制作每日重置的InfluxDB/Grafana累积函数（锯齿图）

我将能源使用数据记录为计数器，我希望将其显示为每天重置的累积图表，类似地在这里问.

我可以按如下方式生成累积值：

SELECT mean("value") \
  FROM "energy" \
  WHERE $timeFilter \
  GROUP BY time($__interval)

以及每日价值：

SELECT max("value") \
  FROM "energy" \
  WHERE $timeFilter \
  GROUP BY time(1d)

但我无法减去它或在一个查询中得到它，因为 GROUP BY 时间不同。

（如何）这在 influxdb 中可能吗？我看过 INTEGRATE() 但这还没有找到一种方法来使其工作。

数据如下所示（示例仅限 1 天）：

time                 value
----                 ----
2018-12-10T17:00:00Z 7
2018-12-10T18:00:00Z 9
2018-12-10T19:00:00Z 10
2018-12-10T20:00:00Z 11
2018-12-10T21:00:00Z 13
2018-12-10T22:00:00Z 14
2018-12-10T23:00:00Z 15
2018-12-11T00:00:00Z 16
2018-12-11T01:00:00Z 17
2018-12-11T02:00:00Z 20
2018-12-11T03:00:00Z 24
2018-12-11T04:00:00Z 25
2018-12-11T05:00:00Z 26
2018-12-11T06:00:00Z 27
2018-12-11T07:00:00Z 28
2018-12-11T08:00:00Z 29
2018-12-11T09:00:00Z 31
2018-12-11T10:00:00Z 32
2018-12-11T11:00:00Z 33
2018-12-11T12:00:00Z 34
2018-12-11T13:00:00Z 35
2018-12-11T14:00:00Z 36
2018-12-11T15:00:00Z 37
2018-12-11T16:00:00Z 38
2018-12-11T17:00:00Z 39

I can plot the following:

But I want something like:

I 找到了解决方案，最后很简单：

SELECT kaifa-kaifa_fill as Energy FROM
  (SELECT first(kaifa) as kaifa_fill from energyv2 WHERE $timeFilter group by time(1d) TZ('Europe/Amsterdam')),
  (SELECT first(kaifa) as kaifa from energyv2 WHERE $timeFilter GROUP BY time($__interval))
fill(previous)

请注意，需要 fill(previous) 以确保 kaifa_fill 和 kaifa 重叠。

示例数据：

time                 kaifa     kaifa_fill kaifa_kaifa_fill
----                 -----     ---------- ----------------
2019-08-03T00:00:00Z 179688195 179688195  0
2019-08-03T01:00:00Z 179746833 179688195  58638
2019-08-03T02:00:00Z 179803148 179688195  114953
2019-08-03T03:00:00Z 179859464 179688195  171269
2019-08-03T04:00:00Z 179914038 179688195  225843
2019-08-03T05:00:00Z 179967450 179688195  279255
2019-08-03T06:00:00Z 179905910 179688195  217715
2019-08-03T07:00:00Z 179847272 179688195  159077
2019-08-03T08:00:00Z 179698065 179688195  9870
2019-08-03T09:00:00Z 179378170 179688195  -310025
2019-08-03T10:00:00Z 179341013 179688195  -347182
2019-08-03T11:00:00Z 179126201 179688195  -561994
2019-08-03T12:00:00Z 179039116 179688195  -649079
2019-08-03T13:00:00Z 178935193 179688195  -753002
2019-08-03T14:00:00Z 178687870 179688195  -1000326
2019-08-03T15:00:00Z 178517762 179688195  -1170433
2019-08-03T16:00:00Z 178409776 179688195  -1278420
2019-08-03T17:00:00Z 178376102 179688195  -1312093
2019-08-03T18:00:00Z 178388875 179688195  -1299320
2019-08-03T19:00:00Z 178780181 179688195  -908015
2019-08-03T20:00:00Z 178928226 179688195  -759969
2019-08-03T21:00:00Z 179065241 179688195  -622954
2019-08-03T22:00:00Z 179183098 179688195  -505098
2019-08-03T23:00:00Z 179306179 179688195  -382016
2019-08-04T00:00:00Z 179306179 179370042  -63863
2019-08-04T00:00:00Z 179370042 179370042  0
2019-08-04T01:00:00Z 179417649 179370042  47607
2019-08-04T02:00:00Z 179464094 179370042  94053
2019-08-04T03:00:00Z 179509960 179370042  139918
2019-08-04T04:00:00Z 179591820 179370042  221779
2019-08-04T05:00:00Z 179872817 179370042  502775
2019-08-04T06:00:00Z 180056278 179370042  686236
2019-08-04T07:00:00Z 179929713 179370042  559671
2019-08-04T08:00:00Z 179514604 179370042  144562
2019-08-04T09:00:00Z 179053049 179370042  -316992
2019-08-04T10:00:00Z 178683225 179370042  -686817
2019-08-04T11:00:00Z 178078269 179370042  -1291773
2019-08-04T12:00:00Z 177650387 179370042  -1719654
2019-08-04T13:00:00Z 177281724 179370042  -2088317
2019-08-04T14:00:00Z 177041367 179370042  -2328674
2019-08-04T15:00:00Z 176807397 179370042  -2562645
2019-08-04T16:00:00Z 176737148 179370042  -2632894
2019-08-04T17:00:00Z 176677349 179370042  -2692693
2019-08-04T18:00:00Z 176690702 179370042  -2679340
2019-08-04T19:00:00Z 176734825 179370042  -2635216
2019-08-04T20:00:00Z 176810300 179370042  -2559742
2019-08-04T21:00:00Z 176866035 179370042  -2504007
2019-08-04T22:00:00Z 176914803 179370042  -2455239
2019-08-04T23:00:00Z 176965893 179370042  -2404149
2019-08-05T00:00:00Z 176965893 177016983  -51090
2019-08-05T00:00:00Z 177016983 177016983  0

示例图：