elasticsearch group by sum avg max min

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

java ES连接工具类

1. public class ESClientConnectionUtil {
   
2. public static TransportClient client=null;
   
3. public final static String HOST = "192.168.200.211"; //服务器部署
   
4. public final static Integer PORT = 9301; //端口
5.  
6. public static TransportClient getESClient(){
   
7. System.setProperty("es.set.netty.runtime.available.processors", "false");
   
8. if (client == null) {
   
9. synchronized (ESClientConnectionUtil.class) {
   
10. try {
    
11. //设置集群名称
    
12. Settings settings = Settings.builder().put("cluster.name", "es5").put("client.transport.sniff", true).build();
    
13. //创建client
    
14. client = new PreBuiltTransportClient(settings).addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(HOST), PORT));
    
15. } catch (Exception ex) {
    
16. ex.printStackTrace();
17.  
18. System.out.println(ex.getMessage());
    
19. }
    
20. }
    
21. }
    
22. return client;
    
23. }
    
24. public static TransportClient getESClientConnection(){
    
25. if (client == null) {
    
26. System.setProperty("es.set.netty.runtime.available.processors", "false");
    
27. try {
    
28. //设置集群名称
    
29. Settings settings = Settings.builder().put("cluster.name", "es5").put("client.transport.sniff", true).build();
    
30. //创建client
    
31. client = new PreBuiltTransportClient(settings).addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(HOST), PORT));
    
32. } catch (Exception ex) {
    
33. ex.printStackTrace();
    
34. System.out.println(ex.getMessage());
    
35. }
    
36. }
    
37. return client;
    
38. }
39.  
40. //判断索引是否存在
    
41. public static boolean judgeIndex(String index){
    
42. client= getESClientConnection();
    
43. IndicesAdminClient adminClient;
    
44. //查询索引是否存在
    
45. adminClient= client.admin().indices();
    
46. IndicesExistsRequest request = new IndicesExistsRequest(index);
    
47. IndicesExistsResponse responses = adminClient.exists(request).actionGet();
48.  
49. if (responses.isExists()) {
    
50. return true;
    
51. }
    
52. return false;
    
53. }
    
54. }

java ES语句（根据单列进行分组求和）

1. //根据 任务id分组进行求和
   
2. 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
   
3. TermsAggregationBuilder termsBuilder = AggregationBuilders.terms("sum").field("taskid");
   
4. sbuilder.addAggregation(termsBuilder);
   
5. SearchResponse responses= sbuilder.execute().actionGet();
   
6. //得到这个分组的数据集合
   
7. Terms terms = responses.getAggregations().get("sum");
   
8. List<BsKnowledgeInfoDTO> lists = new ArrayList<>();
   
9. for(int i=0;i<terms.getBuckets().size();i++){
   
10. //statistics
    
11. String id =terms.getBuckets().get(i).getKey().toString();//id
    
12. Long sum =terms.getBuckets().get(i).getDocCount();//数量
    
13. System.out.println("=="+terms.getBuckets().get(i).getDocCount()+"------"+terms.getBuckets().get(i).getKey());
    
14. }
    //分别打印出统计的数量和id值

根据多列进行分组求和

1. //根据 任务id分组进行求和
   
2. SearchRequestBuilder sbuilder = client.prepareSearch("hottopic").setTypes("hot");
   
3. //根据taskid进行分组统计，统计出的列别名叫sum
   
4. TermsAggregationBuilder termsBuilder = AggregationBuilders.terms("sum").field("taskid");
   
5. //根据第二个字段进行分组
   
6. TermsAggregationBuilder aAggregationBuilder2 = AggregationBuilders.terms("region_count").field("birthplace");
   //如果存在第三个，以此类推；
   
7. sbuilder.addAggregation(termsBuilder.subAggregation(aAggregationBuilder2));
   
8. SearchResponse responses= sbuilder.execute().actionGet();
   
9. //得到这个分组的数据集合
   
10. Terms terms = responses.getAggregations().get("sum");
    
11. List<BsKnowledgeInfoDTO> lists = new ArrayList<>();
    
12. for(int i=0;i<terms.getBuckets().size();i++){
    
13. //statistics
    
14. String id =terms.getBuckets().get(i).getKey().toString();//id
    
15. Long sum =terms.getBuckets().get(i).getDocCount();//数量
    
16. System.out.println("=="+terms.getBuckets().get(i).getDocCount()+"------"+terms.getBuckets().get(i).getKey());
    
17. }
    
18. //分别打印出统计的数量和id值

对多个field求max/min/sum/avg

1. SearchRequestBuilder requestBuilder = client.prepareSearch("hottopic").setTypes("hot");
   
2. //根据taskid进行分组统计，统计别名为sum
   
3. TermsAggregationBuilder aggregationBuilder1 = AggregationBuilders.terms("sum").field("taskid")
   //根据tasktatileid进行升序排列
   
4. .order(Order.aggregation("tasktatileid", true));
   // 求tasktitleid 进行求平均数 别名为avg_title
   
5. AggregationBuilder aggregationBuilder2 = AggregationBuilders.avg("avg_title").field("tasktitleid");
   //
   
6. AggregationBuilder aggregationBuilder3 = AggregationBuilders.sum("sum_taskid").field("taskid");
   
7. requestBuilder.addAggregation(aggregationBuilder1.subAggregation(aggregationBuilder2).subAggregation(aggregationBuilder3));
   
8. SearchResponse response = requestBuilder.execute().actionGet();
9.  
10. Terms aggregation = response.getAggregations().get("sum");
    
11. Avg terms2 = null;
    
12. Sum term3 = null;
    
13. for (Terms.Bucket bucket : aggregation.getBuckets()) {
    
14. terms2 = bucket.getAggregations().get("avg_title"); // org.elasticsearch.search.aggregations.metrics.avg.InternalAvg
    
15. term3 = bucket.getAggregations().get("sum_taskid"); // org.elasticsearch.search.aggregations.metrics.sum.InternalSum
    
16. System.out.println("编号=" + bucket.getKey() + ";平均=" + terms2.getValue() + ";总=" + term3.getValue());
    
17. }

标签：group,min,max,sum,get,client,taskid,terms,id
来源： https://www.cnblogs.com/cfas/p/16377834.html