转载:原文地址
Elasticsearch的Aggregation功能也异常强悍。
Aggregation共分为三种：Metric Aggregations、Bucket Aggregations、 Pipeline Aggregations。下面将分别进行总结。
以下所有内容都来自官网：喜欢原汁原味的参看下方网址，不喜欢英文的参看本人总结。
官网（权威）：https://www.elastic.co/guide/en/elasticsearch/reference/2.4/search-aggregations-metrics-avg-aggregation.html
#########################################
1、Metric Aggregations
1>Avg Aggregation #计算出字段平均值
{ "aggs" : { "avg_grade" : { "avg" : { "field" : "grade" } } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "avg_grade": { "avg": { "field": "grade" } } } }
参数：search_type=count 表示只返回aggregation部分的结果。
2>Cardinality Aggregation #计算出字段的唯一值。相当于sql中的distinct
{ "aggs" : { "author_count" : { "cardinality" : { "field" : "author" } } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "author_count": { "cardinality": { "field": "author" } } } }
3>Extended Stats Aggregation #字段的其他属性，包括最大最小，方差等等。
{ "aggs" : { "grades_stats" : { "extended_stats" : { "field" : "grade" } } } }例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "grades_stats": { "extended_stats": { "field": "grade" } } } }
返回值：
{ ... "aggregations": { "grade_stats": { "count": 9, "min": 72, "max": 99, "avg": 86, "sum": 774, "sum_of_squares": 67028, "variance": 51.55555555555556, "std_deviation": 7.180219742846005, "std_deviation_bounds": { "upper": 100.36043948569201, "lower": 71.63956051430799 } } } }4>Geo Bounds Aggregation #计算出所有的地理坐标将会落在一个矩形区域。比如说朝阳区域有很多饭店，我就可以用一个矩形把这些饭店都圈起来，看看范围。
{ "query" : { "match" : { "business_type" : "shop" } }, "aggs" : { "viewport" : { "geo_bounds" : { "field" : "location", "wrap_longitude" : true } } } }例子：
GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "viewport": { "geo_bounds": { "field": "location", "wrap_longitude": true } } } }
返回值：
{ ... "aggregations": { "viewport": { "bounds": { "top_left": { "lat": 80.45, "lon": -160.22 }, "bottom_right": { "lat": 40.65, "lon": 42.57 } } } } }注释：这个矩形区域左上角坐标，和右下角坐标已经给出。也就是说你查出来的数据将会都落在这个地理范围内。
5>Geo Centroid Aggregation #计算出所有文档的大概的中心点。比如说某个地区盗窃犯罪很多，那我这样就可以看到这片区域到底哪个点（街道）偷盗事件最猖狂。
{ "query" : { "match" : { "crime" : "burglary" } }, "aggs" : { "centroid" : { "geo_centroid" : { "field" : "location" } } } }例子：
GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "centroid": { "geo_centroid": { "field": "location" } } } }
6>Max Aggregation #求最大值
{ "aggs" : { "max_price" : { "max" : { "field" : "price" } } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "max_price": { "max": { "field": "price" } } } }
7>Min Aggregation #求最小值
{ "aggs" : { "min_price" : { "min" : { "field" : "price" } } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "min_price": { "min": { "field": "price" } } } }
8>Percentiles Aggregation #百分比统计。可以看出你网站的所有页面。加载时间的差异。
{ "aggs" : { "load_time_outlier" : { "percentiles" : { "field" : "load_time" } } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "load_time_outlier": { "percentiles": { "field": "load_time" } } } }
返回：可以看出这个网站75%页面在29毫秒左右就加载完毕了。有5%的页面超过了60毫秒。
{ ... "aggregations": { "load_time_outlier": { "values" : { "1.0": 15, "5.0": 20, "25.0": 23, "50.0": 25, "75.0": 29, "95.0": 60, "99.0": 150 } } } }9>Percentile Ranks Aggregation #看看15毫秒和30毫秒内大概有多少页面加载完。
{ "aggs" : { "load_time_outlier" : { "percentile_ranks" : { "field" : "load_time", "values" : [15, 30] } } } }例子：
GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "load_time_outlier": { "percentile_ranks": { "field": "load_time", "values": [ 15, 30 ] } } } }
返回：看出15毫秒时大概92%页面加载完毕。30毫秒时基本都加载完成。
{ ... "aggregations": { "load_time_outlier": { "values" : { "15": 92, "30": 100 } } } }10>Stats Aggregation #最大、最小、和、平均值。一起求出来
{ "aggs" : { "grades_stats" : { "stats" : { "field" : "grade" } } } }例子：
GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "grades_stats": { "stats": { "field": "grade" } } } }
11>Sum Aggregation #求和
"aggs" : { "intraday_return" : { "sum" : { "field" : "change" } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "intraday_return": { "sum": { "field": "change" } } } }
12>Top hits Aggregation #较为常用的统计。获取到每组前n条数据。相当于sql 中 group by 后取出前n条。
{ "aggs": { "top-tags": { "terms": { "field": "tags", "size": 3 }, "aggs": { "top_tag_hits": { "top_hits": { "sort": [ { "last_activity_date": { "order": "desc" } } ], "_source": { "include": [ "title" ] }, "size" : 1 } } } } } } 例子：取100组，每组只要第一条。为了见bain没用order和_source，请自行测试他们。GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "all_interests": { "terms": { "field": "zxw_id", "size": 100 }, "aggs": { "top_tag_hits": { "top_hits": { "size": 1 } } } } } }
14>Value Count Aggregation #数量统计，看看这个字段一共有多少个不一样的数值。
{ "aggs" : { "grades_count" : { "value_count" : { "field" : "grade" } } } } 例子：GET index/type/_search?search_type=count { "query": { "match_all": {} }, "aggs": { "grades_count": { "value_count": { "field": "grade" } } } }
2、Bucket Aggregations 这是第二种类型的统计（用的也是最多的，最实用的。）。后续也是抄写，各位自己看吧。有问题需要讨论的=》1250134974@qq.com发邮件.
网站：https://www.elastic.co/guide/en/elasticsearch/reference/2.4/search-aggregations-bucket-children-aggregation.html
3、Pipeline Aggregations #这是第三中类型的聚合。
转载于:https://www.cnblogs.com/llgogo/p/11493211.html
