Newbie question: have (looks like) valid rrd data, can't get the graph to appear in the status page
Can someone see what I'm missing? I get a 'hobbit graph ncv:mysl' link in the mysql status and trends pages, but no graph.
here's the data as it appears in the status page: (ignore the low values for ThreadsCreated and OpenedTables, these are values over the last sample interval - could probably have done this with a DERIVE rrd dataType - I do the calcs in my collection script)
QueriesPerSecAvg:5177 QueriesPerSec:1851 ThreadsConnected:396 ThreadsCreated:0 OpenFiles:19751 OpenTables:12500 OpenedTables:0
here's my block from hobbitgraph.cfg:
[mysql] TITLE MySQL Queries Per Second YAXIS # DEF:qpsavg=mysql.rrd:QueriesPerSecAvg:GAUGE DEF:qps=mysql.rrd:QueriesPerSec:GAUGE LINE2:qpsavg#0C0C0C:QpsAvg LINE2:qps#C0C0C0:Qps GPRINT:qpsavg:LAST: \: %5.1lf (cur) GPRINT:qpsavg:MAX: \: %5.1lf (max) GPRINT:qpsavg:MIN: \: %5.1lf (min) GPRINT:qpsavg:AVERAGE: \: %5.1lf (avg)\n GPRINT:qps:LAST: \: %5.1lf (cur) GPRINT:qps:MAX: \: %5.1lf (max) GPRINT:qps:MIN: \: %5.1lf (min) GPRINT:qps:AVERAGE: \: %5.1lf (avg)\n
hobbitserver.cfg:
TEST2RRD="cpu=la,disk,inode,qtree,memory,$PINGCOLUMN=tcp,http=tcp,dns=tcp,dig=tcp,time=ntpstat,vmstat,iostat,netstat,temperature,apache,bind,sendmail,mailq,nmailq=mailq,socks,bea,iishealth,citrix,bbgen,bbtest,bbproxy,hobbitd,files,procs=processes,ports,clock,lines,mysql=ncv" NCV_mysql="*:GAUGE"
GRAPHS="la,vmstat,memory,processes,inode,qtree,files,users,disk,iostat,tcp.http,tcp,ncv,netstat,ifstat,mrtg::1,ports,temperature,ntpstat,apache,bind,sendmail,mailq,socks,bea,iishealth,citrix,bbgen,bbtest,bbproxy,hobbitd,clock,lines,mysql"
bb-hosts.cfg
xx.xx.xx.xx greenjeans # conn mysql TRENDS:*,!tcp,!netstat2
here's the output of "rrdtool info ../../data/rrd/greenjeans/mysql.rrd"
filename = "../../data/rrd/greenjeans/mysql.rrd" rrd_version = "0003" step = 300 last_update = 1209502058 ds[QueriesPerSecAvg].type = "GAUGE" ds[QueriesPerSecAvg].minimal_heartbeat = 600 ds[QueriesPerSecAvg].min = 0.0000000000e+00 ds[QueriesPerSecAvg].max = NaN ds[QueriesPerSecAvg].last_ds = "5186" ds[QueriesPerSecAvg].value = 8.1958000000e+05 ds[QueriesPerSecAvg].unknown_sec = 0 ds[QueriesPerSec].type = "GAUGE" ds[QueriesPerSec].minimal_heartbeat = 600 ds[QueriesPerSec].min = 0.0000000000e+00 ds[QueriesPerSec].max = NaN ds[QueriesPerSec].last_ds = "1386" ds[QueriesPerSec].value = 2.5088800000e+05 ds[QueriesPerSec].unknown_sec = 0 ds[ThreadsConnected].type = "GAUGE" ds[ThreadsConnected].minimal_heartbeat = 600 ds[ThreadsConnected].min = 0.0000000000e+00 ds[ThreadsConnected].max = NaN ds[ThreadsConnected].last_ds = "374" ds[ThreadsConnected].value = 6.0887000000e+04 ds[ThreadsConnected].unknown_sec = 0 ds[ThreadsCreated].type = "GAUGE" ds[ThreadsCreated].minimal_heartbeat = 600 ds[ThreadsCreated].min = 0.0000000000e+00 ds[ThreadsCreated].max = NaN ds[ThreadsCreated].last_ds = "0" ds[ThreadsCreated].value = 0.0000000000e+00 ds[ThreadsCreated].unknown_sec = 0 ds[OpenFiles].type = "GAUGE" ds[OpenFiles].minimal_heartbeat = 