将ods的RRDtool数据库数据转换为SNMP计数器值

Question

从网络设备读取RRDTool流量数据，并定期更新SNMP数据库。设备具有非常低的网络负载(实际上只有OSPF加上SNMP本身的数据包)，但从RRDTool数据库创建的图形显示数百Mbps。在调查RDDTool数据库文件发现时，与推送到数据库中的SNMP计数值相比，它包含了奇怪的值。'traf1‘和'traf2’数据源类型的格式为计数器。我做错了什么？

C:\>rrdtool.exe info "Z:\rrdfiles\ubnt\actual\10.12.101.42.rrd"
filename = "Z:\rrdfiles\ubnt\actual\10.12.101.42.rrd"
rrd_version = "0003"
step = 300
last_update = 1483622829
header_size = 8028
ds[min].index = 0
ds[min].type = "GAUGE"
ds[min].minimal_heartbeat = 600
ds[min].min = NaN
ds[min].max = 2,0000000000e+001
ds[min].last_ds = "1"
ds[min].value = 1,2980500000e+002
ds[min].unknown_sec = 0
ds[avg].index = 1
ds[avg].type = "GAUGE"
ds[avg].minimal_heartbeat = 600
ds[avg].min = NaN
ds[avg].max = 1,0000000000e+002
ds[avg].last_ds = "1"
ds[avg].value = 1,2980500000e+002
ds[avg].unknown_sec = 0
ds[max].index = 2
ds[max].type = "GAUGE"
ds[max].minimal_heartbeat = 600
ds[max].min = NaN
ds[max].max = 2,0000000000e+002
ds[max].last_ds = "3"
ds[max].value = 2,8908700000e+002
ds[max].unknown_sec = 0
ds[loss].index = 3
ds[loss].type = "GAUGE"
ds[loss].minimal_heartbeat = 600
ds[loss].min = NaN
ds[loss].max = 1,0100000000e+002
ds[loss].last_ds = "0"
ds[loss].value = 0,0000000000e+000
ds[loss].unknown_sec = 0
ds[uptime].index = 4
ds[uptime].type = "GAUGE"
ds[uptime].minimal_heartbeat = 600
ds[uptime].min = NaN
ds[uptime].max = NaN
ds[uptime].last_ds = "7"
ds[uptime].value = 9,0863500000e+002
ds[uptime].unknown_sec = 0
ds[traf1].index = 5
ds[traf1].type = "COUNTER"
ds[traf1].minimal_heartbeat = 600
ds[traf1].min = NaN
ds[traf1].max = 1,0000000000e+009
ds[traf1].last_ds = "1520759978"
ds[traf1].value = 1,3889824419e+009
ds[traf1].unknown_sec = 0
ds[traf2].index = 6
ds[traf2].type = "COUNTER"
ds[traf2].minimal_heartbeat = 600
ds[traf2].min = NaN
ds[traf2].max = 1,0000000000e+009
ds[traf2].last_ds = "15790760575"
ds[traf2].value = 1,5510842185e+010
ds[traf2].unknown_sec = 0
ds[rssi1].index = 7
ds[rssi1].type = "GAUGE"
ds[rssi1].minimal_heartbeat = 600
ds[rssi1].min = NaN
ds[rssi1].max = 1,0000000000e+002
ds[rssi1].last_ds = "46"
ds[rssi1].value = 3,6634860000e+003
ds[rssi1].unknown_sec = 50
ds[rssi2].index = 8
ds[rssi2].type = "GAUGE"
ds[rssi2].minimal_heartbeat = 600
ds[rssi2].min = NaN
ds[rssi2].max = 1,0000000000e+002
ds[rssi2].last_ds = "46"
ds[rssi2].value = 3,6634860000e+003
ds[rssi2].unknown_sec = 50
ds[signal].index = 9
ds[signal].type = "GAUGE"
ds[signal].minimal_heartbeat = 600
ds[signal].min = NaN
ds[signal].max = 1,0000000000e+002
ds[signal].last_ds = "-46"
ds[signal].value = -3,6634860000e+003
ds[signal].unknown_sec = 0
ds[rssi].index = 10
ds[rssi].type = "GAUGE"
ds[rssi].minimal_heartbeat = 600
ds[rssi].min = NaN
ds[rssi].max = 1,0000000000e+002
ds[rssi].last_ds = "50"
ds[rssi].value = 3,9820500000e+003
ds[rssi].unknown_sec = 0
ds[ccq].index = 11
ds[ccq].type = "GAUGE"
ds[ccq].minimal_heartbeat = 600
ds[ccq].min = NaN
ds[ccq].max = 1,0000000000e+002
ds[ccq].last_ds = "33"
ds[ccq].value = 2,6281530000e+003
ds[ccq].unknown_sec = 0
ds[noisef].index = 12
ds[noisef].type = "GAUGE"
ds[noisef].minimal_heartbeat = 600
ds[noisef].min = NaN
ds[noisef].max = 1,0000000000e+002
ds[noisef].last_ds = "-87"
ds[noisef].value = -6,9287670000e+003
ds[noisef].unknown_sec = 0
ds[txrate].index = 13
ds[txrate].type = "GAUGE"
ds[txrate].minimal_heartbeat = 600
ds[txrate].min = NaN
ds[txrate].max = 1,0000000000e+003
ds[txrate].last_ds = "173"
ds[txrate].value = 1,3777893000e+004
ds[txrate].unknown_sec = 0
ds[rxrate].index = 14
ds[rxrate].type = "GAUGE"
ds[rxrate].minimal_heartbeat = 600
ds[rxrate].min = NaN
ds[rxrate].max = 1,0000000000e+003
ds[rxrate].last_ds = "173"
ds[rxrate].value = 1,3777893000e+004
ds[rxrate].unknown_sec = 0
ds[cinr].index = 15
ds[cinr].type = "GAUGE"
ds[cinr].minimal_heartbeat = 600
ds[cinr].min = NaN
ds[cinr].max = 3,0000000000e+001
ds[cinr].last_ds = "30"
ds[cinr].value = 2,3892300000e+003
ds[cinr].unknown_sec = 50
rra[0].cf = "AVERAGE"
rra[0].rows = 105120
rra[0].cur_row = 65786
rra[0].pdp_per_row = 1
rra[0].xff = 0,0000000000e+000
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[0].cdp_prep[2].value = NaN
