聚合分析的格式：
"aggregations" : {
"<aggregation_name>" : {
"<aggregation_type>" : {
<aggregation_body>
}
[,"meta" : { [<meta_data_body>] } ]?
[,"aggregations" : { [<sub_aggregation>]+ } ]?
}
[,"<aggregation_name_2>" : { ... } ]*
}
举个栗子-------------------------------
GET test_index/doc/_search { "size":0 "aggs": { #聚合关键字 "avg_age": { #聚合的名字 "max": { #聚合分析的类型 "field": "age" #body }}}}
聚合分析有四种：
metrics,指标分析聚合
bucket,分桶类型
pipeline,管道分析
matrix，矩阵分析
SELECT COUNT(color) FROM table GROUP BY color # GROUP BY相当于做分桶（bucket）的工作，COUNT是统计指标（metrics）。
- Metrics
单值分析
- min 返回数值类字段的最小值
View CodeGET test_index/_search { "size": 0, "aggs": { "min_age": { "min": { "field": "age" } } } } ---> "aggregations": { "min_age": { "value": 10 } }
- max
View Code
GET test_index/_search { "size": 0, "aggs": { "max_age": { "max": { "field": "age" } } } } ---------> "aggregations": { "max_age": { "value": 50 } }
- avg
View Code
GET test_index/_search { "size": 0, "aggs": { "avg_age": { "avg": { "field": "age" } } } } -------------> "aggregations": { "avg_age": { "value": 24.666666666666668 } }
- sum 求和
View Code
GET test_index/_search { "size": 0, "aggs": { "sum_age": { "sum": { "field": "age" } } } } --------------> "aggregations": { "sum_age": { "value": 148 } }
- cardinality 基数，不同值的个数，类似sql里面的distinct count概念
View Code
GET test_index/_search { "size": 0, "aggs": { "cardinality_age": { "cardinality": { "field": "age" } } } } -----------> "aggregations": { "cardinality_age": { "value": 5 } }
- value_count 值计数
View Code
GET test_index/_search { "size": 0, "aggs": { "count_age": { "value_count": { "field": "name" #值计数 } } } }
- 一次可以多个聚合：
GET syslog-2018.07.13/_search { "size": 0, "aggs": { "min_facility": { #聚合名称 "min": { #聚合类型 "field": "facility" } }, "max_factility":{ #聚合名称 "max":{ #聚合类型 "field": "facility" } }, "avg_facility":{ #聚合名称 "avg": { #聚合类型 "field": "facility" } }, "sum_facility":{ "sum": { "field": "facility" }}}}
多值分析
- stats 返回一系列数值类型的统计值。min，max，sum，count，avg
View Code
GET test_index/_search { "size": 0, "aggs": { "stats_age": { "stats": { "field": "age" }}}} #结果------------> "aggregations": { "stats_age": { "count": 6, "min": 10, "max": 50, "avg": 24.666666666666668, "sum": 148 }}
- extended stats 在stats的基础上加了方差标准差等
View Code
GET test_index/_search { "size": 0, "aggs": { "age": { "extended_stats": { "field": "age" }}}} -----------> "aggregations": { "age": { "count": 6, "min": 10, "max": 50, "avg": 24.666666666666668, "sum": 148, "sum_of_squares": 4560, "variance": 151.55555555555557, "std_deviation": 12.310790208412925, "std_deviation_bounds": { "upper": 49.28824708349252, "lower": 0.04508624984081777 }}}
- percentiles 百分位数统计，了解数据分布情况
View Code
GET test_index/_search { "size": 0, "aggs": { "age": { "percentiles": { "field": "age", "percents": [1,5,25,50,75,95,99] # 不加percents默认为[1,5,25,50,75,95,99] }}}} ---------> "aggregations": { "age": { "values": { "1.0": 10, "5.0": 10, "25.0": 18, "50.0": 23, "75.0": 24, "95.0": 50, "99.0": 50 }}}
-
计算第p百分位数的步骤： 第1步：以递增顺序排列原始数据（即从小到大排列）。 第2步：计算指数i=np% （n等于count，p%等于百分位） 第3步： l）若 i 不是整数，将 i 向上取整。 2) 若i是整数，则第p百分位数是第i项与第（i＋l）项数据的平均值。
-
- percentiles ranks 字段的数值所占的百分位是多少
View Code
GET test_index/_search { "size": 0, "aggs": { "agg": { "percentile_ranks": { "field": "age", "values": [24,50] }}}} -----------> "aggregations": { "agg": { "values": { "24.0": 66.66666666666666, "50.0": 100 }}}
- top hits 用于分桶后获取桶内最匹配的顶部文档
