ElasticSearch-整合SpringBoot

操作ES只需要发送请求就可以，相对来说整合起来比较简单

一、9300：TCP

spring-data-elasticsearch:transport-api.jar；
springboot 版本不同， transport-api.jar 不同，不能适配es 版本
7.x 已经不建议使用，8 以后就要废弃

二、9200：HTTP

1.JestClient：非官方，更新慢
2.RestTemplate：模拟发HTTP 请求，ES 很多操作需要自己封装，麻烦
3.HttpClient：同上
4.Elasticsearch-Rest-Client：官方RestClient，封装了ES 操作，API 层次分明，上手简单（最终选择）

1.导入依赖

版本要跟自己的ES版本对应
>
     >org.elasticsearch.client>
     >elasticsearch-rest-high-level-client>
     >7.4.2>
>

导入后注意依赖版本，因为是springboot项目会规定ES版

需要规定ES版本

>
        >1.8>
        >7.4.2>
    >

2.编写配置类

使用spring data elasticsearch 也需要配置连接信息等

编写配置给容器中注入一个RestHighLevelClient 对象

@Configuration
public class XmallElasticSearchConfig {


    //在请求ES时携带请求头信息
    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();
    }


    @Bean
    public RestHighLevelClient esRestClient(){
        //如果是多个ES 可以配置多个Host
        RestClientBuilder builder = RestClient.builder(new HttpHost("101.43.122.84", 9200,
                "http"));
        return new RestHighLevelClient(builder);
    }

}

3.编写测试类

@RunWith(SpringRunner.class)
@SpringBootTest
public class XmallSearchApplicationTests {

    //注入ES对象
    @Autowired
    RestHighLevelClient client;

    /**
     * 测试往ES中存储数据
     */
    @Test
    public void indexData() throws IOException {
        IndexRequest indexRequest = new IndexRequest("users");
        indexRequest.id("1"); //数据的id

        /*
         * 要保存的数据
         */
        user user = new user();
        user.setUsername("辛鹏");
        user.setAge(18);
        user.setGender("男");
        String s = JSON.toJSONString(user);
        indexRequest.source(s, XContentType.JSON);

        //同步的方式保存数据
        IndexResponse index = client.index(indexRequest, XmallElasticSearchConfig.COMMON_OPTIONS);


        //响应数据
        System.out.println(index);
    }

    @Data
    class user{
        private String username;
        private String gender;
        private Integer age;

    }



    @Test
    public void contextLoads() {
        System.out.println(client);
    }

}

4.根据业务来新增Mapping（映射关系）

ES区别于关系型数据库的地方，宽表设计，不能去考虑数 据库范式。

• index： 默认true，如果为false，表示该字段不会被索引，但是检索结果里面有，但字段本身不能 当做检索条件。**

• doc_values：
  默认true，设置为false，表示不可以做排序、聚合以及脚本操作，这样更节省磁盘空间。
  还可以通过设定doc_values 为true，index 为false 来让字段不能被搜索但可以用于排序、聚
  合以及脚本操作：**

PUT product
{
  "mappings": {
    "properties": {
      "skuId": {
        "type": "long"
      },
      "spuId": {
        "type": "keyword"  //精确匹配
      },
      "skuTitle": {
        "type": "text",
        "analyzer": "ik_smart"
      },
      "skuPrice": {
        "type": "keyword"
      },
      "skuImg": {
        "type": "keyword",
        "index": false,      //不被检索
        "doc_values": false  
      },
      "saleCount": {
        "type": "long"
      },
      "hasStock": {
        "type": "boolean"
      },
      "hotScore": {
        "type": "long"
      },
      "brandId": {
        "type": "long"
      },
      "catalogId": {
        "type": "long"
      },
      "brandName": {
        "type": "keyword",
        "index": false,
        "doc_values": false
      },
      "brandImg": {
        "type": "keyword",
        "index": false,
        "doc_values": false
      },
      "catalogName": {
        "type": "keyword",
        "index": false,
        "doc_values": false
      },
      "attrs": {
        "type": "nested",
        "properties": {
          "attrId": {
            "type": "long"
          },
          "attrName": {
            "type": "keyword",
            "index": false,
            "doc_values": false
          },
          "attrValue": {
            "type": "keyword"
          }
        }
      }
    }
  }
}

nested数据类型

如果存储的是数组的话，此数据会被扁平化处理，检索会出现误差，使用nested类型可以使用嵌入式数据，让数据不被扁平化处理

检索实现

检索条件分析

1. 全文检索：skuTitle-》keyword
2. 排序：saleCount（销量）、hotScore（热度分）、skuPrice（价格）
3. 过滤：hasStock、skuPrice区间、brandId、catalog3Id、attrs
4. 聚合：attrs

