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Jan-Otto KröpkeandClaude Opus 5.5
798570e9aa
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Jan-Otto Kröpke <mail@jkroepke.de>
204 lines
9.2 KiB
Jsonnet
204 lines
9.2 KiB
Jsonnet
local b = import 'builders.libsonnet';
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local host = '{job=~"$job", instance="$instance"}';
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local cores = 'scalar(max(windows_cpu_logical_processor' + host + '))';
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local mode(m) = 'sum(rate(windows_cpu_time_total{job=~"$job", instance="$instance", mode="' + m + '"}[$__rate_interval])) / ' + cores;
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function(on)
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b.tab(
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'CPU',
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b.rows([
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if on('cpu') then b.summary([160, 161, 162, if on('system') then 163, if on('system') then 164, 165]),
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if on('cpu') then b.row('Utilization', b.flow([[24, 12, 10], [25, 12, 10]])),
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if on('cpu') && on('system') then b.row('Scheduling', b.flow([[26, 8, 8], [27, 8, 8], [28, 8, 8]])),
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if on('cpu') then b.row('System calls and frequency', b.flow([if on('system') then [29, 12, 8], [30, 12, 8]])),
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]),
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[
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b.stat(160, 'CPU busy', 'Non-idle CPU time averaged across logical processors.', '1 - avg(clamp_max(rate(windows_cpu_time_total{job=~"$job", instance="$instance", mode="idle"}[$__rate_interval]), 1))', 'percentunit', steps=b.levels(0.8, 0.9))
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+ b.panel.withDefaults({ decimals: 1, max: 1, min: 0 }),
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b.stat(161, 'User', 'CPU time spent in user mode, as a share of all logical processors.', mode('user'), 'percentunit')
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+ b.panel.withDefaults({ decimals: 1, min: 0 }),
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b.stat(162, 'Privileged', 'CPU time spent in kernel mode, excluding interrupts and DPCs, as a share of all logical processors.', mode('privileged'), 'percentunit')
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+ b.panel.withDefaults({ decimals: 1, min: 0 }),
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b.stat(163, 'Queue per CPU', 'Threads waiting for a CPU per logical processor. A sustained value above 2 indicates CPU contention.', 'windows_system_processor_queue_length' + host + ' / ' + cores, 'short', steps=b.levels(1, 2))
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+ b.panel.withDefaults({ decimals: 2, min: 0 }),
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b.stat(164, 'Ctx switches', 'Context switches per second across the host.', 'rate(windows_system_context_switches_total' + host + '[$__rate_interval])', 'ops'),
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b.stat(165, 'Frequency', 'Average effective frequency across logical processors. Values above the nominal frequency mean turbo boost.', 'avg(1e4 * windows_cpu_core_frequency_mhz' + host + ' * rate(windows_cpu_processor_performance_total' + host + '[$__rate_interval]) / rate(windows_cpu_processor_mperf_total' + host + '[$__rate_interval]))', 'hertz'),
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b.panel.new(24, 'CPU utilization by mode', 'timeseries')
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+ b.panel.withDescription('Share of total CPU time across all logical processors.')
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+ b.panel.withQueries([
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b.query.new('sum by (mode) (rate(windows_cpu_time_total{job=~"$job", instance="$instance", mode!="idle"}[$__rate_interval])) / scalar(count(windows_cpu_time_total{job=~"$job", instance="$instance", mode="idle"}))', 'A')
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+ b.query.withLegendFormat('{{mode}}'),
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])
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+ b.panel.withDefaults({
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custom: {
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fillOpacity: 40,
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stacking: {
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mode: 'normal',
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},
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},
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max: 1,
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min: 0,
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}),
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b.panel.new(25, 'CPU utilization per core', 'state-timeline')
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+ b.panel.withDescription('Busy time of each logical processor, labelled processor group,core. Green is idle, red is fully busy.')
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+ b.panel.withQueries([
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b.query.new('label_replace(clamp(1 - rate(windows_cpu_time_total{job=~"$job", instance="$instance", mode="idle"}[$__rate_interval]), 0, 1), "core", "$1,0$2", "core", "(\\\\d+),(\\\\d)")', 'A')
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+ b.query.withLegendFormat('{{core}}'),
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])
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+ b.panel.withDefaults({
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color: {
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mode: 'continuous-GrYlRd',
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},
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thresholds: {
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steps: [
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{
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color: 'green',
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value: null,
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},
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],
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},
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unit: 'percentunit',
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}),
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b.panel.new(26, 'Processor queue length', 'timeseries')
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+ b.panel.withDescription('Threads that are ready to run but waiting for a CPU. A sustained value above 2 per core indicates CPU contention.')
