【ElasticSearch系列-05】SpringBoot整合elasticSearch

缺乏、安全感 2024-02-19 07:46 49阅读 0赞

ElasticSearch系列整体栏目

--------------------

<table> 
 <thead> 
  <tr> 
   <th align="left">内容</th> 
   <th>链接地址</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="left"><font>【一】ElasticSearch下载和安装</font></td> 
   <td><a href="https://zhenghuisheng.blog.csdn.net/article/details/129260827" rel="nofollow">https://zhenghuisheng.blog.csdn.net/article/details/129260827</a></td> 
  </tr> 
  <tr> 
   <td align="left"><font>【二】ElasticSearch概念和基本操作</font></td> 
   <td><a href="https://blog.csdn.net/zhenghuishengq/article/details/134121631" rel="nofollow">https://blog.csdn.net/zhenghuishengq/article/details/134121631</a></td> 
  </tr> 
  <tr> 
   <td align="left"><font>【三】ElasticSearch的高级查询Query DSL</font></td> 
   <td><a href="https://blog.csdn.net/zhenghuishengq/article/details/134159587" rel="nofollow">https://blog.csdn.net/zhenghuishengq/article/details/134159587</a></td> 
  </tr> 
  <tr> 
   <td align="left"><font>【四】ElasticSearch的聚合查询操作</font></td> 
   <td><a href="https://blog.csdn.net/zhenghuishengq/article/details/134159587" rel="nofollow">https://blog.csdn.net/zhenghuishengq/article/details/134159587</a></td> 
  </tr> 
  <tr> 
   <td align="left"><font>【五】SpringBoot整合elasticSearch</font></td> 
   <td><a href="https://blog.csdn.net/zhenghuishengq/article/details/134212200" rel="nofollow">https://blog.csdn.net/zhenghuishengq/article/details/134212200</a></td> 
  </tr> 
 </tbody> 
</table>

--------------------

#### SpringBoot整合elasticSearch ####

*  一，SpringBoot整合ElasticSearch
 *   *  1，需要的依赖以及版本
     *  2，创建config配置类并测试连接
     *  3，增删改查测试
     *   *  3.1，索引插入数据
         *  3.2，根据id查询数据
         *  3.3，删除一条数据
     *  4，普通查询
     *   *  4.1，match条件查询
         *  4.2，term精确匹配
         *  4.3，prefix前缀查询
         *  4.4，通配符查询wildcard
         *  4.5，范围查询
         *  4.6，fuzzy模糊查询
         *  4.7，highlight高亮查询
     *  5，聚合查询
     *   *  5.1，aggs聚合查询
         *  5.2，获取最终结果

## 一，SpringBoot整合ElasticSearch ##

前面几篇讲解了es的安装，dsl语法，聚合查询等，接下来这篇主要就是讲解通过java的方式来操作es，这里选择通过springboot的方式整合ElasticSearchSearch

在学习这个整合之前，可以查看对应的官网资料：[https://www.elastic.co/guide/en/elasticsearch/client/java-api-client/7.17/connecting.html][https_www.elastic.co_guide_en_elasticsearch_client_java-api-client_7.17_connecting.html]

### 1，需要的依赖以及版本 ###

首先创建springboot项目，然后需要的依赖如下，我前面用的是7.7.0的版本，因此这里继续使用这个版本。其他的依赖根据个人需要选择

<properties>
        <java.version>8</java.version>
        <elasticsearch.version>7.7.0</elasticsearch.version>
    </properties>
    <dependencies>
    	<dependency>
    	    <groupId>org.elasticsearch.client</groupId>
    	    <artifactId>elasticsearch-rest-high-level-client</artifactId>
    	    <version>7.7.0</version>
    	</dependency>
    </dependencies>

### 2，创建config配置类并测试连接 ###

随后创建一个config的配置类，用于连接上ElasticSearch，我这边是单机版，并没有集群

/**
     * 连接es的工具类
     */
    @Configuration
    public class ElasticSearchConfig {
         
        public static final RequestOptions COMMON_OPTIONS;
        static {
        
            RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
            COMMON_OPTIONS = builder.build();
        }
        @Bean
        public RestHighLevelClient esRestClient(){
        