完整查询条件
完整查询条件可能会有很多
keyword=小米
&sort=saleCount_desc/asc
&hasStock=0/1
&skuPrice=400_1900
&brandId=1
&catalog3Id=1
&attrs=1_3G:4G:5G
&attrs=2_骁龙845
&attrs=4_高清屏

可以将所有的查询条件封装成一个VO对象

package cn.cloud.xmall.search.vo;

import lombok.Data;

import java.util.List;

/**
 * @Description: 封装页面所有可能传递过来的查询条件，检索关键字，三级分类Id...
 * @author: Freedom
 * @QQ: 1556507698
 * @date:2022/3/17 21:18
 */
@Data
public class SearchParam {
    /**
     * 页面传递过来的全文匹配关键字
     */
    private String keyword;

    /**
     * 品牌id,可以多选
     */
    private List<Long> brandId;

    /**
     * 三级分类id
     */
    private Long catalog3Id;

    /**
     * 排序条件：sort=price/salecount/hotscore_desc/asc
     */
    private String sort;

    /**
     * 是否显示有货
     */
    private Integer hasStock;

    /**
     * 价格区间查询
     */
    private String skuPrice;

    /**
     * 按照属性进行筛选
     */
    private List<String> attrs;

    /**
     * 页码
     */
    private Integer pageNum = 1;

    /**
     * 原生的所有查询条件
     */
    private String _queryString;
}

package com.xunqi.gulimall.search.service.impl;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.TypeReference;
import com.xunqi.common.es.SkuEsModel;
import com.xunqi.common.utils.R;
import com.xunqi.gulimall.search.config.GulimallElasticSearchConfig;
import com.xunqi.gulimall.search.constant.EsConstant;
import com.xunqi.gulimall.search.service.MallSearchService;
import com.xunqi.gulimall.search.vo.AttrResponseVo;
import com.xunqi.gulimall.search.vo.SearchParam;
import com.xunqi.gulimall.search.vo.SearchResult;
import com.xunqi.gulimall.search.feign.ProductFeignService;
import lombok.extern.slf4j.Slf4j;
import org.apache.lucene.search.join.ScoreMode;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.index.query.BoolQueryBuilder;
import org.elasticsearch.index.query.NestedQueryBuilder;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.index.query.RangeQueryBuilder;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.bucket.nested.NestedAggregationBuilder;
import org.elasticsearch.search.aggregations.bucket.nested.ParsedNested;
import org.elasticsearch.search.aggregations.bucket.terms.ParsedLongTerms;
import org.elasticsearch.search.aggregations.bucket.terms.ParsedStringTerms;
import org.elasticsearch.search.aggregations.bucket.terms.Terms;
import org.elasticsearch.search.aggregations.bucket.terms.TermsAggregationBuilder;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.elasticsearch.search.fetch.subphase.highlight.HighlightBuilder;
import org.elasticsearch.search.fetch.subphase.highlight.HighlightField;
import org.elasticsearch.search.sort.SortOrder;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import org.springframework.util.StringUtils;

import javax.annotation.Resource;
import java.io.IOException;
import java.io.UnsupportedEncodingException;
import java.net.URLEncoder;
import java.util.ArrayList;
import java.util.List;
import java.util.stream.Collectors;

/**
 * @Description:
 * @Created: with IntelliJ IDEA.
 * @author: 夏沫止水
 * @createTime: 2020-06-13 14:19
 **/

@Slf4j
@Service
public class MallSearchServiceImpl implements MallSearchService {

    @Autowired
    private RestHighLevelClient esRestClient;

    @Resource
    private ProductFeignService productFeignService;

    @Override
    public SearchResult search(SearchParam param) {

        //1、动态构建出查询需要的DSL语句
        SearchResult result = null;

        //1、准备检索请求
        SearchRequest searchRequest = buildSearchRequest(param);

        try {
            //2、执行检索请求
            SearchResponse response = esRestClient.search(searchRequest, GulimallElasticSearchConfig.COMMON_OPTIONS);

