一、Elasticsearch介绍（Elasticsearch：官方分布式搜索和分析引擎 | Elastic）
Elasticsearch 是一个分布式、RESTful 风格的搜索和数据分析引擎，能够解决不断涌现出的各种用例。 作为 Elastic Stack 的核心，它集中存储您的数据，帮助您发现意料之中以及意料之外的情况。
它可以用来快速地储存、搜索和分析海量的数据。用来代替MySql作数据的搜索和分析，可以秒级的从海量的数据中作检索和分析。
官方学习文档：Elasticsearch Guide [7.5] | Elastic
1.基本概念
1.1、Index（索引)
动词：相当于MySQL的insert
名词：相当于MySQL的Database
1.2、Type（类型）
在Index中可以定义多个Type。每一种类型的数据放在一起。
类似于MySQL中的Table。
1.3、Document（文档）
保存在某个索引（Index）下，某种类型（Type）的一个数据（Document）。
文档是Json格式的，Documnet类似MySQL某个Table的内容。
## ES6及以后的版本已经删除了TYPE的概念
二、Docker安装Elasticsearch
1、下载镜像文件
docker pull elasticsearch:7.4.2
docker pull kibana:7.4.2
//将es中配置文件挂载到外面的目录，通过修改虚拟机外面的文件夹es配置，进而修改docker中es的配置
mkdir -p /mydata/elasticsearch/config
mkdir -p /mydata/elasticsearch/data
//写了一个配置 http.host:0.0.0.0 代表es可以被远程的任何机器访问，注意这里host：后需要有空格
echo "http.host: 0.0.0.0">> /mydata/elasticsearch/config/elasticsearch.yml2、创建实例
1. Elasticsearch
//运行elasticsearch命令，
//为容器起一个名字为elasticsearch，-p暴露两个端口 9200 9300， 9200是发送http请求——restapi的端口，9300是es在分布式集群状态下，结点之间的通信端口， \代表换行下一行，
//-e single-node 是以单节点方式运行，ES_JAVA_OPTS不指定的话，es一启动，会将内存全部占用，整个虚拟机就卡死了，
//-v 进行挂载，目录中配置，数据等一一关联 -d 后台启动es使用指定的镜像 z
docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms64m -Xmx128m" \
-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 安装完 elasticsearch 后我们来启动一下，docker ps会发现使用docker ps命令查看启动的容器时没有找到我们的 es，这是因为目前 es 的配置文件的权限导致的，因此我们还需要修改一下 es 的配置文件的权限：
chmod -R 777 /mydata/elasticsearch/
若仍然启动失败，则说明运行代码有误，使用 docker log elasticsearch 查看报错
同时，需要 docker pa -a 查看容器id，将elasticsearch容器删除，再执行elasticsearch命令
启动成功后，访问http://192.168.56.10:9200，输出以下内容说明启动成功。
{ "name" : "e514c560d500", "cluster_name" : "elasticsearch", "cluster_uuid" : "Rq6lZhNVRhiVkjTljaCGig", "version" : { "number" : "7.4.2", "build_flavor" : "default", "build_type" : "docker", "build_hash" : "2f90bbf7b93631e52bafb59b3b049cb44ec25e96", "build_date" : "2019-10-28T20:40:44.881551Z", "build_snapshot" : false, "lucene_version" : "8.2.0", "minimum_wire_compatibility_version" : "6.8.0", "minimum_index_compatibility_version" : "6.0.0-beta1" }, "tagline" : "You Know, for Search" }
2、安装Kibana
//访问5601端口，访问到可视化界面kibana，kibana再先发送请求到es9200
docker run --name kibana -e ELASTICSEARCH_HOSTS=http://192.168.56.10:9200 -p 5601:5601 -d kibana:7.4.2
//http://192.168.56.10:9200 要改成自己的Ip端口访问可视化界面 http://192.168.56.10:5601
三、基本操作
1、_cat
Get/_cat/nodes //查看节点
Get/_cat/health //查看健康状况
Get/_cat/master //查看主节点
Get/_cat/indices //查看索引
2、索引一个文档（保存）
PUT/customer/external/1 ，发送多次是更新操作
POST/customer/external/1 ，发送多次是更新操作
POST/customer/external，自动生成id
http://192.168.56.10:9200/customer/external/1
{
"name":"John Doe"
}响应数据
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 0,//并发控制字段，每次更新就会+1，用来做乐观锁
"_primary_term": 1, //同上，主分片重新分配，如重启，就会变化
}PUT/customer/external/1?if_seq_no=7&if_primary_term=1
通过_seq_no和_primary_term的值进行乐观锁操作
{
"error": {
"root_cause": [
{
"type": "illegal_argument_exception",
"reason": "request [/customer/external/1] contains unrecognized parameters: [if_primary_term], [if_seq_no]"
}
],
"type": "illegal_argument_exception",
"reason": "request [/customer/external/1] contains unrecognized parameters: [if_primary_term], [if_seq_no]"
},
"status": 400
}3、查询文档
GET/customer/external/1
{
"_index": "customer",
"_type": "external",
"_id": "1", //Id号
"_version": 1, //版本号
"_seq_no": 0, //并发控制字段，每次更新就会+1，用来做乐观锁
"_primary_term": 1, //同上，主分片重新分配，如重启，就会变化