# 选项：from，size，sort
# 按照host分组，分组后取出每组里面时间最近的一条数据 GET syslog-2018.07.12/_search { "size": 0, "aggs": { "host": { "terms": { "field": "host" }, "aggs": { "sort_date": { "top_hits": { "size":1, "sort": [ { "@timestamp": { "order":"desc" } } ], "_source": { "includes": ["host","message"] }}}}}}}}
- Bucket 按照一定规则将文档分配到不同的桶里，分类分析
- terms 每个唯一值一个桶，返回字段的值和值的个数doc_count。 如果是text类型，则按照分词后的结果分桶
View Code
GET test_index/_search { "size": 0, "aggs": { "agg": { "terms": { "field": "age", "size": 5 #默认情况,返回按排序的前十个。可用size来更改。 }}}} ---------> "buckets": [ { "key": 24, "doc_count": 2 }, { "key": 10, "doc_count": 1 }, { "key": 18, "doc_count": 1 }, { "key": 22, "doc_count": 1 }, { "key": 50, "doc_count": 1 } ]
- range 指定数值范围来设定分桶
View Code
GET test_index/_search { "size": 0, "aggs": { "agg": { "range": { "field": "age", "ranges": [ { "from": 20, "to": 30 }]}}}} --------------------> "aggregations": { "agg": { "buckets": [ { "key": "20.0-30.0", "from": 20, "to": 30, "doc_count": 3 }]}} #示例2： 可以设定这里的key GET syslog-2018.07.13/_search { "size": 0, "aggs": { "priority_range": { "range": { "field": "priority", "ranges": [ { "key": "<50", "to": 50 }, { "from": 50, "to": 80 }, { "key": ">80", "from": 80 }]}}}} -------------------> "aggregations": { "priority_range": { "buckets": [ { "key": "<50", "to": 50, "doc_count": 1990 }, { "key": "50.0-80.0", "from": 50, "to": 80, "doc_count": 31674 }, { "key": ">80", "from": 80, "doc_count": 5828 } ] }
- date_range
View Code
#跟range的区别是 date range可以设置date match expression，+1h,-1d等。还可以指定返回字段的日期格式 format GET syslog-2018.07*/_search { "size": 0, "aggs": { "timestamp_range":{ "date_range": { "field": "@timestamp", "format": "yyyy/MM/dd", #可以设置日期格式 "ranges": [ { "from": "now-10d/d", #可以使用date match "to": "now-5d/d" }, { "from": "now-5d/d" } ]}}}} ------------------> "aggregations": { "timestamp_range": { "buckets": [ { "key": "2018/07/03-2018/07/08", "from": 1530576000000, "from_as_string": "2018/07/03", "to": 1531008000000, "to_as_string": "2018/07/08", "doc_count": 739175 }, { "key": "2018/07/08-*", "from": 1531008000000, "from_as_string": "2018/07/08", "doc_count": 760635 } ] } }
- histogram 直方图
View Code
GET test_index/_search { "size": 0, "aggs": { "age": { "histogram": { #关键字 "field": "age", "interval": 10 #指定间隔大小 "extended_bounds": #指定数据范围 { "min": 0, "max": 50 }}}}} #结果---------------> "buckets": [ { "key": 10, "doc_count": 2 }, { "key": 20, "doc_count": 3 }, { "key": 30, "doc_count": 0 }, { "key": 40, "doc_count": 0 }, { "key": 50, "doc_count": 1 }]
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date_histogram 日期直方图
View CodeGET syslog-2018.07.1*/_search { "size": 0, "aggs": { "range": { "date_histogram": { "field": "@timestamp", "format": "yyyy/MM/dd", #设置返回日期格式 "interval": "day" # 以年月日小时分钟为间隔 }}}} ---------------> "aggregations": { "range": { "buckets": [ { "key_as_string": "2018/07/10", "key": 1531180800000, "doc_count": 146354 }, { "key_as_string": "2018/07/11", "key": 1531267200000, "doc_count": 143784 }, { "key_as_string": "2018/07/12", "key": 1531353600000, "doc_count": 143137 }, { "key_as_string": "2018/07/13", "key": 1531440000000, "doc_count": 43206 } ] }
- filter 给聚合加过滤条件
View Code
GET test_index/_search { "size": 0, "aggs": { "salary": { "filter": { #先过滤 "range": { "salary": { "gte": 8000 } } }, "aggs": { "avg_age": { #后聚合 "avg": { "field": "age" }}}}}} ---------------------> "aggregations": { "salary": { "doc_count": 4, "avg_age": { "value": 23.25 } } }
