[rrd-users] How do i create Graphs, that show the aggregation fuzzyness?

[Andreas Schuldei]

Hi,

Some time ago I saw graphs, that indicated the MIN, AVG and MAX value in
the graph with help of shaded stacked areas between MIN and MAX, and AVG
plotted in a line. I imagine this should best be done with translucent
colors, too, in case of several overlapping values.

However i can not find an example for this. Can you please help me and
point me to a good example(s)?


BTW, Collectd created the rrd file like this (see below) - is that the
right layout and the proper layout and usable consolidation function for
showing the aggregation fuzzyness?

rrdtool info counter-Kessel_Durchsatz.rrd
filename = "counter-Kessel_Durchsatz.rrd"
rrd_version = "0003"
step = 10
last_update = 1507841723
header_size = 4744
ds[value].index = 0
ds[value].type = "COUNTER"
ds[value].minimal_heartbeat = 20
ds[value].min = 0.0000000000e+00
ds[value].max = 5.0000000000e+00
ds[value].last_ds = "80990"
ds[value].value = 0.0000000000e+00
ds[value].unknown_sec = 0
rra[0].cf = "AVERAGE"
rra[0].rows = 2400
rra[0].cur_row = 494
rra[0].pdp_per_row = 1
rra[0].xff = 1.0000000000e-01
rra[0].cdp_prep[0].value = NaN
rra[0].cdp_prep[0].unknown_datapoints = 0
rra[1].cf = "MIN"
rra[1].rows = 2400
rra[1].cur_row = 1091
rra[1].pdp_per_row = 1
rra[1].xff = 1.0000000000e-01
rra[1].cdp_prep[0].value = NaN
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[2].cf = "MAX"
rra[2].rows = 2400
rra[2].cur_row = 848
rra[2].pdp_per_row = 1
rra[2].xff = 1.0000000000e-01
rra[2].cdp_prep[0].value = NaN
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[3].cf = "AVERAGE"
rra[3].rows = 2880
rra[3].cur_row = 828
rra[3].pdp_per_row = 3
rra[3].xff = 1.0000000000e-01
rra[3].cdp_prep[0].value = 0.0000000000e+00
rra[3].cdp_prep[0].unknown_datapoints = 0
rra[4].cf = "MIN"
rra[4].rows = 2880
rra[4].cur_row = 1538
rra[4].pdp_per_row = 3
rra[4].xff = 1.0000000000e-01
rra[4].cdp_prep[0].value = 0.0000000000e+00
rra[4].cdp_prep[0].unknown_datapoints = 0
rra[5].cf = "MAX"
rra[5].rows = 2880
rra[5].cur_row = 692
rra[5].pdp_per_row = 3
rra[5].xff = 1.0000000000e-01
rra[5].cdp_prep[0].value = 0.0000000000e+00
rra[5].cdp_prep[0].unknown_datapoints = 0
rra[6].cf = "AVERAGE"
rra[6].rows = 2420
rra[6].cur_row = 251
rra[6].pdp_per_row = 25
rra[6].xff = 1.0000000000e-01
rra[6].cdp_prep[0].value = 0.0000000000e+00
rra[6].cdp_prep[0].unknown_datapoints = 0
rra[7].cf = "MIN"
rra[7].rows = 2420
rra[7].cur_row = 820
rra[7].pdp_per_row = 25
rra[7].xff = 1.0000000000e-01
rra[7].cdp_prep[0].value = 0.0000000000e+00
rra[7].cdp_prep[0].unknown_datapoints = 0
rra[8].cf = "MAX"
rra[8].rows = 2420
rra[8].cur_row = 885