rra[0].cdp_prep[2].unknown_datapoints = 0
rra[0].cdp_prep[3].value = NaN
rra[0].cdp_prep[3].unknown_datapoints = 0
rra[0].cdp_prep[4].value = NaN
rra[0].cdp_prep[4].unknown_datapoints = 0
rra[0].cdp_prep[5].value = NaN
rra[0].cdp_prep[5].unknown_datapoints = 0
rra[0].cdp_prep[6].value = NaN
rra[0].cdp_prep[6].unknown_datapoints = 0
rra[0].cdp_prep[7].value = NaN
rra[0].cdp_prep[7].unknown_datapoints = 0
rra[0].cdp_prep[8].value = NaN
rra[0].cdp_prep[8].unknown_datapoints = 0
rra[0].cdp_prep[9].value = NaN
rra[0].cdp_prep[9].unknown_datapoints = 0
rra[0].cdp_prep[10].value = NaN
rra[0].cdp_prep[10].unknown_datapoints = 0
rra[0].cdp_prep[11].value = NaN
rra[0].cdp_prep[11].unknown_datapoints = 0
rra[0].cdp_prep[12].value = NaN
rra[0].cdp_prep[12].unknown_datapoints = 0
rra[0].cdp_prep[13].value = NaN
rra[0].cdp_prep[13].unknown_datapoints = 0
rra[0].cdp_prep[14].value = NaN
rra[0].cdp_prep[14].unknown_datapoints = 0
rra[0].cdp_prep[15].value = NaN
rra[0].cdp_prep[15].unknown_datapoints = 0
rra[1].cf = "AVERAGE"
rra[1].rows = 1051200
rra[1].cur_row = 39803
rra[1].pdp_per_row = 5
rra[1].xff = 0,0000000000e+000
rra[1].cdp_prep[0].value = 4,0000000000e+000
rra[1].cdp_prep[0].unknown_datapoints = 0
rra[1].cdp_prep[1].value = 6,7364766667e+000
rra[1].cdp_prep[1].unknown_datapoints = 0
rra[1].cdp_prep[2].value = 1,7704723333e+001
rra[1].cdp_prep[2].unknown_datapoints = 0
rra[1].cdp_prep[3].value = 0,0000000000e+000
rra[1].cdp_prep[3].unknown_datapoints = 0
rra[1].cdp_prep[4].value = 2,8000000000e+001
rra[1].cdp_prep[4].unknown_datapoints = 0
rra[1].cdp_prep[5].value = 2,8633496716e+007
rra[1].cdp_prep[5].unknown_datapoints = 0
rra[1].cdp_prep[6].value = 2,3707567836e+008
rra[1].cdp_prep[6].unknown_datapoints = 0
rra[1].cdp_prep[7].value = 8,9847846153e+001
rra[1].cdp_prep[7].unknown_datapoints = 2
rra[1].cdp_prep[8].value = 9,0845644557e+001
rra[1].cdp_prep[8].unknown_datapoints = 2
rra[1].cdp_prep[9].value = -2,4966400000e+001
rra[1].cdp_prep[9].unknown_datapoints = 0
rra[1].cdp_prep[10].value = 2,6028800000e+001
rra[1].cdp_prep[10].unknown_datapoints = 0
rra[1].cdp_prep[11].value = 1,7529600000e+001
rra[1].cdp_prep[11].unknown_datapoints = 0
rra[1].cdp_prep[12].value = -4,5948513333e+001
rra[1].cdp_prep[12].unknown_datapoints = 0
rra[1].cdp_prep[13].value = 9,1897600000e+001
rra[1].cdp_prep[13].unknown_datapoints = 0
rra[1].cdp_prep[14].value = 9,1897600000e+001
rra[1].cdp_prep[14].unknown_datapoints = 0
rra[1].cdp_prep[15].value = 5,8899743702e+001
rra[1].cdp_prep[15].unknown_datapoints = 2
rra[2].cf = "AVERAGE"
rra[2].rows = 105120
rra[2].cur_row = 21504
rra[2].pdp_per_row = 60
rra[2].xff = 0,0000000000e+000
rra[2].cdp_prep[0].value = 2,9000000000e+001
rra[2].cdp_prep[0].unknown_datapoints = 0
rra[2].cdp_prep[1].value = 4,8360020000e+001
rra[2].cdp_prep[1].unknown_datapoints = 0
rra[2].cdp_prep[2].value = 1,0648201333e+002
rra[2].cdp_prep[2].unknown_datapoints = 0
rra[2].cdp_prep[3].value = 0,0000000000e+000
rra[2].cdp_prep[3].unknown_datapoints = 0
rra[2].cdp_prep[4].value = 2,0300000000e+002
rra[2].cdp_prep[4].unknown_datapoints = 0
rra[2].cdp_prep[5].value = 2,0276988858e+008
rra[2].cdp_prep[5].unknown_datapoints = 0
rra[2].cdp_prep[6].value = 1,6192898195e+009
rra[2].cdp_prep[6].unknown_datapoints = 0
rra[2].cdp_prep[7].value = 6,3192685679e+002
rra[2].cdp_prep[7].unknown_datapoints = 15
rra[2].cdp_prep[8].value = 6,3690843755e+002
rra[2].cdp_prep[8].unknown_datapoints = 15
rra[2].cdp_prep[9].value = -1,7221466000e+002
rra[2].cdp_prep[9].unknown_datapoints = 0
rra[2].cdp_prep[10].value = 1,7956678000e+002
rra[2].cdp_prep[10].unknown_datapoints = 0
rra[2].cdp_prep[11].value = 1,2092487000e+002
rra[2].cdp_prep[11].unknown_datapoints = 0
rra[2].cdp_prep[12].value = -3,1694801333e+002
rra[2].cdp_prep[12].unknown_datapoints = 0
rra[2].cdp_prep[13].value = 6,2624983000e+002
rra[2].cdp_prep[13].unknown_datapoints = 0
rra[2].cdp_prep[14].value = 6,2943175000e+002
rra[2].cdp_prep[14].unknown_datapoints = 0
rra[2].cdp_prep[15].value = 3,8770676286e+002
rra[2].cdp_prep[15].unknown_datapoints = 16

