高并发高可用之ElasticSearch

ES里面的数据怎么保持与mysql实时同步？
都存内存 数据不会越来越多吗？有过期时间吗？

ES对比MySql数据库

ES的数据存储在磁盘中，数据操作在内存中。

• 索引：数据库
• 类型：数据表
• 文档：表里的数据
• 属性：表列名

注意：ElasticSearch6.0之后移除了类型的概念。7.x使用类型会警告，8.x将彻底废除。

Docker下安装ES和kibana

安装ES

# 将docker里的目录挂载到linux的/mydata目录中
# 修改/mydata就可以改掉docker里的
mkdir -p /mydata/elasticsearch/config
mkdir -p /mydata/elasticsearch/data

# es可以被远程任何机器访问
echo "http.host: 0.0.0.0" >/mydata/elasticsearch/config/elasticsearch.yml

# 递归更改文件访问权限，es需要访问
chmod -R 777 /mydata/elasticsearch/

docker pull elasticsearch:7.4.2
docker pull kibana:7.4.2
版本要统一

# 9200是用户交互端口 9300是集群心跳端口
# -e指定是单阶段运行
# -e ES_JAVA_OPTS="-Xms64m -Xmx512m"指定初始占用内存大小和最大占用大小
# 反斜杠表示换行
docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms64m -Xmx512m" \
-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


# 设置开机启动elasticsearch
docker update elasticsearch --restart=always

查看日志命令：docker logs elasticsearch
查看docker镜像ID命令：docker ps -a
运行docker镜像：docker start 镜像ID
访问：

安装kibana

# 指定了ES交互端口9200和IP地址
docker run --name kibana -e ELASTICSEARCH_HOSTS=http://192.168.239.134:9200 -p 5601:5601 -d kibana:7.4.2


# 设置开机启动kibana
docker update kibana  --restart=always

kibana访问地址：http://192.168.239.134:5601/

增删改查操作

（1）GET /_cat/nodes：查看所有节点
（2）GET /_cat/health：查看es健康状况
（3）GET /_cat/master：查看主节点
（4）GET /_cat/indices：查看所有索引 ，等价于mysql数据库的show databases;

新增/更新
PUT/POST /索引名/类型名/ID

http://192.168.56.10:9200/索引名/类型名/ID
请求参数Json：
{
 "name":"John Doe"
}

支持put和post，post不写ID可以自动生产。对一个ID多次操作都会变为update操作。

查询
GET /索引名/类型名/ID

更新

POST /索引名/类型名/ID/_update
{
    "doc":{
        "name":"111"
    }
}

加_update参数就要加doc。
POST时带_update会对比元数据，如果一样就不进行任何操作。

删除
删除文档数据
DELETE /索引名/类型名/ID
删除索引
DELETE /索引名

注：elasticsearch并没有提供删除类型的操作，只提供了删除索引和文档的操作。

批量执行
在指定索引和类型下批量执行
POST /索引名/类型名/_bulk
在整个ES中批量执行
POST /_bulk

高级检索Query DSL

1. query/match匹配查询
   如果是非字符串，会进行精确匹配。如果是字符串，会进行全文检索

   GET bank/_search
   {
     "query": {
       "match": {
         "account_number": "20"
       }
     }
   }
   

2. query/match_phrase 【不拆分匹配】
   将需要匹配的值当成一整个单词（不分词）进行检索。
   – match_phrase：不拆分字符串进行检索，包含就匹配成功。
   – 字段.keyword：必须全匹配上才检索成功。

   GET bank/_search
   {
     "query": {
       "match_phrase": {
         "address": "990 Mill"
       }
     }
   }
   

   GET bank/_search
   {
     "query": {
       "match": {
         "address.keyword": "990 Mill"  # 字段后面加上 .keyword
       }
     }
   }
   

3. query/multi_math 【多字段匹配】

   GET bank/_search
   {
     "query": {
       "multi_match": {  # 前面的match仅指定了一个字段。
         "query": "mill",
         "fields": [ # state和address有mill子串  不要求都有
           "state",
           "address"
         ]
       }
     }
   }
   

4. query/bool/must 【复合查询】
   – must：必须达到must所列举的所有条件
   – must_not：必须不匹配must_not所列举的所有条件。
   – should：应该满足should所列举的条件。满足条件最好，不满足也可以，满足得分更高

   GET bank/_search
   {
     "query": {
       "bool": {
         "must": [
           {
             "match": {
               "gender": "M"
             }
           },
           {
             "match": {
               "address": "mill"
             }
           }
         ],
         "must_not": [
           {
             "match": {
               "age": "18"
             }
           }
         ],
         "should": [
           {
             "match": {
               "lastname": "Wallace"
             }
           }
         ]
       }
     }
   }
   

5. query/filter 【结果过滤】
   must 贡献得分
   should 贡献得分
   must_not 不贡献得分
   filter 不贡献得分

   GET bank/_search
   {
     "query": {
       "bool": {
         "must": [
           { "match": {"address": "mill" } }
         ],
         "filter": {  # query.bool.filter
           "range": {
             "balance": {  # 哪个字段
               "gte": "10000",
               "lte": "20000"
             }
           }
         }
       }
     }
   }
   

