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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