本文内容参考自: https://gitee.com/macrozheng/mall
如有侵权,请联系作者删除
项目中所使用代码已开源 : https://gitee.com/szwei/elasticsearch
项目中使用依赖版本:
依赖 | 版本 |
---|---|
spring-boot | 2.3.1.RELEASE |
elasticsearch | 7.9.3-windows-x86_64 |
kibana | 7.8.0-windows-x86_64 |
一、介绍
回忆时光
许多年前,一个刚结婚的名叫 Shay Banon 的失业开发者,跟着他的妻子去了伦敦,他的妻子在那里学习厨师。 在寻找一个赚钱的工作的时候,为了给他的妻子做一个食谱搜索引擎,他开始使用 Lucene 的一个早期版本。
直接使用 Lucene 是很难的,因此 Shay 开始做一个抽象层,Java 开发者使用它可以很简单的给他们的程序添加搜索功能。 他发布了他的第一个开源项目 Compass。
后来 Shay 获得了一份工作,主要是高性能,分布式环境下的内存数据网格。这个对于高性能,实时,分布式搜索引擎的需求尤为突出, 他决定重写 Compass,把它变为一个独立的服务并取名 Elasticsearch。
第一个公开版本在2010年2月发布,从此以后,Elasticsearch 已经成为了 Github 上最活跃的项目之一,他拥有超过300名 contributors(目前736名 contributors )。 一家公司已经开始围绕 Elasticsearch 提供商业服务,并开发新的特性,但是,Elasticsearch 将永远开源并对所有人可用。
据说,Shay 的妻子还在等着她的食谱搜索引擎…
不得不说,Elasticsearch 的作者是一个很幽默的人,大概这就是大佬普遍的特性吧,随手一写,就可以开发出足以影响世界的代码。
ES 的特性:
- 一个分布式的实时文档存储,每个字段 可以被索引与搜索
- 一个分布式实时分析搜索引擎
- 能胜任上百个服务节点的扩展,并支持 PB 级别的结构化或者非结构化数据
二、安装
注意: Elasticsearch 的版本和 springboot 版本 和 Spring Data Elasticsearch 版本需要对应,否则会有各种各样未知错误
Spring Data Release Train | Spring Data Elasticsearch | Elasticsearch | Spring Boot |
---|---|---|---|
2020.0.0[1] | 4.1.x[1] | 7.9.3 | 2.4.x[1] |
Neumann | 4.0.x | 7.6.2 | 2.3.x |
Moore | 3.2.x | 6.8.12 | 2.2.x |
Lovelace | 3.1.x | 6.2.2 | 2.1.x |
Kay[2] | 3.0.x[2] | 5.5.0 | 2.0.x[2] |
Ingalls[2] | 2.1.x[2] | 2.4.0 | 1.5.x[2] |
1. 安装 Elasticsearch
下载地址:
官网下载地址(不推荐,较慢)
华为镜像加速(推荐,速度 嗖嗖嗖~~)
-
首先通过官网下载 需要的版本,笔者下载的是Windows 版本的,等待下载完成
-
Windows 版本下载好之后,解压文件夹,双击 bin 目录下的 elasticsearch.bat 启动 ES
-
等待启动完成后,访问 http://localhost:9200
看到这个返回结果,就是启动完成了。
安装中文分词器,elasticsearch-analysis-ik
下载地址:https://github.com/medcl/elasticsearch-analysis-ik/releases
下载相对应的版本后,解压,在 elasticsearch 的安装目录,plugins 目录下新建 ik 文件夹,将解压后的文件放到里面,重启 ES
那么现在我们还差一个可视化界面
2.安装 kibana
同样的国外的较慢,直接华为下载 => 华为kibana镜像
下载后,解压
bin 目录,kibana.bat 启动,kibana 会自动的连接我们刚刚启动的 ES
看到这个页面,说明启动完成
访问 http://localhost:5601/ 即可看到页面
我们选择 左侧的 Dev Tools 进入命令输入页面
三、简单使用
索引操作
-
列出所有索引
GET /_cat/indices
-
创建新的索引
PUT /mygoods
-
查询某个索引信息
GET /mygoods
-
删除某个索引
DELETE /mygoods
文档操作
-
添加文档
PUT /mygoods2/_doc/188 { "id":"188", "name":"添加索引文档", "price":"188", "keywords":"添加索引文档", "subtitle":"添加索引文档", "brandId" : 7, "categoryId" : 5 }
-
删除文档
DELETE /mygoods/_doc/188
-
简单查询
- 查询单个
GET /mygoods/_doc/188
- 查询所有
GET /mygoods/_search
-
表达式查询
-
查询所有
GET /mygoods/_search { "query" : { "match_all": {} } }
-
查询条件查询
GET /mygoods/_search { "query" : { "match" : { "name" : "添加索引" } } }
-
四、Springboot 中使用
### 1. 引入依赖
查询Springboot 和 Spring Data Elasticsearch 版本,下载对应的 ES , 引入依赖
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>
2. 新建实体类
package cn.couldme.es.entity;
import com.baomidou.mybatisplus.annotation.TableId;
import com.baomidou.mybatisplus.annotation.TableName;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.springframework.data.elasticsearch.annotations.Document;
import org.springframework.data.elasticsearch.annotations.Field;
import org.springframework.data.elasticsearch.annotations.FieldType;
import java.io.Serializable;
/**
* 商品表(Goods)表实体类
*
* @Author szwei
* @Date 2020-12-06 21:45:37
*/
@Data
@TableName
@EqualsAndHashCode(callSuper = false)
@Document(indexName = "mygoods",type = "_doc", shards = 1,replicas = 0)
public class Goods implements Serializable {
private static final long serialVersionUID = -1;
/**
* 自增 => @TableId(type = IdType.AUTO)
*/
@TableId
private Long id;
/*名称*/
@Field(analyzer = "ik_max_word",type = FieldType.Text)
private String name;
/*价格*/
private Double price;
/*关键词*/
@Field(analyzer = "ik_max_word",type = FieldType.Text)
private String keywords;
/*标题*/
@Field(analyzer = "ik_max_word",type = FieldType.Text)