6. query/term
   和match一样。匹配某个属性的值。
   – 全文检索字段用match，
   – 其他非text文本字段匹配用term。

7. aggs 【聚合】
   复杂子聚合例子：查出所有年龄分布，并且这些年龄段中M的平均薪资和F的平均薪资以及这个年龄段的总体平均薪资

   GET bank/_search
   {
     "query": {
       "match_all": {}
     },
     "aggs": {
       "ageAgg": {
         "terms": {  #  看age分布
           "field": "age",
           "size": 100
         },
         "aggs": { # 子聚合
           "genderAgg": {
             "terms": { # 看gender分布
               "field": "gender.keyword" # 注意这里，文本字段应该用.keyword
             },
             "aggs": { # 子聚合
               "balanceAvg": {
                 "avg": { # 男性的平均
                   "field": "balance"
                 }
               }
             }
           },
           "ageBalanceAvg": {
             "avg": { #age分布的平均（男女）
               "field": "balance"
             }
           }
         }
       }
     },
     "size": 0
   }
   

更多Aggregations聚合函数请参考官方文档：https://www.elastic.co/guide/en/elasticsearch/reference/7.5/search-aggregations.html

映射

存入数据后ES会把字段自动映射一个数据类型。如果自动映射的数据类型不正确还可以手动指定映射。
创建索引并指定映射

PUT /my_index
{
  "mappings": {
    "properties": {
      "age": {
        "type": "integer"
      },
      "email": {
        "type": "keyword" # 指定为keyword
      },
      "name": {
        "type": "text" # 全文检索。保存时候分词，检索时候进行分词匹配
      }
    }
  }
}

查看映射：GET /my_index

有映射的情况下添加新的字段并指定映射

PUT /my_index/_mapping
{
  "properties": {
    "employee-id": {
      "type": "keyword",
      "index": false # 字段不能被检索。检索
    }
  }
}

更新映射
由于改变映射会影响到该字段下的数据，故想要更新映射只支持把数据迁移到新的映射规则下。
数据迁移：

POST _reindex
{
  "source": {
    "index": "bank",		#数据源索引
    "type": "account" 		#6.0后没有类型可以不写该行
  },
  "dest": {
    "index": "newbank"		#要迁移到的新索引
  }
}

安装中文IK分词器

下载并解压elasticsearch-analysis-ik-7.4.2到安装ES时挂载的插件外部目录/mydata/elasticsearch/plugins
配置ik插件目录访问权限并重启ES容器
注意：IK版本必须和ES版本一致

使用：
支持两种分词模式：ik_smart , ik_max_word

GET _analyze
{
   "analyzer": "ik_smart", 
   "text":"我是中国人"
}

扩展IK分词器有两种方式

1. 编写一个项目让IK访问。
2. 词条配置到一个nginx让IK访问
   – 在nginx的html目录下创建es目录并创建fenci.txt文件，在fenci.txt中写入自定义的词语，每行一条。
   – 修改/plugins/ik/config中的IKAnalyzer.cfg.xml文件：
   – 配置远程扩展字典访问地址

   <?xml version="1.0" encoding="UTF-8"?>
   <!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
   <properties>
   	<comment>IK Analyzer 扩展配置</comment>
   	<!--用户可以在这里配置自己的扩展字典 -->
   	<entry key="ext_dict"></entry>
   	 <!--用户可以在这里配置自己的扩展停止词字典-->
   	<entry key="ext_stopwords"></entry>
   	<!--用户可以在这里配置远程扩展字典 -->
   	<entry key="remote_ext_dict">http://192.168.56.10/es/fenci.txt</entry> 
   	<!--用户可以在这里配置远程扩展停止词字典-->
   	<!-- <entry key="remote_ext_stopwords">words_location</entry> -->
   </properties>
   

参考：https://github.com/medcl/elasticsearch-analysis-ik

SpringBoot整合ES

推荐使用Elasticsearch-Rest-Client：官方RestClient，封装了ES操作，API层次分明，上手简单。

1. 创建一个es-search微服务，可以勾选spring web组件，依赖common模块，配置注册中心，配置中心等配置

2. 引入maven依赖，依赖版本要和ES版本保持一致

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

   由于当前spring-boot版本默认依赖管理的ES版本是6.8.5，故要改为手动管理ES版本

   <properties>
       <java.version>1.8java.version>
       <elasticsearch.version>7.4.2elasticsearch.version>
   properties>
   

3. 编写ES配置类

   @Configuration
   public class ESConfig {
       //对所有请求进行配置项
       public static final RequestOptions COMMON_OPTIONS;
   
       static {
           RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
           COMMON_OPTIONS = builder.build();
       }
   
       @Bean
       public RestHighLevelClient esRestClient() {
           // 这里可以一次性指定多个es
           RestClientBuilder builder = RestClient.builder(new HttpHost("192.168.239.134", 9200, "http"));
           RestHighLevelClient client = new RestHighLevelClient(builder);
           return client;
       }
   