"found": true,
"_source": { //真正的内容
"name": "John Doe"
}
}4、更新文档
POST/customer/external/1/_update 该语句会对比数据内容，数据一样则不会进行update操作，版本号不会更新
{
"_index": "customer",
"_type": "external",
"_id": "1",
"_version": 2,
"result": "noop", //检测到数据一致，无操作执行
"_shards": {
"total": 0,
"successful": 0,
"failed": 0
},
"_seq_no": 1,
"_primary_term": 1
}不带_update的语句会进行新增或更新操作
5、删除文档
DEL/customer/external/1 删除文档
DEL/customer 删除索引
6、批量API
bulk批量API操作每个语句是独立的，其中一条出现错误，整个语句不会进行回滚
使用Kibana的Dev Tool进行测试
命令：
POST /customer/external/_bulk
{"index":{"_id":"1"}}
{"name":"tang"}
{"index":{"_id":"2"}}
{"name":"yao"}
结果：
#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
"took" : 501,
"errors" : false,
"items" : [
{
"index" : {
"_index" : "customer",
"_type" : "external",
"_id" : "1",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 3,
"_primary_term" : 1,
"status" : 201
}
},
{
"index" : {
"_index" : "customer",
"_type" : "external",
"_id" : "2",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 4,
"_primary_term" : 1,
"status" : 201
}
}
]
}复杂的批量API操作
POST /_bulk
{"delete":{"_index":"website","_type":"blog","_id":"123"}}
{"create":{"_index":"website","_type":"blog","_id":"123"}}
{"title":"My first blog post"}
{"index":{"_index":"website","_type":"blog"}}
{"title":"My second blog post"}
{"update":{"_index":"website","_type":"blog","_id":"123"}}
{"doc":{"title":"My updated blog post"}}官方测试数据：elasticsearch/accounts.json at v7.4.2 · elastic/elasticsearch (github.com)
四、进阶操作
1、SearchAPI（检索信息）
1.1、检索bank下所有信息
GET bank/_search
1.2、请求参数方式检索
//?检索条件,q=* 查询所有，sort=account_number:asc排序规则按照该字段升序排列
GET bank/_search?q=*&sort=account_number:asc1.3、请求体+URL检索 即QueryDSL检索
GET /bank/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"balance":"desc"
},
{
"account_number": "asc"
}
]
}2、QueryDSL
请求体+URL检索的方式进行检索
2.1、基本语法
GET /bank/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"balance":"desc"
},
{
"account_number": "asc"
}
],
"from": 0,
"size": 5,
"_source": ["balance","firstname"]
}2.2、match 匹配查询
##match 全文检索按照评分进行排序，会对检索条件进行分词匹配
GET /bank/_search
{
"query": {
"match": {
"account_number": "20"
}
}
}
GET /bank/_search
{
"query": {
"match": {
"address": "mill lane"
}
}
}2.3、match_phrase
##短语匹配
GET /bank/_search
{
"query":{
"match_phrase":{
"address":"mill lane"
}
}
}2.4、multi_match 多字段匹配
GET /bank/_search
{
"query": {
"multi_match": {
"query": "mill",
"fields": ["address","city"]
}
}
}2.5、bool 合并查询
GET /bank/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"age": "40"
}
}
],
"must_not": [
{
"match": {
"state": "ID"
}
}
],
"should": [
{
"match": {
"lastname": "Ross"
}
}
]
}
}
}2.6、filter 结果过滤
GET /bank/_search
{
"query": {
"bool": {
"must": { "match_all": {} },
"filter": {
"range": {
"balance": {
"gte": 20000,
"lte": 30000
}
}
}
}
}
}2.7、term
和match一样，匹配某个属性的值。全文检索字段用match，非text字段用trem
##term 非TEXT字段
GET bank/_search
{
"query":{
"term":{
"balance":"32838"
}
}
}
GET /_search
{
"query": {
"match_phrase": {
"address": "789 Madison"
}
}
}
##精准查询
GET /_search
{
"query": {
"match": {
"address.keyword": "789 Madison"
}
}
}2.8、aggregations 聚合执行
GET bank/_search
{
"query": {
"match": {
"address": "mill"
}
},
"aggs": {
"ageaggs": {
"terms": {
"field": "age",
"size": 10 //假设年龄有100种可能，只取出前10种可能
}
}
}
}复杂：
# 按照年龄聚合，并且请求这些年龄段的这些人的平均薪资
GET bank/_search
{
"query": {
"match_all": {}
},
"aggs": {
"ageAgg": {
"terms": {
"field": "age",
"size": 100
