询会匹配以用户输入内容开头的词条并返回。为了提高补全查询的效率，对于文档中字段的类型有一些约束： 参与补全查询的字段必须是completion类型。 字段的内容一般是用来补全的多个词条形成的数组。
# 自动补全的索引库
PUT test1
{
"mappings": {
"properties": {
"title":{
"type": "completion"
}
}
}
}
# 示例数据
POST test1/_doc
{
"title": ["Sony", "WH-1000XM3"]
}
POST test1/_doc
{
"title": ["SK-II", "PITERA"]
}
POST test1/_doc
{
"title": ["Nintendo", "switch"]
# 自动补全查询
POST /test1/_search
{
"suggest": {
"title_suggest": {
"text": "s",
"completion": {
"field": "title",
"skip_duplicates": true,
"size": 10
}
}
}
}
自动补全对字段的要求：
类型是completion类型
字段值是多词条的数组
案例——实现hotel索引库的自动补全、拼音搜索功能
修改hotel索引库结构，设置自定义拼音分词器
修改索引库的name、all字段，使用自定义分词器
索引库添加一个新字段suggestion，类型为completion类型，使用自定义的分词器
# 酒店数据索引库
PUT /hotel
{
"settings": {
"analysis": {
"analyzer": {
"text_anlyzer": {
"tokenizer": "ik_max_word",
"filter": "py"
},
"completion_analyzer": {
"tokenizer": "keyword",
"filter": "py"
}
},
"filter": {
"py": {
"type": "pinyin",
"keep_full_pinyin": false,
"keep_joined_full_pinyin": true,
"keep_original": true,
"limit_first_letter_length": 16,
"remove_duplicated_term": true,
"none_chinese_pinyin_tokenize": false
}
}
}
},
"mappings": {
"properties": {
"id":{
"type": "keyword"
},
"name":{
"type": "text",
"analyzer": "text_anlyzer",
"search_analyzer": "ik_smart",
"copy_to": "all"
},
"address":{
"type": "keyword",
"index": false
},
"price":{
"type": "integer"
},
"score":{
"type": "integer"
},
"brand":{
"type": "keyword",
"copy_to": "all"
},
"city":{
"type": "keyword"
},
"starName":{
"type": "keyword"
},
"business":{
"type": "keyword",
"copy_to": "all"
},
"location":{
"type": "geo_point"
},
"pic":{
"type": "keyword",
"index": false
},
"all":{
"type": "text",
"analyzer": "text_anlyzer",
"search_analyzer": "ik_smart"
},
"suggestion":{
"type": "completion",
"analyzer": "completion_analyzer"
}
}
}
}
给HotelDoc类添加suggestion字段，内容包含brand、business
重新导入数据到hotel库
name、all是可分词的，自动补全的brand、business是不可分词的，要使用不同的分词器组合
测试
GET /hotel/_search
{
"suggest": {
"suggestions": {
"text": "sh",
"completion": {
"field": "suggestion",
"skip_duplicates":true,
"size":10
}
}
}
}
实现酒店搜索框自动补全
后端控制层接口
// 搜索自动补全
@GetMapping("/suggestion")
public List<String> suggestion(String key){
return hotelService.suggestion(key);
}业务层代码
@Override
public List<String> suggestion(String key) {
List<String> list=new ArrayList<>();
SearchRequest request=new SearchRequest("hotel");
request.source().suggest(new SuggestBuilder().addSuggestion(
"mySuggestion",
SuggestBuilders.completionSuggestion("suggestion")
.prefix(key)
.skipDuplicates(true)
.size(10)
));
SearchResponse response = null;
try {
response = restHighLevelClient.search(request, RequestOptions.DEFAULT);
} catch (IOException e) {
throw new RuntimeException(e);
}
Suggest suggest = response.getSuggest();
CompletionSuggestion mySuggestion = suggest.getSuggestion("mySuggestion");
for (CompletionSuggestion.Entry.Option option : mySuggestion.getOptions()) {
String s = option.getText().toString();
list.add(s);
}
return list;
}
