[rrd-developers] rrd resize

[Mark Mills]

I noticed today when doing a resize (GROW) that some of the historical data in the not-being-changed rra's was zero'ed out.
 
This is the first time I've ever done a resize so I was rather shocked when it mangled the rolled up archive data. Worse, it didn't do it consistently, leaving the last rra untouched! This left me with 3 graphs truncated to 50 hours of 5 minute readings (pretty skinny on that 2 month wide, 2 hour graph) and a yearly graph that stretched back 3 months.
 
Has this come up in the past? Preferably, there should be a non-destructive option in there but at the very least it should perform consistently across all the RRAs. Is there a reason why it might not, on purpose? I don't want to waste time looking for bugs if it has been fixed or is intended behavior.
 
Also, if anyone could suggest a workaround where I could keep the rolledup archives' data intact while extending the first rra, I'd appreciate it greatly. I'd hate to lose my historical data just to add new features to my system.
 
A dump of the info (post change) follows. The first rra was resized from 600 to 17820. (62 days of historical at full resolution makes for cool comparison printouts. I lock the high-low settings and print 2 months and 1 month back daily graphs to compare traffic changes.)
 
--mark
 
filename = "/opt/cricket/cricket-data/switch-ports/xswtch010.sdf.xodiax.net-1.rrd"
rrd_version = "0001"
step = 300
last_update = 1031249476
ds[ds0].type = "DERIVE"
ds[ds0].minimal_heartbeat = 1800
ds[ds0].min = 0.0000000000e+00
ds[ds0].max = NaN
ds[ds0].last_ds = "931789036"
ds[ds0].value = 9.2983466667e+03
ds[ds0].unknown_sec = 0
ds[ds1].type = "DERIVE"
ds[ds1].minimal_heartbeat = 1800
ds[ds1].min = 0.0000000000e+00
ds[ds1].max = NaN
ds[ds1].last_ds = "88172450"
ds[ds1].value = 8.2232000000e+02
ds[ds1].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 18720
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[1].cf = "AVERAGE"
rra[1].rows = 600
rra[1].pdp_per_row = 6
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 2.4924051111e+02
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[1].cdp_prep[1].value = 2.1928933333e+01
rra[1].cdp_prep[1].unknown_datapoints = 0
rra[2].cf = "AVERAGE"
rra[2].rows = 600
rra[2].pdp_per_row = 24
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = 2.4924051111e+02
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[2].cdp_prep[1].value = 2.1928933333e+01
rra[2].cdp_prep[1].unknown_datapoints = 0
rra[3].cf = "MAX"
rra[3].rows = 600
rra[3].pdp_per_row = 24
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = 1.2566466887e+02
rra[3].cdp_prep[0].unknown_datapoints = 0
rra[3].cdp_prep[1].value = 1.1076754967e+01
rra[3].cdp_prep[1].unknown_datapoints = 0
rra[4].cf = "AVERAGE"
rra[4].rows = 600
rra[4].pdp_per_row = 288
rra[4].xff = 5.0000000000e-01
rra[4].cdp_prep[0].value = 2.6126946962e+04
rra[4].cdp_prep[0].unknown_datapoints = 11
rra[4].cdp_prep[1].value = 2.3128132399e+03
rra[4].cdp_prep[1].unknown_datapoints = 11
rra[5].cf = "MAX"
rra[5].rows = 600
rra[5].pdp_per_row = 288
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = 1.5079793478e+02
rra[5].cdp_prep[0].unknown_datapoints = 11
rra[5].cdp_prep[1].value = 3.5044190254e+01
rra[5].cdp_prep[1].unknown_datapoints = 11


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