},
"aggs": {
"balanceAvg": {
"avg": {
"field": "balance"
}
}
}
}
}
}# 查出所有的年龄分布，并且这些年龄段中性别为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": {
"field": "age",
"size": 100
},
"aggs": {
"gender": {
"terms": {
"field": "gender.keyword"
},
"aggs": {
"genderBalance": {
"avg": {
"field": "balance"
}
}
}
},
"ageBlance":{
"avg": {
"field": "balance"
}
}
}
}
},
"size": 0
}3、Mapping
3.1 字段类型
参考文档 Field datatypes | Elasticsearch Guide [7.5] | Elastic
3.2 映射
Mapping（映射）用来定义一个文档（document），以及他所包含属性（feild）是如何存储和索引的。
查看mapping信息 GET bank/_mapping
每个属性的映射类型,type为text默认就会就全文检索，检索起来就会分词，想要精确检索address的值，就要用address.keyword
3.3 创建映射规则
PUT /my_index
{
"mappings": {
"properties": {
"age": { "type": "integer" },
"email": { "type": "keyword" },
"name": { "type": "text" }
}
}
}
GET my_index/_mapping3.4 添加新的字段映射
PUT /my_index/_mapping
{
"properties": {
"employee-id": {
"type": "keyword",
"index": false // 设置不可以被索引
}
}
}3.5 更新映射
对于已经存在的映射字段，我们不能更新。更新必须创建新的索引进行数据迁移
3.6 数据迁移
根据bank的属性，复制过来进行修改而生成新的索引和映射规则
newbank映射规则
PUT /newbank
{
"mappings": {
"properties" : {
"account_number" : {
"type" : "long"
},
"address" : {
"type" : "text"
},
"age" : {
"type" : "integer"
},
"balance" : {
"type" : "long"
},
"city" : {
"type" : "keyword"
},
"email" : {
"type" : "keyword"
},
"employer" : {
"type" : "keyword"
},
"firstname" : {
"type" : "text"
},
"gender" : {
"type" : "keyword"
},
"lastname" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"state" : {
"type" : "keyword"
}
}
}
}将老bank数据迁移到newbank
POST _reindex
{
"source": {
"index": "bank",
"type": "account"
},
"dest": {
"index": "newbank"
}
}
GET newbank/_search4、分词 Standard Analyzer | Elasticsearch Guide [7.4] | Elastic
将完成的大段话分词，利用单词的相关性匹配，最终完成全文检索功能。默认使用标准分词器
POST _analyze
{
"analyzer": "standard",
"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}默认是英文分词器，如果text为中文的话就会分割成一个个汉字，需要自己安装分词器。
4.1 ik分词器
github地址：https://github.com/medcl/elasticsearch-analysis-ik/releases/tag/v7.4.2
# -it 交互模式 /bin/bash 进入控制台
docker exec -it [进程号] /bin/bash复制链接 https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.4.2/elasticsearch-analysis-ik-7.4.2.zip
wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.4.2/elasticsearch-analysis-ik-7.4.2.zipwget命令没找到，使用Xshell和Xftp进行上传安装
修改配置允许vagrant账号密码登录
vi /etc/ssh/sshd_config
使用which ssh检查是否已经安装shh，若没安装需要先安装ssh
CentOS7安装ssh（自行查找自己版本的安装方式）
yum install -y openssl openssh-server
确认安装成功后，进入XShell连接虚拟机
连接成功
下载ES7.4.2.zip https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.4.2/elasticsearch-analysis-ik-7.4.2.zip
解压后再通过Xftp上传到 /mydata/elasticsearch/plugins/ 目录下
修改ik文件权限为可读可写可执行 chmod -R 777 ik/
测试ik分词器
docker exec -it [ES端口号] /bin/bash #进入内部环境
cd bin
elasticsearch-plugin list #输出ik则说明安装成功
docker restart elasticsearch #重启ES实例
ik_smart
POST _analyze
{
"analyzer": "ik_smart",
"text": "我是中国人"
}
{
"tokens" : [
{
"token" : "我",
"start_offset" : 0,
"end_offset" : 1,
"type" : "CN_CHAR",
"position" : 0
},
{
"token" : "是",
"start_offset" : 1,
"end_offset" : 2,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "中国人",
"start_offset" : 2,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 2
}
]
}
5、修改Linux网络设置&开启root密码访问
cd /etc/sysconfig/network-scripts/
vi ifcfg-eth1
##添加以下配置
GATEWAY=192.168.56.1
DNS1=114.114.114.114
DNS2=8.8.8.8
##重启网卡
service network restart 
连接成功，就可以进行安装一些必要的工具
yum install -y wget
yum install -y unzip
6、自定义扩展词库