- filters
-
View Code
GET /logs/_search { "aggs": { "count_debug":{ #agg name "filters": { #关键字 "filters": { "error": { # 过滤器名字 "match":{ #查询语句关键字match "body":"error" #匹配body字段中带有error的 } }, "warnings":{ #过滤器名字 "term":{ #查询语句关键字term "body":"warning" #匹配body字段中带有warning的 }}}}}}} ---------结果--------》 "buckets": { "error": { "doc_count": 1 }, "warnings": { "doc_count": 2 }}
-
nested 嵌套类型聚合
View CodePUT test_index { "mappings": { "doc": { "properties": { "man":{ "type": "nested", #设置man字段为nested类型 "properties": { #子字段 "age":{ "type":"integer" }, "name":{ "type":"text" }}}}}}}} PUT test_index/doc/1 { "man":[ { "name":"alice white", "age":34 }, { "name":"peter brown", "age":26 } ] } GET test_index/_search { "size": 0, "aggs": { #聚合关键字 "man": { #聚合名字 "nested": { #关键字 "path": "man" #嵌套字段 }, "aggs": { "avg_age": { "avg": { "field": "man.age" #子字段 } } } } } }
嵌套聚合
- bucket+bucket
GET bank/_search { "size": 0, "aggs": { "state": { #名字 "terms": { #关键字 "field": "state.keyword" #按照不同国家分桶 }, "aggs": { #嵌套 "range_age": { #名字 "range": { #关键字 "field": "age", "ranges": [ { "from": 20, "to": 30 } ] } } } } } } --------------------------> "aggregations": { "state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "ID", "doc_count": 27, "range_age": { "buckets": [ { "key": "20.0-30.0", "from": 20, "to": 30, "doc_count": 9 } ] } }, { "key": "TX", "doc_count": 27, "range_age": { "buckets": [ { "key": "20.0-30.0", "from": 20, "to": 30, "doc_count": 17 } ] } }, { "key": "AL", "doc_count": 25, "range_age": { "buckets": [ { "key": "20.0-30.0", "from": 20, "to": 30, "doc_count": 12 } ] } },................
- bucket+metrics
GET bank/_search { "size": 0, "aggs": { "state": { #桶名字 "terms": { #bucket聚合分析，按国家名分桶 "field": "state.keyword" }, "aggs": { #嵌套 "avg_age": { #桶名字 "avg": { # metric聚合分析，求不同桶的age平均值 "field": "age" }}}}}} #结果-------------> "aggregations": { "state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "ID", "doc_count": 27, "avg_age": { "value": 31.59259259259259 } }, { "key": "TX", "doc_count": 27, "avg_age": { "value": 28.77777777777778 } }, { "key": "AL", "doc_count": 25, "avg_age": { "value": 29.16 } }, { "key": "MD", "doc_count": 25, "avg_age": { "value": 31.04 } }, { "key": "TN", "doc_count": 23, "avg_age": { "value": 30.91304347826087 } }, { "key": "MA", "doc_count": 21, "avg_age": { "value": 27.761904761904763 } }, { "key": "NC", "doc_count": 21, "avg_age": { "value": 31.333333333333332 } }
聚合分析的作用范围：
- filter 只为某个聚合分析设定过滤条件，不改变整体过滤条件
View Code
# filter过滤条件只作用于host_priority_little聚合，不作用于host聚合 GET syslog-2018.07.13/_search { "size": 0, "aggs": { "host_priority_little": { "filter": { "range": { "priority": { "to":50 } } }, "aggs": { "host": { "terms": { "field": "host", "size": 2 } } } }, "host":{ "terms": { "field": "host", "size":2 } } } } -------------> "aggregations": { "host_priority_little": { "doc_count": 2530, "host": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 196, "buckets": [ { "key": "10.10.14.16", "doc_count": 2108 }, { "key": "10.10.12.171", "doc_count": 226 } ] } }, "host": { "doc_count_error_upper_bound": 593, "sum_other_doc_count": 37640, "buckets": [ { "key": "10.10.14.248", "doc_count": 7198 }, { "key": "10.10.14.4", "doc_count": 6494 } ] } }
- post_filter 过滤文档，但不影响聚合
View Code