   }
   

4. 使用，参考官方文档 https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-getting-started-initialization.html

   package com.example.essearch;
   
   import com.alibaba.fastjson.JSON;
   import com.example.essearch.config.ESConfig;
   import org.elasticsearch.action.index.IndexRequest;
   import org.elasticsearch.action.index.IndexResponse;
   import org.elasticsearch.action.search.SearchRequest;
   import org.elasticsearch.action.search.SearchResponse;
   import org.elasticsearch.client.RestHighLevelClient;
   import org.elasticsearch.common.xcontent.XContentType;
   import org.elasticsearch.index.query.QueryBuilders;
   import org.elasticsearch.search.SearchHit;
   import org.elasticsearch.search.SearchHits;
   import org.elasticsearch.search.aggregations.AggregationBuilders;
   import org.elasticsearch.search.aggregations.Aggregations;
   import org.elasticsearch.search.aggregations.bucket.terms.Terms;
   import org.elasticsearch.search.aggregations.bucket.terms.TermsAggregationBuilder;
   import org.elasticsearch.search.aggregations.metrics.Avg;
   import org.elasticsearch.search.aggregations.metrics.AvgAggregationBuilder;
   import org.elasticsearch.search.builder.SearchSourceBuilder;
   import org.junit.jupiter.api.Test;
   import org.springframework.beans.factory.annotation.Autowired;
   import org.springframework.boot.test.context.SpringBootTest;
   
   import java.io.IOException;
   
   @SpringBootTest
   class EsSearchApplicationTests {
   
       @Autowired
       private RestHighLevelClient client;
       
       /**
        * 创建/更新索引
        * @throws IOException
        */
       @Test
       public void indexData() throws IOException {
           User user = new User();
           user.setUserName("张三");
           user.setAge(20);
           user.setGender("男");
           String jsonString = JSON.toJSONString(user);
   
           // 设置索引，索引名为users
           IndexRequest indexRequest = new IndexRequest ("users");
           indexRequest.id("1");
           //设置要保存的内容，指定数据和类型
           indexRequest.source(jsonString, XContentType.JSON);
   
           //执行创建索引和保存数据
           IndexResponse index = client.index(indexRequest, ESConfig.COMMON_OPTIONS);
   
           System.out.println(index);
       }
   
   
       /**
        * 高级检索与聚合分析
        * @throws IOException
        */
       @Test
       public void searchData() throws IOException {
           SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
           // 构造检索条件
           //sourceBuilder.query();
           //sourceBuilder.from();
           //sourceBuilder.size();
           //sourceBuilder.aggregation();
           sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
   
           // 聚合
           //AggregationBuilders工具类构建AggregationBuilder
           // 构建第一个聚合条件:按照年龄的值分布
           TermsAggregationBuilder agg1 = AggregationBuilders.terms("agg1").field("age").size(10);// 设置聚合名称为agg1
           sourceBuilder.aggregation(agg1);
           // 构建第二个聚合条件:平均薪资
           AvgAggregationBuilder agg2 = AggregationBuilders.avg("agg2").field("balance");// 设置聚合名称为agg2
           sourceBuilder.aggregation(agg2);
   
           System.out.println("检索条件"+sourceBuilder.toString());
   
           // 1 创建检索请求
           SearchRequest searchRequest = new SearchRequest();
           searchRequest.indices("bank");  //设置请求索引为bank
           searchRequest.source(sourceBuilder);
   
           // 2 执行检索
           SearchResponse response = client.search(searchRequest, ESConfig.COMMON_OPTIONS);
           // 3 分析响应结果
           System.out.println(response.toString());
   
   
           // 3.1 获取java bean
           SearchHits hits = response.getHits();
           SearchHit[] hitsList = hits.getHits();
           for (SearchHit hit : hitsList) {
               hit.getId();
               hit.getIndex();
               String sourceAsString = hit.getSourceAsString();
               Account account = JSON.parseObject(sourceAsString, Account.class);
               System.out.println(account);
   
           }
   
           // 3.2 获取检索到的聚合分析信息
           Aggregations aggregations = response.getAggregations();
           Terms agg1Terms = aggregations.get("agg1");
           for (Terms.Bucket bucket : agg1Terms.getBuckets()) {
               String keyAsString = bucket.getKeyAsString();
               System.out.println("年龄："+keyAsString+"=====>"+bucket.getDocCount());
           }
   
           Avg agg2Avg = aggregations.get("agg2");
           System.out.println("平均薪资："+agg2Avg.getValue());
       }
   
   
   
   
   
   
       class User{
           private String userName;
           private Integer age;
           private String gender;
   
           public String getUserName() {
               return userName;
           }
   
           public void setUserName(String userName) {
               this.userName = userName;
           }
   
           public Integer getAge() {
               return age;
           }
   
           public void setAge(Integer age) {
               this.age = age;
           }
   
           public String getGender() {
               return gender;
           }
   
           public void setGender(String gender) {
               this.gender = gender;
           }
       }
   
   
       static class Account
       {
           private int account_number;
   
           private int balance;
   
           private String firstname;
   
           private String lastname;
   
           private int age;
   
           private String gender;
   
           private String address;
   
           private String employer;
   
           private String email;
   
           private String city;
   
           private String state;
   
