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InfuxDB函数说明

InfuxDB函数说明

作者: 恬恬她爹 | 来源:发表于2017-04-09 15:55 被阅读0次

    1. 聚合函数

    1.1 COUNT()

    描述：返回单个字段中非空(non-null)值的数量
    语法

    SELECT COUNT(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子：

    • 计算字段water_level非空值的数量
    > SELECT COUNT("water_level") FROM "h2o_feet"
name: h2o_feet
--------------
time                           count
1970-01-01T00:00:00Z     15258
    • 计算字段water_level非空值的数量,按时间间隔分组
    > SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time < '2015-09-18T17:00:00Z' GROUP BY time(4d)
name: h2o_feet
--------------
time                           count
2015-08-17T00:00:00Z     1440
2015-08-21T00:00:00Z     1920
2015-08-25T00:00:00Z     1920
2015-08-29T00:00:00Z     1920
2015-09-02T00:00:00Z     1915
2015-09-06T00:00:00Z     1920
2015-09-10T00:00:00Z     1920
2015-09-14T00:00:00Z     1920
2015-09-18T00:00:00Z     335
    • 表h2o_feet有两个数据字段level description和water_level,可以通过count(*) 统计所有的字段
    > SELECT COUNT(*) FROM "h2o_feet"
name: h2o_feet
--------------
time                   count_level description      count_water_level
1970-01-01T00:00:00Z   15258                       15258

    1.2 DISTINCT()

    说明：返回字段的唯一值(去重)
    语法

    SELECT DISTINCT(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子：

    • 全表单字段
    > SELECT DISTINCT("level description") FROM "h2o_feet"
name: h2o_feet
--------------
time                           distinct
1970-01-01T00:00:00Z     between 6 and 9 feet
1970-01-01T00:00:00Z     below 3 feet
1970-01-01T00:00:00Z     between 3 and 6 feet
1970-01-01T00:00:00Z     at or greater than 9 feet
    • 分组单字段
    > SELECT DISTINCT("level description") FROM "h2o_feet" GROUP BY "location"
name: h2o_feet
tags: location=coyote_creek
time                            distinct
----                            --------
1970-01-01T00:00:00Z      between 6 and 9 feet
1970-01-01T00:00:00Z      between 3 and 6 feet
1970-01-01T00:00:00Z      below 3 feet
1970-01-01T00:00:00Z      at or greater than 9 feet

name: h2o_feet
tags: location=santa_monica
time                            distinct
----                            --------
1970-01-01T00:00:00Z      below 3 feet
1970-01-01T00:00:00Z      between 3 and 6 feet
1970-01-01T00:00:00Z      between 6 and 9 feet
    • 多字段
    > SELECT DISTINCT(*) FROM "h2o_feet" LIMIT 5
name: h2o_feet
--------------
time                   distinct_level description    distinct_water_level
1970-01-01T00:00:00Z   below 3 feet                  2.064
1970-01-01T00:00:00Z   between 6 and 9 feet          8.12
1970-01-01T00:00:00Z                                 2.116
1970-01-01T00:00:00Z                                 8.005
1970-01-01T00:00:00Z                                 2.028

    1.3 MEAN()

    说明:算术平均值,字段类型必须为整型(int64)或浮点型(float64)
    语法

    SELECT MEAN(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子:

    • 时间在2016-08-18T00:00:00Z和2016-09-18T17:00:00Z之间，按4天(time(4d))进行分组
    > SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time < '2015-09-18T17:00:00Z' GROUP BY time(4d)
name: h2o_feet
--------------
time                     mean
2016-08-17T00:00:00Z     4.322029861111125
2016-08-21T00:00:00Z     4.251395512375667
2016-08-25T00:00:00Z     4.285036458333324
2016-08-29T00:00:00Z     4.469495801899061
2016-09-02T00:00:00Z     4.382785378590083
2016-09-06T00:00:00Z     4.28849666349042
...

