谷粒商城微服务分布式高级篇十一——

商城检索-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

商城检索- 完整查询参数

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

controller

@GetMapping("/list.html")public String listPage(SearchParam param, Model model){SearchResult result =  mallSearchService.search(param);model.addAttribute("result",result);return  "list";}

SearchParam

/*** @Description 封装页面所有可能传递过来的查询条件*/
@Data
public class SearchParam {/*** 页面传递的关键字*/private String keyword;/*** 三类分级Id*/private Long catalog3Id;/*** 排序条件*/private String sort;/*** 是否有货 hasStock0/1 0无库存 1有库存*/private Integer hasStock;/*** 价格区间  skuPrice1_100*/private String skuPrice;/*** 品牌*/private List<Long> brandId;/*** 属性*/private List<String> attrs;/*** 页码*/private Integer pageNum;}

MallSearchServiceImpl

@Service
public class MallSearchServiceImpl implements MallSearchService {@AutowiredRestHighLevelClient restHighLevelClient;@Overridepublic SearchResult search(SearchParam param) {//动态构建查询DSL语句SearchResult result = null;//准备检索请求SearchRequest searchRequest = buildSearchRequest(param);try {//执行检索请求SearchResponse searchResponse = restHighLevelClient.search(searchRequest, EsConfig.COMMON_OPTIONS);//分析响应数据封装成我们所需要的格式result = buildSearchResult(searchResponse, param);} catch (IOException e) {e.printStackTrace();}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 sourceBuilder = new SearchSourceBuilder();/*** 模糊匹配 、过滤、（按照属性、分类、品牌、价格区间、库存）*/BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();if (StringUtils.isNotBlank(param.getKeyword()))boolQuery.must(QueryBuilders.matchQuery("skuTitle", param.getKeyword()));if (param.getCatalog3Id() != null)boolQuery.filter(QueryBuilders.termQuery("catalogId", param.getCatalog3Id()));if (param.getBrandId() != null && param.getBrandId().size() > 0)boolQuery.filter(QueryBuilders.termsQuery("brandId", param.getBrandId()));if (param.getHasStock() != null)boolQuery.filter(QueryBuilders.termQuery("hasStock", param.getHasStock() == 1));if (StringUtils.isNotBlank(param.getSkuPrice())) {RangeQueryBuilder rangeQuery = QueryBuilders.rangeQuery("skuPrice");String[] s = param.getSkuPrice().split("_");if (s.length == 2) {rangeQuery.gte(s[0]).lte(s[1]);} else if (s.length == 1) {if (param.getSkuPrice().startsWith("_"))rangeQuery.lte(s[1]);if (param.getSkuPrice().endsWith("_"))rangeQuery.gte(s[0]);}boolQuery.filter(rangeQuery);}if (param.getAttrs() != null && param.getAttrs().size() > 0) {//attrs=1_5寸:8寸&attrs2_16G:8Gfor (String attr : param.getAttrs()) {String[] s = attr.split("_");String attrId = s[0];//检索的属性idString[] attrValues = s[1].split(":");BoolQueryBuilder nestedBoolQuery = QueryBuilders.boolQuery();nestedBoolQuery.must(QueryBuilders.termQuery("attrs.attrId", attrId));nestedBoolQuery.must(QueryBuilders.termsQuery("attrs.attrValue", attrValues));//每一个都得生成一个nested查询NestedQueryBuilder nestedQuery = QueryBuilders.nestedQuery("attrs", nestedBoolQuery, ScoreMode.None);boolQuery.filter(nestedQuery);}}//所有条件进行封装sourceBuilder.query(boolQuery);/*** 排序、分页、高亮*/if (StringUtils.isNotBlank(param.getSort())) {String sort = param.getSkuPrice();String[] s = sort.split("_");SortOrder sortOrder = s[1].equalsIgnoreCase("asc") ? SortOrder.ASC : SortOrder.DESC;sourceBuilder.sort(s[0], sortOrder);}System.out.println("PageNum" + param.getPageNum());if (param.getPageNum() == null) {//sourceBuilder.from((50 - 1) * EsConstant.PRODUCT_PAGESIZE);} else {sourceBuilder.from((param.getPageNum() - 1) * EsConstant.PRODUCT_PAGESIZE);}sourceBuilder.size(EsConstant.PRODUCT_PAGESIZE);if (StringUtils.isNotBlank(param.getKeyword())) {HighlightBuilder builder = new HighlightBuilder();builder.field("skuTitle");builder.preTags("<b style='color:red'>");builder.postTags("</b>");sourceBuilder.highlighter(builder);}/*** 聚合分析*///品牌聚合TermsAggregationBuilder brand_agg = AggregationBuilders.terms("brand_agg").field("brandId").size(50);//品牌聚合的子聚合brand_agg.subAggregation(AggregationBuilders.terms("brand_name_agg").field("brandName").size(1));brand_agg.subAggregation(AggregationBuilders.terms("brand_img_agg").field("brandImg").size(1));sourceBuilder.aggregation(brand_agg);//分类聚合TermsAggregationBuilder catalog_agg = AggregationBuilders.terms("catalog_agg").field("catalogId").size(50);sourceBuilder.aggregation(catalog_agg);//分类子聚合catalog_agg.subAggregation(AggregationBuilders.terms("catalog_name_agg").field("catalogName").size(1));//属性聚合NestedAggregationBuilder attr_agg = AggregationBuilders.nested("attr_agg", "attrs");TermsAggregationBuilder attr_id_agg = AggregationBuilders.terms("attr_id_agg").field("attrs.attrId");attr_id_agg.subAggregation(AggregationBuilders.terms("attr_name_agg").field("attrs.attrName").size(1));attr_id_agg.subAggregation(AggregationBuilders.terms("attr_value_agg").field("attrs.attrValue").size(1));attr_agg.subAggregation(attr_id_agg);//聚合attrsourceBuilder.aggregation(attr_agg);System.out.println("检索请求" + sourceBuilder.toString());SearchRequest searchRequest = new SearchRequest(new String[]{EsConstant.PRODUCT_INDEX}, sourceBuilder);return searchRequest;}/*** 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.setProducts(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idlong 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idlong 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idString 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);return result;}}

这时查询会报错大概意思是Elasticsearch索引只读

[FORBIDDEN/12/index read-only / allow delete (api)];

解决：
在虚拟机上执行

curl -XPUT -H "Content-Type: application/json" http://localhost:9200/_all/_settings -d '{"index.blocks.read_only_allow_delete": null}'

或kibana执行

PUT _settings{"index": {"blocks": {"read_only_allow_delete": "false"}}}

还有一个查询错误是之前的数据格式问题，新建一个索引（主要是type为keyword）

PUT mall_product
{"mappings": {"properties": {"attrs": {"type": "nested","properties": {"attrId": {"type": "long"},"attrName": {"type": "keyword"},"attrValue": {"type": "keyword"}}},"brandId": {"type": "long"},"brandImg": {"type": "keyword"},"brandName": {"type": "keyword"},"catalogId": {"type": "long"},"catalogName": {"type": "keyword"},"hasStock": {"type": "boolean"},"hotScore": {"type": "long"},"saleCount": {"type": "long"},"skuId": {"type": "long"},"skuImg": {"type": "keyword"},"skuPrice": {"type": "keyword"},"skuTitle": {"type": "text","analyzer": "ik_smart"},"spuId": {"type": "keyword"}}}
}

然后把旧数据迁移到新索引

#数据迁移
POST _reindex
{"source": {"index": "product" //旧索引},"dest": {"index": "mall_product" //新索引}
}

效果

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