Plotting Weather Forecast Data

time
query
grafana
#1

I’m writing weather forecast data to InfluxDB. For each write point there will be three timestamps:

  1. Time of collection
  2. Time which data point occurs (in future)
  3. Time of forecast issue

My question is: how can I create a query that uses 2) Time which data point occurs (in future), rather than 1) Time of collection ?

And as a larger question: Is there a better, or more correct way of structuring my write points (see below)?

My current query:

> SELECT "temperatureHigh" FROM "darksky_daily" WHERE $timeFilter

To illustrate, my graph in Grafana currently looks like this:

Some additional details…

This is how I’m writing points to InfluxDB. Each point is tagged as daily_n where n is typically 1-8:

{'fields': {'ReportTime': 1553929200, 'temperatureLow': 46.04, 'temperatureLowTime': 1554040800,
 'temperatureHigh': 68.82, 'temperatureHighTime': 1553983200, ...}, 'time': '2019-03-23T14:59:03Z',
 'tags': {'location': 'San Pablito', 'daily': 'daily_8'}, 'measurement': 'darksky_daily'}

The results in InfluxDB:

> select temperatureLow,temperatureLowTime,temperatureHigh,temperatureHighTime from "darksky_daily" order by time desc limit 20
name: darksky_daily
time                 temperatureLow temperatureLowTime temperatureHigh temperatureHighTime
----                 -------------- ------------------ --------------- -------------------
2019-03-23T15:00:03Z 46.04          1554040800         68.82           1553983200
2019-03-23T15:00:03Z 37.94          1553436000         60.06           1553378400
2019-03-23T15:00:03Z 43.23          1553954400         64.42           1553896800
2019-03-23T15:00:03Z 44.18          1553781600         53.68           1553727600
2019-03-23T15:00:03Z 47.76          1553695200         56.48           1553641200
2019-03-23T15:00:03Z 41.36          1553868000         60.47           1553810400
2019-03-23T15:00:03Z 45.12          1553608800         57.45           1553554800
2019-03-23T15:00:03Z 48.05          1553497200         62.9            1553468400
-------------------------------------------------------------
2019-03-23T14:30:02Z 45.12          1553608800         57.45           1553554800
2019-03-23T14:30:02Z 48.05          1553497200         62.9            1553468400
2019-03-23T14:30:02Z 43.23          1553954400         64.42           1553896800
2019-03-23T14:30:02Z 46.04          1554040800         68.82           1553983200
2019-03-23T14:30:02Z 37.94          1553436000         60.04           1553378400
2019-03-23T14:30:02Z 41.36          1553868000         60.47           1553810400
2019-03-23T14:30:02Z 44.18          1553781600         53.68           1553727600
2019-03-23T14:30:02Z 47.76          1553695200         56.48           1553641200