Elasticsearch 的API 分为 REST Client API(http请求形式)以及 transportClient
API两种。相比来说transportClient API效率更高,transportClient
是通过Elasticsearch内部RPC的形式进行请求的,连接可以是一个长连接,相当于是把客户端的请求当成
Elasticsearch 集群的一个节点,当然 REST Client API 也支持http
keepAlive形式的长连接,只是非内部RPC形式。但是从Elasticsearch 7 后就会移除transportClient
。主要原因是transportClient 难以向下兼容版本。
利用9300端口的是spring-data-elasticsearch:transport-api.jar,但是这种方式因为对应的SpringBoot版本不一致,造成对应的transport-api.jar也不同,不能适配es的版本,而且ElasticSearch7.x中已经不推荐使用了,ElasticSearch 8之后更是废弃了,所以我们不做过多的介绍
基于9200端口的方式也有多种
我们在商城服务中创建一个检索的SpringBoot服务
添加对应的依赖:官方地址:https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-getting-started-maven.html#java-rest-high-getting-started-maven-maven
公共依赖不要忘了,同时我们在公共依赖中依赖了MyBatisPlus所以我们需要在search服务中排除数据源,不然启动报错
然后我们需要把这个服务注册到Nacos注册中心中,这块操作了很多遍,不重复
添加对应的ElasticSearch的配置类
/**
* ElasticSearch的配置类
*/
@Configuration
public class MallElasticSearchConfiguration {
@Bean
public RestHighLevelClient restHighLevelClient(){
RestClientBuilder builder = RestClient.builder(new HttpHost("192.168.56.100", 9200, "http"));
RestHighLevelClient client = new RestHighLevelClient(builder);
return client;
}
}
测试:
我们就在ElasticSearch的配置文件中设置
然后就可以结合官方文档来实现文档数据的存储
package com.msb.mall.mallsearch;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.util.JSONPObject;
import com.msb.mall.mallsearch.config.MallElasticSearchConfiguration;
import lombok.Data;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.action.index.IndexResponse;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.common.xcontent.XContentType;
import org.json.JSONObject;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
@SpringBootTest
class MallSearchApplicationTests {
@Autowired
private RestHighLevelClient client;
@Test
void contextLoads() {
System.out.println("--->"+client);
}
/**
* 测试保存文档
*/
@Test
void saveIndex() throws Exception {
IndexRequest indexRequest = new IndexRequest("system");
indexRequest.id("1");
// indexRequest.source("name","bobokaoya","age",18,"gender","男");
User user = new User();
user.setName("bobo");
user.setAge(22);
user.setGender("男");
// 用Jackson中的对象转json数据
ObjectMapper objectMapper = new ObjectMapper();
String json = objectMapper.writeValueAsString(user);
indexRequest.source(json, XContentType.JSON);
// 执行操作
IndexResponse index = client.index(indexRequest, MallElasticSearchConfiguration.COMMON_OPTIONS);
// 提取有用的返回信息
System.out.println(index);
}
@Data
class User{
private String name;
private Integer age;
private String gender;
}
}
之后成功
参考官方文档可以获取到处理各种检索情况的API
案例1:检索出所有的bank索引的所有文档
@Test
void searchIndexAll() throws IOException {
// 1.创建一个 SearchRequest 对象
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank"); // 设置我们要检索的数据对应的索引库
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
/*sourceBuilder.query();
sourceBuilder.from();
sourceBuilder.size();
sourceBuilder.aggregation();*/
searchRequest.source(sourceBuilder);
// 2.如何执行检索操作
SearchResponse response = client.search(searchRequest, MallElasticSearchConfiguration.COMMON_OPTIONS);
// 3.获取检索后的响应对象,我们需要解析出我们关心的数据
System.out.println("ElasticSearch检索的信息:"+response);
}
案例2:根据address全文检索
@Test
void searchIndexByAddress() throws IOException {
// 1.创建一个 SearchRequest 对象
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank"); // 设置我们要检索的数据对应的索引库
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 查询出bank下 address 中包含 mill的记录
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
searchRequest.source(sourceBuilder);
// System.out.println(searchRequest);
// 2.如何执行检索操作
SearchResponse response = client.search(searchRequest, MallElasticSearchConfiguration.COMMON_OPTIONS);