GET syslog-2018.06.13/_search { "size":0, "aggs": { "host": { "terms": { "field": "host" } } }, "post_filter": { "range": { "priority": { "gte": 100 }}}} ----------------> "hits": { "total": 106, #post_filter只作用于命中的文档数，跟聚合无关 "max_score": 0, "hits": [] }, "aggregations": { #聚合不管post_filter的过滤条件 "host": { "doc_count_error_upper_bound": 430, "sum_other_doc_count": 53134, "buckets": [ { "key": "10.10.14.248", "doc_count": 19590 }, { "key": "172.16.10.37", "doc_count": 17625 }, ........... # 如果使用的query，则是命中文档数，并作用于聚合分析 GET syslog-2018.06.13/_search { "size":0, "aggs": { "host": { "terms": { "field": "host" } } }, "query": { "range": { "priority": { "gte": 100 }}}} --------------------> "hits": { "total": 106, #根据query条件 命中的文档数 "max_score": 0, "hits": [] }, "aggregations": { # 对query查询后的文档进行聚合 "host": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "172.16.10.253", "doc_count": 106 } ] } }
- global 无视query过滤条件，基于全部文档分析
View Code
GET test_index/_search { "size": 0, "query": { "match": { "name": "lin" } }, "aggs": { "lin_age_avg": { "avg": { "field": "age" } }, "all":{ "global": {}, "aggs": { "avg_age": { "avg": { "field": "age" }}}}}} ----------------------------> "aggregations": { "all": { "doc_count": 4, "avg_age": { "value": 25.25 } }, "lin_age_avg": { "value": 24.5 } } }
聚合分析的排序：
- 使用自带的关键数据进行排序
-
_count 按照文档数doc_count排序 。如果不指定order则默认按_count倒排
-
_key 按照key值排序
-
-
View Code
GET test_index/_search { "size": 0, "aggs": { "age": { "terms": { "field": "salary" }}}} -#-----------------> 不指定order时默认情况下，使用_count倒排 "aggregations": { "age": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": 8000, "doc_count": 3 }, { "key": 5000, "doc_count": 2 }, { "key": 4000, "doc_count": 1 }, { "key": 9000, "doc_count": 1 } ] } } # 使用排序的_term类型 ，按照key进行排序 GET test_index/_search { "size": 0, "aggs": { "age": { "terms": { "field": "salary", "order": { # order关键词 "_key": "asc" #按照key升序排序 }}}}}} ------------------> "aggregations": { "age": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": 4000, "doc_count": 1 }, { "key": 5000, "doc_count": 2 }, { "key": 8000, "doc_count": 3 }, { "key": 9000, "doc_count": 1 } ] }
- 可以使用子聚合的结果进行排序
View Code
GET test_index/_search { "size": 0, "aggs": { "salary": { "terms": { "field": "salary", "order": { "avg_age": "asc" # 按照子聚合里面的avg_age升序排序 } }, "aggs": { #子聚合 "avg_age": { "avg": { "field": "age" }}}}}}} -----------------> "buckets": [ { "key": 9000, "doc_count": 1, "avg_age": { "value": 21 } }, { "key": 4000, "doc_count": 1, "avg_age": { "value": 22 } }, { "key": 8000, "doc_count": 3, "avg_age": { "value": 24 } }, { "key": 5000, "doc_count": 2, "avg_age": { "value": 25.5 } } ] # 在有多值指标的情况下，需要修改一下 GET test_index/_search { "size": 0, "aggs": { "salary": { "terms": { "field": "salary", "order": { "stats_age.avg": "asc" # 按照 stats中的avg 排序,使用. } }, "aggs": { "stats_age": { "stats": { "field": "age" }}}}}} -------------> "buckets": [ { "key": 9000, "doc_count": 1, "stats_age": { "count": 1, "min": 21, "max": 21, "avg": 21, "sum": 21 } }, { "key": 4000, "doc_count": 1, "stats_age": { "count": 1, "min": 22, "max": 22, "avg": 22, "sum": 22 } }, ..................... ] #例2： GET test_index/_search { "size": 0, "aggs": { "salary": { "terms": { "field": "salary", "order": { "filter_age>stats_age.sum": "asc" #注意这里的写法 } }, "aggs": { #子聚合 "filter_age":{ "filter": { #过滤age>21的 "range": { "age": { "gt": 21 } } }, "aggs": { "stats_age": { "stats": { #多值分析 "field": "age" }}}}}}}} -------------------------> "buckets": [ { "key": 9000, "doc_count": 1, "filter_age": { "doc_count": 0, "stats_age": { "count": 0, "min": null, "max": null, "avg": null, "sum": null } } }, { "key": 4000, "doc_count": 1, "filter_age": { "doc_count": 1, "stats_age": { "count": 1, "min": 22, "max": 22, "avg": 22, "sum": 22 } } }, { "key": 5000, "doc_count": 2, "filter_age": { "doc_count": 2, "stats_age": { "count": 2, "min": 23, "max": 28, "avg": 25.5, "sum": 51 } } }, { "key": 8000, "doc_count": 3, "filter_age": { "doc_count": 3, "stats_age": { "count": 3, "min": 22, "max": 26, "avg": 24, "sum": 72 } } } ]
