[rrd-users] (bug?) Incorrectly selecting low-res RRA

[Phil Radden]

Hi all,

I'm suddenly getting a situation where rrdtool is picking up a 
low-resolution RRA instead of the one it used to use - I can't spot 
anything that has changed since it last worked, so I'm a little confused.

I'm using 1.0.41 - the same problem started in 1.0.40, and I've just 
upgraded because the entry 'fix borderline problem with rrd_fetch 
selecting the wrong rra under certain conditions' in the changelog seemed 
relevant!

I'm using ganglia (http://ganglia.sourceforge.net/) to populate my RRDs, 
and have RRAs for hour/day/week/month/year/decade - see an example output 
from rrdtool info below.  When plotting the monthly graphs, it should be 
using 240 datapoints; however, it's suddenly switched to just thirty - see 
sample output from rrdtool fetch below.  And this is consistent across 
every graph I'm plotting.

Ah - it gets more entertaining; it all works again now.  And I definitely 
didn't change anything!  The window of bad behaviour was maybe an hour 
(and I'm not normally looking at these graphs at this time of day/week!)

Is it possible there's another bug along the lines of the one fixed in the 
changelog entry mentioned above?  Is there anything I can do to help 
narrow down the problem?

Thanks for any help!
Phil

=== sample rrdtool info ===
filename = "transfers.rrd"
rrd_version = "0001"
step = 15
last_update = 1048379989
ds[sum].type = "GAUGE"
ds[sum].minimal_heartbeat = 30
ds[sum].min = NaN
ds[sum].max = NaN
ds[sum].last_ds = "UNKN"
ds[sum].value = 0.0000000000e+00
ds[sum].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 240
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[1].cf = "AVERAGE"
rra[1].rows = 240
rra[1].pdp_per_row = 24
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 0.0000000000e+00
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[2].cf = "AVERAGE"
rra[2].rows = 240
rra[2].pdp_per_row = 168
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = 0.0000000000e+00
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[3].cf = "AVERAGE"
rra[3].rows = 240
rra[3].pdp_per_row = 672
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = 2.2533333333e+01
rra[3].cdp_prep[0].unknown_datapoints = 0
rra[4].cf = "AVERAGE"
rra[4].rows = 370
rra[4].pdp_per_row = 5760
rra[4].xff = 5.0000000000e-01
rra[4].cdp_prep[0].value = 0.0000000000e+00
rra[4].cdp_prep[0].unknown_datapoints = 0
rra[5].cf = "AVERAGE"
rra[5].rows = 370
rra[5].pdp_per_row = 17280
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = 5.9533333333e+02
rra[5].cdp_prep[0].unknown_datapoints = 6
rra[6].cf = "AVERAGE"
rra[6].rows = 370
rra[6].pdp_per_row = 57600
rra[6].xff = 5.0000000000e-01
rra[6].cdp_prep[0].value = 5.4878666667e+03
rra[6].cdp_prep[0].unknown_datapoints = 7471


=== sample broken rrdtool fetch ===
$ rrdtool fetch transfers.rrd AVERAGE -s -2419200 -e N
                      sum

1045958400: nan
1046044800: nan
1046131200: nan
1046217600: nan
1046304000: nan
1046390400: nan
1046476800: nan
1046563200: nan
1046649600: nan
1046736000: nan
1046822400: nan
1046908800: nan
1046995200: nan
1047081600: 1.8783181554e-01
1047168000: 5.7869212963e-01
1047254400: 5.2446759259e-01
1047340800: 7.0638888889e-01
1047427200: 1.6115046296e+00
1047513600: 2.6965277778e+00
1047600000: 1.9215046296e+00
1047686400: 8.6528671653e-01
1047772800: 7.7846064815e-01
1047859200: 6.6748842593e-01
1047945600: 1.3293171296e+00
1048032000: 2.7433796296e+00
1048118400: nan
1048204800: nan
1048291200: 9.3993055556e-02
1048377600: 9.3731896652e-03
1048464000: nan


=== sample working rrdtool fetch ===
$ rrdtool fetch transfers.rrd AVERAGE -s -2419200 -e N
                      sum

1045961280: nan
1045971360: nan
1045981440: nan
1045991520: nan
1046001600: nan
1046011680: nan
1046021760: nan
1046031840: nan
1046041920: nan
1046052000: nan
1046062080: nan
[...snip lots here, rather than post 240 lines of stuff...]
1048269600: 0.0000000000e+00
1048279680: 0.0000000000e+00
1048289760: 0.0000000000e+00
1048299840: 0.0000000000e+00
1048309920: 0.0000000000e+00
1048320000: 1.1706349206e-02
1048330080: 0.0000000000e+00
1048340160: 1.7460317460e-02
1048350240: 6.4484126984e-03
1048360320: 5.5555555556e-03
1048370400: 5.5555555556e-03
1048380480: 3.3531746032e-02
1048390560: nan

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