我使用RRD来监控数据源。我们看到RRD存储NaN结果的很多场合，尽管我们知道收到数据，因为我们还将接收的数据附加到文件进行测试。当我们检查差异时，我们会看到以下内容：
我尝试将数据粘贴为两列，但它没有正确的结构，但实质上我们在下面看到的是电子表格的两列。左列是rrd转储，右列是当时到达的实际数据。
" <!-- 2017-09-28 06:00:00 UTC / 1506578400 --> <row><v>1.1999200000e+06</v></row>" 1506578412:1202000
" <!-- 2017-09-28 06:05:00 UTC / 1506578700 --> <row><v>1.2538400000e+06</v></row>" 1506578712:1256000
" <!-- 2017-09-28 06:10:00 UTC / 1506579000 --> <row><v>1.2310400000e+06</v></row>" 1506579012:1230000
" <!-- 2017-09-28 06:15:00 UTC / 1506579300 --> <row><v>1.2415200000e+06</v></row>" 1506579312:1242000
" <!-- 2017-09-28 06:20:00 UTC / 1506579600 --> <row><v>1.2304800000e+06</v></row>" 1506579612:1230000
" <!-- 2017-09-28 06:25:00 UTC / 1506579900 --> <row><v>1.2357600000e+06</v></row>" 1506579912:1236000
" <!-- 2017-09-28 06:30:00 UTC / 1506580200 --> <row><v>1.1284800000e+06</v></row>" 1506580212:1124000
" <!-- 2017-09-28 06:35:00 UTC / 1506580500 --> <row><v>1.2238400000e+06</v></row>" 1506580512:1228000
" <!-- 2017-09-28 06:40:00 UTC / 1506580800 --> <row><v>NaN</v></row>" 1506580813:1222000
" <!-- 2017-09-28 06:45:00 UTC / 1506581100 --> <row><v>1.2400000000e+06</v></row>" 1506581112:1240000
" <!-- 2017-09-28 06:50:00 UTC / 1506581400 --> <row><v>1.2284800000e+06</v></row>" 1506581412:1228000
" <!-- 2017-09-28 06:55:00 UTC / 1506581700 --> <row><v>8.9392000000e+05</v></row>" 1506581712:880000
" <!-- 2017-09-28 07:00:00 UTC / 1506582000 --> <row><v>NaN</v></row>" 1506582014:1000000
" <!-- 2017-09-28 07:05:00 UTC / 1506582300 --> <row><v>NaN</v></row>" 1506582315:738000
" <!-- 2017-09-28 07:10:00 UTC / 1506582600 --> <row><v>1.1760000000e+06</v></row>" 1506582613:1176000
" <!-- 2017-09-28 07:15:00 UTC / 1506582900 --> <row><v>1.1874800000e+06</v></row>" 1506582912:1188000
" <!-- 2017-09-28 07:20:00 UTC / 1506583200 --> <row><v>1.2033600000e+06</v></row>" 1506583212:1204000
" <!-- 2017-09-28 07:25:00 UTC / 1506583500 --> <row><v>1.2097600000e+06</v></row>" 1506583512:1210000
" <!-- 2017-09-28 07:30:00 UTC / 1506583800 --> <row><v>1.0717600000e+06</v></row>" 1506583811:1066000
" <!-- 2017-09-28 07:35:00 UTC / 1506584100 --> <row><v>NaN</v></row>" 1506584112:1222000
" <!-- 2017-09-28 07:40:00 UTC / 1506584400 --> <row><v>1.1760000000e+06</v></row>" 1506584412:1176000
" <!-- 2017-09-28 07:45:00 UTC / 1506584700 --> <row><v>1.2048000000e+06</v></row>" 1506584712:1206000
" <!-- 2017-09-28 07:50:00 UTC / 1506585000 --> <row><v>1.0255200000e+06</v></row>" 1506585012:1018000
" <!-- 2017-09-28 07:55:00 UTC / 1506585300 --> <row><v>1.2004000000e+06</v></row>" 1506585312:1208000
" <!-- 2017-09-28 08:00:00 UTC / 1506585600 --> <row><v>1.1676800000e+06</v></row>" 1506585612:1166000
" <!-- 2017-09-28 08:05:00 UTC / 1506585900 --> <row><v>1.2024800000e+06</v></row>" 1506585912:1204000
" <!-- 2017-09-28 08:10:00 UTC / 1506586200 --> <row><v>1.2116800000e+06</v></row>" 1506586212:1212000
" <!-- 2017-09-28 08:15:00 UTC / 1506586500 --> <row><v>NaN</v></row>" 1506586513:886000
" <!-- 2017-09-28 08:20:00 UTC / 1506586800 --> <row><v>1.1940000000e+06</v></row>" 1506586812:1194000
" <!-- 2017-09-28 08:25:00 UTC / 1506587100 --> <row><v>1.1959200000e+06</v></row>" 1506587112:1196000
" <!-- 2017-09-28 08:30:00 UTC / 1506587400 --> <row><v>NaN</v></row>" 1506587413:1206000
