elasticsearch的搜索补全提示

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

拼音分词器

下载 

要实现根据字母做补全，就必须对文档按照拼音分词，GitHub上有拼音分词插件

GitHub - medcl/elasticsearch-analysis-pinyin: This Pinyin Analysis plugin is used to do conversion between Chinese characters and Pinyin.

解压

解压到一个文件夹中去

上传 

上传到服务器中，elasticsearch的plugin目录  

重启 

重启elasticsearch  

docker restart es

 测试

POST /_analyze
{"text": "如家酒店还不错","analyzer": "pinyin"
}

返回拼音 

自定义分词器

默认的拼音分词器会将每个汉字单独分为拼音，而我们希望的是每个词条形成一组拼音，需要对拼音分词器做个性化定制，形成自定义分词器。

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

自定义分词器 

PUT /myanalyzer
{"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"}}}
}

• analyzer自定义分词器 
• my_analyzer分词器名称
• filter自定义tokenizer filter
• py过滤器名称
• filter.type过滤器类型，这里是pinyin
• name分词的字段

 测试

POST /myanalyzer/_analyze
{"text": ["华美达酒店还不错"],"analyzer": "my_analyzer"
}

结果

自动补全查询 

创建索引库

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"}}}
}

HotelDoc实体

import lombok.Data;
import lombok.NoArgsConstructor;import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;@Data
@NoArgsConstructor
public class HotelDoc {private Long id;private String name;private String address;private Integer price;private Integer score;private String brand;private String city;private String starName;private String business;private String location;private String pic;private Object distance;private Boolean isAD;private List<String> suggestion;public HotelDoc(Hotel hotel) {this.id = hotel.getId();this.name = hotel.getName();this.address = hotel.getAddress();this.price = hotel.getPrice();this.score = hotel.getScore();this.brand = hotel.getBrand();this.city = hotel.getCity();this.starName = hotel.getStarName();this.business = hotel.getBusiness();this.location = hotel.getLatitude() + ", " + hotel.getLongitude();this.pic = hotel.getPic();// 组装suggestionif(this.business.contains("/")){// business有多个值，需要切割String[] arr = this.business.split("/");// 添加元素this.suggestion = new ArrayList<>();this.suggestion.add(this.brand);Collections.addAll(this.suggestion, arr);}else {this.suggestion = Arrays.asList(this.brand, this.business);}}
}

导入数据

 @Testvoid testBulkRequest() throws IOException {// 批量查询酒店数据List<Hotel> hotels = hotelService.list();// 1.创建RequestBulkRequest request = new BulkRequest();// 2.准备参数，添加多个新增的Requestfor (Hotel hotel : hotels) {// 2.1.转换为文档类型HotelDocHotelDoc hotelDoc = new HotelDoc(hotel);// 2.2.创建新增文档的Request对象request.add(new IndexRequest("hotel").id(hotelDoc.getId().toString()).source(JSON.toJSONString(hotelDoc), XContentType.JSON));}// 3.发送请求client.bulk(request, RequestOptions.DEFAULT);}

controller类

import cn.itcast.hotel.pojo.PageResult;
import cn.itcast.hotel.pojo.RequestParams;
import cn.itcast.hotel.service.IHotelService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;import java.util.List;
import java.util.Map;@RestController
@RequestMapping("/hotel")
public class HotelController {@Autowiredprivate IHotelService hotelService;// 搜索酒店数据@GetMapping("suggestion")public List<String> getSuggestions(@RequestParam("key") String prefix) {return hotelService.getSuggestions(prefix);}
}

service类

import cn.itcast.hotel.mapper.HotelMapper;
import cn.itcast.hotel.pojo.Hotel;
import cn.itcast.hotel.pojo.HotelDoc;
import cn.itcast.hotel.pojo.PageResult;
import cn.itcast.hotel.pojo.RequestParams;
import cn.itcast.hotel.service.IHotelService;
import com.alibaba.fastjson.JSON;
import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.common.geo.GeoPoint;
import org.elasticsearch.common.unit.DistanceUnit;
import org.elasticsearch.index.query.BoolQueryBuilder;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.index.query.functionscore.FunctionScoreQueryBuilder;
import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilders;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.Aggregations;
import org.elasticsearch.search.aggregations.bucket.terms.Terms;
import org.elasticsearch.search.sort.SortBuilders;
import org.elasticsearch.search.sort.SortOrder;
import org.elasticsearch.search.suggest.Suggest;
import org.elasticsearch.search.suggest.SuggestBuilder;
import org.elasticsearch.search.suggest.SuggestBuilders;
import org.elasticsearch.search.suggest.completion.CompletionSuggestion;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;@Service
public class HotelService extends ServiceImpl<HotelMapper, Hotel> implements IHotelService {@Autowiredprivate RestHighLevelClient client;@Overridepublic List<String> getSuggestions(String prefix) {try {// 1.准备RequestSearchRequest request = new SearchRequest("hotel");// 2.准备DSLrequest.source().suggest(new SuggestBuilder().addSuggestion("suggestions",SuggestBuilders.completionSuggestion("suggestion").prefix(prefix).skipDuplicates(true).size(10)));// 3.发起请求SearchResponse response = client.search(request, RequestOptions.DEFAULT);// 4.解析结果Suggest suggest = response.getSuggest();// 4.1.根据补全查询名称，获取补全结果CompletionSuggestion suggestions = suggest.getSuggestion("suggestions");// 4.2.获取optionsList<CompletionSuggestion.Entry.Option> options = suggestions.getOptions();// 4.3.遍历List<String> list = new ArrayList<>(options.size());for (CompletionSuggestion.Entry.Option option : options) {String text = option.getText().toString();list.add(text);}return list;} catch (IOException e) {throw new RuntimeException(e);}}}

测试