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 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 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()); } 以上是“java如何使用elasticsearch分组进行聚合查询”这篇文章的所有内容，感谢各位的阅读！希望分享的内容对大家有帮助，更多相关知识，欢迎关注创新互联行业资讯频道！
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