    1.4 MEDIAN()

    说明：返回中位数，字段类型必须为整型(int64)或浮点型(float64)，*表示所有字段
    语法

    SELECT MEDIAN(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子：

    > SELECT MEDIAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time < '2015-08-18T00:36:00Z' GROUP BY "location"
name: h2o_feet
tags: location = coyote_creek
time                           median
----                           ------
2015-08-18T00:00:00Z     7.8245

name: h2o_feet
tags: location = santa_monica
time                           median
----                           ------
2015-08-18T00:00:00Z     2.0575

    1.5 MODE()

    说明：返回最频繁的(出现频率最高的)值，如果有多个频率相同的值则返回第一个(最早的)
    语法

    SELECT MODE(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子：

    > SELECT MODE("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time < '2015-08-18T00:36:00Z' GROUP BY "location"
name: h2o_feet
tags: location = coyote_creek
time                           mode
----                           ------
2015-08-18T00:00:00Z     7

name: h2o_feet
tags: location = santa_monica
time                           mode
----                           ------
2015-08-18T00:00:00Z     2

    1.5 SPREAD()

    说明：返回字段的最小值和最大值之间的差值。 该字段必须是int64或float64类型; *表示表中的所有int64或float64字段。

    语法

    SELECT SPREAD(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子：

    > SELECT SPREAD("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-09-18T17:00:00Z' AND time < '2015-09-18T20:30:00Z' GROUP BY time(30m)
name: h2o_feet
--------------
time                            spread
2015-09-18T17:00:00Z      0.16699999999999982
2015-09-18T17:30:00Z      0.5469999999999997
2015-09-18T18:00:00Z      0.47499999999999964
2015-09-18T18:30:00Z      0.2560000000000002
2015-09-18T19:00:00Z      0.23899999999999988
2015-09-18T19:30:00Z      0.1609999999999996
2015-09-18T20:00:00Z      0.16800000000000015

    1.6 SUM()

    说明：返回单个字段中所有值的总和。 该字段必须是int64或float64类型; *表示表中的所有int64或float64字段。

    语法

    SELECT SUM(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    例子：

    > SELECT SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time < '2015-09-18T17:00:00Z' GROUP BY time(5d)
name: h2o_feet
--------------
time                           sum
2015-08-18T00:00:00Z     10334.908999999983
2015-08-23T00:00:00Z     10113.356999999995
2015-08-28T00:00:00Z     10663.683000000006
2015-09-02T00:00:00Z     10451.321
2015-09-07T00:00:00Z     10871.817999999994
2015-09-12T00:00:00Z     11459.00099999999
2015-09-17T00:00:00Z     3627.762000000003

    1.7 STDDEV()

    说明：返回单个字段中的值的标准偏差。 该字段必须是int64或float64类型。
    语法

    SELECT STDDEV(<field_key>) FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2. 选择器函数

    2.1 BOTTOM()

    说明：返回单个字段中最小的N个值。 字段类型必须为int64或float64。当同步指定tag时，设定tag的记录数是X，如果N>X时只返回X条记录,每个tag一条，否则返回最小的N条数据。
    语法

    SELECT BOTTOM(<field_key>[,<tag_keys>],<N>)[,<tag_keys>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2.2 FIRST()

    说明：返回单个字段的最旧值（由时间戳确定）。
    语法

    SELECT FIRST(<field_key>)[,<tag_key(s)>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2.3 LAST()

    说明：返回单个字段的最新值（由时间戳确定）。

    SELECT LAST(<field_key>)[,<tag_key(s)>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2.4 MAX()

    说明：返回单个字段中的最大值。 该字段必须是int64，float64或boolean。
    语法

    SELECT MAX(<field_key>)[,<tag_key(s)>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2.5 MIN()

    说明：返回单个字段中的最小值。 该字段必须是int64，float64或boolean。
    语法

    SELECT MIN(<field_key>)[,<tag_key(s)>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2.6 PERCENTILE()

    说明：返回单个字段的排序值的第N个百分位数值。 该字段必须是int64或float64类型。 百分位数N必须是0到100之间的整数或浮点数，包括0和100。算法找度娘。
    语法

    SELECT PERCENTILE(<field_key>, <N>)[,<tag_key(s)>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    2.7 SAMPLE()

    说明：返回指定字段的N个点的随机抽样。 InfluxDB使用储层采样来生成随机点。 SAMPLE()支持所有字段类型。
    语法

    SELECT SAMPLE(<field_key>,<N>) FROM_clause [WHERE_clause] [GROUP_BY_clause]

    2.8 TOP()

    说明：返回单个字段中最大的N个值。 字段类型必须为int64或float64。
    语法

    SELECT TOP(<field_key>[,<tag_keys>],<N>)[,<tag_keys>] FROM <measurement_name> [WHERE <stuff>] [GROUP BY <stuff>]

    3. 转换函数

    3.1 CUMULATIVE_SUM()

    说明：返回单个字段的连续字段值的累积和。 字段类型必须为int64或float64。
    基础语法

    SELECT CUMULATIVE_SUM(<field_key>) FROM_clause WHERE_clause

    高级语法
    支持如下嵌套函数类型：COUNT()
    , MEAN()
    , MEDIAN()
    , MODE()
    , SUM()
    , FIRST()
    , LAST()
    , MIN()
    ,MAX()
    , and PERCENTILE()
    .

