[rrd-users] unexpected NANs

Wed Nov 28 13:15:13 CET 2007

Hi,

I'm having problems with some of my rrd's because all the PDP's are nans eventhough the values which go into each PDP are present at the required 60 second intervals. A few values may come in slightly over the 60 seconds, but I have set the heartbeat 120, so as long as there is data within this time, I would have expected to get a PDP. Can anyone see where I'm going wrong?

Many thanks,

Ed.

This is the info for one of the rrd's:

filename = "Windows_Memory_Paging_Usage.rrd"
rrd_version = "0003"
step = 60
last_update = 1196244176
ds[MEMmmPgsInptPrSc].type = "GAUGE"
ds[MEMmmPgsInptPrSc].minimal_heartbeat = 120
ds[MEMmmPgsInptPrSc].min = NaN
ds[MEMmmPgsInptPrSc].max = NaN
ds[MEMmmPgsInptPrSc].last_ds = "UNKN"
ds[MEMmmPgsInptPrSc].value = 0.0000000000e+00
ds[MEMmmPgsInptPrSc].unknown_sec = 56
ds[MEMmmPgsOtptPrSc].type = "GAUGE"
ds[MEMmmPgsOtptPrSc].minimal_heartbeat = 120
ds[MEMmmPgsOtptPrSc].min = NaN
ds[MEMmmPgsOtptPrSc].max = NaN
ds[MEMmmPgsOtptPrSc].last_ds = "UNKN"
ds[MEMmmPgsOtptPrSc].value = 0.0000000000e+00
ds[MEMmmPgsOtptPrSc].unknown_sec = 56
rra[0].cf = "AVERAGE"
rra[0].rows = 26352
rra[0].pdp_per_row = 5
rra[0].xff = 5.0000000000e-01
rra[0].cdp_prep[0].value = NaN
rra[0].cdp_prep[0].unknown_datapoints = 2
rra[0].cdp_prep[1].value = NaN
rra[0].cdp_prep[1].unknown_datapoints = 2
rra[1].cf = "AVERAGE"
rra[1].rows = 8784
rra[1].pdp_per_row = 30
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = NaN
rra[1].cdp_prep[0].unknown_datapoints = 2
rra[1].cdp_prep[1].value = NaN
rra[1].cdp_prep[1].unknown_datapoints = 2
rra[2].cf = "AVERAGE"
rra[2].rows = 4392
rra[2].pdp_per_row = 120
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = NaN
rra[2].cdp_prep[0].unknown_datapoints = 2
rra[2].cdp_prep[1].value = NaN
rra[2].cdp_prep[1].unknown_datapoints = 2
rra[3].cf = "AVERAGE"
rra[3].rows = 1098
rra[3].pdp_per_row = 1440
rra[3].xff = 5.0000000000e-01
rra[3].cdp_prep[0].value = NaN
rra[3].cdp_prep[0].unknown_datapoints = 602
rra[3].cdp_prep[1].value = 1.0000000000e+00
rra[3].cdp_prep[1].unknown_datapoints = 600
rra[4].cf = "MAX"
rra[4].rows = 26352
rra[4].pdp_per_row = 5
rra[4].xff = 5.0000000000e-01
rra[4].cdp_prep[0].value = NaN
rra[4].cdp_prep[0].unknown_datapoints = 2
rra[4].cdp_prep[1].value = NaN
rra[4].cdp_prep[1].unknown_datapoints = 2
rra[5].cf = "MAX"
rra[5].rows = 8784
rra[5].pdp_per_row = 30
rra[5].xff = 5.0000000000e-01
rra[5].cdp_prep[0].value = NaN
rra[5].cdp_prep[0].unknown_datapoints = 2
rra[5].cdp_prep[1].value = NaN
rra[5].cdp_prep[1].unknown_datapoints = 2
rra[6].cf = "MAX"
rra[6].rows = 4392
rra[6].pdp_per_row = 120
rra[6].xff = 5.0000000000e-01
rra[6].cdp_prep[0].value = NaN
rra[6].cdp_prep[0].unknown_datapoints = 2
rra[6].cdp_prep[1].value = NaN
rra[6].cdp_prep[1].unknown_datapoints = 2
rra[7].cf = "MAX"
rra[7].rows = 1098
rra[7].pdp_per_row = 1440
rra[7].xff = 5.0000000000e-01
rra[7].cdp_prep[0].value = NaN
rra[7].cdp_prep[0].unknown_datapoints = 602
rra[7].cdp_prep[1].value = 5.0000000000e-01
rra[7].cdp_prep[1].unknown_datapoints = 600
rra[8].cf = "MIN"
rra[8].rows = 26352
rra[8].pdp_per_row = 5
rra[8].xff = 5.0000000000e-01
rra[8].cdp_prep[0].value = NaN
rra[8].cdp_prep[0].unknown_datapoints = 2
rra[8].cdp_prep[1].value = NaN
rra[8].cdp_prep[1].unknown_datapoints = 2
rra[9].cf = "MIN"
rra[9].rows = 8784
rra[9].pdp_per_row = 30
rra[9].xff = 5.0000000000e-01
rra[9].cdp_prep[0].value = NaN
rra[9].cdp_prep[0].unknown_datapoints = 2
rra[9].cdp_prep[1].value = NaN
rra[9].cdp_prep[1].unknown_datapoints = 2
rra[10].cf = "MIN"
rra[10].rows = 4392
rra[10].pdp_per_row = 120
rra[10].xff = 5.0000000000e-01
rra[10].cdp_prep[0].value = NaN
rra[10].cdp_prep[0].unknown_datapoints = 2
rra[10].cdp_prep[1].value = NaN
rra[10].cdp_prep[1].unknown_datapoints = 2
rra[11].cf = "MIN"
rra[11].rows = 1098
rra[11].pdp_per_row = 1440
rra[11].xff = 5.0000000000e-01
rra[11].cdp_prep[0].value = NaN
rra[11].cdp_prep[0].unknown_datapoints = 602
rra[11].cdp_prep[1].value = 5.0000000000e-01
rra[11].cdp_prep[1].unknown_datapoints = 600