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+ b.panel.withQueries([
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b.query.new('windows_system_processor_queue_length{job=~"$job", instance="$instance"}', 'A')
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+ b.query.withLegendFormat('queue length'),
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])
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+ b.panel.withDefaults({
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min: 0,
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unit: 'short',
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})
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+ b.panel.withOptions({
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legend: {
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calcs: [],
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displayMode: 'list',
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},
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}),
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b.panel.new(27, 'Context switches and interrupts', 'timeseries')
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+ b.panel.withDescription('Context switches, hardware interrupts and deferred procedure calls per second across the host. High rates can reflect scheduling or driver activity; correlate changes with CPU usage and workload rather than assuming they are faults.')
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+ b.panel.withQueries([
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b.query.new('rate(windows_system_context_switches_total{job=~"$job", instance="$instance"}[$__rate_interval])', 'A')
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+ b.query.withLegendFormat('context switches'),
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b.query.new('sum(rate(windows_cpu_interrupts_total{job=~"$job", instance="$instance"}[$__rate_interval]))', 'B')
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+ b.query.withLegendFormat('interrupts'),
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b.query.new('sum(rate(windows_cpu_dpcs_total{job=~"$job", instance="$instance"}[$__rate_interval]))', 'C')
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+ b.query.withLegendFormat('DPCs'),
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])
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+ b.panel.withDefaults({
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min: 0,
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unit: 'ops',
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}),
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b.panel.new(28, 'Processes and threads', 'timeseries')
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+ b.panel.withDescription('Current process and thread counts on the host. Sustained growth can help identify runaway process creation or thread leaks; thread counts use the right-hand axis.')
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+ b.panel.withQueries([
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b.query.new('windows_system_processes{job=~"$job", instance="$instance"}', 'A')
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+ b.query.withLegendFormat('processes'),
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b.query.new('windows_system_threads{job=~"$job", instance="$instance"}', 'B')
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+ b.query.withLegendFormat('threads'),
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])
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+ b.panel.withDefaults({
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min: 0,
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unit: 'short',
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})
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+ b.panel.withOverrides([
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{
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matcher: {
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id: 'byName',
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options: 'threads',
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},
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properties: [
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{
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id: 'custom.axisPlacement',
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value: 'right',
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},
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],
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},
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]),
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b.panel.new(29, 'System calls and exceptions', 'timeseries')
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+ b.panel.withDescription('System calls and exception dispatches per second. Exception dispatches include handled exceptions and do not directly count application crashes; compare with application logs. Exceptions use the right-hand axis.')
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+ b.panel.withQueries([
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b.query.new('rate(windows_system_system_calls_total{job=~"$job", instance="$instance"}[$__rate_interval])', 'A')
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+ b.query.withLegendFormat('system calls'),
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b.query.new('rate(windows_system_exception_dispatches_total{job=~"$job", instance="$instance"}[$__rate_interval])', 'B')
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+ b.query.withLegendFormat('exception dispatches'),
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])
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+ b.panel.withDefaults({
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min: 0,
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unit: 'ops',
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})
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+ b.panel.withOverrides([
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{
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matcher: {
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id: 'byName',
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options: 'exception dispatches',
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},
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properties: [
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{
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id: 'custom.axisPlacement',
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value: 'right',
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},
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],
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},
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]),
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b.panel.new(30, 'CPU frequency', 'timeseries')
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+ b.panel.withDescription('Effective frequency from the APERF/MPERF counters, averaged and maximum across logical processors, against the nominal frequency. Values above nominal mean turbo boost.')
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+ b.panel.withQueries([
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b.query.new('avg(1e4 * windows_cpu_core_frequency_mhz{job=~"$job", instance="$instance"} * rate(windows_cpu_processor_performance_total{job=~"$job", instance="$instance"}[$__rate_interval]) / rate(windows_cpu_processor_mperf_total{job=~"$job", instance="$instance"}[$__rate_interval]))', 'A')
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+ b.query.withLegendFormat('average'),
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b.query.new('max(1e4 * windows_cpu_core_frequency_mhz{job=~"$job", instance="$instance"} * rate(windows_cpu_processor_performance_total{job=~"$job", instance="$instance"}[$__rate_interval]) / rate(windows_cpu_processor_mperf_total{job=~"$job", instance="$instance"}[$__rate_interval]))', 'B')
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+ b.query.withLegendFormat('maximum'),
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b.query.new('avg(windows_cpu_core_frequency_mhz{job=~"$job", instance="$instance"}) * 1e6', 'C')
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+ b.query.withLegendFormat('nominal'),
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])
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+ b.panel.withDefaults({
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min: 0,
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unit: 'hertz',
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})
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+ b.panel.withOptions({
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legend: {
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calcs: [],
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displayMode: 'list',
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},
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})
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+ b.panel.withOverrides([
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{
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matcher: {
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id: 'byName',
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options: 'nominal',
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},
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properties: [
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{
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id: 'color',
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value: {
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fixedColor: 'text',
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mode: 'fixed',
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},
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},
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{
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id: 'custom.fillOpacity',
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value: 0,
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},
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{
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id: 'custom.lineStyle',
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value: {
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dash: [
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10,
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10,
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],
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fill: 'dash',
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},
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},
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],
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},
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]),
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]
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)
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