           public void setAccount_number(int account_number){
               this.account_number = account_number;
           }
           public int getAccount_number(){
               return this.account_number;
           }
           public void setBalance(int balance){
               this.balance = balance;
           }
           public int getBalance(){
               return this.balance;
           }
           public void setFirstname(String firstname){
               this.firstname = firstname;
           }
           public String getFirstname(){
               return this.firstname;
           }
           public void setLastname(String lastname){
               this.lastname = lastname;
           }
           public String getLastname(){
               return this.lastname;
           }
           public void setAge(int age){
               this.age = age;
           }
           public int getAge(){
               return this.age;
           }
           public void setGender(String gender){
               this.gender = gender;
           }
           public String getGender(){
               return this.gender;
           }
           public void setAddress(String address){
               this.address = address;
           }
           public String getAddress(){
               return this.address;
           }
           public void setEmployer(String employer){
               this.employer = employer;
           }
           public String getEmployer(){
               return this.employer;
           }
           public void setEmail(String email){
               this.email = email;
           }
           public String getEmail(){
               return this.email;
           }
           public void setCity(String city){
               this.city = city;
           }
           public String getCity(){
               return this.city;
           }
           public void setState(String state){
               this.state = state;
           }
           public String getState(){
               return this.state;
           }
       }
   
   
   }
   
   

实战应用

ES数据模型结构的设计
空间和时间不可兼得两种只能选其一。
方案1：

{
    skuId:1
    spuId:11
    skyTitile:华为xx
    price:999
    saleCount:99
    attr:[
        {尺寸:5},
        {CPU:高通945},
        {分辨率:全高清}
	]
缺点：如果每个sku都存储规格参数(如尺寸)，会有冗余存储，因为每个spu对应的sku的规格参数都一样

方案2：

sku索引
{
    spuId:1
    skuId:11
}
attr索引
{
    skuId:11
    attr:[
        {尺寸:5},
        {CPU:高通945},
        {分辨率:全高清}
	]
}
先找到4000个符合要求的spu，再根据4000个spu查询对应的属性，封装了4000个id，
每次传输大小：如id为long类型,8B*4000=32000B=32KB
1K个人检索，就是32MB，高并发下会造成严重阻塞。

结论：如果将规格参数单独建立索引，会出现检索时出现大量数据传输的问题，会引起网络网络

创建索引并设置映射

PUT product
{
    "mappings":{
        "properties": {
            "skuId":{ "type": "long" },
            "spuId":{ "type": "keyword" },  # 不可分词
            "skuTitle": {
                "type": "text",
                "analyzer": "ik_smart"  # 中文分词器
            },
            "skuPrice": { "type": "keyword" },  
            "skuImg"  : { 
            	"type": "keyword" ,
            	"index": false,  # 降低占用空间，不可被检索，不生成索引，只用做页面展示
                "doc_values": false # 降低占用空间，不可被聚合，默认为true
            }, 
            "saleCount":{ "type":"long" },
            "hasStock": { "type": "boolean" },
            "hotScore": { "type": "long"  },
            "brandId":  { "type": "long" },
            "catalogId": { "type": "long"  },
            "brandName": { "type": "keyword" }, 
            "brandImg":{
                "type": "keyword",
                "index": false,  
                "doc_values": false 
            },
            "catalogName": {"type": "keyword" }, 
            "attrs": {
                "type": "nested",	# 重要！！！表示嵌入式，防止被ES自动扁平化处理
                "properties": {
                    "attrId": {"type": "long"  },
                    "attrName": {
                        "type": "keyword",
                        "index": false,
                        "doc_values": false
                    },
                    "attrValue": {"type": "keyword" }
                }
            }
        }
    }
}

创建ES数据模型实体类

@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 catalogId;
    private String brandName;
    private String brandImg;
    private String catalogName;
    private List<Attr> attrs;

    @Data
    public static class Attr{
        private Long attrId;
        private String attrName;
        private String attrValue;
    }
}

封装数据到ES数据模型实体类并存入ES
商品上架的同时进行封装商品数据并远程调用ES微服务保存到ES中
（封装代码略）

编写ES微服务保存数据的Controller层

/*** 上架商品*/
@PostMapping("/product") // ElasticSaveController
public R productStatusUp(@RequestBody List<SkuEsModel> skuEsModels){

    boolean status;
    try {
        status = productSaveService.productStatusUp(skuEsModels);
    } catch (IOException e) {
        log.error("ElasticSaveController商品上架错误: {}", e);
        return R.error(BizCodeEnum.PRODUCT_UP_EXCEPTION.getCode(), BizCodeEnum.PRODUCT_UP_EXCEPTION.getMsg());
    }
    if(!status){
        return R.ok();
    }
    return R.error(BizCodeEnum.PRODUCT_UP_EXCEPTION.getCode(), BizCodeEnum.PRODUCT_UP_EXCEPTION.getMsg());
}

编写ES微服务保存数据的Service层

public class ProductSaveServiceImpl implements ProductSaveService {

	@Resource
	private RestHighLevelClient client;