            //3、分析响应数据，封装成我们需要的格式
            result = buildSearchResult(response,param);
        } catch (IOException e) {
            e.printStackTrace();
        }

        return result;
    }

    /**
     * 构建结果数据
     * 模糊匹配，过滤（按照属性、分类、品牌，价格区间，库存），完成排序、分页、高亮,聚合分析功能
     * @param response
     * @return
     */
    private SearchResult buildSearchResult(SearchResponse response,SearchParam param) {

        SearchResult result = new SearchResult();

        //1、返回的所有查询到的商品
        SearchHits hits = response.getHits();

        List<SkuEsModel> esModels = new ArrayList<>();
        //遍历所有商品信息
        if (hits.getHits() != null && hits.getHits().length > 0) {
            for (SearchHit hit : hits.getHits()) {
                String sourceAsString = hit.getSourceAsString();
                SkuEsModel esModel = JSON.parseObject(sourceAsString, SkuEsModel.class);

                //判断是否按关键字检索，若是就显示高亮，否则不显示
                if (!StringUtils.isEmpty(param.getKeyword())) {
                    //拿到高亮信息显示标题
                    HighlightField skuTitle = hit.getHighlightFields().get("skuTitle");
                    String skuTitleValue = skuTitle.getFragments()[0].string();
                    esModel.setSkuTitle(skuTitleValue);
                }
                esModels.add(esModel);
            }
        }
        result.setProduct(esModels);

        //2、当前商品涉及到的所有属性信息
        List<SearchResult.AttrVo> attrVos = new ArrayList<>();
        //获取属性信息的聚合
        ParsedNested attrsAgg = response.getAggregations().get("attr_agg");
        ParsedLongTerms attrIdAgg = attrsAgg.getAggregations().get("attr_id_agg");
        for (Terms.Bucket bucket : attrIdAgg.getBuckets()) {
            SearchResult.AttrVo attrVo = new SearchResult.AttrVo();
            //1、得到属性的id
            long attrId = bucket.getKeyAsNumber().longValue();
            attrVo.setAttrId(attrId);

            //2、得到属性的名字
            ParsedStringTerms attrNameAgg = bucket.getAggregations().get("attr_name_agg");
            String attrName = attrNameAgg.getBuckets().get(0).getKeyAsString();
            attrVo.setAttrName(attrName);

            //3、得到属性的所有值
            ParsedStringTerms attrValueAgg = bucket.getAggregations().get("attr_value_agg");
            List<String> attrValues = attrValueAgg.getBuckets().stream().map(item -> item.getKeyAsString()).collect(Collectors.toList());
            attrVo.setAttrValue(attrValues);

            attrVos.add(attrVo);
        }

        result.setAttrs(attrVos);

        //3、当前商品涉及到的所有品牌信息
        List<SearchResult.BrandVo> brandVos = new ArrayList<>();
        //获取到品牌的聚合
        ParsedLongTerms brandAgg = response.getAggregations().get("brand_agg");
        for (Terms.Bucket bucket : brandAgg.getBuckets()) {
            SearchResult.BrandVo brandVo = new SearchResult.BrandVo();

            //1、得到品牌的id
            long brandId = bucket.getKeyAsNumber().longValue();
            brandVo.setBrandId(brandId);

            //2、得到品牌的名字
            ParsedStringTerms brandNameAgg = bucket.getAggregations().get("brand_name_agg");
            String brandName = brandNameAgg.getBuckets().get(0).getKeyAsString();
            brandVo.setBrandName(brandName);

            //3、得到品牌的图片
            ParsedStringTerms brandImgAgg = bucket.getAggregations().get("brand_img_agg");
            String brandImg = brandImgAgg.getBuckets().get(0).getKeyAsString();
            brandVo.setBrandImg(brandImg);

            brandVos.add(brandVo);
        }
        result.setBrands(brandVos);

        //4、当前商品涉及到的所有分类信息
        //获取到分类的聚合
        List<SearchResult.CatalogVo> catalogVos = new ArrayList<>();
        ParsedLongTerms catalogAgg = response.getAggregations().get("catalog_agg");
        for (Terms.Bucket bucket : catalogAgg.getBuckets()) {
            SearchResult.CatalogVo catalogVo = new SearchResult.CatalogVo();
            //得到分类id
            String keyAsString = bucket.getKeyAsString();
            catalogVo.setCatalogId(Long.parseLong(keyAsString));

