灰灰商城-整合RestHighLevelClient笔记（尚硅谷谷粒商城2020）够豪横！

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灰灰商城-分布式高级篇-2

整合 RestHighLevelClient

导入es的rest-high-level-client

<dependency>
	<groupId>org.elasticsearch.clientgroupId>    
	<artifactId>elasticsearch-rest-high-level-clientartifactId>  
	<version>7.4.2version>
dependency>

• 注意Spring Boot（2.2.5版本）中默认整合es版本为6.8.7

需要在pom中指定

<properties>      
    <elasticsearch.version>7.4.2elasticsearch.version>
properties>

添加配置类

• 给容器中注入一个RestHighLevelClient

@Configuration
public class GreymallElasticSearchConfig {
     

    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();
    }

    /**
     * 配置ElasticSearch RestHighLevelClient
     * @return
     */
    @Bean
    public RestHighLevelClient esRestClient(){
     

        return new RestHighLevelClient(
                RestClient.builder(
                        new HttpHost("ES部署的ip", 9200, "http")
                ));
    }
}

保存索引

 // 测试保存数据到es
 @Test
 void indexData() throws IOException {
     
     IndexRequest request = new IndexRequest("users");
     request.id("1");
     User user = new User();
     user.setUserName("wei-xhh");
     user.setAge(20);
     user.setGender("男");
     String jsonString = JSON.toJSONString(user);
     request.source(jsonString, XContentType.JSON); // 保存的内容

     IndexResponse index = client.index(request, GreymallElasticSearchConfig.COMMON_OPTIONS);

 }

更多可以参考文档

查询文档

    // 测试查询数据
    @Test
    void searchData2() throws IOException {
     
//        GET /bank/_search
//        {
     
//            "query": {
     
//            "term": {
     
//                "address": {
     
//                    "value": "mill"
//                }
//            }
//        },
//            "aggs": {
     
//            "aggAvg": {
     
//                "avg": {
     
//                    "field":"age"
//                }
//            },
//            "balanceAgg":{
     
//                "terms": {
     
//                    "field": "balance",
//                            "size": 10
//                }
//            }
//        }
//        }
        SearchRequest searchRequest = new SearchRequest();
        searchRequest.indices("bank");

        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.termQuery("address", "mill"));

        // 按照薪资的值进行分布
        TermsAggregationBuilder balance = AggregationBuilders.terms("balanceAgg")
                .field("balance");
        searchSourceBuilder.aggregation(balance);

        // 计算平均年龄
        AvgAggregationBuilder age = AggregationBuilders.avg("ageAvg").field("age");
        searchSourceBuilder.aggregation(age);

        searchRequest.source(searchSourceBuilder);
        System.out.println(searchSourceBuilder.toString());

        SearchResponse search = client.search(searchRequest, GreymallElasticSearchConfig.COMMON_OPTIONS);

        System.out.println(search.toString());

        // 封装结果
        SearchHits hits = search.getHits();
        SearchHit[] data = hits.getHits();
        for (SearchHit datum : data) {
     
            String sourceAsString = datum.getSourceAsString();
            ResultData resultData = JSON.parseObject(sourceAsString, ResultData.class);
            System.out.println(resultData);
        }

        Aggregations aggregations = search.getAggregations();
//        List aggregationsData = aggregations.asList();
//        for (Aggregation aggregationsDatum : aggregationsData) {
     
//            String name = aggregationsDatum.getName();
//            System.out.println(name);
//        }
        Terms balanceAgg = aggregations.get("balanceAgg");
        for (Terms.Bucket bucket : balanceAgg.getBuckets()) {
     
            String keyAsString = bucket.getKeyAsString();
            System.out.println("薪资" + keyAsString);
        }

        Avg aggAvg = aggregations.get("ageAvg");
        double value = aggAvg.getValue();
        System.out.println("平均年龄" + value);