指定一个远程词库，让ik分词器自己向远程发送请求，要到最新的单词，最新的单词就会作为新的单词进行分解
1）自己写一个项目，处理这个请求，返回我们新的单词，让ik分词器给我们项目发送请求
2）安装nginx，将最新词库放到nginx里面，让ik分词器给nginx发送请求，由它（也是个web服务器）返回最新的词库，就能把新词库和原来的词库合并起来
先确保linux虚拟机有足够内存，关闭虚拟机进行内存设置
之前设置es的jvm堆内存比较小，最大只有128m，想要设置成512m，最快的方式停掉原来的创建一个新的。
这样做数据并不会丢失，因为之前创建的时候，将es中数据映射到外面的文件data下，即使删掉了容器，可是外面的文件夹还在，再创建新的容器和外面文件夹关联起来，数据也不会丢失。
docker stop elasticsearch
docker rm elasticsearch
##重写启动ES
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:7.4.26.1、安装nginx
// 在/mydata 目录下面创建nginx
mkdir nginx
// 本地没有找到镜像，自动去远程下载
docker run -p 80:80 --name nginx -d nginx:1.10
docker container cp nginx:/etc/nginx .
docker stop nginx
docker rm nginx
// 切换到mydata后 把nginx改名字为conf
mv nginx conf
mkdir nginx
// 把整个文件夹移动到 nginx下，以后nginx就在conf下面了
mv conf nginx/注意，\前面是有空格的
docker run -p 80:80 --name nginx \
-v /mydata/nginx/html:/usr/share/nginx/html \
-v /mydata/nginx/logs:/var/log/nginx \
-v /mydata/nginx/conf:/etc/nginx \
-d nginx:1.10测试nginx
访问默认端口号80，进入nginx欢迎页
#在nginx文件夹下
cd /html
#只要这个文件夹下有index.html,就会默认展示
vi index.html
#在html文件夹下，创建es，把es中ik分词器用到的资源放在这里
mkdir es
cd es
vi fenci.txt
#添加尚硅谷和乔碧萝两个词
192.168.56.10/es/fenci.txt6.2、自定义词库
cd /mydata/elasticsearch/plugins/ik/config/
vi IKAnalyzer.cfg.xml
#修改为词库地址
http://192.168.56.10/es/fenci.txt
#修改后，重启elasticsearch
docker restart elasticsearch 
分词成功
五、Elasticsearch-Rest-Client
Elasticsearch Clients：https://www.elastic.co/guide/en/elasticsearch/client/index.html
java REST Client：https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/index.html
1、创建一个新项目专门用于检索
引入依赖
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.4.2</version>
</dependency>发现版本不配套，原因是springboot项目依赖里面t对es的版本也做了管理，当前Springboot默认整合springData，来操作es
在自己的pom文件里添加版本管理
<elasticsearch.version>7.4.2</elasticsearch.version>
创建config.GulimallElasticSearchConfig类
import org.springframework.context.annotation.Configuration;
@Configuration
public class GulimallElasticSearchConfig {
}导入common依赖
<dependency>
<groupId>com.atguigu.gulimall</groupId>
<artifactId>gulimall-common</artifactId>
<version>0.0.1-SNAPSHOT</version>
</dependency>配置application.properties
spring.application.name=gulimall-search
spring.cloud.nacos.discovery.server-addr=127.0.0.1:8848参考 https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/java-rest-high-getting-started-initialization.html
/**
* 1、导入依赖
* 2、编写配置给容器中注入一个RestHighlevelClient
* 3、参照API https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/java-rest-high-getting-started-initialization.html
*/
@Configuration
public class GulimallElasticSearchConfig {
@Bean
public RestHighLevelClient esRestClient() {
RestClientBuilder builder = null;
//final String hostname, final int port, final String scheme
builder = RestClient.builder(new HttpHost("192.168.56.10", 9200, "http"));
RestHighLevelClient client = new RestHighLevelClient(builder);
// RestHighLevelClient client = new RestHighLevelClient(
// //如果es有多个，指定es的地址和端口号以及协议名
// RestClient.builder(
// new HttpHost("192.168.56.10", 9200, "http")));
return client;
}
}2、编写测试类
import org.elasticsearch.client.RestHighLevelClient;
import org.junit.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
@RunWith(SpringRunner.class)
@SpringBootTest
public class GulimailSearchApplicationTests {
@Autowired
private RestHighLevelClient client;
@Test
public void contextLoads() {
System.out.println(client);
}
}在common里面默认是有数据源的，就要配合数据源有关的配置,比如mysql的驱动，包括mybatis-plus的依赖，但是search项目不操作数据库，所以需要去掉