rra[8].pdp_per_row = 25
rra[8].xff = 1.0000000000e-01
rra[8].cdp_prep[0].value = 0.0000000000e+00
rra[8].cdp_prep[0].unknown_datapoints = 0
rra[9].cf = "AVERAGE"
rra[9].rows = 2413
rra[9].cur_row = 203
rra[9].pdp_per_row = 111
rra[9].xff = 1.0000000000e-01
rra[9].cdp_prep[0].value = 0.0000000000e+00
rra[9].cdp_prep[0].unknown_datapoints = 0
rra[10].cf = "MIN"
rra[10].rows = 2413
rra[10].cur_row = 189
rra[10].pdp_per_row = 111
rra[10].xff = 1.0000000000e-01
rra[10].cdp_prep[0].value = 0.0000000000e+00
rra[10].cdp_prep[0].unknown_datapoints = 0
rra[11].cf = "MAX"
rra[11].rows = 2413
rra[11].cur_row = 926
rra[11].pdp_per_row = 111
rra[11].xff = 1.0000000000e-01
rra[11].cdp_prep[0].value = 0.0000000000e+00
rra[11].cdp_prep[0].unknown_datapoints = 0
rra[12].cf = "AVERAGE"
rra[12].rows = 2402
rra[12].cur_row = 266
rra[12].pdp_per_row = 1317
rra[12].xff = 1.0000000000e-01
rra[12].cdp_prep[0].value = 0.0000000000e+00
rra[12].cdp_prep[0].unknown_datapoints = 0
rra[13].cf = "MIN"
rra[13].rows = 2402
rra[13].cur_row = 2096
rra[13].pdp_per_row = 1317
rra[13].xff = 1.0000000000e-01
rra[13].cdp_prep[0].value = 0.0000000000e+00
rra[13].cdp_prep[0].unknown_datapoints = 0
rra[14].cf = "MAX"
rra[14].rows = 2402
rra[14].cur_row = 2324
rra[14].pdp_per_row = 1317
rra[14].xff = 1.0000000000e-01
rra[14].cdp_prep[0].value = 0.0000000000e+00
rra[14].cdp_prep[0].unknown_datapoints = 0
rra[15].cf = "AVERAGE"
rra[15].rows = 2400
rra[15].cur_row = 502
rra[15].pdp_per_row = 13149
rra[15].xff = 1.0000000000e-01
rra[15].cdp_prep[0].value = 0.0000000000e+00
rra[15].cdp_prep[0].unknown_datapoints = 3
rra[16].cf = "MIN"
rra[16].rows = 2400
rra[16].cur_row = 1277
rra[16].pdp_per_row = 13149
rra[16].xff = 1.0000000000e-01
rra[16].cdp_prep[0].value = 0.0000000000e+00
rra[16].cdp_prep[0].unknown_datapoints = 3
rra[17].cf = "MAX"
rra[17].rows = 2400
rra[17].cur_row = 2326
rra[17].pdp_per_row = 13149
rra[17].xff = 1.0000000000e-01
rra[17].cdp_prep[0].value = 0.0000000000e+00
rra[17].cdp_prep[0].unknown_datapoints = 3
rra[18].cf = "AVERAGE"
rra[18].rows = 2400
rra[18].cur_row = 1345
rra[18].pdp_per_row = 39447
rra[18].xff = 1.0000000000e-01
rra[18].cdp_prep[0].value = 0.0000000000e+00
rra[18].cdp_prep[0].unknown_datapoints = 15
rra[19].cf = "MIN"
rra[19].rows = 2400
rra[19].cur_row = 971
rra[19].pdp_per_row = 39447
rra[19].xff = 1.0000000000e-01
rra[19].cdp_prep[0].value = 0.0000000000e+00
rra[19].cdp_prep[0].unknown_datapoints = 15
rra[20].cf = "MAX"
rra[20].rows = 2400
rra[20].cur_row = 2146
rra[20].pdp_per_row = 39447
rra[20].xff = 1.0000000000e-01
rra[20].cdp_prep[0].value = 0.0000000000e+00
rra[20].cdp_prep[0].unknown_datapoints = 15
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