导出的RRDTool数据：

C:\>rrdtool.exe xport "Z:\rrdfiles\ubnt\actual\10.12.101.42.rrd"   
<xport>
      <meta>
        <start>1483613100</start>
        <step>300</step>
        <end>1483613100</end>
        <rows>13</rows>
        <columns>2</columns>
        <legend>
          <entry>tra1</entry>
          <entry>traf2</entry>
        </legend>
      </meta>
      <data>
        <row><t>1483613100</t><v>2.2345456150e+006</v><v>2.2345456150e+006</v></row>
        <row><t>1483613400</t><v>1.2072292921e+007</v><v>1.2072292921e+007</v></row>
        <row><t>1483613700</t><v>2.2444571321e+006</v><v>2.2444571321e+006</v></row>
        <row><t>1483614000</t><v>1.1981343532e+007</v><v>1.1981343532e+007</v></row>
        <row><t>1483614300</t><v>2.3354061100e+006</v><v>2.3354061100e+006</v></row>
        <row><t>1483614600</t><v>1.2097984951e+007</v><v>1.2097984951e+007</v></row>
        <row><t>1483614900</t><v>2.2187643548e+006</v><v>2.2187643548e+006</v></row>
        <row><t>1483615200</t><v>1.2097093297e+007</v><v>1.2097093297e+007</v></row>
        <row><t>1483615500</t><v>2.2196560491e+006</v><v>2.2196560491e+006</v></row>
        <row><t>1483615800</t><v>1.2104058787e+007</v><v>1.2104058787e+007</v></row>
        <row><t>1483616100</t><v>2.2126911123e+006</v><v>2.2126911123e+006</v></row>
        <row><t>1483616400</t><v>1.2066756766e+007</v><v>1.2066756766e+007</v></row>
        <row><t>1483616700</t><v>NaN</v><v>NaN</v></row>
      </data>
    </xport>