    SELECT CUMULATIVE_SUM(<function>(<field_key>)) FROM_clause WHERE_clause GROUP BY time(<interval>)[,<tag_key>]

    例子

    ##原数据
> SELECT "water_level" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time                   water_level
----                   -----------
2015-08-18T00:00:00Z   2.064
2015-08-18T00:06:00Z   2.116
2015-08-18T00:12:00Z   2.028
2015-08-18T00:18:00Z   2.126
2015-08-18T00:24:00Z   2.041
2015-08-18T00:30:00Z   2.051
##转换后数据
> SELECT CUMULATIVE_SUM("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' AND "location" = 'santa_monica'
name: h2o_feet
time                   cumulative_sum
----                   --------------
2015-08-18T00:00:00Z   2.064
2015-08-18T00:06:00Z   4.18
2015-08-18T00:12:00Z   6.208
2015-08-18T00:18:00Z   8.334
2015-08-18T00:24:00Z   10.375
2015-08-18T00:30:00Z   12.426

    3.2 DERIVATIVE()

    说明：返回系列中单个字段中值的变化率。 InfluxDB计算时间域值之间的差异，并将这些结果转换为单位变化率。 单位参数是可选的，如果未指定，则默认为1秒（1秒）。
    基础语法

    SELECT DERIVATIVE(<field_key>, [<unit>]) FROM <measurement_name> [WHERE <stuff>]

    单位unit可以有如下值：

    • u or µ微妙(microseconds)
    • ms 毫秒(milliseconds)
    • s 秒(seconds)
    • m 分(minutes)
    • h 时(hours)
    • d 天(days)
    • w 周(weeks)

    使用聚合函数语法

    SELECT DERIVATIVE(AGGREGATION_FUNCTION(<field_key>),[<unit>]) FROM <measurement_name> WHERE <stuff> GROUP BY time(<aggregation_interval>)

    例子

    #原数据,两条数据直接间隔6分钟
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' LIMIT 5
name: h2o_feet
--------------
time                           water_level
2015-08-18T00:00:00Z     2.064
2015-08-18T00:06:00Z     2.116
2015-08-18T00:12:00Z     2.028
2015-08-18T00:18:00Z     2.126
2015-08-18T00:24:00Z     2.041
2015-08-18T00:30:00Z     2.051

#不指定单位时，默认为每秒变化率，如第一条记录(`(2.116-2.064)/(6m/1s)`)
> SELECT DERIVATIVE("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' LIMIT 5
name: h2o_feet
--------------
time                           derivative
2015-08-18T00:06:00Z     0.00014444444444444457
2015-08-18T00:12:00Z     -0.00024444444444444465
2015-08-18T00:18:00Z     0.0002722222222222218
2015-08-18T00:24:00Z     -0.000236111111111111
2015-08-18T00:30:00Z     2.777777777777842e-05

#每6分钟变化率，如第一条(`(2.116-2.064)/(6m/6m)`)
> SELECT DERIVATIVE("water_level",6m) FROM "h2o_feet" WHERE "location" = 'santa_monica' LIMIT 5
name: h2o_feet
--------------
time                           derivative
2015-08-18T00:06:00Z     0.052000000000000046
2015-08-18T00:12:00Z     -0.08800000000000008
2015-08-18T00:18:00Z     0.09799999999999986
2015-08-18T00:24:00Z     -0.08499999999999996
2015-08-18T00:30:00Z     0.010000000000000231

#每12分钟变化率,如第一条(`(2.116-2.064)/(6m/12m)`)
> SELECT DERIVATIVE("water_level",12m) FROM "h2o_feet" WHERE "location" = 'santa_monica' LIMIT 5
name: h2o_feet
--------------
time                           derivative
2015-08-18T00:06:00Z     0.10400000000000009
2015-08-18T00:12:00Z     -0.17600000000000016
2015-08-18T00:18:00Z     0.19599999999999973
2015-08-18T00:24:00Z     -0.16999999999999993
2015-08-18T00:30:00Z     0.020000000000000462

