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f08abdb48b
irate is a rate function that only looks at the most recent two data points, and calucaltes a per-second value from that. This produces much more granular graphs for fast moving data, and works sanely across many scrape intervals. It doesn't do so well for slowly moving data.
35 lines
1.4 KiB
HTML
35 lines
1.4 KiB
HTML
{{ template "head" . }}
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{{ template "prom_right_table_head" }}
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<tr>
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<th>Node</th>
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<th>{{ template "prom_query_drilldown" (args "sum(up{job='node'})") }} / {{ template "prom_query_drilldown" (args "count(up{job='node'})") }}</th>
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</tr>
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{{ template "prom_right_table_tail" }}
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{{ template "prom_content_head" . }}
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<h1>Node</h1>
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<table class="table table-condensed table-striped table-bordered" style="width: 0%">
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<tr>
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<th>Node</th>
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<th>Up</th>
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<th>CPU<br/>Used</th>
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<th>Memory<br/> Available</th>
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</tr>
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{{ range query "up{job='node'}" | sortByLabel "instance" }}
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<tr>
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<td><a href="node-overview.html?instance={{ .Labels.instance }}">{{ reReplaceAll "(.*?://)([^:/]+?)(:\\d+)?/.*" "$2" .Labels.instance }}</a></td>
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<td{{ if eq (. | value) 1.0 }}>Yes{{ else }} class="alert-danger">No{{ end }}</td>
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<td>{{ template "prom_query_drilldown" (args (printf "100 * (1 - avg by(instance)(irate(node_cpu{job='node',mode='idle',instance='%s'}[5m])))" .Labels.instance) "%" "printf.1f") }}</td>
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<td>{{ template "prom_query_drilldown" (args (printf "node_memory_MemFree{job='node',instance='%s'} + node_memory_Cached{job='node',instance='%s'} + node_memory_Buffers{job='node',instance='%s'}" .Labels.instance .Labels.instance .Labels.instance) "B" "humanize1024") }}</td>
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</tr>
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{{ else }}
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<tr><td colspan=4>No nodes found.</td></tr>
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{{ end }}
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{{ template "prom_content_tail" . }}
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{{ template "tail" }}
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