600 ds[OpenFiles].min = 0.0000000000e+00 ds[OpenFiles].max = NaN ds[OpenFiles].last_ds = "19685" ds[OpenFiles].value = 3.1083160000e+06 ds[OpenFiles].unknown_sec = 0 ds[OpenTables].type = "GAUGE" ds[OpenTables].minimal_heartbeat = 600 ds[OpenTables].min = 0.0000000000e+00 ds[OpenTables].max = NaN ds[OpenTables].last_ds = "12500" ds[OpenTables].value = 1.9750000000e+06 ds[OpenTables].unknown_sec = 0 ds[OpenedTables].type = "GAUGE" ds[OpenedTables].minimal_heartbeat = 600 ds[OpenedTables].min = 0.0000000000e+00 ds[OpenedTables].max = NaN ds[OpenedTables].last_ds = "0" ds[OpenedTables].value = 7.8000000000e+01 ds[OpenedTables].unknown_sec = 0 rra[0].cf = "AVERAGE" rra[0].rows = 576 rra[0].pdp_per_row = 1 rra[0].xff = 5.0000000000e-01 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[1].cf = "AVERAGE" rra[1].rows = 576 rra[1].pdp_per_row = 6 rra[1].xff = 5.0000000000e-01 rra[1].cdp_prep[0].value = 1.5580810000e+04 rra[1].cdp_prep[0].unknown_datapoints = 0 rra[1].cdp_prep[1].value = 4.7480566667e+03 rra[1].cdp_prep[1].unknown_datapoints = 0 rra[1].cdp_prep[2].value = 4.0307553957e+02 rra[1].cdp_prep[2].unknown_datapoints = 2 rra[1].cdp_prep[3].value = 0.0000000000e+00 rra[1].cdp_prep[3].unknown_datapoints = 2 rra[1].cdp_prep[4].value = 1.9674374101e+04 rra[1].cdp_prep[4].unknown_datapoints = 2 rra[1].cdp_prep[5].value = 1.2499100719e+04 rra[1].cdp_prep[5].unknown_datapoints = 2 rra[1].cdp_prep[6].value = 1.0107913669e+00 rra[1].cdp_prep[6].unknown_datapoints = 2 rra[2].cf = "AVERAGE" rra[2].rows = 576 rra[2].pdp_per_row = 24 rra[2].xff = 5.0000000000e-01 rra[2].cdp_prep[0].value = 4.6847800000e+04 rra[2].cdp_prep[0].unknown_datapoints = 0 rra[2].cdp_prep[1].value = 1.3392856667e+04 rra[2].cdp_prep[1].unknown_datapoints = 0 rra[2].cdp_prep[2].value = 1.6543988729e+03 rra[2].cdp_prep[2].unknown_datapoints = 5 rra[2].cdp_prep[3].value = 5.0000000000e-02 rra[2].cdp_prep[3].unknown_datapoints = 5 rra[2].cdp_prep[4].value = 7.8531830767e+04 rra[2].cdp_prep[4].unknown_datapoints = 5 rra[2].cdp_prep[5].value = 4.9999100719e+04 rra[2].cdp_prep[5].unknown_datapoints = 5 rra[2].cdp_prep[6].value = 1.4874580336e+00 rra[2].cdp_prep[6].unknown_datapoints = 5 rra[3].cf = "AVERAGE" rra[3].rows = 576 rra[3].pdp_per_row = 288 rra[3].xff = 5.0000000000e-01 rra[3].cdp_prep[0].value = 1.7319647667e+05 rra[3].cdp_prep[0].unknown_datapoints = 216 rra[3].cdp_prep[1].value = 5.8770938095e+04 rra[3].cdp_prep[1].unknown_datapoints = 216 rra[3].cdp_prep[2].value = 1.0685098873e+04 rra[3].cdp_prep[2].unknown_datapoints = 221 rra[3].cdp_prep[3].value = 7.9333333333e-01 rra[3].cdp_prep[3].unknown_datapoints = 221 rra[3].cdp_prep[4].value = 5.4433304886e+05 rra[3].cdp_prep[4].unknown_datapoints = 221 rra[3].cdp_prep[5].value = 3.4992956739e+05 rra[3].cdp_prep[5].unknown_datapoints = 221 rra[3].cdp_prep[6].value = 1.1470791367e+01 rra[3].cdp_prep[6].unknown_datapoints = 221
john burk
Got it....
the last parameter on the DEF line is the consolidation function, not the dataType. Changed it to 'AVERAGE', and I've got graphs.