在启动类上修改注释
@SpringBootApplication(exclude = DataSourceAutoConfiguration.class)测试成功
3、实例测试
参考文档：https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/java-rest-high-getting-started-request-options.html
3.1、请求设置项
所有请求，比如es添加了安全访问规则，通过RequestOptions进行设置
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();
}3.2、保存/更新实例
/**
* 测试存储数据到es
* 保存更新二合一
*/
@Test
public void indexData() throws IOException{
IndexRequest indexRequest = new IndexRequest("user");
indexRequest.id("1");
//indexRequest.source("userName","ZhangSan","age",18,"gender","男");
User user = new User();
user.setUserName("zhangsan");
user.setAge(22);
user.setGender("男");
String jsonString = JSON.toJSONString(user);
indexRequest.source(jsonString, XContentType.JSON); //要保存得内容
//执行操作
IndexResponse index = null;
index = client.index(indexRequest, GulimailElasticSearchConfig.COMMON_OPTIONS);
//响应数据
System.out.println(index);
}
@Data
class User{
private String userName;
private String gender;
private Integer age;
} 
3.3、复杂检索实例
参考文档：https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.4/java-rest-high-search.html
复杂检索实例
@Test
void searchData() throws IOException {
//1.创建一个检索请求
SearchRequest searchRequest = new SearchRequest();
//制定索引
searchRequest.indices("bank");
//制定DSL，检索条件
// SearchSourceBuilder searchSourceBuilder 封装检索条件
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
//1.1)构造检索条件
// searchSourceBuilder.query();
// searchSourceBuilder.from();
// searchSourceBuilder.size();
// searchSourceBuilder.aggregation();
searchSourceBuilder.query(QueryBuilders.matchQuery("address", "mill"));
//1.2)按照年龄的值分布进行聚合
TermsAggregationBuilder ageAgg = AggregationBuilders.terms("ageAgg").field("age").size(10);
searchSourceBuilder.aggregation(ageAgg);
//1.3)计算平均薪资
AvgAggregationBuilder balanceAvg = AggregationBuilders.avg("balanceAvg").field("balance");
searchSourceBuilder.aggregation(balanceAvg);
System.out.println("检索条件" + searchSourceBuilder.toString());
SearchRequest source = searchRequest.source(searchSourceBuilder);
//2.执行检索
SearchResponse searchResponse = client.search(source, GulimallElasticSearchConfig.COMMON_OPTIONS);
//3、分析结果
System.out.println(searchResponse.toString());
// Map map = JSON.parseObject(searchResponse.toString(), Map.class);
//3.1)、获取所有查到的数据
SearchHits hits = searchResponse.getHits();
SearchHit[] searchHits = hits.getHits();
for (SearchHit hit : searchHits) {
//在此之前根据json生成java对象Account
String sourceAsString = hit.getSourceAsString();
Account account = JSON.parseObject(sourceAsString, Account.class);
System.out.println("account = " + account);
}
//3.2)获取检索到的分析信息
Aggregations aggregations = searchResponse.getAggregations();
// for (Aggregation aggregation : aggregations.asList()) {
// System.out.println("name = " + aggregation.getName());
// }
System.out.println("aggregations = " + aggregations.toString());
Terms ageAgg1 = aggregations.get("ageAgg");
for (Terms.Bucket bucket : ageAgg1.getBuckets()) {
String keyAsString = bucket.getKeyAsString();
System.out.println("年龄 = " + keyAsString + "===>" + bucket.getDocCount());
}
Avg balanceAvg1 = aggregations.get("balanceAvg");
System.out.println("平均薪资" + balanceAvg1.getValue());
System.out.println(aggregations);
}
@Data
static class Accout
{
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;
}
至此，Elasticsearch介绍和搭建已经完成。接下来是商品业务的实现：
posted on
