Influxdb 流入通量

这个查询的等价物是什么

select sum("count") from "measurement_name" where time<now() and time>now()-4d group by time(100s),"source"

及

但它们似乎都产生了不同的结果

这是按时间间隔=100s和源进行分组。假设，按时间分组和总和聚合是隐式的？使用Flux中的窗口函数完成，但InfluxQL查询选择的结果。。。它们是：

name: measurement_name
tags: source=source_name
time                sum
----                ---
1601022500000000000 39
1601022600000000000 191
1601022700000000000 232
1601022800000000000 145
1601022900000000000 207
1601023000000000000 277
1601023100000000000 160
1601023200000000000 228
1601023300000000000 253
1601023400000000000 167

而来自Flux查询的是

Table: keys: [source]
source:string        _time:time                                 _value:int    _field:string  
----------------------  ------------------------------  --------------------------  -------- 

source_name  2020-09-25T11:46:51.390000000Z                           6              count  
source_name  2020-09-25T11:46:54.124000000Z                           5              count  
source_name  2020-09-25T11:46:57.616000000Z                           6              count  
source_name  2020-09-25T11:46:57.999000000Z                           9              count  
source_name  2020-09-25T11:46:58.064000000Z                           3              count  
source_name  2020-09-25T11:46:58.307000000Z                           6              count  
source_name  2020-09-25T11:47:01.011000000Z                           8              count  
source_name  2020-09-25T11:47:03.634000000Z                           6              count  
source_name  2020-09-25T11:47:03.700000000Z                           8              count  
source_name  2020-09-25T11:47:04.144000000Z                           8              count 

最终目标是在格拉法纳规划出这一点

是否也有可能在这两种范式之间来回转换？当然，只要有可能，您就需要显式地包含sum函数。我建议也使用

通常，组不保留排序顺序，而是在执行其工作之前按时间进行聚合并进行排序。这是值得寻找的。此外，Flux和InfluxQL查询之间的时间界限可能并不完全一致。我想他们会的，但要仔细检查一下

name: measurement_name
tags: source=source_name
time                sum
----                ---
1601022500000000000 39
1601022600000000000 191
1601022700000000000 232
1601022800000000000 145
1601022900000000000 207
1601023000000000000 277
1601023100000000000 160
1601023200000000000 228
1601023300000000000 253
1601023400000000000 167

Table: keys: [source]
source:string        _time:time                                 _value:int    _field:string  
----------------------  ------------------------------  --------------------------  -------- 

source_name  2020-09-25T11:46:51.390000000Z                           6              count  
source_name  2020-09-25T11:46:54.124000000Z                           5              count  
source_name  2020-09-25T11:46:57.616000000Z                           6              count  
source_name  2020-09-25T11:46:57.999000000Z                           9              count  
source_name  2020-09-25T11:46:58.064000000Z                           3              count  
source_name  2020-09-25T11:46:58.307000000Z                           6              count  
source_name  2020-09-25T11:47:01.011000000Z                           8              count  
source_name  2020-09-25T11:47:03.634000000Z                           6              count  
source_name  2020-09-25T11:47:03.700000000Z                           8              count  
source_name  2020-09-25T11:47:04.144000000Z                           8              count 

from(bucket:"metrics/default_metrics") 
 |> range(start: -4d) 
 |> filter(fn: (r)=> r._measurement == "measurement_name")
 |> group(columns: ["source"])
 |> aggregateWindow(every: 100s, fn: sum)
 |> drop(columns:["_start","_stop","_measurement","column_a","column_b"]) 
 |> yield()