Yeah !
On 29/04/2008, John Burk <john.d.burk at gmail.com> wrote:
Can someone see what I'm missing? I get a 'hobbit graph ncv:mysl' link in the mysql status and trends pages, but no graph.
here's the data as it appears in the status page: (ignore the low values for ThreadsCreated and OpenedTables, these are values over the last sample interval - could probably have done this with a DERIVE rrd dataType - I do the calcs in my collection script)
QueriesPerSecAvg:5177 QueriesPerSec:1851 ThreadsConnected:396 ThreadsCreated:0 OpenFiles:19751 OpenTables:12500 OpenedTables:0
here's my block from hobbitgraph.cfg:
[mysql] TITLE MySQL Queries Per Second YAXIS # DEF:qpsavg=mysql.rrd:QueriesPerSecAvg:GAUGE DEF:qps=mysql.rrd:QueriesPerSec:GAUGE LINE2:qpsavg#0C0C0C:QpsAvg LINE2:qps#C0C0C0:Qps GPRINT:qpsavg:LAST: \: %5.1lf (cur) GPRINT:qpsavg:MAX: \: %5.1lf (max) GPRINT:qpsavg:MIN: \: %5.1lf (min) GPRINT:qpsavg:AVERAGE: \: %5.1lf (avg)\n GPRINT:qps:LAST: \: %5.1lf (cur) GPRINT:qps:MAX: \: %5.1lf (max) GPRINT:qps:MIN: \: %5.1lf (min) GPRINT:qps:AVERAGE: \: %5.1lf (avg)\n
hobbitserver.cfg:
TEST2RRD="cpu=la,disk,inode,qtree,memory,$PINGCOLUMN=tcp,http=tcp,dns=tcp,dig=tcp,time=ntpstat,vmstat,iostat,netstat,temperature,apache,bind,sendmail,mailq,nmailq=mailq,socks,bea,iishealth,citrix,bbgen,bbtest,bbproxy,hobbitd,files,procs=processes,ports,clock,lines,mysql=ncv" NCV_mysql="*:GAUGE"
GRAPHS="la,vmstat,memory,processes,inode,qtree,files,users,disk,iostat,tcp.http,tcp,ncv,netstat,ifstat,mrtg::1,ports,temperature,ntpstat,apache,bind,sendmail,mailq,socks,bea,iishealth,citrix,bbgen,bbtest,bbproxy,hobbitd,clock,lines,mysql"
bb-hosts.cfg
xx.xx.xx.xx greenjeans # conn mysql TRENDS:*,!tcp,!netstat2
here's the output of "rrdtool info ../../data/rrd/greenjeans/mysql.rrd"
filename = "../../data/rrd/greenjeans/mysql.rrd" rrd_version = "0003" step = 300 last_update = 1209502058 ds[QueriesPerSecAvg].type = "GAUGE" ds[QueriesPerSecAvg].minimal_heartbeat = 600 ds[QueriesPerSecAvg].min = 0.0000000000e+00 ds[QueriesPerSecAvg].max = NaN ds[QueriesPerSecAvg].last_ds = "5186" ds[QueriesPerSecAvg].value = 8.1958000000e+05 ds[QueriesPerSecAvg].unknown_sec = 0 ds[QueriesPerSec].type = "GAUGE" ds[QueriesPerSec].minimal_heartbeat = 600 ds[QueriesPerSec].min = 0.0000000000e+00 ds[QueriesPerSec].max = NaN ds[QueriesPerSec].last_ds = "1386" ds[QueriesPerSec].value = 2.5088800000e+05 ds[QueriesPerSec].unknown_sec = 0 ds[ThreadsConnected].type = "GAUGE" ds[ThreadsConnected].minimal_heartbeat = 600 ds[ThreadsConnected].min = 0.0000000000e+00 ds[ThreadsConnected].max = NaN ds[ThreadsConnected].last_ds = "374" ds[ThreadsConnected].value = 6.0887000000e+04 ds[ThreadsConnected].unknown_sec = 0 ds[ThreadsCreated].type = "GAUGE" ds[ThreadsCreated].minimal_heartbeat = 600 ds[ThreadsCreated].min = 0.0000000000e+00 ds[ThreadsCreated].max = NaN ds[ThreadsCreated].last_ds = "0" ds[ThreadsCreated].value = 0.0000000000e+00 ds[ThreadsCreated].unknown_sec = 0 ds[OpenFiles].type = "GAUGE" ds[OpenFiles].minimal_heartbeat = 600 ds[OpenFiles].min = 0.0000000000e+00 ds[OpenFiles].max = NaN ds[OpenFiles].last_ds = "19685" ds[OpenFiles].value = 3.1083160000e+06 ds[OpenFiles].unknown_sec = 0 ds[OpenTables].type = "GAUGE" ds[OpenTables].minimal_heartbeat = 600 ds[OpenTables].min = 0.0000000000e+00 ds[OpenTables].max = NaN ds[OpenTables].last_ds = "12500" ds[OpenTables].value = 1.9750000000e+06 ds[OpenTables].unknown_sec = 0 ds[OpenedTables].type = "GAUGE" ds[OpenedTables].minimal_heartbeat = 600 ds[OpenedTables].min = 0.0000000000e+00 ds[OpenedTables].max = NaN ds[OpenedTables].last_ds = "0" ds[OpenedTables].value = 7.8000000000e+01 ds[OpenedTables].unknown_sec = 0 rra[0].cf = "AVERAGE" rra[0].rows = 576 rra[0].pdp_per_row = 1 rra[0].xff = 5.0000000000e-01 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[1].cf = "AVERAGE" rra[1].rows = 576 rra[1].pdp_per_row = 6 rra[1].xff = 5.0000000000e-01 rra[1].cdp_prep[0].value = 1.5580810000e+04 rra[1].cdp_prep[0].unknown_datapoints = 0 rra[1].cdp_prep[1].value = 4.7480566667e+03 rra[1].cdp_prep[1].unknown_datapoints = 0 rra[1].cdp_prep[2].value = 4.0307553957e+02 rra[1].cdp_prep[2].unknown_datapoints = 2 rra[1].cdp_prep[3].value = 0.0000000000e+00 rra[1].cdp_prep[3].unknown_datapoints = 2 rra[1].cdp_prep[4].value = 1.9674374101e+04 rra[1].cdp_prep[4].unknown_datapoints = 2 rra[1].cdp_prep[5].value = 1.2499100719e+04 rra[1].cdp_prep[5].unknown_datapoints = 2 rra[1].cdp_prep[6].value = 1.0107913669e+00 rra[1].cdp_prep[6].unknown_datapoints = 2 rra[2].cf = "AVERAGE" rra[2].rows = 576 rra[2].pdp_per_row = 24 rra[2].xff = 5.0000000000e-01 rra[2].cdp_prep[0].value = 4.6847800000e+04 rra[2].cdp_prep[0].unknown_datapoints = 0 rra[2].cdp_prep[1].value = 1.3392856667e+04 rra[2].cdp_prep[1].unknown_datapoints = 0 rra[2].cdp_prep[2].value = 1.6543988729e+03 rra[2].cdp_prep[2].unknown_datapoints = 5 rra[2].cdp_prep[3].value = 5.0000000000e-02 rra[2].cdp_prep[3].unknown_datapoints = 5 rra[2].cdp_prep[4].value = 7.8531830767e+04 rra[2].cdp_prep[4].unknown_datapoints = 5 rra[2].cdp_prep[5].value = 4.9999100719e+04 rra[2].cdp_prep[5].unknown_datapoints = 5 rra[2].cdp_prep[6].value = 1.4874580336e+00 rra[2].cdp_prep[6].unknown_datapoints = 5 rra[3].cf = "AVERAGE" rra[3].rows = 576 rra[3].pdp_per_row = 288 rra[3].xff = 5.0000000000e-01 rra[3].cdp_prep[0].value = 1.7319647667e+05 rra[3].cdp_prep[0].unknown_datapoints = 216 rra[3].cdp_prep[1].value = 5.8770938095e+04 rra[3].cdp_prep[1].unknown_datapoints = 216 rra[3].cdp_prep[2].value = 1.0685098873e+04 rra[3].cdp_prep[2].unknown_datapoints = 221 rra[3].cdp_prep[3].value = 7.9333333333e-01 rra[3].cdp_prep[3].unknown_datapoints = 221 rra[3].cdp_prep[4].value = 5.4433304886e+05 rra[3].cdp_prep[4].unknown_datapoints = 221 rra[3].cdp_prep[5].value = 3.4992956739e+05 rra[3].cdp_prep[5].unknown_datapoints = 221 rra[3].cdp_prep[6].value = 1.1470791367e+01 rra[3].cdp_prep[6].unknown_datapoints = 221
john burk
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john.d.burk@gmail.com