            //得到分类名
            ParsedStringTerms catalogNameAgg = bucket.getAggregations().get("catalog_name_agg");
            String catalogName = catalogNameAgg.getBuckets().get(0).getKeyAsString();
            catalogVo.setCatalogName(catalogName);
            catalogVos.add(catalogVo);
        }

        result.setCatalogs(catalogVos);
        //===============以上可以从聚合信息中获取====================//
        //5、分页信息-页码
        result.setPageNum(param.getPageNum());
        //5、1分页信息、总记录数
        long total = hits.getTotalHits().value;
        result.setTotal(total);

        //5、2分页信息-总页码-计算
        int totalPages = (int)total % EsConstant.PRODUCT_PAGESIZE == 0 ?
                (int)total / EsConstant.PRODUCT_PAGESIZE : ((int)total / EsConstant.PRODUCT_PAGESIZE + 1);
        result.setTotalPages(totalPages);

        List<Integer> pageNavs = new ArrayList<>();
        for (int i = 1; i <= totalPages; i++) {
            pageNavs.add(i);
        }
        result.setPageNavs(pageNavs);


        //6、构建面包屑导航
        if (param.getAttrs() != null && param.getAttrs().size() > 0) {
            List<SearchResult.NavVo> collect = param.getAttrs().stream().map(attr -> {
                //1、分析每一个attrs传过来的参数值
                SearchResult.NavVo navVo = new SearchResult.NavVo();
                String[] s = attr.split("_");
                navVo.setNavValue(s[1]);
                R r = productFeignService.attrInfo(Long.parseLong(s[0]));
                if (r.getCode() == 0) {
                    AttrResponseVo data = r.getData("attr", new TypeReference<AttrResponseVo>() {
                    });
                    navVo.setNavName(data.getAttrName());
                } else {
                    navVo.setNavName(s[0]);
                }

                //2、取消了这个面包屑以后，我们要跳转到哪个地方，将请求的地址url里面的当前置空
                //拿到所有的查询条件，去掉当前
                String encode = null;
                try {
                    encode = URLEncoder.encode(attr,"UTF-8");
                    encode.replace("+","%20");  //浏览器对空格的编码和Java不一样，差异化处理
                } catch (UnsupportedEncodingException e) {
                    e.printStackTrace();
                }
                String replace = param.get_queryString().replace("&attrs=" + attr, "");
                navVo.setLink("http://search.gulimall.com/list.html?" + replace);

                return navVo;
            }).collect(Collectors.toList());

            result.setNavs(collect);
        }


        return result;
    }


    /**
     * 准备检索请求
     * 模糊匹配，过滤（按照属性，分类，品牌，价格区间，库存），排序，分页，高亮，聚合分析
     * @return
     */
    private SearchRequest buildSearchRequest(SearchParam param) {

        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        /**
         * 模糊匹配，过滤（按照属性，分类，品牌，价格区间，库存）
         */
        //1. 构建bool-query
        BoolQueryBuilder boolQueryBuilder=new BoolQueryBuilder();

        //1.1 bool-must
        if(!StringUtils.isEmpty(param.getKeyword())){
            boolQueryBuilder.must(QueryBuilders.matchQuery("skuTitle",param.getKeyword()));
        }

        //1.2 bool-fiter
        //1.2.1 catelogId
        if(null != param.getCatalog3Id()){
            boolQueryBuilder.filter(QueryBuilders.termQuery("catalogId",param.getCatalog3Id()));
        }

        //1.2.2 brandId
        if(null != param.getBrandId() && param.getBrandId().size() >0){
            boolQueryBuilder.filter(QueryBuilders.termsQuery("brandId",param.getBrandId()));
        }

        //1.2.3 attrs
        if(param.getAttrs() != null && param.getAttrs().size() > 0){

            param.getAttrs().forEach(item -> {
                //attrs=1_5寸:8寸&2_16G:8G
                BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();


                //attrs=1_5寸:8寸
                String[] s = item.split("_");
                String attrId=s[0];
                String[] attrValues = s[1].split(":");//这个属性检索用的值
                boolQuery.must(QueryBuilders.termQuery("attrs.attrId",attrId));
                boolQuery.must(QueryBuilders.termsQuery("attrs.attrValue",attrValues));

