使用ElasticSearch的准备工作

一、在Linux上安装ElasticSearch

1、docker下载elasticSearch和kibana的镜像

docker pull elasticsearch:7.4.2 存储和检索数据
docker pull kibana:7.4.2 可视化检索数据

2、创建目录

mkdir -p /mydata/elasticsearch/config 
mkdir -p /mydata/elasticsearch/data 
echo "http.host: 0.0.0.0" >> /mydata/elasticsearch/config/elasticsearch.yml

3、启动并挂载Elasticsearch

chmod -R 777 /mydata/elasticsearch/ 保证权限 
docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \ -e "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms64m -Xmx1024m" \ 
-v /mydata/elasticsearch/config/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml \ 
-v /mydata/elasticsearch/data:/usr/share/elasticsearch/data \ 
-v /mydata/elasticsearch/plugins:/usr/share/elasticsearch/plugins \ 
-d elasticsearch:7.4.2特别注意： -e ES_JAVA_OPTS="-Xms64m -Xmx256m" \ 
测试环境下，设置 ES 的初始内存和最大内存，否则导 致过大启动不了 ES

4、启动kibana

docker run --name kibana -e ELASTICSEARCH_HOSTS=http://虚拟机Linux的ip地址:9200 -p 5601:5601 \ -d kibana:7.4.2http://192.168.56.10:9200 一定改为自己虚拟机的地址

二、整合SpringBoot

1、导入Elasticsearch客户端的依赖

注意这里的7.4.2要写成和自己Linux的elasticsearch版本，如果不写这句那就用的是SpringBoot默认的版本，然后上面的linux中的elasticSearch和kibana版本最好保持一致性

1.87.4.22021.0.0
org.elasticsearch.clientelasticsearch-rest-high-level-client7.4.2

2、ElasticSearch的配置类

package com.saodai.saodaimall.search.config;import org.apache.http.HttpHost;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;/**
* ElasticSearch的配置类
**/
@Configuration
public class SaodaimallElasticSearchConfig {public static final RequestOptions COMMON_OPTIONS;static {RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();// builder.addHeader("Authorization", "Bearer " + TOKEN);// builder.setHttpAsyncResponseConsumerFactory(//         new HttpAsyncResponseConsumerFactory//                 .HeapBufferedResponseConsumerFactory(30 * 1024 * 1024 * 1024));COMMON_OPTIONS = builder.build();}@Beanpublic RestHighLevelClient esRestClient(){RestClientBuilder builder = null;builder = RestClient.builder(new HttpHost("192.168.241.128", 9200, "http"));RestHighLevelClient client = new RestHighLevelClient(builder);return  client;}}

本商城项目哪里用到了ElasticSearch

商品服务

商品服务的商品上架功能就是把上架的商品的所有信息存到ElasticSearch

1、在Vscode中发送上架请求

2、商品服务的SpuInfoController来处理请求/{spuId}/up

/*** 商品维护的spu管理的上架功能*/@PostMapping("/{spuId}/up")public R spuUp(@PathVariable("spuId") Long spuId){spuInfoService.up(spuId);return R.ok();}

分流程：

（1）查找出spuId对应的所有sku对象

（2）查出当前sku的所有可以被用来检索的规格属性

  //商品维护的spu管理的上架功能@Overridepublic void up(Long spuId) {//查找出spuId对应的所有sku对象List skuInfoEntities = skuInfoService.getSkusBySpuId(spuId);//查出当前sku的所有可以被用来检索的规格属性List productAttrValueEntities = attrValueService.baseAttrlistforspu(spuId);List attrIds = productAttrValueEntities.stream().map(attr -> {return attr.getAttrId();}).collect(Collectors.toList());
/*        //在批量规格参数attrids找到具有检索属性的规格参数的id集List sarchAttrIds = attrService.selectSearchAttrsIds(attrIds);*/Set idSet = new HashSet<>(attrIds);//找到符合要求的数据List attrsList = productAttrValueEntities.stream().filter(item -> {//下面返回的是Listreturn idSet.contains(item.getAttrId());}).map(item -> {//把上面过滤的List对象转为ListSkuEsModel.Attrs attrs = new SkuEsModel.Attrs();BeanUtils.copyProperties(item, attrs);return attrs;}).collect(Collectors.toList());//发送远程调用，库存服务查询是否有库存//获取skuIds集合List skuIdList = skuInfoEntities.stream().map(SkuInfoEntity::getSkuId).collect(Collectors.toList());Map stockMap =null;//进行异常处理（因为远程调用可能因为网络问题导致失败，进行异常处理就不会影响下面的代码运行）try{//远程调用仓库接口R r = wareFeignService.getSkusHasStock(skuIdList);TypeReference> typeReference = new TypeReference>() {};//获取返回R中的数据并封装成mapstockMap = r.getData(typeReference).stream().collect(Collectors.toMap(SkuHasStockVo::getSkuId, item -> item.getHasStock()));} catch (Exception e) {log.error("库存服务查询异常：原因{}",e);}Map finalStockMap =stockMap;List collect = skuInfoEntities.stream().map(skuInfoEntity -> {//组装需要的数据SkuEsModel skuEsModel = new SkuEsModel();BeanUtils.copyProperties(skuInfoEntity,skuEsModel);skuEsModel.setSkuPrice(skuInfoEntity.getPrice());skuEsModel.setSkuImg(skuInfoEntity.getSkuDefaultImg());//hasStock,hotScore//设置库存信息if (finalStockMap == null) {skuEsModel.setHasStock(true);} else {skuEsModel.setHasStock(finalStockMap.get(skuInfoEntity.getSkuId()));}//热度评分//查询品牌和分类的名称信息BrandEntity brandEntity = brandService.getById(skuInfoEntity.getBrandId());skuEsModel.setBrandName(brandEntity.getName());skuEsModel.setBrandId(brandEntity.getBrandId());skuEsModel.setBrandImg(brandEntity.getLogo());CategoryEntity categoryEntity = categoryService.getById(skuInfoEntity.getCatalogId());skuEsModel.setCatalogId(categoryEntity.getCatId());skuEsModel.setCatalogName(categoryEntity.getName());//设置可以检索的规格参数skuEsModel.setAttrs(attrsList);return skuEsModel;}).collect(Collectors.toList());//TODO 5、远程调用将数据发给es进行保存：saodaimall-searchR r = searchFeginService.productStatusUp(collect);if (r.getCode() == 0) {//远程调用成功//TODO 6、修改当前spu的状态System.out.println("远程调成功！");this.baseMapper.updaSpuStatus(spuId, ProductConstant.ProductStatusEnum.SPU_UP.getCode());} else {//远程调用失败//TODO 7、重复调用？接口幂等性:重试机制}}