[rrd-users] lots of NaNs in fetch

[Matthew M. Boedicker]

I am creating an rrd with this command:

rrdtool create capacity.rrd \
DS:capacity:GAUGE:600:U:U \
DS:in:GAUGE:600:U:U \
DS:out:GAUGE:600:U:U \
RRA:AVERAGE:0.5:1:2016 \
RRA:AVERAGE:0.5:12:720 \
RRA:AVERAGE:0.5:288:1095 \
RRA:AVERAGE:0.5:2016:520 \
RRA:AVERAGE:0.5:8064:1200 \
RRA:AVERAGE:0.5:96768:100

I am updating it every 5 minutes with data like this:

rrdtool updatev capacity.rrd -t capacity:in:out N:672:74:0

When doing a fetch it seems to have values that aren't NaN only every once
in a while.

The fetch looks like this:

rrdtool fetch capacity.rrd AVERAGE

... bunch of NaN cut ...
1208145000: NaN NaN NaN
1208145300: NaN NaN NaN
1208145600: NaN NaN NaN
1208145900: 6.7200000000e+02 4.3869431287e+01 0.0000000000e+00
1208146200: 6.7200000000e+02 4.1264112557e+01 0.0000000000e+00
1208146500: 6.7200000000e+02 3.9534401613e+01 0.0000000000e+00
1208146800: 6.7200000000e+02 3.7267041573e+01 0.0000000000e+00
1208147100: 6.7200000000e+02 3.9903616280e+01 0.0000000000e+00
1208147400: 6.7200000000e+02 3.9589101013e+01 0.0000000000e+00
1208147700: 6.7200000000e+02 3.5552953440e+01 0.0000000000e+00
1208148000: 6.7200000000e+02 3.2084010387e+01 0.0000000000e+00
1208148300: 6.7200000000e+02 3.8332196400e+01 0.0000000000e+00
... bunch of NaN cut ...
1208156400: NaN NaN NaN
1208156700: 6.7200000000e+02 3.5141270800e+00 0.0000000000e+00
1208157000: NaN NaN NaN
... bunch of NaN cut ...
1208190900: NaN NaN NaN
1208191200: 6.7200000000e+02 6.2971792813e+01 0.0000000000e+00
1208191500: NaN NaN NaN

When it does have a value besides NaN, the values are what I expect to see. I
am pretty sure that there is data going in about every 5 minutes and the
minimal heartbeat is is 600. Any ideas on why I am seeing all these NaNs
when I fetch the data?

When I clear the rrd and start over it works fine for a while then starts
to get NaNs.

This is the rrdinfo:

filename = "capacity.rrd"
rrd_version = "0003"
step = 300
last_update = 1208193669
ds[capacity].type = "GAUGE"
ds[capacity].minimal_heartbeat = 600
ds[capacity].min = NaN
ds[capacity].max = NaN
ds[capacity].last_ds = "672"
ds[capacity].value = 4.6624522560e+04
ds[capacity].unknown_sec = 0
ds[in].type = "GAUGE"
ds[in].minimal_heartbeat = 600
ds[in].min = NaN
ds[in].max = NaN
ds[in].last_ds = "84"
ds[in].value = 5.8280653200e+03
ds[in].unknown_sec = 0
ds[out].type = "GAUGE"
ds[out].minimal_heartbeat = 600
ds[out].min = NaN
ds[out].max = NaN
ds[out].last_ds = "0"
ds[out].value = 0.0000000000e+00
ds[out].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 2016
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[1].cf = "AVERAGE"
rra[1].rows = 720
rra[1].pdp_per_row = 12
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 2.6880000000e+03
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[1].cdp_prep[1].value = 2.8002402588e+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[2].cf = "AVERAGE"
rra[2].rows = 1095
rra[2].pdp_per_row = 288
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = 1.3977600000e+05
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[2].cdp_prep[1].value = 9.1576284286e+03
rra[2].cdp_prep[1].unknown_datapoints = 0
rra[2].cdp_prep[2].value = 0.0000000000e+00
rra[2].cdp_prep[2].unknown_datapoints = 0
rra[3].cf = "AVERAGE"
rra[3].rows = 520
rra[3].pdp_per_row = 2016
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = 5.8664983155e+05
rra[3].cdp_prep[0].unknown_datapoints = 487
rra[3].cdp_prep[1].value = 4.0400658203e+04
rra[3].cdp_prep[1].unknown_datapoints = 487
rra[3].cdp_prep[2].value = 0.0000000000e+00
rra[3].cdp_prep[2].unknown_datapoints = 487
rra[4].cf = "AVERAGE"
rra[4].rows = 1200
rra[4].pdp_per_row = 8064
rra[4].xff = 5.0000000000e-01
rra[4].cdp_prep[0].value = 5.8664983155e+05
rra[4].cdp_prep[0].unknown_datapoints = 2503
rra[4].cdp_prep[1].value = 4.0400658203e+04
rra[4].cdp_prep[1].unknown_datapoints = 2503
rra[4].cdp_prep[2].value = 0.0000000000e+00
rra[4].cdp_prep[2].unknown_datapoints = 2503
rra[5].cf = "AVERAGE"
rra[5].rows = 100
rra[5].pdp_per_row = 96768
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = 5.8664983155e+05
rra[5].cdp_prep[0].unknown_datapoints = 58951
rra[5].cdp_prep[1].value = 4.0400658203e+04
rra[5].cdp_prep[1].unknown_datapoints = 58951
rra[5].cdp_prep[2].value = 0.0000000000e+00
rra[5].cdp_prep[2].unknown_datapoints = 58951

Thanks,
Matthew M. Boedicker