            RestHighLevelClient client = new RestHighLevelClient(
                    RestClient.builder(new HttpHost("xx.xx.xx.xx", 9200, "http")));
            return  client;
        }
    }

在创建好了之后，可以直接在test类中进行测试，看能否连接成功

@RunWith(SpringRunner.class)
    @SpringBootTest
    public class StudyApplicationTests {
        
        @Resource
        private RestHighLevelClient client;
    
        @Test
        public void contextLoads() {
        
            System.out.println(restHighLevelClient);
        }
    }

在运行之后，如果打印出了以下这句话，表示整合成功

org.elasticsearch.client.RestHighLevelClient@7d151a

### 3，增删改查测试 ###

#### 3.1，索引插入数据 ####

首先先创建一个users的索引，并向里面插入一条数据。插入和更新都可以用这个方法

//创建一个user索引，并且插入一条数据
    @Test
    public void addData() throws IOException {
        
        //创建一个索引
        IndexRequest userIndex = new IndexRequest("users");
        User user = new User();
        user.setId(1);
        user.setUsername("Tom");
        user.setPassword("123456");
        user.setAge(18);
        user.setSex("女");
       //添加数据
       userIndex.source(JSON.toJSONString(user), XContentType.JSON);
       IndexResponse response = client.index(userIndex, ElasticSearchConfig.COMMON_OPTIONS);
    	//响应数据
    	System.out.println(response);
    }

随后再在kibana中查询这个索引，可以看到这条数据是已经插入成功的，并且索引页创建成功

![在这里插入图片描述][c17f57042ec748738ad2209433910097.png]

#### 3.2，根据id查询数据 ####

查询id为1的数据，需要通过QueryBuild构造器查询

@Test
    public void getById() throws IOException {
        
        SearchRequest request = new SearchRequest("users");
        SearchSourceBuilder builder = new SearchSourceBuilder();
        builder.query(QueryBuilders.matchQuery("id", "1"));
        request.source(builder);
        SearchResponse response = client.search(request, RequestOptions.DEFAULT);
        System.out.println(response);
    }

#### 3.3，删除一条数据 ####

删除刚刚创建的这条数据，这里直接设置id为1即可

@Test
    public void deleteById() throws Exception{
        
        DeleteRequest request = new DeleteRequest("users");
        request.id("1");
        DeleteResponse delete = client.delete(request, ElasticSearchConfig.COMMON_OPTIONS);
        System.out.println(delete);
    }

### 4，普通查询 ###

这里主要是结合本人写的第三篇Query DSL的语法，通过java的方式写出依旧是先创建一个员工的信息索引，并且设置字段得我属性

PUT /employees
    {
        
      "mappings": {
        
        "properties": {
        
          "name":{
        
            "type": "keyword"
          },
          "job":{
        
            "type": "keyword"
          },
          "salary":{
        
            "type": "integer"
          }
        }
      }
    }

随后批量的插入10条数据

PUT /employees/_bulk
    {
         "index" : {
          "_id" : "1" } }
    {
         "name" : "huisheng1","job":"python","salary":35000 }
    {
         "index" : {
          "_id" : "2" } }
    {
         "name" : "huisheng2","job":"java","salary": 50000}
    {
         "index" : {
          "_id" : "3" } }
    {
         "name" : "huisheng3","job":"python","salary":18000 }
    {
         "index" : {
          "_id" : "4" } }
    {
         "name" : "huisheng4","job":"java","salary": 22000}
    {
         "index" : {
          "_id" : "5" } }
    {
         "name" : "huisheng5","job":"javascript","salary":18000 }
    {
         "index" : {
          "_id" : "6" } }
    {
         "name" : "huisheng6","job":"javascript","salary": 25000}
    {
         "index" : {
          "_id" : "7" } }
    {
         "name" : "huisheng7","job":"c++","salary":20000 }
    {
         "index" : {
          "_id" : "8" } }
    {
         "name" : "huisheng8","job":"c++","salary": 20000}
    {
         "index" : {
          "_id" : "9" } }
    {
         "name" : "huisheng9","job":"java","salary":22000 }
    {
         "index" : {
          "_id" : "10" } }
    {
         "name" : "huisheng10","job":"java","salary": 9000}

