[rrd-users] Issue (?) with rrdtool resize

[Michael Grice]

I'm having a problem getting rrdtool resize to do what I want. I'm not
sure if I'm missing something or if I'm trying to do something I
shouldn't be.

I am trying to take rrd files generated by MRTG and move them onto
another platform. So I do an rrdtool dump and rrdtool restore to get
the rrd files into the appropriate format. However, I would like to keep
more data than the default rrd data, so I want to use rrdtool resize to
add a bunch of rows (156880, to be precise) to rra[0] and rra[4].

The thing is, afterwards I can't graph or fetch any data older than about
2 days (i.e., older than the oldest of the 800 rows of 300-second-interval
data stored in the rrd). This may simply mean that I haven't been able
to get at it (although I have dinked around with the resolution with
rrdtool fetch). Or it may mean the resize operation has truncated the
data. I've also monkeyed around with resizing the other RRA's prior to
resizing the first one, but I haven't gotten it to work.

If I'm trying something unrealistic, what other approach should I take?
While my primary goal is to capture all data at 5 minute intervals going
forward, I'd also like as much historical data as possible (and I
realize it won't be at the same resolution).

Thanks,

Michael

Here's info for a typical file before resizing:

filename = "foo.rrd"
rrd_version = "0001"
step = 300
last_update = 1074110737
ds[ds0].type = "GAUGE"
ds[ds0].minimal_heartbeat = 600
ds[ds0].min = 0.0000000000e+00
ds[ds0].max = 1.0000000000e+02
ds[ds0].last_ds = "UNKN"
ds[ds0].value = 1.2950000000e+03
ds[ds0].unknown_sec = 0
ds[ds1].type = "GAUGE"
ds[ds1].minimal_heartbeat = 600
ds[ds1].min = 0.0000000000e+00
ds[ds1].max = 1.0000000000e+02
ds[ds1].last_ds = "UNKN"
ds[ds1].value = 1.2950000000e+03
ds[ds1].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 800
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 = 800
rra[1].pdp_per_row = 6
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 3.4856666667e+01
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[1].cdp_prep[1].value = 3.4856666667e+01
rra[1].cdp_prep[1].unknown_datapoints = 0
rra[2].cf = "AVERAGE"
rra[2].rows = 800
rra[2].pdp_per_row = 24
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = 3.4856666667e+01
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[2].cdp_prep[1].value = 3.4856666667e+01
rra[2].cdp_prep[1].unknown_datapoints = 0
rra[3].cf = "AVERAGE"
rra[3].rows = 800
rra[3].pdp_per_row = 288
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = 5.5996466667e+03
rra[3].cdp_prep[0].unknown_datapoints = 0
rra[3].cdp_prep[1].value = 5.5996466667e+03
rra[3].cdp_prep[1].unknown_datapoints = 0
rra[4].cf = "MAX"
rra[4].rows = 800
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[5].cf = "MAX"
rra[5].rows = 800
rra[5].pdp_per_row = 6
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = 3.4856666667e+01
rra[5].cdp_prep[0].unknown_datapoints = 0
rra[5].cdp_prep[1].value = 3.4856666667e+01
rra[5].cdp_prep[1].unknown_datapoints = 0
rra[6].cf = "MAX"
rra[6].rows = 800
rra[6].pdp_per_row = 24
rra[6].xff = 5.0000000000e-01
rra[6].cdp_prep[0].value = 3.4856666667e+01
rra[6].cdp_prep[0].unknown_datapoints = 0
rra[6].cdp_prep[1].value = 3.4856666667e+01
rra[6].cdp_prep[1].unknown_datapoints = 0
rra[7].cf = "MAX"
rra[7].rows = 800
rra[7].pdp_per_row = 288
rra[7].xff = 5.0000000000e-01
rra[7].cdp_prep[0].value = 3.6000000000e+01
rra[7].cdp_prep[0].unknown_datapoints = 0
rra[7].cdp_prep[1].value = 3.6000000000e+01

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