This is an example of some of the raw data for one of the datasources:

head -30 NT_MEMORY##NT_MEMORY##MEMmmPgsInptPrSc.csv
1195985611,0.016667
1195985671,2.01667
1195985738,7.08955
1195985799,0
1195985859,0
1195985919,0.737705
1195985982,0
1195986043,0
1195986103,0
1195986163,0
1195986227,0
1195986288,0
1195986348,0
1195986408,0
1195986471,0
1195986531,0
1195986591,0
1195986651,0.655738
1195986716,0.015625
1195986776,0
1195986836,0.016393
1195986896,0
1195986961,0
1195987021,0
1195987081,0
1195987141,0
1195987206,0
1195987266,0
1195987327,0
1195987387,0.016667

which translates to the following times:

$ head -30 NT_MEMORY##NT_MEMORY##MEMmmPgsInptPrSc.csv  | cut -d, -f1 | xargs -i s2d {}
Sun Nov 25 09:13:31 GMT 2007
Sun Nov 25 09:14:31 GMT 2007
Sun Nov 25 09:15:38 GMT 2007
Sun Nov 25 09:16:39 GMT 2007
Sun Nov 25 09:17:39 GMT 2007
Sun Nov 25 09:18:39 GMT 2007
Sun Nov 25 09:19:42 GMT 2007
Sun Nov 25 09:20:43 GMT 2007
Sun Nov 25 09:21:43 GMT 2007
Sun Nov 25 09:22:43 GMT 2007
Sun Nov 25 09:23:47 GMT 2007
Sun Nov 25 09:24:48 GMT 2007
Sun Nov 25 09:25:48 GMT 2007
Sun Nov 25 09:26:48 GMT 2007
Sun Nov 25 09:27:51 GMT 2007
Sun Nov 25 09:28:51 GMT 2007
Sun Nov 25 09:29:51 GMT 2007
Sun Nov 25 09:30:51 GMT 2007
Sun Nov 25 09:31:56 GMT 2007
Sun Nov 25 09:32:56 GMT 2007
Sun Nov 25 09:33:56 GMT 2007
Sun Nov 25 09:34:56 GMT 2007
Sun Nov 25 09:36:01 GMT 2007
Sun Nov 25 09:37:01 GMT 2007
Sun Nov 25 09:38:01 GMT 2007
Sun Nov 25 09:39:01 GMT 2007
Sun Nov 25 09:40:06 GMT 2007
Sun Nov 25 09:41:06 GMT 2007
Sun Nov 25 09:42:07 GMT 2007
Sun Nov 25 09:43:07 GMT 2007

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