// 3.获取检索后的响应对象,我们需要解析出我们关心的数据
System.out.println("ElasticSearch检索的信息:"+response);
}
案例3:嵌套的聚合操作:检索出bank下的年龄分布和每个年龄段的平均薪资
/**
* 聚合:嵌套聚合
* @throws IOException
*/
@Test
void searchIndexAggregation() throws IOException {
// 1.创建一个 SearchRequest 对象
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank"); // 设置我们要检索的数据对应的索引库
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 查询出bank下 所有的文档
sourceBuilder.query(QueryBuilders.matchAllQuery());
// 聚合 aggregation
// 聚合bank下年龄的分布和每个年龄段的平均薪资
AggregationBuilder aggregationBuiler = AggregationBuilders.terms("ageAgg")
.field("age")
.size(10);
// 嵌套聚合
aggregationBuiler.subAggregation(AggregationBuilders.avg("balanceAvg").field("balance"));
sourceBuilder.aggregation(aggregationBuiler);
sourceBuilder.size(0); // 聚合的时候就不用显示满足条件的文档内容了
searchRequest.source(sourceBuilder);
System.out.println(sourceBuilder);
// 2.如何执行检索操作
SearchResponse response = client.search(searchRequest, MallElasticSearchConfiguration.COMMON_OPTIONS);
// 3.获取检索后的响应对象,我们需要解析出我们关心的数据
System.out.println(response);
}
案例4:并行的聚合操作:查询出bank下年龄段的分布和总的平均薪资
/**
* 聚合
* @throws IOException
*/
@Test
void searchIndexAggregation1() throws IOException {
// 1.创建一个 SearchRequest 对象
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank"); // 设置我们要检索的数据对应的索引库
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 查询出bank下 所有的文档
sourceBuilder.query(QueryBuilders.matchAllQuery());
// 聚合 aggregation
// 聚合bank下年龄的分布和平均薪资
AggregationBuilder aggregationBuiler = AggregationBuilders.terms("ageAgg")
.field("age")
.size(10);
sourceBuilder.aggregation(aggregationBuiler);
// 聚合平均年龄
AvgAggregationBuilder balanceAggBuilder = AggregationBuilders.avg("balanceAgg").field("age");
sourceBuilder.aggregation(balanceAggBuilder);
sourceBuilder.size(0); // 聚合的时候就不用显示满足条件的文档内容了
searchRequest.source(sourceBuilder);
System.out.println(sourceBuilder);
// 2.如何执行检索操作
SearchResponse response = client.search(searchRequest, MallElasticSearchConfiguration.COMMON_OPTIONS);
// 3.获取检索后的响应对象,我们需要解析出我们关心的数据
System.out.println(response);
}
案例5:处理检索后的结果
@Test
void searchIndexResponse() throws IOException {
// 1.创建一个 SearchRequest 对象
SearchRequest searchRequest = new SearchRequest();
searchRequest.indices("bank"); // 设置我们要检索的数据对应的索引库
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
// 查询出bank下 address 中包含 mill的记录
sourceBuilder.query(QueryBuilders.matchQuery("address","mill"));
searchRequest.source(sourceBuilder);
// System.out.println(searchRequest);
// 2.如何执行检索操作
SearchResponse response = client.search(searchRequest, MallElasticSearchConfiguration.COMMON_OPTIONS);
// 3.获取检索后的响应对象,我们需要解析出我们关心的数据
// System.out.println("ElasticSearch检索的信息:"+response);
RestStatus status = response.status();
TimeValue took = response.getTook();
SearchHits hits = response.getHits();
TotalHits totalHits = hits.getTotalHits();
TotalHits.Relation relation = totalHits.relation;
long value = totalHits.value;
float maxScore = hits.getMaxScore(); // 相关性的最高分
SearchHit[] hits1 = hits.getHits();
for (SearchHit documentFields : hits1) {
/*"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025*/
//documentFields.getIndex(),documentFields.getType(),documentFields.getId(),documentFields.getScore();
String json = documentFields.getSourceAsString();
//System.out.println(json);
// JSON字符串转换为 Object对象
ObjectMapper mapper = new ObjectMapper();
Account account = mapper.readValue(json, Account.class);
System.out.println("account = " + account);
}
//System.out.println(relation.toString()+"--->" + value + "--->" + status);
}
@ToString
@Data
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;
}
数据的结果: