這篇文章主要介紹了java使用elasticsearch分組進行聚合查詢過程解析,文中通過示例程式碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值,需要的朋友可以參考下

java連線elasticsearch 進行聚合查詢進行相應操作

一：對單個欄位進行分組求和

1、表結構圖片：

根據任務id分組，分別統計出每個任務id下有多少個文字標題

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

java ES連線工具類

public class ESClientConnectionUtil {
  public static TransportClient client=null;
  public final static String HOST = "192.168.200.211"; //伺服器部署
  public final static Integer PORT = 9301; //埠

  public static TransportClient getESClient(){
    System.setProperty("es.set.netty.runtime.available.processors","false");
    if (client == null) {
      synchronized (ESClientConnectionUtil.class) {
        try {
          //設定叢集名稱
          Settings settings = Settings.builder().put("cluster.name","es5").put("client.transport.sniff",true).build();
          //建立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 {
          //設定叢集名稱
          Settings settings = Settings.builder().put("cluster.name",PORT));
        } catch (Exception ex) {
          ex.printStackTrace();
          System.out.println(ex.getMessage());
      }
    }
    return client;
  }

  //判斷索引是否存在
  public static boolean judgeIndex(String index){
    client= getESClientConnection();
     IndicesAdminClient adminClient;
    //查詢索引是否存在
    adminClient= client.admin().indices();
    IndicesExistsRequest request = new IndicesExistsRequest(index);
    IndicesExistsResponse responses = adminClient.exists(request).actionGet();

    if (responses.isExists()) {
      return true;
    }
    return false;
  }
}

java ES語句（根據單列進行分組求和）

//根據 任務id分組進行求和
 SearchRequestBuilder sbuilder = client.prepareSearch("hottopic").setTypes("hot");
//根據taskid進行分組統計，統計出的列別名叫sum
 TermsAggregationBuilder termsBuilder = AggregationBuilders.terms("sum").field("taskid");

 sbuilder.addAggregation(termsBuilder);
 SearchResponse responses= sbuilder.execute().actionGet();
//得到這個分組的資料集合
 Terms terms = responses.getAggregations().get("sum");
 List<BsKnowledgeInfoDTO> lists = new ArrayList<>();
for(int i=0;i<terms.getBuckets().size();i++){
  //statistics
  String id =terms.getBuckets().get(i).getKey().toString();//id
  Long sum =terms.getBuckets().get(i).getDocCount();//數量
System.out.println("=="+terms.getBuckets().get(i).getDocCount()+"------"+terms.getBuckets().get(i).getKey());
}
//分別打印出統計的數量和id值

根據多列進行分組求和

//根據 任務id分組進行求和
 SearchRequestBuilder sbuilder = client.prepareSearch("hottopic").setTypes("hot");
//根據taskid進行分組統計，統計出的列別名叫sum
 TermsAggregationBuilder termsBuilder = AggregationBuilders.terms("sum").field("taskid");
//根據第二個欄位進行分組
 TermsAggregationBuilder aAggregationBuilder2 = AggregationBuilders.terms("region_count").field("birthplace");
//如果存在第三個，以此類推；
 sbuilder.addAggregation(termsBuilder.subAggregation(aAggregationBuilder2));
 SearchResponse responses= sbuilder.execute().actionGet();
//得到這個分組的資料集合
 Terms terms = responses.getAggregations().get("sum");
 List<BsKnowledgeInfoDTO> lists = new ArrayList<>();
for(int i=0;i<terms.getBuckets().size();i++){
  //statistics
  String id =terms.getBuckets().get(i).getKey().toString();//id
  Long sum =terms.getBuckets().get(i).getDocCount();//數量
System.out.println("=="+terms.getBuckets().get(i).getDocCount()+"------"+terms.getBuckets().get(i).getKey());
}
//分別打印出統計的數量和id值

對多個field求max/min/sum/avg

SearchRequestBuilder requestBuilder = client.prepareSearch("hottopic").setTypes("hot");
//根據taskid進行分組統計，統計別名為sum
    TermsAggregationBuilder aggregationBuilder1 = AggregationBuilders.terms("sum").field("taskid") 
//根據tasktatileid進行升序排列
        .order(Order.aggregation("tasktatileid",true));
// 求tasktitleid 進行求平均數 別名為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("編號=" + bucket.getKey() + ";平均=" + terms2.getValue() + ";總=" + term3.getValue());
    }

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