Only keeping 2 days worth of data

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bretthoward
Posts: 2
Joined: Mon Mar 27, 2017 8:50 am

Only keeping 2 days worth of data

Post by bretthoward »

I've read about this and I've read that its generally related to an RRD problem. I re-did things and turned off my hourly RRA and deleted my RRD file and let it start over but I'm still looking at only 2 days on the weekly graph that just fifo's through.

I also don't understand why my fills show a bit low when they first come up... Essentially I have a grey line that shows thermostat set temp, the red line shows the current temp in the house. Then if the system is heating a set "heating" equal to ambient and if the system is cooling I set "cooling" equal to ambient. The theory is that the heating and cooling variables will be graphed as fills to the ambient line so that I can see when the system runs. But for some reason the fill always shows slightly short of the red line even though the script will never return a value where heating or cooling is not equal to ambient. On the daily graph you can see it really clearly at the front edge of each red bar... Any thoughts and/or help would be greatly appreciated!

I've checked all the values in the RRA's and these RRA's work just fine with the built in traffic graphs and what not... The only graph that isn't working is the one based on my own script.

Running rrdtool info on my rrd file returns the following:
--------> rrdtool info Thermo_temps.rrd
filename = "Thermo_temps.rrd"
rrd_version = "0003"
step = 300
last_update = 1490620501
header_size = 4656
ds[Target].index = 0
ds[Target].type = "GAUGE"
ds[Target].minimal_heartbeat = 600
ds[Target].min = 0.0000000000e+00
ds[Target].max = 1.2000000000e+02
ds[Target].last_ds = "66"
ds[Target].value = 6.6000000000e+01
ds[Target].unknown_sec = 0
ds[Ambient].index = 1
ds[Ambient].type = "GAUGE"
ds[Ambient].minimal_heartbeat = 600
ds[Ambient].min = 0.0000000000e+00
ds[Ambient].max = 1.2000000000e+02
ds[Ambient].last_ds = "66"
ds[Ambient].value = 6.6000000000e+01
ds[Ambient].unknown_sec = 0
ds[Heat].index = 2
ds[Heat].type = "GAUGE"
ds[Heat].minimal_heartbeat = 600
ds[Heat].min = 0.0000000000e+00
ds[Heat].max = 1.2000000000e+02
ds[Heat].last_ds = "0"
ds[Heat].value = 0.0000000000e+00
ds[Heat].unknown_sec = 0
ds[Cool].index = 3
ds[Cool].type = "GAUGE"
ds[Cool].minimal_heartbeat = 600
ds[Cool].min = 0.0000000000e+00
ds[Cool].max = 1.2000000000e+02
ds[Cool].last_ds = "0"
ds[Cool].value = 0.0000000000e+00
ds[Cool].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 600
rra[0].cur_row = 163
rra[0].pdp_per_row = 1
rra[0].xff = 5.0000000000e-01
rra[0].cdp_prep[0].value = NaN
rra[0].cdp_prep[0].unknown_datapoints = 0
rra[0].cdp_prep[1].value = NaN
rra[0].cdp_prep[1].unknown_datapoints = 0
rra[0].cdp_prep[2].value = NaN
rra[0].cdp_prep[2].unknown_datapoints = 0
rra[0].cdp_prep[3].value = NaN
rra[0].cdp_prep[3].unknown_datapoints = 0
rra[1].cf = "AVERAGE"
rra[1].rows = 700
rra[1].cur_row = 29
rra[1].pdp_per_row = 6
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 1.9800000000e+02
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[1].cdp_prep[1].value = 1.9800000000e+02
rra[1].cdp_prep[1].unknown_datapoints = 0
rra[1].cdp_prep[2].value = 0.0000000000e+00
rra[1].cdp_prep[2].unknown_datapoints = 0
rra[1].cdp_prep[3].value = 0.0000000000e+00
rra[1].cdp_prep[3].unknown_datapoints = 0
