ElasticSearch系列整体栏目

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内容	链接地址
【一】ElasticSearch下载和安装	https://zhenghuisheng.blog.csdn.net/article/details/129260827
【二】ElasticSearch概念和基本操作	https://blog.csdn.net/zhenghuishengq/article/details/134121631
【三】ElasticSearch的高级查询Query DSL	https://blog.csdn.net/zhenghuishengq/article/details/134159587
【四】ElasticSearch的聚合查询操作	https://blog.csdn.net/zhenghuishengq/article/details/134159587
【五】SpringBoot整合elasticSearch	https://blog.csdn.net/zhenghuishengq/article/details/134212200

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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>

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();}@Beanpublic 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 {@Resourceprivate RestHighLevelClient client;@Testpublic 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

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());
}