elasticsearch整合SpringCloud的步骤

发布时间：2020-06-22 22:32:46 作者：元一
来源：亿速云阅读：594

这篇文章将为大家详细讲解有关elasticsearch整合SpringCloud的步骤，文章内容质量较高，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。

ElasticSearch是一个基于Lucene的搜索服务器。它提供了一个分布式多用户能力的全文搜索引擎，基于RESTful web接口。Elasticsearch是用Java开发的，并作为Apache许可条款下的开放源码发布，是当前流行的企业级搜索引擎。设计用于云计算中，能够达到实时搜索，稳定，可靠，快速，安装使用方便。

注意：适用于springboot或者springcloud框架

1.首先下载相关文件
2.然后需要去启动相关的启动文件

elasticsearch整合SpringCloud的步骤

3、导入相关jar包（如果有相关的依赖包不需要导入）以及配置配置文件，并且写一个dao接口继承一个类，在启动类上标注地址

<dependency>
 <groupId>org.projectlombok</groupId>
 <artifactId>lombok</artifactId>
</dependency>

<dependency>
 <groupId>org.springframework.boot</groupId>
 <artifactId>spring-boot-starter-web</artifactId>
</dependency>

<dependency>
 <groupId>org.springframework.boot</groupId>
 <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>

## ElasticSearch - start
#开启 Elasticsearch 仓库(默认值:true)
spring.data.elasticsearch.repositories.enabled=true
spring.data.elasticsearch.cluster-nodes=localhost:9300
spring.data.elasticsearch.cluster-name=myes

Shop：是下面创建的实体类名称（不能写错），String（传参时的类型，我这里id也给的String，因为integer报错）

import com.jk.user.model.Shop;
import org.springframework.data.elasticsearch.repository.ElasticsearchRepository;
public interface EsDao extends ElasticsearchRepository<Shop,String> {
}

启动类上加上注解，后面跟的是dao的包名

@EnableElasticsearchRepositories(basePackages = "com.jk.web.dao")

4.实体类
indexName相当于数据库名， type 相当于表名，必须加上id，type 类型，analyzer 分词器名称（ik分词）

@Document(indexName = "zth",type = "t_shangpin")
public class Shop implements Serializable {
 private static final long serialVersionUID = 2006762641515872124L;

 private String id;

 @Field(type = FieldType.Text, analyzer = "ik_max_word")
 //商品名称
 private String shopname;

 //优惠价格
 private Long reducedprice;
}

5.然后写controller层（这里直接注入dao接口），这里新增我选的是对象循环赋值，其实可以直接赋集合（参考）

//elasticsearch 生成表
 // @RequestMapping("el")
 // @ResponseBody
 // public void el(){

 // List<ElasticsearchBean> list=shoppService.queryelasticsearch();
 // for (ElasticsearchBean ss: list) {
 //  ss.setScrenicName(ss.getScrenicName()+""+ss.getHotelName());
 // }
 // elasticsearch.saveAll(list);

 // }

@Autowired
private EsDao esDao;
// 查询时需要
@Autowired
private ElasticsearchTemplate elasticsearchTemplate ;

//更新es服务器数据
@RequestMapping("addEs")
public boolean addShopEs() {
 List<TShangpin> list = webUserService.queryShouye();//先去后台查出数据在赋值
 Shop shop = new Shop();
 try {
 for (int i = 0; i < list.size(); i++) {
  shop.setId(list.get(i).getShopid().toString());
  shop.setShopname(list.get(i).getShopname());
  esDao.save(shop);
 }
 return true;
 } catch (Exception e) {
 e.printStackTrace();
 return false;
 }
}
//es搜索商品
@RequestMapping("queryShop")
public List ellist(String name, HttpSession session, Integer page, Integer rows){
 if (name==null||"".equals(name)){
 name = session.getAttribute("name").toString();
 }
 page=1;
 rows=3;
 HashMap<String, Object> resultMap = new HashMap<>();
 //创建一个要搜索的索引库
 SearchRequestBuilder searchRequestBuilder = elasticsearchTemplate.getClient().prepareSearch("zth").setTypes("t_shangpin");