private String subtitle;
/*品牌商*/
@Field(type=FieldType.Keyword)
private Long brandId;
/*分类名称*/
@Field(type=FieldType.Keyword)
private Long categoryId;
}
3. 继承 ElasticsearchRepository
package cn.couldme.es.repository;
import cn.couldme.es.entity.Goods;
import org.springframework.data.domain.Page;
import org.springframework.data.domain.Pageable;
import org.springframework.data.elasticsearch.repository.ElasticsearchRepository;
/**
* 搜索商品ES操作类
* Created by macro on 2018/6/19.
*/
public interface EsGoodsRepository extends ElasticsearchRepository<Goods, Long> {
/**
* 搜索查询
*
* @param name 商品名称
* @param keywords 商品关键字
* @param page 分页信息
*/
Page<Goods> findByNameOrKeywords(String name, String keywords, Pageable page);
}
4. EsGoodstService
package cn.couldme.es.service;
import cn.couldme.es.entity.Goods;
import org.springframework.data.domain.Page;
import java.util.List;
/**
* 搜索商品管理Service
* Created by macro on 2018/6/19.
*/
public interface EsGoodstService {
/**
* 从数据库中导入所有商品到ES
*/
int importAll();
/**
* 根据id删除商品
*/
void delete(Long id);
/**
* 根据id创建商品
*/
Goods create(Long id);
/**
* 批量添加商品
*/
void batchCreate(List<Long> ids);
/**
* 批量删除商品
*/
void batchDelete(List<Long> ids);
/**
* 根据关键字搜索名称或者副标题
*/
Page<Goods> search(String keyword, Integer pageNum, Integer pageSize);
/**
* 根据关键字搜索名称或者副标题复合查询
*/
Page<Goods> search(String keyword, Long brandId, Long categoryId, Integer pageNum, Integer pageSize, Integer sort);
/**
* 根据商品id推荐相关商品
*/
Page<Goods> recommend(Long id, Integer pageNum, Integer pageSize);
}
5. EsGoodstServiceImpl
@Slf4j
@Service
public class EsGoodstServiceImpl implements EsGoodstService {
@Autowired
private GoodsMapper goodsMapper;
@Autowired
private EsGoodsRepository productRepository;
@Autowired
private ElasticsearchRestTemplate elasticsearchRestTemplate;
@Override
public int importAll() {
List<Goods> GoodsList = goodsMapper.selectList(null);
Iterable<Goods> GoodsIterable = productRepository.saveAll(GoodsList);
Iterator<Goods> iterator = GoodsIterable.iterator();
int result = 0;
while (iterator.hasNext()) {
result++;
iterator.next();
}
return result;
}
@Override
public void delete(Long id) {
productRepository.deleteById(id);
}
@Override
public Goods create(Long id) {
Goods result = null;
Goods goods = goodsMapper.selectById(id);
result = productRepository.save(goods);
return result;
}
@Override
public void batchCreate(List<Long> ids) {
if (!CollectionUtils.isEmpty(ids)) {
List<Goods> spxxList = goodsMapper.selectBatchIds(ids);
productRepository.saveAll(spxxList);
}
}
@Override
public void batchDelete(List<Long> ids) {
if (!CollectionUtils.isEmpty(ids)) {
List<Goods> GoodsList = new ArrayList<>();
for (Long id : ids) {
Goods goods = new Goods();
goods.setId(id);
GoodsList.add(goods);
}
productRepository.deleteAll(GoodsList);
}
}
@Override
public Page<Goods> search(String keyword, Integer pageNum, Integer pageSize) {
Pageable pageable = PageRequest.of(pageNum, pageSize);
return productRepository.findByNameOrKeywords(keyword, keyword, pageable);
}
@Override
public Page<Goods> search(String keyword, Long brandId, Long categoryId, Integer pageNum, Integer pageSize, Integer sort) {
Pageable pageable = PageRequest.of(pageNum, pageSize);
NativeSearchQueryBuilder nativeSearchQueryBuilder = new NativeSearchQueryBuilder();
//分页
nativeSearchQueryBuilder.withPageable(pageable);
nativeSearchQueryBuilder.withSort(SortBuilders.scoreSort());
//过滤
if (brandId != null || categoryId != null) {
BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
if (brandId != null) {
boolQueryBuilder.must(QueryBuilders.termQuery("brandId", brandId));
