这篇文章主要介绍了java使用elasticsearch分组进行聚合查询过程解析,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下

java连接elasticsearch 进行聚合查询进行相应操作

一：对单个字段进行分组求和

1、表结构图片：

根据任务id分组，分别统计出每个任务id下有多少个文字标题

1.SQL：select id, count(*) as sum from task group by taskid;

java ES连接工具类

public class ESClientConnectionUtil {

public static TransportClient client=null;

public final static String HOST = "192.168.200.211"; //服务器部署

public final static Integer PORT = 9301; //端口

public static TransportClient getESClient(){

System.setProperty("es.set.netty.runtime.available.processors", "false");

if (client == null) {

synchronized (ESClientConnectionUtil.class) {

try {

//设置集群名称

Settings settings = Settings.builder().put("cluster.name", "es5").put("client.transport.sniff", true).build();

//创建client

client = new PreBuiltTransportClient(settings).addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(HOST), PORT));

} catch (Exception ex) {

ex.printStackTrace();

System.out.println(ex.getMessage());

}

}

}

return client;

}

public static TransportClient getESClientConnection(){

if (client == null) {

System.setProperty("es.set.netty.runtime.available.processors", "false");

try {

//设置集群名称

Settings settings = Settings.builder().put("cluster.name", "es5").put("client.transport.sniff", true).build();

//创建client

client = new PreBuiltTransportClient(settings).addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(HOST), PORT));

} catch (Exception ex) {

ex.printStackTrace();

System.out.println(ex.getMessage());

}

}

return client;

}

//判断索引是否存在

public static boolean judgeIndex(String index){

client= getESClientConnection();

IndicesAdminClient adminClient;

//查询索引是否存在

adminClient= client.admin().indices();

IndicesExistsRequest request = new IndicesExistsRequest(index);

IndicesExistsResponse responses = adminClient.exists(request).actionGet();

if (responses.isExists()) {

return true;

}

return false;

}

}

java ES语句(根据单列进行分组求和)

//根据 任务id分组进行求和

SearchRequestBuilder sbuilder = client.prepareSearch("hottopic").setTypes("hot");

//根据taskid进行分组统计，统计出的列别名叫sum

TermsAggregationBuilder termsBuilder = AggregationBuilders.terms("sum").field("taskid");

sbuilder.addAggregation(termsBuilder);

SearchResponse responses= sbuilder.execute().actionGet();

//得到这个分组的数据集合

Terms terms = responses.getAggregations().get("sum");

List lists = new ArrayList<>();

for(int i=0;i

//statistics

String id =terms.getBuckets().get(i).getKey().toString();//id

Long sum =terms.getBuckets().get(i).getDocCount();//数量

System.out.println("=="+terms.getBuckets().get(i).getDocCount()+"------"+terms.getBuckets().get(i).getKey());

}

//分别打印出统计的数量和id值

根据多列进行分组求和

//根据 任务id分组进行求和

SearchRequestBuilder sbuilder = client.prepareSearch("hottopic").setTypes("hot");

//根据taskid进行分组统计，统计出的列别名叫sum

TermsAggregationBuilder termsBuilder = AggregationBuilders.terms("sum").field("taskid");

//根据第二个字段进行分组

TermsAggregationBuilder aAggregationBuilder2 = AggregationBuilders.terms("region_count").field("birthplace");

//如果存在第三个，以此类推；

sbuilder.addAggregation(termsBuilder.subAggregation(aAggregationBuilder2));

SearchResponse responses= sbuilder.execute().actionGet();

//得到这个分组的数据集合

Terms terms = responses.getAggregations().get("sum");

List lists = new ArrayList<>();

for(int i=0;i

//statistics

String id =terms.getBuckets().get(i).getKey().toString();//id

Long sum =terms.getBuckets().get(i).getDocCount();//数量

System.out.println("=="+terms.getBuckets().get(i).getDocCount()+"------"+terms.getBuckets().get(i).getKey());

}

//分别打印出统计的数量和id值

对多个field求max/min/sum/avg

SearchRequestBuilder requestBuilder = client.prepareSearch("hottopic").setTypes("hot");

//根据taskid进行分组统计，统计别名为sum

TermsAggregationBuilder aggregationBuilder1 = AggregationBuilders.terms("sum").field("taskid")

//根据tasktatileid进行升序排列

.order(Order.aggregation("tasktatileid", true));

// 求tasktitleid 进行求平均数 别名为avg_title

AggregationBuilder aggregationBuilder2 = AggregationBuilders.avg("avg_title").field("tasktitleid");

//

AggregationBuilder aggregationBuilder3 = AggregationBuilders.sum("sum_taskid").field("taskid");

requestBuilder.addAggregation(aggregationBuilder1.subAggregation(aggregationBuilder2).subAggregation(aggregationBuilder3));

SearchResponse response = requestBuilder.execute().actionGet();

Terms aggregation = response.getAggregations().get("sum");

Avg terms2 = null;

Sum term3 = null;

for (Terms.Bucket bucket : aggregation.getBuckets()) {

terms2 = bucket.getAggregations().get("avg_title"); // org.elasticsearch.search.aggregations.metrics.avg.InternalAvg

term3 = bucket.getAggregations().get("sum_taskid"); // org.elasticsearch.search.aggregations.metrics.sum.InternalSum

System.out.println("编号=" + bucket.getKey() + ";平均=" + terms2.getValue() + ";总=" + term3.getValue());

}

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。