【ElasticSearch系列-05】SpringBoot整合elasticSearch

ElasticSearch系列整体栏目

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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

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>
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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;
    }
}
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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);
    }
}
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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
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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);
}
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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

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);
}
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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);
}
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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"
      }
    }
  }
}
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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}
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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"
    }
  }
}
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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);
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短语匹配的语法如下

builder.query(QueryBuilders.matchPhraseQuery("job","java"));
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多字段查询的语法如下

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

builder.query(QueryBuilders.queryStringQuery("java"));
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4.2，term精确匹配

GET /employees/_search
{
  "query": {
    "term": {
      "job": "java"
    }
  }
}
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精确匹配通过java的方式如下

builder.query(QueryBuilders.termQuery("job","java"));
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4.3，prefix前缀查询

PUT /employees/_search
{
    "query":{
        "prefix":{
            "name":{
                "value":"huisheng1"
            }
        }
    }
}
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前缀查询的java方式如下

builder.query(QueryBuilders.prefixQuery("name","huisheng1"));
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4.4，通配符查询wildcard

GET /employees/_search
{
  "query": {
    "wildcard": {
      "job": {
        "value": "*py*"
      }
    }
  }
}
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通配符查询的java方式如下

builder.query(QueryBuilders.wildcardQuery("job","py"));
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4.5，范围查询

POST /employees/_search
{
  "query": {
    "range": {
      "salary": {
        "gte": 25000
      }
    }
  }
}
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范围查询的java方式如下

builder.query(QueryBuilders.rangeQuery("salary").gte(25000));
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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
      }
    }
  }
}
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模糊查询的java方式如下

builder.query(QueryBuilders.fuzzyQuery("job","javb").fuzziness(Fuzziness.ONE));
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4.7，highlight高亮查询

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

}
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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());
}
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