}
if (categoryId != null) {
boolQueryBuilder.must(QueryBuilders.termQuery("categoryId", categoryId));
}
nativeSearchQueryBuilder.withFilter(boolQueryBuilder);
}
//搜索
if (StringUtils.isEmpty(keyword)) {
nativeSearchQueryBuilder.withQuery(QueryBuilders.matchAllQuery());
} else {
List<FunctionScoreQueryBuilder.FilterFunctionBuilder> filterFunctionBuilders = new ArrayList<>();
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("name", keyword),
ScoreFunctionBuilders.weightFactorFunction(10)));
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("subTitle", keyword),
ScoreFunctionBuilders.weightFactorFunction(5)));
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("keywords", keyword),
ScoreFunctionBuilders.weightFactorFunction(2)));
FunctionScoreQueryBuilder.FilterFunctionBuilder[] builders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[filterFunctionBuilders.size()];
filterFunctionBuilders.toArray(builders);
MultiMatchQueryBuilder matchQuery = QueryBuilders.multiMatchQuery(keyword, "name", "subTitle", "keywords");
FunctionScoreQueryBuilder functionScoreQueryBuilder = QueryBuilders.functionScoreQuery(matchQuery,builders)
.scoreMode(FunctionScoreQuery.ScoreMode.SUM)
.setMinScore(2);
nativeSearchQueryBuilder.withQuery(functionScoreQueryBuilder);
}
//排序
if(sort==1){
//按新品从新到旧
nativeSearchQueryBuilder.withSort(SortBuilders.fieldSort("id").order(SortOrder.DESC));
}else if(sort==2){
//按销量从高到低
nativeSearchQueryBuilder.withSort(SortBuilders.fieldSort("sale").order(SortOrder.DESC));
}else if(sort==3){
//按价格从低到高
nativeSearchQueryBuilder.withSort(SortBuilders.fieldSort("price").order(SortOrder.ASC));
}else if(sort==4){
//按价格从高到低
nativeSearchQueryBuilder.withSort(SortBuilders.fieldSort("price").order(SortOrder.DESC));
}else{
//按相关度
nativeSearchQueryBuilder.withSort(SortBuilders.scoreSort().order(SortOrder.DESC));
}
nativeSearchQueryBuilder.withSort(SortBuilders.scoreSort().order(SortOrder.DESC));
NativeSearchQuery searchQuery = nativeSearchQueryBuilder.build();
log.info("DSL:{}", searchQuery.getQuery().toString());
SearchHits<Goods> searchHits = elasticsearchRestTemplate.search(searchQuery, Goods.class);
if(searchHits.getTotalHits()<=0){
return new PageImpl<>(new ArrayList<>(),pageable,0);
}
List<Goods> searchProductList = searchHits.stream().map(SearchHit::getContent).collect(Collectors.toList());
return new PageImpl<>(searchProductList,pageable,searchHits.getTotalHits());
}
@Override
public Page<Goods> recommend(Long id, Integer pageNum, Integer pageSize) {
Pageable pageable = PageRequest.of(pageNum, pageSize);
Goods goods = goodsMapper.selectById(id);
if (Objects.nonNull(goods)) {
String keyword = goods.getName();
Long brandId = goods.getBrandId();
Long categoryId = goods.getCategoryId();
//根据商品标题、品牌、分类进行搜索
List<FunctionScoreQueryBuilder.FilterFunctionBuilder> filterFunctionBuilders = new ArrayList<>();
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("name", keyword),
ScoreFunctionBuilders.weightFactorFunction(8)));
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("subTitle", keyword),
ScoreFunctionBuilders.weightFactorFunction(2)));
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("keywords", keyword),
ScoreFunctionBuilders.weightFactorFunction(2)));
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("brandId", brandId),
ScoreFunctionBuilders.weightFactorFunction(5)));
filterFunctionBuilders.add(new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchQuery("productCategoryId", categoryId),
ScoreFunctionBuilders.weightFactorFunction(3)));