#### 4.1，match条件查询 ####

首先是分页查询，分页查询的queryDSL的语法如下

GET /employees/_search
    {
        
      "query": {
        
        "match": {
        
          "job": "java"
        }
      }
    }

java的语法如下

SearchRequest request = new SearchRequest("employees");
    SearchSourceBuilder builder = new SearchSourceBuilder();
    builder.query(QueryBuilders.matchQuery("job", "java"));
    request.source(builder);
    SearchResponse response = client.search(request, RequestOptions.DEFAULT);
    System.out.println(response);

短语匹配的语法如下

builder.query(QueryBuilders.matchPhraseQuery("job","java"));

多字段查询的语法如下

String fields[] = {
        "job","name"};
    builder.query(QueryBuilders.multiMatchQuery("java",fields));

queryString的语法如下

builder.query(QueryBuilders.queryStringQuery("java"));

#### 4.2，term精确匹配 ####

GET /employees/_search
    {
        
      "query": {
        
        "term": {
        
          "job": "java"
        }
      }
    }

精确匹配通过java的方式如下

builder.query(QueryBuilders.termQuery("job","java"));

#### 4.3，prefix前缀查询 ####

PUT /employees/_search
    {
        
        "query":{
        
            "prefix":{
        
                "name":{
        
                    "value":"huisheng1"
                }
            }
        }
    }

前缀查询的java方式如下

builder.query(QueryBuilders.prefixQuery("name","huisheng1"));

#### 4.4，通配符查询wildcard ####

GET /employees/_search
    {
        
      "query": {
        
        "wildcard": {
        
          "job": {
        
            "value": "*py*"
          }
        }
      }
    }

通配符查询的java方式如下

builder.query(QueryBuilders.wildcardQuery("job","py"));

#### 4.5，范围查询 ####

POST /employees/_search
    {
        
      "query": {
        
        "range": {
        
          "salary": {
        
            "gte": 25000
          }
        }
      }
    }

范围查询的java方式如下

builder.query(QueryBuilders.rangeQuery("salary").gte(25000));

#### 4.6，fuzzy模糊查询 ####

GET /employees/_search
    {
        
      "query": {
        
        "fuzzy": {
        
          "job": {
        
            "value": "javb",
            "fuzziness": 1    //表示允许错一个字
          }
        }
      }
    }

模糊查询的java方式如下

builder.query(QueryBuilders.fuzzyQuery("job","javb").fuzziness(Fuzziness.ONE));

#### 4.7，highlight高亮查询 ####

GET /employees/_search
    {
        
      "query": {
        
        "term": {
        
          "job": {
        
            "value": "java"
          }
        }
      },
      "highlight": {
        
        "fields": {
        
          "*":{
        }
        }
      }
    }

高亮查询的java方式如下

builder.query(QueryBuilders.termQuery("job","java"));
    HighlightBuilder highlightBuilder = new HighlightBuilder();
    highlightBuilder.field("job");
    builder.highlighter(highlightBuilder);

### 5，聚合查询 ###

#### 5.1，aggs聚合查询 ####

先通过job进行分组查询，再拿到结果后再进行stats查询，求最大值，最小值，平均值等

POST /employees/_search
    {
        
      "size": 0,
      "aggs": {
        
        "name": {
        
          "terms": {
        
            "field": "job"
          },
          "aggs": {
        
            "stats_salary": {
        
              "stats": {
        
                "field": "salary"
              }
            }
          }
        }
      }
    }

其java代码如下，需要注意的点就是，如果存在二级聚合，那么需要调用这个 **subAggregation** 方法，如果只需要聚合的结果而不需要查询的结果，可以直接在SearchSourceBuilder的实例设置为0即可。

