本文收录于github和gitee ，里面有我完整的Java系列文章，学习或面试都可以看看

（一）概述

在前面关于ES的一系列文章中，已经介绍了ES的概念、常用操作、JavaAPI以及实际的一个小demo，但是在真实的应用场景中，还有可能会有更高阶的一些用法，今天主要介绍两种相对来说会更难一些的操作，聚合查询。该文档基于ElasticSearch7.6，将介绍restful查询语法以及JavaApi。

阅读本文需要你有ElasticSearch的基础。

（二）前期数据准备

这里准备了包含姓名、年龄、教室、性别和成绩五个字段的数据

PUT /test4
{"mappings" : {"properties" : {"name" : {"type" : "text"},"age":{"type": "integer"},"classroom":{"type": "keyword"},"gender":{"type": "keyword"},"grade":{"type": "integer"}}}
}

PUT /test4/_bulk
{"index": {"_id": 1}}
{"name":"张三","age":18,"classroom":"1","gender":"男","grade":80}
{"index": {"_id": 2}}
{"name":"李四","age":20,"classroom":"2","gender":"男","grade":60}
{"index": {"_id": 3}}
{"name":"王五","age":20,"classroom":"2","gender":"女","grade":70}
{"index": {"_id": 4}}
{"name":"赵六","age":19,"classroom":"1","gender":"女","grade":90}
{"index": {"_id": 5}}
{"name":"毛七","age":20,"classroom":"1","gender":"男","grade":90}

（三）聚合查询

ES中的聚合操作提供了强大的分组及数理计算的能力，ES中聚合从大体上可以分为四种方式：

1、Metrics Aggregation 提供了诸如Max，Min，Avg的数值计算能力

2、Bucket Aggregation 提供了分桶的能力，简单来讲就是将一类相同的数据聚合到一起

3、Pipeline Aggregation 管道聚合，对其他聚合进行二次聚合

4、Matrix Aggregation 对多个字段进行操作并返回矩阵结果

ES官网提供了全部聚合查询文档，这篇文章将介绍常用的几种聚合查询的语法以及JavaApi：

https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.6/java-rest-high-aggregation-builders.html#_metrics_aggregations

（四）Metrics Aggregation

4.1 AVG

avg用于计算聚合文档中提取的数值的平均值，restful查询语法如下：

POST /test4/_search
{"aggs": {"avg_grade": {"avg": {"field": "grade"}}}
}

查询得到的结果如下:

接着是JavaApi，核心在于使用AggregationBuilders的avg方法，第七行代码对应于上面的操作。

@Test
public void testAvg() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.avg("agg_grade").field("grade")).size(0);request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);//注意这里要把Aggregation类型转化为ParsedAvg类型ParsedAvg aggregation = search.getAggregations().get("agg_grade");System.out.println(aggregation.getValue()); //返回78.0
}

接下来就直接贴代码了

4.2 Min

获取聚合数据的最小值：

POST /test4/_search
{"aggs": {"min_grade": {"min": {"field": "grade"}}}
}

@Test
public void testMin() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.min("min_grade").field("grade")).size(0);request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);ParsedMin aggregation = search.getAggregations().get("min_grade");System.out.println(aggregation.getValue());
}

4.3 Max

获取聚合数据的最大值：

POST /test4/_search
{"aggs": {"max_grade": {"max": {"field": "grade"}}}
}

@Test
public void testMax() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.max("max_grade").field("grade")).size(0);request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);ParsedMax aggregation = search.getAggregations().get("max_grade");System.out.println(aggregation.getValue());
}

4.4 Sum

获取聚合数据的和：

POST /test4/_search
{"aggs": {"sum_grade": {"sum": {"field": "grade"}}}
}

@Test
public void testSum() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.sum("sum_grade").field("grade")).size(0);request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);ParsedSum aggregation = search.getAggregations().get("sum_grade");System.out.println(aggregation.getValue());
}