	/**
	 * 将数据保存到ES
	 * 用bulk代替index，进行批量保存
	 * BulkRequest bulkRequest, RequestOptions options
	 */
	@Override // ProductSaveServiceImpl
	public boolean productStatusUp(List<SkuEsModel> skuEsModels) throws IOException {
		// 1.给ES建立一个索引 product
		BulkRequest bulkRequest = new BulkRequest();
		// 2.构造保存请求
		for (SkuEsModel esModel : skuEsModels) {
			// 设置es索引
			IndexRequest indexRequest = new IndexRequest(EsConstant.PRODUCT_INDEX);
			// 设置索引id
			indexRequest.id(esModel.getSkuId().toString());
			// json格式
			String jsonString = JSON.toJSONString(esModel);
			indexRequest.source(jsonString, XContentType.JSON);
			// 添加到文档
			bulkRequest.add(indexRequest);
		}
		// bulk批量保存
		BulkResponse bulk = client.bulk(bulkRequest, GuliESConfig.COMMON_OPTIONS);
		// TODO 是否拥有错误
		boolean hasFailures = bulk.hasFailures();
		if(hasFailures){
			List<String> collect = Arrays.stream(bulk.getItems()).map(item -> item.getId()).collect(Collectors.toList());
			log.error("商品上架错误：{}",collect);
		}
		return hasFailures;
	}
}

检索查询参数模型分析
可能用到的参数：
全文检索：skuTitle->keyword
排序：saleCount（销量）、hotScore（热度分）、skuPrice（价格）
过滤：hasStock、skuPrice区间、brandId、catalog3Id、attrs（规格属性）
聚合：attrs

/**
封装页面所有可能传递过来的关键字
 * catalog3Id=225&keyword=华为&sort=saleCount_asc&hasStock=0/1&brandId=25&brandId=30
 */
@Data
public class SearchParam {

    // 页面传递过来的全文匹配关键字
    private String keyword;

    /** 三级分类id*/
    private Long catalog3Id;
    //排序条件：sort=price/salecount/hotscore_desc/asc
    private String sort;
    // 仅显示有货
    private Integer hasStock;

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

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

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

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

    /*** 原生所有查询属性*/
    private String _queryString;
}

检索返回结果模型分析

/**
 * 
Title: SearchResponse
 * Description：包含页面需要的所有信息
 */
@Data
public class SearchResult {

    /** * 查询到的所有商品信息（即前面的ES数据模型实体类）*/
    private List<SkuEsModel> products;

    /*** 当前页码*/
    private Integer pageNum;
    /** 总记录数*/
    private Long total;
    /** * 总页码*/
    private Integer totalPages;

    /** 当前查询到的结果, 所有涉及到的品牌*/
    private List<BrandVo> brands;
    /*** 当前查询到的结果, 所有涉及到的分类*/
    private List<CatalogVo> catalogs;
	/** * 当前查询的结果 所有涉及到所有属性*/
    private List<AttrVo> attrs;

	/** 导航页   页码遍历结果集(分页)  */
	private List<Integer> pageNavs;
//	================以上是返回给页面的所有信息================

    /** 导航数据*/
    private List<NavVo> navs = new ArrayList<>();

    /** 便于判断当前id是否被使用*/
    private List<Long> attrIds = new ArrayList<>();

    @Data
    public static class NavVo {
        private String name;
        private String navValue;
        private String link;
    }

    @Data
    public static class BrandVo {

        private Long brandId;
        private String brandName;
        private String brandImg;
    }

    @Data
    public static class CatalogVo {
        private Long catalogId;
        private String catalogName;
    }

    @Data
    public static class AttrVo {

        private Long attrId;
        private String attrName;
        private List<String> attrValue;
    }
}

写出DSL检索语句，（如果是嵌入式的映射属性字段，检索查询，聚合，分析等都应该用相应的嵌入式语法nested）

GET gulimall_product/_search
{
  "query": {
    "bool": {
      "must": [ {"match": {  "skuTitle": "华为" }} ], # 检索出华为
      "filter": [ # 过滤
        { "term": { "catalogId": "225" } },
        { "terms": {"brandId": [ "2"] } }, 
        { "term": { "hasStock": "false"} },
        {
          "range": {
            "skuPrice": { # 价格1K~7K
              "gte": 1000,
              "lte": 7000
            }
          }
        },
        {
          "nested": {
            "path": "attrs", # 聚合名字
            "query": {
              "bool": {
                "must": [
                  {
                    "term": { "attrs.attrId": { "value": "6"} }
                  }
                ]
              }
            }
          }
        }
      ]
    }
  },
  "sort": [ {"skuPrice": {"order": "desc" } } ],
  "from": 0,
  "size": 5,
  "highlight": {  
    "fields": {"skuTitle": {}}, # 高亮的字段
    "pre_tags": "",  # 前缀
    "post_tags": ""
  },
  "aggs": { # 查完后聚合
    "brandAgg": {
      "terms": {
        "field": "brandId",
        "size": 10
      },
      "aggs": { # 子聚合
        "brandNameAgg": {  # 每个商品id的品牌
          "terms": {
            "field": "brandName",
            "size": 10
          }
        },
      
        "brandImgAgg": {
          "terms": {
            "field": "brandImg",
            "size": 10
          }
        }
        