@Test
    public void toAgg() throws  Exception{
        
        //创建检索请求
        SearchRequest searchRequest = new SearchRequest();
        //指定索引
        searchRequest.indices("employees");
        //构建检索条件
        SearchSourceBuilder builder = new SearchSourceBuilder();
        //构建聚合条件
        TermsAggregationBuilder aggregationBuilder = AggregationBuilders.terms("jobData").field("job");
            aggregationBuilder.subAggregation(AggregationBuilders.stats("salaryData").field("salary"));
    	//将聚合条件加入到检索条件中
    	builder.aggregation(aggregationBuilder);
    	//只要聚合的结果，不需要查询的结果
    	builder.size(0);
    	searchRequest.source(builder);
    	//执行检索
    	SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        System.out.println("检索结果：" + searchResponse);
    }

打印的结果如下，和预期要打印的结果是一致的

{
        "took":4,"timed_out":false,"_shards":{
        "total":1,"successful":1,"skipped":0,"failed":0},"hits":{
        "total":{
        "value":10,"relation":"eq"},"max_score":null,"hits":[]},"aggregations":{
        "sterms#jobData":{
        "doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{
        "key":"java","doc_count":4,"stats#salaryData":{
        "count":4,"min":9000.0,"max":50000.0,"avg":25750.0,"sum":103000.0}},{
        "key":"c++","doc_count":2,"stats#salaryData":{
        "count":2,"min":20000.0,"max":20000.0,"avg":20000.0,"sum":40000.0}},{
        "key":"javascript","doc_count":2,"stats#salaryData":{
        "count":2,"min":18000.0,"max":25000.0,"avg":21500.0,"sum":43000.0}},{
        "key":"python","doc_count":2,"stats#salaryData":{
        "count":2,"min":18000.0,"max":35000.0,"avg":26500.0,"sum":53000.0}}]}}}

除了上面的state求全部的最大值，最小值等，还可以分别的求最大值，最小值，平均值，个数等，求平均值的的示例如下，需要使用到这个 **AvgAggregationBuilder** 构造器

AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders.avg("salaryData").field("salary");
    //将聚合条件加入到检索条件中
    builder.aggregation(avgAggregationBuilder);

求最大值的示例如下，需要使用到这个 **MaxAggregationBuilder** 构造器

MaxAggregationBuilder maxAggregationBuilder = AggregationBuilders.max("maxData").field("salary");
    //将聚合条件加入到检索条件中
     builder.aggregation(maxAggregationBuilder);

求最小值的示例如下，需要使用到这个 **MinAggregationBuilder** 构造器

MinAggregationBuilder minAggregationBuilder = AggregationBuilders.min("minData").field("salary");
    //将聚合条件加入到检索条件中
    builder.aggregation(minAggregationBuilder);

求总个数的示例如下，需要使用到这个 **ValueCountAggregationBuilder** 构造器

ValueCountAggregationBuilder countBuilder = AggregationBuilders.count("countData").field("salary");
    //将聚合条件加入到检索条件中
    builder.aggregation(countBuilder);

#### 5.2，获取最终结果 ####

上面在查询之后，会获取 **SearchResponse** 的对象，这里面就值执行查询后返回的结果

SearchResponse searchResponse

随后可以直接过滤结果，通过for循环去遍历这个 getHits

SearchHits hits = searchResponse.getHits();
    SearchHit[] searchHits = hits.getHits();
    for (SearchHit searchHit : searchHits) {
        
        String sourceAsString = searchHit.getSourceAsString();
        Employees employees = JSON.parseObject(sourceAsString, Employees.class);
        System.out.println(employees);
    
    }

或者直接获取聚合操作结果的值

//获取jobData聚合。还有Avg、Max、Min等
    Terms maxData = aggregations.get("jobData");
    for (Terms.Bucket bucket : maxData.getBuckets()) {
        
    	String keyAsString = bucket.getKeyAsString();
    	System.out.println("job职业：" + keyAsString + " 数量==> " + bucket.getDocCount());
    }

[https_www.elastic.co_guide_en_elasticsearch_client_java-api-client_7.17_connecting.html]: https://www.elastic.co/guide/en/elasticsearch/client/java-api-client/7.17/connecting.html
[c17f57042ec748738ad2209433910097.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/19/fec78058117d40aeaf3cf82157064888.png