这篇文章主要介绍java如何使用elasticsearch分组进行聚合查询，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！

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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

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

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());    }

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