#每隔12分钟最大值
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time < '2015-08-18T00:36:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time                           max
2015-08-18T00:00:00Z     2.116
2015-08-18T00:12:00Z     2.126
2015-08-18T00:24:00Z     2.051
#每隔12分钟变化率，如第一条(`(2.126-2.116)/(12m-12m)`),带group by 语句时unit默认为time函数指定的间隔
> SELECT DERIVATIVE(MAX("water_level")) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time < '2015-08-18T00:36:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time                           derivative
2015-08-18T00:12:00Z     0.009999999999999787
2015-08-18T00:24:00Z     -0.07499999999999973

#每隔18分钟汇总数据
> SELECT SUM("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time < '2015-08-18T00:36:00Z' GROUP BY time(18m)
name: h2o_feet
--------------
time                           sum
2015-08-18T00:00:00Z     6.208
2015-08-18T00:18:00Z     6.218

#每隔18分钟汇总数据没6分钟变化率，如第一条(`(6.218-6.208)/(18m/6m)`)
> SELECT DERIVATIVE(SUM("water_level"),6m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time < '2015-08-18T00:36:00Z' GROUP BY time(18m)
name: h2o_feet
--------------
time                           derivative
2015-08-18T00:18:00Z     0.0033333333333332624

    3.3 DIFFERENCE()

    说明：返回单个字段中连续的时间顺序值之间的差异。 字段类型必须为int64或float64。
    基础语法

    SELECT DIFFERENCE(<field_key>) FROM <measurement_name> [WHERE <stuff>]

    高级语法

    SELECT DIFFERENCE(<function>(<field_key>)) FROM <measurement_name> WHERE <stuff> GROUP BY time(<time_interval>)

    支持的内嵌函数包括：COUNT()
    , MEAN()
    , MEDIAN()
    , SUM()
    , FIRST()
    ,LAST()
    , MIN()
    , MAX()
    , and PERCENTILE()
    。
    例子

    #带处理数据
> SELECT "water_level" FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z'
name: h2o_feet
--------------
time                            water_level
2015-08-18T00:00:00Z      2.064
2015-08-18T00:06:00Z      2.116
2015-08-18T00:12:00Z      2.028
2015-08-18T00:18:00Z      2.126
2015-08-18T00:24:00Z      2.041
2015-08-18T00:30:00Z      2.051
2015-08-18T00:36:00Z      2.067
#处理后
> SELECT DIFFERENCE("water_level") FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z'
name: h2o_feet
--------------
time                            difference
2015-08-18T00:06:00Z      0.052000000000000046
2015-08-18T00:12:00Z      -0.08800000000000008
2015-08-18T00:18:00Z      0.09799999999999986
2015-08-18T00:24:00Z      -0.08499999999999996
2015-08-18T00:30:00Z      0.010000000000000231
2015-08-18T00:36:00Z      0.016000000000000014
#分组统计后数据
> SELECT MIN("water_level") FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time                            min
2015-08-18T00:00:00Z    2.064
2015-08-18T00:12:00Z    2.028
2015-08-18T00:24:00Z    2.041
2015-08-18T00:36:00Z    2.067
#分组统计处理后数据
> SELECT DIFFERENCE(MIN("water_level")) FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time                            difference
2015-08-18T00:12:00Z      -0.03600000000000003
2015-08-18T00:24:00Z      0.0129999999999999
2015-08-18T00:36:00Z      0.026000000000000245

    3.4 ELAPSED()

    说明：返回单个字段中后续时间戳之间的差异。 持续时间(unit)参数是可选的，如果未指定，则默认为一纳秒。
    语法

    SELECT ELAPSED(<field_key>, <unit>) FROM <measurement_name> [WHERE <stuff>]