      }
    },
    "catalogAgg":{
      "terms": {
        "field": "catalogId",
        "size": 10
      },
      "aggs": {
        "catalogNameAgg": {
          "terms": {
            "field": "catalogName",
            "size": 10
          }
        }
      }
    },
    "attrs":{
      "nested": {"path": "attrs" },
      "aggs": {
        "attrIdAgg": {
          "terms": {
            "field": "attrs.attrId",
            "size": 10
          },
          "aggs": {
            "attrNameAgg": {
              "terms": {
                "field": "attrs.attrName",
                "size": 10
              }
            }
          }
        }
      }
    }
  }
}

检索查询代码实现
controller

@GetMapping(value = {"/search.html","/"})
public String getSearchPage(SearchParam searchParam, // 检索参数，
                            Model model, HttpServletRequest request) {
    searchParam.set_queryString(request.getQueryString());//_queryString是个字段
    SearchResult result=searchService.getSearchResult(searchParam);
    model.addAttribute("result", result);
    return "search";
}

service

@Slf4j
@Service
public class ProductSearchServiceImpl {

    @Resource
    private RestHighLevelClient restHighLevelClient;

    /**
     *  根据请求参数检索ES数据，并将检索结果封装为系统返回响应实体类
     * @param searchParam
     * @return
     */
    public SearchResult getSearchResult(SearchParam searchParam) {//根据带来的请求内容封装
        SearchResult searchResult= null;
        // 通过请求参数构建es查询请求
        SearchRequest request = bulidSearchRequest(searchParam);
        try {
            SearchResponse searchResponse = restHighLevelClient.search(request,
                    ESConfig.COMMON_OPTIONS);
            // 将es响应数据封装成结果
            searchResult = bulidSearchResult(searchParam,searchResponse);
        } catch (IOException e) {
            e.printStackTrace();
        }
        return searchResult;
    }


    /**
     * 通过请求参数构建ES查询请求
     * @param searchParam
     * @return
     */
    private SearchRequest bulidSearchRequest(SearchParam searchParam) {
        // 用于构建DSL语句
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        //1. 构建bool query
        BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();
        //1.1 bool must
        if (!StringUtils.isEmpty(searchParam.getKeyword())) {
            boolQueryBuilder.must(QueryBuilders.matchQuery("skuTitle", searchParam.getKeyword()));
        }

        //使用不参与评分的filter，性能效率更高
        //1.2 bool filter
        //1.2.1 catalog
        if (searchParam.getCatalog3Id()!=null){
            boolQueryBuilder.filter(QueryBuilders.termQuery("catalogId", searchParam.getCatalog3Id()));
        }
        //1.2.2 brand
        if (searchParam.getBrandId()!=null&&searchParam.getBrandId().size()>0) {
            //值有多个为List时termsQuery
            boolQueryBuilder.filter(QueryBuilders.termsQuery("brandId",searchParam.getBrandId()));
        }
        //1.2.3 hasStock
        if (searchParam.getHasStock() != null) {
            boolQueryBuilder.filter(QueryBuilders.termQuery("hasStock", searchParam.getHasStock() == 1));
        }
        //1.2.4 priceRange
        //解析自定义的区间参数格式，这里为0_6000，_6000，6000_分别表示大于0小于6000，小于6000，大于6000
        RangeQueryBuilder rangeQueryBuilder = QueryBuilders.rangeQuery("skuPrice");
        if (!StringUtils.isEmpty(searchParam.getSkuPrice())) {
            String[] prices = searchParam.getSkuPrice().split("_");
            if (prices.length == 1) {
                if (searchParam.getSkuPrice().startsWith("_")) {
                    rangeQueryBuilder.lte(Integer.parseInt(prices[0]));
                }else {
                    rangeQueryBuilder.gte(Integer.parseInt(prices[0]));
                }
            } else if (prices.length == 2) {
                //_6000会截取成["","6000"]
                if (!prices[0].isEmpty()) {
                    rangeQueryBuilder.gte(Integer.parseInt(prices[0]));
                }
                rangeQueryBuilder.lte(Integer.parseInt(prices[1]));
            }
            boolQueryBuilder.filter(rangeQueryBuilder);
        }
        //1.2.5 attrs-nested  嵌入式属性使用嵌入式语法
        //attrs=1_5寸:8寸&2_16G:8G
        List<String> attrs = searchParam.getAttrs();
        BoolQueryBuilder queryBuilder = new BoolQueryBuilder();
        if (attrs!=null&&attrs.size() > 0) {
            attrs.forEach(attr->{
                String[] attrSplit = attr.split("_");
                queryBuilder.must(QueryBuilders.termQuery("attrs.attrId", attrSplit[0]));
                String[] attrValues = attrSplit[1].split(":");
                queryBuilder.must(QueryBuilders.termsQuery("attrs.attrValue", attrValues));
            });
        }
        NestedQueryBuilder nestedQueryBuilder = QueryBuilders.nestedQuery("attrs", queryBuilder, ScoreMode.None);
        boolQueryBuilder.filter(nestedQueryBuilder);
        //1.X bool query构建完成
        searchSourceBuilder.query(boolQueryBuilder);




        //2. sort  eg:sort=saleCount_desc/asc
        if (!StringUtils.isEmpty(searchParam.getSort())) {
            String[] sortSplit = searchParam.getSort().split("_");
            searchSourceBuilder.sort(sortSplit[0], sortSplit[1].equalsIgnoreCase("asc") ? SortOrder.ASC : SortOrder.DESC);
        }