    }

更多可以参考文档

商品上架

分析应该怎么存数据到ES

• 按照Sku的信息检索
• 按照品牌检索
• 按照当前分类下的产品检索
• 按照Spu的信息检索

保存的文档应该怎样呢？

情况1：

保存sku信息和属性信息（都在一个索引下）
如：

{
     
    "skuId":1,
    "skuTitle":"华为xx",
    "price":998,
    "saleCount":99,
    "attrs":[
        {
     "尺寸":5寸},
        {
     "CPU":"高通945"}，
        {
     "分辨率":"全高清"}
    ]
}

后果：产生冗余字段

假设
100万个产品，有20个属性（20个属性假设有2kb）
1000000 * 2kb = 2000MB = 2G 内存

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情况2：

保存有用的信息（不同索引）

如：

sku索引

{
     
    "skuId":1,
    "spuId":11,
    "xxx":"xxx"
}

attr索引, 可以看到属性只存了一次

{
     
    "spuId":11,
    "attrs":[
        {
     "尺寸":5寸},
        {
     "CPU":"高通945"}，
        {
     "分辨率":"全高清"}
    ]
}

没有像第一种情况的冗余了。

但实际项目中需要根据attrs动态查询到sku：

如 搜索 小米；

假设带小米的商品有10000个，涉及到4000个spu

需要做分步查询：查出4000个spu对应的所有可能属性。

一个请求：
esClient : spuId:[4000个spuId] 4000 * 8个字节 = 32kb

10000个人请求：

32kb * 10000 = 32000Mb = 32GB

只能选第一个：空间换时间

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商品的映射信息

在kibana中执行

PUT product

{
     
  "mappings": {
     
    "skuId": {
     
      "type": "long"
    },
    "spuId": {
     
      "type": "keyword"
    },
    "skuTitle": {
     
      "type": "text",
      "analyzer":"ik_smart"
    },
    "skuPrice":{
     
      "type":"keyword"
    },
    "skuImg":{
     
      "type":"keyword",
      "index":false,
      "doc_values":false
    },
    "saleCount":{
     
      "type":"long"
    },
    "hasStock":{
     
      "type":"boolean"
    },
    "hotScore":{
     
      "type":"long"
    },
    "brandId":{
     
      "type":"long"
    },
    "catelogId":{
     
      "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的数据类型介绍

[https://www.elastic.co/guide/en/elasticsearch/reference/7.x/nested.html]

扁平化处理

如ES开发文档的例子

PUT my_index/_doc/1

{
     
  "group" : "fans",
  "user" : [ 
    {
     
      "first" : "John",
      "last" :  "Smith"
    },
    {
     
      "first" : "Alice",
      "last" :  "White"
    }
  ]
}

保存了上述数据后 ，会被扁平化处理

上述数据：存入了两个用户，有first和last属性
保存被扁平化成这样：

{
     
  "group" :        "fans",
  "user.first" : [ "alice", "john" ],
  "user.last" :  [ "smith", "white" ]
}

假设检索了Alice,Smith他们对应着不同的用户，但却可以被检索到

GET my_index/_search

{
     
  "query": {
     
    "bool": {
     
      "must": [
        {
      "match": {
      "user.first": "Alice" }},
        {
      "match": {
      "user.last":  "Smith" }}
      ]
    }
  }
}

所以使用nested嵌入式属性，使用后就不会出现扁平化。

商品在ES中保存的数据模型

@Data
public class SkuEsModel {
     
    private Long skuId;

    private Long spuId;

    private String skuTitle;

    private BigDecimal skuPrice;

    private String skuImg;

    private Long saleCount;

    private Boolean hasStock;

    private Long hotScore;

    private Long brandId;

    private Long catelogId;

    private String brandName;

    private String brandImg;

    private String catalogName;

    private List<Attrs> attrs;

    @Data
    public static class Attrs{
     

        private Long attrId;

        private String attrName;

        private String attrValue;

    }
}