rra[2].cf = "AVERAGE"
rra[2].rows = 775
rra[2].cur_row = 146
rra[2].pdp_per_row = 24
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = 9.9000000000e+02
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[2].cdp_prep[1].value = 9.9000000000e+02
rra[2].cdp_prep[1].unknown_datapoints = 0
rra[2].cdp_prep[2].value = 6.6220000000e+01
rra[2].cdp_prep[2].unknown_datapoints = 0
rra[2].cdp_prep[3].value = 0.0000000000e+00
rra[2].cdp_prep[3].unknown_datapoints = 0
rra[3].cf = "AVERAGE"
rra[3].rows = 797
rra[3].cur_row = 7
rra[3].pdp_per_row = 288
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = 1.0277993333e+04
rra[3].cdp_prep[0].unknown_datapoints = 0
rra[3].cdp_prep[1].value = 1.0595013333e+04
rra[3].cdp_prep[1].unknown_datapoints = 0
rra[3].cdp_prep[2].value = 5.9378333333e+02
rra[3].cdp_prep[2].unknown_datapoints = 0
rra[3].cdp_prep[3].value = 0.0000000000e+00
rra[3].cdp_prep[3].unknown_datapoints = 0
rra[4].cf = "MAX"
rra[4].rows = 600
rra[4].cur_row = 148
rra[4].pdp_per_row = 1
rra[4].xff = 5.0000000000e-01
rra[4].cdp_prep[0].value = NaN
rra[4].cdp_prep[0].unknown_datapoints = 0
rra[4].cdp_prep[1].value = NaN
rra[4].cdp_prep[1].unknown_datapoints = 0
rra[4].cdp_prep[2].value = NaN
rra[4].cdp_prep[2].unknown_datapoints = 0
rra[4].cdp_prep[3].value = NaN
rra[4].cdp_prep[3].unknown_datapoints = 0
rra[5].cf = "MAX"
rra[5].rows = 700
rra[5].cur_row = 577
rra[5].pdp_per_row = 6
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = 6.6000000000e+01
rra[5].cdp_prep[0].unknown_datapoints = 0
rra[5].cdp_prep[1].value = 6.6000000000e+01
rra[5].cdp_prep[1].unknown_datapoints = 0
rra[5].cdp_prep[2].value = 0.0000000000e+00
rra[5].cdp_prep[2].unknown_datapoints = 0
rra[5].cdp_prep[3].value = 0.0000000000e+00
rra[5].cdp_prep[3].unknown_datapoints = 0
rra[6].cf = "MAX"
rra[6].rows = 775
rra[6].cur_row = 546
rra[6].pdp_per_row = 24
rra[6].xff = 5.0000000000e-01
rra[6].cdp_prep[0].value = 6.6000000000e+01
rra[6].cdp_prep[0].unknown_datapoints = 0
rra[6].cdp_prep[1].value = 6.6000000000e+01
rra[6].cdp_prep[1].unknown_datapoints = 0
rra[6].cdp_prep[2].value = 6.6000000000e+01
rra[6].cdp_prep[2].unknown_datapoints = 0
rra[6].cdp_prep[3].value = 0.0000000000e+00
rra[6].cdp_prep[3].unknown_datapoints = 0
rra[7].cf = "MAX"
rra[7].rows = 797
rra[7].cur_row = 521
rra[7].pdp_per_row = 288
rra[7].xff = 5.0000000000e-01
rra[7].cdp_prep[0].value = 6.8000000000e+01
rra[7].cdp_prep[0].unknown_datapoints = 0
rra[7].cdp_prep[1].value = 6.8000000000e+01
rra[7].cdp_prep[1].unknown_datapoints = 0
rra[7].cdp_prep[2].value = 6.7000000000e+01
rra[7].cdp_prep[2].unknown_datapoints = 0
rra[7].cdp_prep[3].value = 0.0000000000e+00
rra[7].cdp_prep[3].unknown_datapoints = 0
Attachments
weekly 30 min avg.png
weekly 30 min avg.png (25.97 KiB) Viewed 359 times
monthly 2 hr avg.png
monthly 2 hr avg.png (23.7 KiB) Viewed 359 times
daily 5 min avg.png
daily 5 min avg.png (20.9 KiB) Viewed 359 times
bretthoward
Posts: 2
Joined: Mon Mar 27, 2017 8:50 am

Re: Only keeping 2 days worth of data

Post by bretthoward »

Still beating my head against the wall on this one... Found another thread where someone was having a similar issue and they said to take the hourly RRA out and rebuild. I took the RRA out and deleted the RRD file and it rebuilt that way... I assume that should have worked? Still running into the same problem.
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