        //3. 分页 // 是检测结果分页
        searchSourceBuilder.from((searchParam.getPageNum() - 1) * EsConstant.PRODUCT_PAGESIZE);
        searchSourceBuilder.size(EsConstant.PRODUCT_PAGESIZE);

        //4. 高亮highlight
        if (!StringUtils.isEmpty(searchParam.getKeyword())) {
            HighlightBuilder highlightBuilder = new HighlightBuilder();
            highlightBuilder.field("skuTitle");
            highlightBuilder.preTags("");
            highlightBuilder.postTags("");
            searchSourceBuilder.highlighter(highlightBuilder);
        }




        //5. 聚合
        //5.1 按照brand聚合
        TermsAggregationBuilder brandAgg = AggregationBuilders.terms("brandAgg").field("brandId");
        TermsAggregationBuilder brandNameAgg = AggregationBuilders.terms("brandNameAgg").field("brandName");
        TermsAggregationBuilder brandImgAgg = AggregationBuilders.terms("brandImgAgg").field("brandImg");
        //通过子聚合的方式就可以获取brand的中文名和图片了！！!
        brandAgg.subAggregation(brandNameAgg);
        brandAgg.subAggregation(brandImgAgg);
        searchSourceBuilder.aggregation(brandAgg);

        //5.2 按照catalog聚合
        TermsAggregationBuilder catalogAgg = AggregationBuilders.terms("catalogAgg").field("catalogId");
        // 子聚合
        TermsAggregationBuilder catalogNameAgg = AggregationBuilders.terms("catalogNameAgg").field("catalogName");
        catalogAgg.subAggregation(catalogNameAgg);
        searchSourceBuilder.aggregation(catalogAgg);

        //5.3 按照attrs聚合  嵌入式属性使用嵌入式聚合语法
        NestedAggregationBuilder nestedAggregationBuilder = new NestedAggregationBuilder("attrs", "attrs");
        //按照attrId聚合     //按照attrId聚合之后再按照attrName和attrValue聚合
        TermsAggregationBuilder attrIdAgg    = AggregationBuilders.terms("attrIdAgg"   ).field("attrs.attrId");
        TermsAggregationBuilder attrNameAgg  = AggregationBuilders.terms("attrNameAgg" ).field("attrs.attrName");
        TermsAggregationBuilder attrValueAgg = AggregationBuilders.terms("attrValueAgg").field("attrs.attrValue");
        attrIdAgg.subAggregation(attrNameAgg);
        attrIdAgg.subAggregation(attrValueAgg);

        nestedAggregationBuilder.subAggregation(attrIdAgg);
        searchSourceBuilder.aggregation(nestedAggregationBuilder);

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

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


    /**
     * 将ES响应数据封装成结果
     * @param searchParam
     * @param searchResponse
     * @return
     */
    private SearchResult bulidSearchResult(SearchParam searchParam, SearchResponse searchResponse) {
        SearchResult result = new SearchResult();

        SearchHits hits = searchResponse.getHits();
        //1. 封装查询到的商品信息
        if (hits.getHits()!=null&&hits.getHits().length>0){
            List<SkuEsModel> skuEsModels = new ArrayList<>();
            for (SearchHit hit : hits) {
                String sourceAsString = hit.getSourceAsString();
                SkuEsModel skuEsModel = JSON.parseObject(sourceAsString, SkuEsModel.class);
                //设置高亮属性
                if (!StringUtils.isEmpty(searchParam.getKeyword())) {
                    HighlightField skuTitle = hit.getHighlightFields().get("skuTitle");
                    String highLight = skuTitle.getFragments()[0].string();
                    skuEsModel.setSkuTitle(highLight);
                }
                skuEsModels.add(skuEsModel);
            }
            result.setProducts(skuEsModels);
        }

        //2. 封装分页信息
        //2.1 当前页码
        result.setPageNum(searchParam.getPageNum());
        //2.2 总记录数
        long total = hits.getTotalHits().value;
        result.setTotal(total);
        //2.3 总页码
        Integer 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);

        //3. 查询结果涉及到的品牌
        List<SearchResult.BrandVo> brandVos = new ArrayList<>();
        Aggregations aggregations = searchResponse.getAggregations();
        //ParsedLongTerms用于接收terms聚合的结果，并且可以把key转化为Long类型的数据
        ParsedLongTerms brandAgg = aggregations.get("brandAgg");
        for (Terms.Bucket bucket : brandAgg.getBuckets()) {
            //3.1 得到品牌id
            Long brandId = bucket.getKeyAsNumber().longValue();

            //获取子聚合拿到brand中文名和图片
            Aggregations subBrandAggs = bucket.getAggregations();
            //3.2 得到品牌图片
            ParsedStringTerms brandImgAgg=subBrandAggs.get("brandImgAgg");
            String brandImg = brandImgAgg.getBuckets().get(0).getKeyAsString();
            //3.3 得到品牌名字
            Terms brandNameAgg=subBrandAggs.get("brandNameAgg");
            String brandName = brandNameAgg.getBuckets().get(0).getKeyAsString();
            SearchResult.BrandVo brandVo = new SearchResult.BrandVo(brandId, brandName, brandImg);
            brandVos.add(brandVo);
        }
        result.setBrands(brandVos);