FunctionScoreQueryBuilder.FilterFunctionBuilder[] builders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[filterFunctionBuilders.size()];
filterFunctionBuilders.toArray(builders);
//设置查询条件
QueryBuilder queryBuilder = QueryBuilders.boolQuery()
.must(QueryBuilders.multiMatchQuery(keyword,"name","subTitle","keywords"));
// .should(QueryBuilders.matchQuery("brandId", brandId))
// .should(QueryBuilders.matchQuery("categoryId", categoryId));
FunctionScoreQueryBuilder functionScoreQueryBuilder = QueryBuilders.functionScoreQuery(queryBuilder, builders)
.scoreMode(FunctionScoreQuery.ScoreMode.SUM)
.setMinScore(2);
//用于过滤掉相同的商品
BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();
boolQueryBuilder.mustNot(QueryBuilders.termQuery("id",id));
//构建查询条件
NativeSearchQueryBuilder builder = new NativeSearchQueryBuilder();
builder.withQuery(functionScoreQueryBuilder);
builder.withFilter(boolQueryBuilder);
builder.withPageable(pageable);
NativeSearchQuery searchQuery = builder.build();
log.info("DSL:{}", searchQuery.getQuery().toString());
SearchHits<Goods> searchHits = elasticsearchRestTemplate.search(searchQuery, Goods.class);
if(searchHits.getTotalHits()<=0){
return new PageImpl<>(new ArrayList<>(),pageable,0);
}
List<Goods> searchProductList = searchHits.stream().map(SearchHit::getContent).collect(Collectors.toList());
return new PageImpl<>(searchProductList,pageable,searchHits.getTotalHits());
}
return new PageImpl<>(new ArrayList<>());
}
}
6. 事件通知类
添加商品事件
package cn.couldme.core.event;
import lombok.Getter;
import org.springframework.context.ApplicationEvent;
import java.util.List;
/**
* @Author: szwei
* @Date: 2020-12-06 23:03
**/
@Getter
public class GoodsAddEvent extends ApplicationEvent {
private List<Long> ids;
public GoodsAddEvent(Object source, List<Long> ids) {
super(source);
this.ids = ids;
}
}
删除商品事件
package cn.couldme.core.event;
import lombok.Getter;
import org.springframework.context.ApplicationEvent;
import java.util.List;
/**
* @Author: szwei
* @Date: 2020-12-06 23:03
**/
@Getter
public class GoodsDelEvent extends ApplicationEvent {
private List<Long> ids;
public GoodsDelEvent(Object source, List<Long> ids) {
super(source);
this.ids = ids;
}
}
7. 监听器
package cn.couldme.es.eventListener;
import cn.couldme.core.event.GoodsAddEvent;
import cn.couldme.core.event.GoodsDelEvent;
import cn.couldme.es.service.EsGoodstService;
import lombok.AllArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.springframework.context.event.EventListener;
import org.springframework.scheduling.annotation.Async;
import org.springframework.stereotype.Component;
import java.util.List;
/**
* @Author: szwei
* @Date: 2020-12-06 23:22
**/
@Slf4j
@Component
@AllArgsConstructor
public class GoodsListener {
private final EsGoodstService esGoodstService;
@EventListener
@Async
public void goodsAddEvent(GoodsAddEvent goodsAddEvent){
List<Long> ids = goodsAddEvent.getIds();
log.debug("往es添加商品: ids => {}",ids);
esGoodstService.batchCreate(ids);
}
@EventListener
@Async
public void goodsDelEvent(GoodsDelEvent goodsDelEvent){
List<Long> ids = goodsDelEvent.getIds();
log.debug("往es删除商品: ids => {}",ids);
esGoodstService.batchDelete(ids);
}
}
8. 配置异步
在启动类添加 @EnableAsync
注解
9. 效果图
五、注意事项
1. 启动项目报错
问题出现原因:
elasticsearch和Redis都需要Netty作为NIO框架,在Redis初始化时已经对Netty进行了初始化处理器数量,当ES再次尝试初始化Netty处理器数量时,Netty就会对此进行保护措施,抛出异常
解决方案:
在启动类上添加:
System.setProperty("es.set.netty.runtime.available.processors", "false");
不让es 的 netty 再次去设置
2. 查询出来的数据没有按照匹配度排序
在导入数据到 es 的时候需要指定 该字段的使用的分词器
例如:
关键词(不会进行分词)@Field(type=FieldType.Keyword)
最大粒度分词@Field(analyzer = "ik_max_word",type = FieldType.Text)