    例子

    #原数据
> SELECT "water_level" FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z'
name: h2o_feet
--------------
time                            water_level
2015-08-18T00:00:00Z      2.064
2015-08-18T00:06:00Z      2.116
2015-08-18T00:12:00Z      2.028
2015-08-18T00:18:00Z      2.126
2015-08-18T00:24:00Z      2.041
#每两个`water_level`数据记录直接的时间间隔是6分钟(即360000000000纳秒)，elapsed = 6m/1纳秒
> SELECT ELAPSED("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:24:00Z'
name: h2o_feet
--------------
time                            elapsed
2015-08-18T00:06:00Z      360000000000
2015-08-18T00:12:00Z      360000000000
2015-08-18T00:18:00Z      360000000000
2015-08-18T00:24:00Z      360000000000
#每两个`water_level`数据记录直接的时间间隔是6分钟，elapsed = 6m/1m
> SELECT ELAPSED("water_level",1m) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:24:00Z'
name: h2o_feet
--------------
time                            elapsed
2015-08-18T00:06:00Z      6
2015-08-18T00:12:00Z      6
2015-08-18T00:18:00Z      6
2015-08-18T00:24:00Z      6
#每两个`water_level`数据记录直接的时间间隔是6分钟，elapsed = 6m / 1h。因为1h>6m,所以数据为0
> SELECT ELAPSED("water_level",1h) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:24:00Z'
name: h2o_feet
--------------
time                            elapsed
2015-08-18T00:06:00Z      0
2015-08-18T00:12:00Z      0
2015-08-18T00:18:00Z      0
2015-08-18T00:24:00Z      0

    3.5 MOVING_AVERAGE()

    说明：返回单个字段的连续window个字段值的滑动平均值。 字段类型必须为int64或float64。算法找度娘
    基础语法

    SELECT MOVING_AVERAGE(<field_key>,<window>) FROM <measurement_name> [WHERE <stuff>]

    高级语法

    SELECT MOVING_AVERAGE(<function>(<field_key>),<window>) FROM <measurement_name> WHERE <stuff> GROUP BY time(<time_interval>)

    支持的函数包括：COUNT()
    , MEAN()
    , MEDIAN()
    , SUM()
    , FIRST()
    ,LAST()
    , MIN()
    , MAX()
    , and PERCENTILE()
    。
    例子

    > SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z'
name: h2o_feet
--------------
time                            water_level
2015-08-18T00:00:00Z      2.064
2015-08-18T00:06:00Z      2.116
2015-08-18T00:12:00Z      2.028
2015-08-18T00:18:00Z      2.126
2015-08-18T00:24:00Z      2.041
2015-08-18T00:30:00Z      2.051
2015-08-18T00:36:00Z      2.067
#第一条(`(2.116+2.064)/2`)，第二条(`(2.028+2.116)/2`)
> SELECT MOVING_AVERAGE("water_level",2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z'
name: h2o_feet
--------------
time                            moving_average
2015-08-18T00:06:00Z      2.09
2015-08-18T00:12:00Z      2.072
2015-08-18T00:18:00Z      2.077
2015-08-18T00:24:00Z      2.0835
2015-08-18T00:30:00Z      2.0460000000000003
2015-08-18T00:36:00Z      2.059
#分组统计
> SELECT MIN("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time                            min
2015-08-18T00:00:00Z      2.064
2015-08-18T00:12:00Z      2.028
2015-08-18T00:24:00Z      2.041
2015-08-18T00:36:00Z      2.067
#分组统计滑动平均值，第一条(`(2.064+2.028)/2`)
> SELECT MOVING_AVERAGE(MIN("water_level"),2) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:36:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time                            moving_average
2015-08-18T00:12:00Z      2.0460000000000003
2015-08-18T00:24:00Z      2.0345000000000004
2015-08-18T00:36:00Z      2.0540000000000003

    3.6 NON_NEGATIVE_DERIVATIVE()

    说明：返回系列中单个字段中的值的非负变化率。 InfluxDB计算时间域值之间的差异，并将这些结果转换为单位变化率。 单位参数是可选的，如果未指定，则默认为1秒（1秒）。改函数和DERIVATIVE()函数类似，只是返回其正数结果。
    语法

    SELECT NON_NEGATIVE_DERIVATIVE(<field_key>, [<unit>]) FROM <measurement_name> [WHERE <stuff>]

SELECT NON_NEGATIVE_DERIVATIVE(AGGREGATION_FUNCTION(<field_key>),[<unit>]) FROM <measurement_name> WHERE <stuff> GROUP BY time(<aggregation_interval>)

    4. 预测函数

    4.1 HOLT_WINTERS()

    说明：霍尔特指数平滑法，该字段必须是int64或float64类型。算法找度娘。
    语法

    SELECT HOLT_WINTERS(FUNCTION(<field_key>),<N>,<S>) FROM <measurement_name> WHERE <stuff> GROUP BY time(<interval>)[,<stuff>]

SELECT HOLT_WINTERS_WITH_FIT(FUNCTION(<field_key>),<N>,<S>) FROM <measurement_name> WHERE <stuff> GROUP BY time(<interval>)[,<stuff>]

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