        //4. 查询涉及到的所有分类
        List<SearchResult.CatalogVo> catalogVos = new ArrayList<>();
        ParsedLongTerms catalogAgg = aggregations.get("catalogAgg");
        for (Terms.Bucket bucket : catalogAgg.getBuckets()) {
            //4.1 获取分类id
            Long catalogId = bucket.getKeyAsNumber().longValue();
            Aggregations subcatalogAggs = bucket.getAggregations();
            //4.2 获取分类名
            ParsedStringTerms catalogNameAgg=subcatalogAggs.get("catalogNameAgg");
            String catalogName = catalogNameAgg.getBuckets().get(0).getKeyAsString();
            SearchResult.CatalogVo catalogVo = new SearchResult.CatalogVo(catalogId, catalogName);
            catalogVos.add(catalogVo);
        }
        result.setCatalogs(catalogVos);

        //5 查询涉及到的所有属性
        List<SearchResult.AttrVo> attrVos = new ArrayList<>();
        //ParsedNested用于接收内置嵌入式属性的聚合
        ParsedNested parsedNested=aggregations.get("attrs");
        ParsedLongTerms attrIdAgg=parsedNested.getAggregations().get("attrIdAgg");
        for (Terms.Bucket bucket : attrIdAgg.getBuckets()) {
            //5.1 查询属性id
            Long attrId = bucket.getKeyAsNumber().longValue();

            //获取子聚合
            Aggregations subAttrAgg = bucket.getAggregations();
            //5.2 查询属性名
            ParsedStringTerms attrNameAgg=subAttrAgg.get("attrNameAgg");
            String attrName = attrNameAgg.getBuckets().get(0).getKeyAsString();
            //5.3 查询属性值
            ParsedStringTerms attrValueAgg = subAttrAgg.get("attrValueAgg");
            List<String> attrValues = new ArrayList<>();
            for (Terms.Bucket attrValueAggBucket : attrValueAgg.getBuckets()) {
                String attrValue = attrValueAggBucket.getKeyAsString();
                attrValues.add(attrValue);
                List<SearchResult.NavVo> navVos = new ArrayList<>();
            }
            SearchResult.AttrVo attrVo = new SearchResult.AttrVo(attrId, attrName, attrValues);
            attrVos.add(attrVo);
        }
        result.setAttrs(attrVos);



        
        return result;
    }


}

ES集群

ELasticsearch的集群是由多个节点组成的，通过cluster.name设置集群名称，并且用于区分其它的集群，每个节点通过node.name指定节点的名称。

ES集群中的节点类型：
1、主节点
主节点负责创建索引、删除索引、分配分片、追踪集群中的节点状态等工作。ElasticSearch中的主节点的工作量相对较轻，用户的请求可以发往集群中任何一个节点，由该节点负责分发和返回结果，而不需要经过主节点转发。而主节点是由候选主节点通过ZenDiscovery机制选举出来的，所以要想成为主节点，首先要先成为候选主节点。

2、候选主节点
在ElasticSearch集群初始化或者主节点宕机的情况下，由候选主节点中选举其中一个作为主节点。指定候选主节点的配置为：node.master:true。

3、数据节点
数据节点负责数据的存储和相关具体操作，比如CRUD、搜索、聚合。所以，数据节点对机器配置要求比较高，首先需要有足够的磁盘空间来存储数据，其次数据操作对系统CPU、Memory和IO的性能消耗都很大。通常随着集群的扩大，需要增加更多的数据节点来提高可用性。指定数据节点的配置：node.data:true。
ElasticSearch是允许一个节点既做候选主节点也做数据节点的，但是数据节点的负载较重，所以需要考虑将二者分离开，设置专用的候选主节点和数据节点，避免因数据节点负责重导致主节点不响应。

4、客户端节点
客户端节点就是既不做候选主节点也不做数据节点的节点，只负责请求的分发、汇总等等，但是这样的工作，其实任何一个节点都可以完成，因为在ElasticSearch中一个集群内的节点都可以执行任何请求，其会负责将请求转发给对应的节点进行处理。所以单独增加这样的节点更多是为了负载均衡。指定该节点的配置为：
node.master:false
node.data:false

分片
为了将数据添加到Elasticsearch，我们需要索引(index)——一个存储关联数据的地方。实际上，索引只是一个用来指向一个或多个分片(shards)的“逻辑命名空间(logical namespace)”.

集群新增节点

• 向集群增加一个节点前后，索引发生了些什么。在左端，索引的主分片全部分配到节点 Node1，而副本分片没有地方分配。在这种状态下，集群是黄色的。

• 一旦第二个节点加入，尚未分配的副本分片就会分配到新的节点 Node2，这使得集群变为了绿色的状态。

• 当另一个节点加入的时候，Elasticsearch 会自动地尝试将分片在所有节点上进行均匀分配。
  

集群参考:http://dljz.nicethemes.cn/news/show-107233.html
集群参考:https://blog.csdn.net/qq_40977118/article/details/123301013