4.5 Stats

stats集成了上面的所有计算操作。

POST /test4/_search
{"aggs": {"stats_grade": {"stats": {"field": "grade"}}}
}

@Test
public void testStats() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.stats("sum_grade").field("grade")).size(0);request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);ParsedStats aggregation = search.getAggregations().get("sum_grade");System.out.println(aggregation.getMax());System.out.println(aggregation.getAvg());System.out.println(aggregation.getCount());System.out.println(aggregation.getMin());System.out.println(aggregation.getSum());
}

（五）Bucket Aggregation

桶聚合是按照某个字段将同类型的数据聚合为一类，最常用对桶聚合就是terms聚合了。

5.1 terms

terms查询类似于group by，返回查询字段分组后的值以及数量，比如我对classroom字段terms查询

POST /test4/_search
{"aggs": {"classroom_term": {"terms": {"field": "classroom"}}}
}

返回值就是classroom的分组后的值以及每个组的数量：classroom是1的有3条记录，classroom是2的有2条记录

"aggregations" : {"classroom_term" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : "1","doc_count" : 3},{"key" : "2","doc_count" : 2}]}

我们也可以对多个字段进行terms分组，比如我现在对classroom和gender两个字段进行分组：

POST /test4/_search
{"aggs": {"classroom_term": {"terms": {"field": "classroom"},"aggs": {"gender": {"terms": {"field": "gender"}}}}}
}

最后对返回值就是classroom和gender分组后的值和数量：

classroom是1，gender是男有两条；

classroom是1，gender是女有一条；

classroom是2，gender是男有一条；

classroom是2，gender是女有一条；

"aggregations" : {"classroom_term" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : "1","doc_count" : 3,"gender" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : "男","doc_count" : 2},{"key" : "女","doc_count" : 1}]}},{"key" : "2","doc_count" : 2,"gender" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : "女","doc_count" : 1},{"key" : "男","doc_count" : 1}]}}]}

对应的JavaApi使用如下：

@Test
public void testTerms() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.terms("classroom_term").field("classroom").subAggregation(AggregationBuilders.terms("gender").field("gender")));request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);//获取数据时首先对classroom分桶，再对gender分桶Terms classroomTerm = search.getAggregations().get("classroom_term");for(Terms.Bucket classroomBucket:classroomTerm.getBuckets()){Terms genderTerm=classroomBucket.getAggregations().get("gender");for (Terms.Bucket genderBucket:genderTerm.getBuckets()){System.out.println("classRoom:"+classroomBucket.getKeyAsString()+"gender:"+genderBucket.getKeyAsString()+"count:"+genderBucket.getDocCount());}}
}

这里比较难理解对是获取数据时的处理，聚合查询时有个桶的概念，在获取数据时需要遍历获取桶，以上面的代码为例，先获取到classroom的桶，再遍历classroom的桶获取gender的桶，从桶中获取到具体的内容。看下图：

5.2 range

range查询可以统计出每个数据区间内的数量：比如我要统计分数为*～70，70～85，80～*的数据，就可以通过下面的方式：

POST /test4/_search
{"aggs": {"grade_range": {"range": {"field": "grade","ranges": [{"to":70},{"from":70,"to":85},{"from":85}]}}}
}

JavaAPI如下：

@Test
public void testRange() throws Exception {//封装了获取RestHighLevelClient的方法RestHighLevelClient client=ElasticSearchClient.getClient();SearchRequest request = new SearchRequest("test4");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.aggregation(AggregationBuilders.range("grade_range").field("grade").addUnboundedTo(70).addRange(70,85).addUnboundedFrom(85));request.source(searchSourceBuilder);SearchResponse search = client.search(request, RequestOptions.DEFAULT);//获取数据时首先对classroom分桶，再对gender分桶Range gradeRange = search.getAggregations().get("grade_range");for(Range.Bucket gradeBucket:gradeRange.getBuckets()){System.out.println("key:"+gradeBucket.getKey()+"count:"+gradeBucket.getDocCount());}
}

（六）总结

至此，关于ES的聚合查询一些常用方法就讲解完毕了，ES提供的其他更多方法可以直接在官方文档中看，讲解的十分详细。我是鱼仔，我们下期再见！

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