" <!-- 2017-09-28 08:35:00 UTC / 1506587700 --> <row><v>1.1440000000e+06</v></row>" 1506587712:1144000
" <!-- 2017-09-28 08:40:00 UTC / 1506588000 --> <row><v>NaN</v></row>" 1506588013:668000
" <!-- 2017-09-28 08:45:00 UTC / 1506588300 --> <row><v>1.2080000000e+06</v></row>" 1506588312:1208000
" <!-- 2017-09-28 08:50:00 UTC / 1506588600 --> <row><v>NaN</v></row>" 1506588613:1156000
" <!-- 2017-09-28 08:55:00 UTC / 1506588900 --> <row><v>1.2080000000e+06</v></row>" 1506588912:1208000
" <!-- 2017-09-28 09:00:00 UTC / 1506589200 --> <row><v>1.1945600000e+06</v></row>" 1506589212:1194000
" <!-- 2017-09-28 09:05:00 UTC / 1506589500 --> <row><v>1.1786400000e+06</v></row>" 1506589512:1178000
" <!-- 2017-09-28 09:10:00 UTC / 1506589800 --> <row><v>1.1396000000e+06</v></row>" 1506589811:1138000
" <!-- 2017-09-28 09:15:00 UTC / 1506590100 --> <row><v>NaN</v></row>" 1506590113:1006000
" <!-- 2017-09-28 09:20:00 UTC / 1506590400 --> <row><v>1.1780000000e+06</v></row>" 1506590412:1178000
" <!-- 2017-09-28 09:25:00 UTC / 1506590700 --> <row><v>1.1799200000e+06</v></row>" 1506590712:1180000
" <!-- 2017-09-28 09:30:00 UTC / 1506591000 --> <row><v>1.1953600000e+06</v></row>" 1506591012:1196000
" <!-- 2017-09-28 09:35:00 UTC / 1506591300 --> <row><v>1.1806400000e+06</v></row>" 1506591312:1180000
" <!-- 2017-09-28 09:40:00 UTC / 1506591600 --> <row><v>1.1588800000e+06</v></row>" 1506591612:1158000
" <!-- 2017-09-28 09:45:00 UTC / 1506591900 --> <row><v>1.2002400000e+06</v></row>" 1506591912:1202000
" <!-- 2017-09-28 09:50:00 UTC / 1506592200 --> <row><v>1.0656800000e+06</v></row>" 1506592212:1060000
" <!-- 2017-09-28 09:55:00 UTC / 1506592500 --> <row><v>1.2078400000e+06</v></row>" 1506592512:1214000
" <!-- 2017-09-28 10:00:00 UTC / 1506592800 --> <row><v>1.1640800000e+06</v></row>" 1506592812:1162000
" <!-- 2017-09-28 10:05:00 UTC / 1506593100 --> <row><v>1.1754400000e+06</v></row>" 1506593112:1176000
我们可以看到数据似乎不被接受的情况几乎总是在它到达的时间有点超出趋势时。
我们如何才能扩大验收标准，以便接受所有这些数据点？
有问题的RRD的RRD信息如下所示：
root@ra:/var/www/genie/public_html# rrdtool info /an/data/SI1.rrd
filename = "/an/data/SI1.rrd"
rrd_version = "0003"
step = 300
last_update = 1506594312
header_size = 1000
ds[probe1-temp].index = 0
ds[probe1-temp].type = "GAUGE"
ds[probe1-temp].minimal_heartbeat = 300
ds[probe1-temp].min = 0.0000000000e+00
ds[probe1-temp].max = 5.0000000000e+06
ds[probe1-temp].last_ds = "1226000"
ds[probe1-temp].value = NaN
ds[probe1-temp].unknown_sec = 12
rra[0].cf = "MIN"
rra[0].rows = 1440
rra[0].cur_row = 238
rra[0].pdp_per_row = 12
rra[0].xff = 5.0000000000e-01
rra[0].cdp_prep[0].value = 1.1754400000e+06
rra[0].cdp_prep[0].unknown_datapoints = 2
rra[1].cf = "MAX"
rra[1].rows = 1440
rra[1].cur_row = 1220
rra[1].pdp_per_row = 12
rra[1].xff = 5.0000000000e-01
rra[1].cdp_prep[0].value = 1.2140000000e+06
rra[1].cdp_prep[0].unknown_datapoints = 2
rra[2].cf = "AVERAGE"
rra[2].rows = 1440
rra[2].cur_row = 1205
rra[2].pdp_per_row = 1
rra[2].xff = 5.0000000000e-01
rra[2].cdp_prep[0].value = NaN
rra[2].cdp_prep[0].unknown_datapoints = 0
root@ra:#
答案 0 :(得分：1)
您已将DS心跳设置为300，但步骤设置为300。
这意味着，如果您的数据相隔300s或更远，那么它们将被存储为NaN，这就是您所看到的。根据您提供的统计数据，您可以看到在NaN行上，实际时间间隔为301或302秒，即> 300，因此在超过心跳时间时会产生NaN。
您通常应将心跳设置为预期数据间隔的两倍，IE是步骤的两倍，以便处理此情况。
尝试将心跳设置为600;这应该可以解决问题。