- Pipeline 根据输出位置的不同 分为两类：
-parent 结果内嵌到现有的聚合分析结果中
- derivative 求导 计算父级histgram（date_histgram）中指定指标的导数
- cumulati average 累计总和 计算父histgram（date_histgram）中指定指标的累计总和。
- moving average 移动平均值 聚合将动态移动数据窗口，生成该窗口数据的平均值。
-sibling 结果与现有聚合结果同级
- max_bucket /min_bucket / avg_bucket / sum_bucket
{ "max_bucket": { "buckets_path": "the_sum" } }
GET bank/_search { "size": 0, "aggs": { "state": { #聚合名字 "terms": { # 按照不同国家分桶 "field": "state.keyword" }, "aggs": { "avg_age": { # 聚合名字 "avg": { # 各个桶的age的平均值 "field": "age" } } } }, "max_state_age":{ #pipeline聚合名字, 跟state聚合同级 "max_bucket": { #关键字 "buckets_path": "state>avg_age" # 各个国家平均值中的最大值 >state表示包含在state里的avg_age } } } } ------------------------> "aggregations": { "state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "MA", "doc_count": 21, "avg_age": { "value": 27.761904761904763 } }, { "key": "NC", "doc_count": 21, "avg_age": { "value": 31.333333333333332 } }, { "key": "ND", "doc_count": 21, "avg_age": { "value": 31.238095238095237 }}] }, "max_state_age": { "value": 31.59259259259259, "keys": [ "ID" ]}}
-
View Code
sql语句： SELECT COUNT(DISTINCT mac,ip) FROM test 对应的es语句： GET nginx-access-log-2018.07.24/_search { "size": 0, "aggs": { "beat": { "terms": { #先对beat分桶 "field": "beat.name" }, "aggs": { "ip": { #桶内ip去重 "cardinality": { "field": "clientip" } } } }, "sum_beat_ip":{ #不同桶的ip总数 "sum_bucket": { "buckets_path": "beat>ip" } } } }
- stats_bucket / extended_stats_bucket
View Code
GET bank/_search { "size": 0, "aggs": { "state": { "terms": { "field": "state.keyword" }, "aggs": { "min_age": { "min": { "field": "age" } } } }, "stats_state_age":{ "stats_bucket": { "buckets_path": "state>min_age" } } } } ---------------> "aggregations": { "state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "ID", "doc_count": 27, "min_age": { "value": 21 } }, { "key": "MD", "doc_count": 25, "min_age": { "value": 20 } }, { "key": "ND", "doc_count": 21, "min_age": { "value": 21 } }, { "key": "ME", "doc_count": 20, "min_age": { "value": 21 } } ] }, "stats_state_age": { "count": 10, "min": 20, "max": 22, "avg": 20.6, "sum": 206 } } }
{ "stats_bucket": { "buckets_path": "the_sum" }
} - percentiles
View Code
GET bank/_search { "size": 0, "aggs": { "state": { "terms": { "field": "state.keyword" }, "aggs": { "avg_age": { "avg": { "field": "age" } } } }, "percen_state_age":{ "percentiles_bucket":{ "buckets_path":"state>avg_age" } } } } --------> "aggregations": { "state": { "doc_count_error_upper_bound": 20, "sum_other_doc_count": 770, "buckets": [ { "key": "ID", "doc_count": 27, "avg_age": { "value": 31.59259259259259 } }, { "key": "TX", "doc_count": 27, "avg_age": { "value": 28.77777777777778 } },.................. ] }, "percen_state_age": { "values": { "1.0": 27.761904761904763, "5.0": 27.761904761904763, "25.0": 28.77777777777778, "50.0": 30.91304347826087, "75.0": 31.238095238095237, "95.0": 31.59259259259259, "99.0": 31.59259259259259 } } }
- Matrix