 //创建组合查询
 BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();

 if (name!=null && !"".equals(name)){
 boolQueryBuilder.should(QueryBuilders.matchQuery("shopname",name));
 }
 //设置查询的类型
 searchRequestBuilder.setSearchType(SearchType.DFS_QUERY_THEN_FETCH);
 searchRequestBuilder.setQuery(boolQueryBuilder);

 //分页
 searchRequestBuilder.setFrom((page-1)*rows);
 searchRequestBuilder.setSize(rows);
 //设置高亮字段
 HighlightBuilder highlightBuilder = new HighlightBuilder();
 highlightBuilder.field("shopname")
  .preTags("<font color='red'>")
  .postTags("</font>");
 searchRequestBuilder.highlighter(highlightBuilder);

 //直接搜索返回响应数据 （json）
 SearchResponse searchResponse = searchRequestBuilder.get();
 SearchHits hits = searchResponse.getHits();
 //获取总条数
 long totalHits = hits.getTotalHits();
 resultMap.put("total",totalHits);

 ArrayList<Map<String,Object>> list = new ArrayList<>();
 //获取Hits中json对象数据
 SearchHit[] hits1 = hits.getHits();
 for (int i=0;i<hits1.length;i++){
 //获取Map对象
 Map<String, Object> sourceAsMap = hits1[i].getSourceAsMap();
 //获取高亮字段
 Map<String, HighlightField> highlightFields = hits1[i].getHighlightFields();
 //！！如果有高亮字段就取出赋给上面sourceAsMap中原有的名字给他替换掉！！
 if (name!=null && !"".equals(name)){
  sourceAsMap.put("shopname",highlightFields.get("shopname").getFragments()[0].toString());
 }
 list.add(sourceAsMap);
 }

 return list;

}

6.最后如果无法搜索，可能是需要加一个ik的json文件，因为在实体类中规定了是ik分词器，如果不规定当它存进去后其实是还没有分词。

film-mapping.json

{ 
"film": 
{ 
"_all":
{
"enabled": true 
},
"properties":
{ "id": 
{ 
"type": "integer"
},"name":
{
"type": "text", "analyzer": "ikSearchAnalyzer", "search_analyzer": "ikSearchAnalyzer", "fields":
{ "pinyin": { 
"type": "text", "analyzer": "pinyinSimpleIndexAnalyzer", "search_analyzer": "pinyinSimpleIndexAnalyzer" 
} } },
"nameOri": { "type": "text" 
},"publishDate": 
{ "type": "text" },"type": 
{ "type": "text"
},"language": 
{ "type": "text" 
},"fileDuration":
{ "type": "text" 
},"director":
{ "type": "text",
 "index": "true", "analyzer": "ikSearchAnalyzer"
 },"created": 
 {
 "type": "date", "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis" 
 } } } }

film-setting.json

elasticsearch整合SpringCloud的步骤

{ "index": { "analysis": 
{ "filter":
{ "edge_ngram_filter": 
{ "type": "edge_ngram", "min_gram": 1, "max_gram": 50 
},"pinyin_simple_filter": 
{
 "type": "pinyin", "first_letter": "prefix", "padding_char": " ", "limit_first_letter_length": 50, "lowercase": true
 } 
},"char_filter": 
 { 
"tsconvert": { "type": "stconvert", "convert_type": "t2s" 
 } 
},"analyzer":
 { "ikSearchAnalyzer": 
 { "type": "custom", "tokenizer": "ik_max_word", "char_filter": [ "tsconvert" ]
 },"pinyinSimpleIndexAnalyzer": 
 { "tokenizer": "keyword", "filter": [ "pinyin_simple_filter", "edge_ngram_filter", "lowercase" ] 
 } } } } }

elasticsearch整合SpringCloud的步骤

以上就是elasticsearch整合SpringCloud的步骤，看完之后是否有所收获呢？如果想了解更多相关内容，欢迎关注亿速云行业资讯，感谢各位的阅读。

elasticsearch整合SpringCloud的步骤

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