从设备读取SNMP本身的数据(时间戳、trafx、trafy、正常运行时间)：

1483612976
.1.3.6.1.2.1.2.2.1.16.10 130854869
.1.3.6.1.2.1.2.2.1.10.10 275599423
.1.3.6.1.2.1.1.3.0 7:13:21:30.00
1483613276
.1.3.6.1.2.1.2.2.1.16.10 130906048
.1.3.6.1.2.1.2.2.1.10.10 275757426
.1.3.6.1.2.1.1.3.0 7:13:26:30.00
1483613576
.1.3.6.1.2.1.2.2.1.16.10 130912462
.1.3.6.1.2.1.2.2.1.10.10 275874209
.1.3.6.1.2.1.1.3.0 7:13:31:30.00
1483613876
.1.3.6.1.2.1.2.2.1.16.10 130963695
.1.3.6.1.2.1.2.2.1.10.10 276030574
.1.3.6.1.2.1.1.3.0 7:13:36:30.00
1483614176
.1.3.6.1.2.1.2.2.1.16.10 130970155
.1.3.6.1.2.1.2.2.1.10.10 276148573
.1.3.6.1.2.1.1.3.0 7:13:41:30.00
1483614476
.1.3.6.1.2.1.2.2.1.16.10 131021246
.1.3.6.1.2.1.2.2.1.10.10 276304600
.1.3.6.1.2.1.1.3.0 7:13:46:30.00
1483614776
.1.3.6.1.2.1.2.2.1.16.10 131027614
.1.3.6.1.2.1.2.2.1.10.10 276418563
.1.3.6.1.2.1.1.3.0 7:13:51:30.00
1483615076
.1.3.6.1.2.1.2.2.1.16.10 131078755
.1.3.6.1.2.1.2.2.1.10.10 276571254
.1.3.6.1.2.1.1.3.0 7:13:56:30.00
1483615376
.1.3.6.1.2.1.2.2.1.16.10 131085169
.1.3.6.1.2.1.2.2.1.10.10 276684807
.1.3.6.1.2.1.1.3.0 7:14:01:30.00
1483615676
.1.3.6.1.2.1.2.2.1.16.10 131136260
.1.3.6.1.2.1.2.2.1.10.10 276842586
.1.3.6.1.2.1.1.3.0 7:14:06:30.00
1483615976
.1.3.6.1.2.1.2.2.1.16.10 131142812
.1.3.6.1.2.1.2.2.1.10.10 276957811
.1.3.6.1.2.1.1.3.0 7:14:11:30.00
1483616276
.1.3.6.1.2.1.2.2.1.16.10 131193949
.1.3.6.1.2.1.2.2.1.10.10 277112644
.1.3.6.1.2.1.1.3.0 7:14:16:30.00

Answer 1

这里有两个因素在起作用：速率转换和数据标准化。

由于您的类型为COUNTER，因此会发生Rate Conversion。如果数据点x1到达时间t1，这将通过简单计算(x1 - x0)/(t1 - t0)将存储的值转换为速率，依此类推。因为大多数被监视的数据都在增加网络字节计数器，并且您希望看到吞吐量的图表，所以这是有意义的；但是，如果您想要用图表表示实际的值，请使用数据类型GAUGE。为了提高效率，RRDTool在数据存储时进行速率计算，而不是在显示时。

数据规范化比较难理解。这会调整存储的值，使它们精确地适应时间窗口边界。RRDTool始终将数据精确地存储在区间边界中，但是由于数据可能在任何时候进入，因此需要使用线性插值来调整这些点以适应这一点。这些调整不会影响总体合计和汇总，对于较大的值通常不会引起注意，但如果使用GAUGE绘制较低的值，则可能会导致混淆。

关于这些的最好的阅读位置是在Alex著名的教程here中。