                NestedQueryBuilder nestedQueryBuilder = QueryBuilders.nestedQuery("attrs",boolQuery, ScoreMode.None);
                boolQueryBuilder.filter(nestedQueryBuilder);
            });

        }

        //1.2.4 hasStock
        if(null != param.getHasStock()){
            boolQueryBuilder.filter(QueryBuilders.termQuery("hasStock",param.getHasStock() == 1));
        }


        //1.2.5 skuPrice
        if(!StringUtils.isEmpty(param.getSkuPrice())){
            //skuPrice形式为：1_500或_500或500_
            RangeQueryBuilder rangeQueryBuilder = QueryBuilders.rangeQuery("skuPrice");
            String[] price = param.getSkuPrice().split("_");
            if(price.length==2){
                rangeQueryBuilder.gte(price[0]).lte(price[1]);
            }else if(price.length == 1){
                if(param.getSkuPrice().startsWith("_")){
                    rangeQueryBuilder.lte(price[1]);
                }
                if(param.getSkuPrice().endsWith("_")){
                    rangeQueryBuilder.gte(price[0]);
                }
            }
            boolQueryBuilder.filter(rangeQueryBuilder);
        }

        //封装所有的查询条件
        searchSourceBuilder.query(boolQueryBuilder);


        /**
         * 排序，分页，高亮
         */

        //排序
        //形式为sort=hotScore_asc/desc
        if(!StringUtils.isEmpty(param.getSort())){
            String sort = param.getSort();
            String[] sortFileds = sort.split("_");

            SortOrder sortOrder="asc".equalsIgnoreCase(sortFileds[1])?SortOrder.ASC:SortOrder.DESC;

            searchSourceBuilder.sort(sortFileds[0],sortOrder);
        }

        //分页
        searchSourceBuilder.from((param.getPageNum()-1)*EsConstant.PRODUCT_PAGESIZE);
        searchSourceBuilder.size(EsConstant.PRODUCT_PAGESIZE);

        //高亮
        if(!StringUtils.isEmpty(param.getKeyword())){

            HighlightBuilder highlightBuilder = new HighlightBuilder();
            highlightBuilder.field("skuTitle");
            highlightBuilder.preTags("");
            highlightBuilder.postTags("");

            searchSourceBuilder.highlighter(highlightBuilder);
        }



        /**
         * 聚合分析
         */
        //1. 按照品牌进行聚合
        TermsAggregationBuilder brand_agg = AggregationBuilders.terms("brand_agg");
        brand_agg.field("brandId").size(50);


        //1.1 品牌的子聚合-品牌名聚合
        brand_agg.subAggregation(AggregationBuilders.terms("brand_name_agg")
                .field("brandName").size(1));
        //1.2 品牌的子聚合-品牌图片聚合
        brand_agg.subAggregation(AggregationBuilders.terms("brand_img_agg")
                .field("brandImg").size(1));

        searchSourceBuilder.aggregation(brand_agg);

        //2. 按照分类信息进行聚合
        TermsAggregationBuilder catalog_agg = AggregationBuilders.terms("catalog_agg");
        catalog_agg.field("catalogId").size(20);

        catalog_agg.subAggregation(AggregationBuilders.terms("catalog_name_agg").field("catalogName").size(1));

        searchSourceBuilder.aggregation(catalog_agg);

        //2. 按照属性信息进行聚合
        NestedAggregationBuilder attr_agg = AggregationBuilders.nested("attr_agg", "attrs");
        //2.1 按照属性ID进行聚合
        TermsAggregationBuilder attr_id_agg = AggregationBuilders.terms("attr_id_agg").field("attrs.attrId");
        attr_agg.subAggregation(attr_id_agg);
        //2.1.1 在每个属性ID下，按照属性名进行聚合
        attr_id_agg.subAggregation(AggregationBuilders.terms("attr_name_agg").field("attrs.attrName").size(1));
        //2.1.1 在每个属性ID下，按照属性值进行聚合
        attr_id_agg.subAggregation(AggregationBuilders.terms("attr_value_agg").field("attrs.attrValue").size(50));
        searchSourceBuilder.aggregation(attr_agg);

        log.debug("构建的DSL语句 {}",searchSourceBuilder.toString());

        SearchRequest searchRequest = new SearchRequest(new String[]{EsConstant.PRODUCT_INDEX},searchSourceBuilder);

        return searchRequest;
    }
}