Elasticsearch 的 metrics（度量）包含 count、sum、avg、max、min、percentiles (百分位数)、Unique count（基数 || 去重计数）、Median（中位数）、扩展度量（含方差、平方和、标准差、标准差界限）、Percentile ranks（百分位等级）
- count（数量）：
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| SELECT count(log_date.d) AS Count FROM INDEX-2017-12
|
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "COUNT" ],
"excludes" : [ ]
},
"aggregations" : {
"Count" : {
"value_count" : {
"field" : "log_date.d"
}
}
}
}
|
- sum（和）：
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| SELECT sum(log_date.d) AS SUM FROM INDEX-2017-12
|
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "SUM" ],
"excludes" : [ ]
},
"aggregations" : {
"SUM" : {
"sum" : {
"field" : "log_date.d"
}
}
}
}
|
- avg（平均数）：
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| SELECT avg(log_date.d) AS AVG FROM INDEX-2017-12
|
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "AVG" ],
"excludes" : [ ]
},
"aggregations" : {
"AVG" : {
"avg" : {
"field" : "log_date.d"
}
}
}
}
|
- max（最大值）：
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| SELECT max(log_date.d) AS MAX FROM INDEX-2017-12
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "MAX" ],
"excludes" : [ ]
},
"aggregations" : {
"MAX" : {
"max" : {
"field" : "log_date.d"
}
}
}
}
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- min（最小值）：
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| SELECT min(log_date.d) AS MIN FROM INDEX-2017-12
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "MIN" ],
"excludes" : [ ]
},
"aggregations" : {
"MIN" : {
"min" : {
"field" : "log_date.d"
}
}
}
}
|
- percentiles（百分位数）：
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| SELECT percentiles(log_date.d,1.0,15.0,31.0) AS Percentiles FROM INDEX-2017-12
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "percentiles" ],
"excludes" : [ ]
},
"aggregations" : {
"Percentiles" : {
"percentiles" : {
"field" : "log_date.d",
"percents" : [ 1.0, 15.0, 31.0 ]
}
}
}
}
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- Unique count（基数 || 去重计数，就是 SQL 中的 distinct ）：
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| SELECT count(distinct(log_date.d)) AS UniqueCount FROM INDEX-2017-12
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "COUNT" ],
"excludes" : [ ]
},
"aggregations" : {
"UniqueCount" : {
"cardinality" : {
"field" : "log_date.d",
"precision_threshold" : 40000
}
}
}
}
|
- Median（中位数）：
中位数没找到单独的获取方法，不过在 Kibana 中看到获取中位数时请求中的参数，其实就是获取的某个字段50的百分位数，所以可能有：中位数=50的百分位数1
| SELECT percentiles(log_date.d,50.0) AS percentiles FROM INDEX-2017-12
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "percentiles" ],
"excludes" : [ ]
},
"aggregations" : {
"percentiles" : {
"percentiles" : {
"field" : "log_date.d",
"percents" : [ 50.0 ]
}
}
}
}
|
- 方差、平方和、标准差、标准差界限：
这几个度量没有单独方法去获取，都是用 EXTENDED_STATS 一个请求全部获取下来，然后从中取自己需要的结果1
| SELECT EXTENDED_STATS(log_date.d) AS EXTENDED_STATS FROM INDEX-2017-12
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| {
"from" : 0,
"size" : 0,
"_source" : {
"includes" : [ "EXTENDED_STATS" ],
"excludes" : [ ]
},
"aggregations" : {
"EXTENDED_STATS" : {
"extended_stats" : {
"field" : "log_date.d"
}
}
}
}
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EXTENDED_STATS 查询结果包含：方差、平方和、标准差、标准差界限以及最大值、平均数等基础度量，具体如下：
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| "aggregations": {
"1": {
"count": 15304326,
"min": 1,
"max": 31,
"avg": 15.068216202399244,
"sum": 230608893,
"sum_of_squares": 4588588661,
"variance": 72.7718426201877,
"std_deviation": 8.530641395591992,
"std_deviation_bounds": {
"upper": 32.129498993583226,
"lower": -1.9930665887847407
}
}
}
|
- Percentile ranks（百分位等级）
暂时没找到求百分位等级的 SQL 语句，只能用原生 ES 查询语句获取了；
ES原生查询语句如下：1
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| {
"size": 0,
......
"aggs": {
"1": {
"percentile_ranks": {
"field": "log_date.d",
"values": [
6,
15,
31
],
"keyed": false
}
}
}
}
|
Histogram、Date Histogram、Range、Date Range、Terms、IPv4 Range、Significant Terms 等各种 buckets 桶查询：https://blog.iaiot.com/20171228/Elasticsearch-buckets.html
附 elasticsearch-sql 的 GitHub 地址：https://github.com/NLPchina/elasticsearch-sql
Elasticsearch 官方文档（中文版）地址：https://www.elastic.co/guide/cn/elasticsearch/guide/cn/aggregations.html
