mirror of
https://github.com/ClusterCockpit/cc-backend
synced 2024-09-21 11:57:25 +02:00
266 lines
9.8 KiB
Svelte
266 lines
9.8 KiB
Svelte
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<script>
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import { init } from './utils.js'
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import { getContext, onMount } from 'svelte'
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import { operationStore, query } from '@urql/svelte'
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import { Row, Col, Spinner, Card, Table } from 'sveltestrap'
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import Filters from './filters/Filters.svelte'
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import PlotSelection from './PlotSelection.svelte'
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import Histogram, { binsFromFootprint } from './plots/Histogram.svelte'
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import ScatterPlot from './plots/Scatter.svelte'
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import PlotTable from './PlotTable.svelte'
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import Roofline from './plots/Roofline.svelte'
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const { query: initq } = init()
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export let filterPresets
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// By default, look at the jobs of the last 6 hours:
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if (filterPresets?.startTime == null) {
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if (filterPresets == null)
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filterPresets = {}
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let now = new Date(Date.now())
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let hourAgo = new Date(now)
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hourAgo.setHours(hourAgo.getHours() - 6)
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filterPresets.startTime = { from: hourAgo.toISOString(), to: now.toISOString() }
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}
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let cluster
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let filters
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let rooflineMaxY
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let colWidth
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let numBins = 50
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const ccconfig = getContext('cc-config'),
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metricConfig = getContext('metrics')
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let metricsInHistograms = ccconfig.analysis_view_histogramMetrics,
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metricsInScatterplots = ccconfig.analysis_view_scatterPlotMetrics
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$: metrics = [...new Set([...metricsInHistograms, ...metricsInScatterplots.flat()])]
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getContext('on-init')(({ data }) => {
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if (data != null) {
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cluster = data.clusters.find(c => c.name == filterPresets.cluster)
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console.assert(cluster != null, `This cluster could not be found: ${filterPresets.cluster}`)
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rooflineMaxY = cluster.subClusters.reduce((max, part) => Math.max(max, part.flopRateSimd), 0)
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$rooflineQuery.variables.maxY = rooflineMaxY
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$rooflineQuery.context.pause = false
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$rooflineQuery.reexecute()
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}
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})
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const statsQuery = operationStore(`
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query($filter: [JobFilter!]!) {
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stats: jobsStatistics(filter: $filter) {
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totalJobs
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shortJobs
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totalWalltime
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totalCoreHours
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histDuration { count, value }
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histNumNodes { count, value }
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}
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topUsers: jobsCount(filter: $filter, groupBy: USER, weight: NODE_HOURS, limit: 5) { name, count }
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}
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`, { filter: [] }, { pause: true })
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const footprintsQuery = operationStore(`
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query($filter: [JobFilter!]!, $metrics: [String!]!) {
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footprints: jobsFootprints(filter: $filter, metrics: $metrics) {
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nodehours,
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metrics { metric, data }
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}
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}
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`, { filter: [], metrics }, { pause: true })
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$: $footprintsQuery.variables = { ...$footprintsQuery.variables, metrics }
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const rooflineQuery = operationStore(`
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query($filter: [JobFilter!]!, $rows: Int!, $cols: Int!,
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$minX: Float!, $minY: Float!, $maxX: Float!, $maxY: Float!) {
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rooflineHeatmap(filter: $filter, rows: $rows, cols: $cols,
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minX: $minX, minY: $minY, maxX: $maxX, maxY: $maxY)
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}
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`, {
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filter: [],
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rows: 50, cols: 50,
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minX: 0.01, minY: 1., maxX: 1000., maxY: -1
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}, { pause: true });
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query(statsQuery)
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query(footprintsQuery)
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query(rooflineQuery)
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onMount(() => filters.update())
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</script>
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<Row>
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{#if $initq.fetching || $statsQuery.fetching || $footprintsQuery.fetching}
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<Col xs="auto">
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<Spinner />
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</Col>
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{/if}
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<Col xs="auto">
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{#if $initq.error}
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<Card body color="danger">{$initq.error.message}</Card>
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{:else if cluster}
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<PlotSelection
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availableMetrics={cluster.metricConfig.map(mc => mc.name)}
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bind:metricsInHistograms={metricsInHistograms}
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bind:metricsInScatterplots={metricsInScatterplots} />
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{/if}
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</Col>
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<Col xs="auto">
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<Filters
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bind:this={filters}
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filterPresets={filterPresets}
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disableClusterSelection={true}
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startTimeQuickSelect={true}
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on:update={({ detail }) => {
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$statsQuery.context.pause = false
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$statsQuery.variables = { filter: detail.filters }
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$footprintsQuery.context.pause = false
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$footprintsQuery.variables = { metrics, filter: detail.filters }
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$rooflineQuery.variables = { ...$rooflineQuery.variables, filter: detail.filters }
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}} />
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</Col>
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</Row>
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<br/>
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{#if $statsQuery.error}
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<Row>
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<Col>
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<Card body color="danger">{$statsQuery.error.message}</Card>
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</Col>
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</Row>
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{:else if $statsQuery.data}
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<Row>
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<div class="col-3" bind:clientWidth={colWidth}>
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<div style="height: 40%">
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<Table>
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<tr>
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<th scope="col">Total Jobs</th>
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<td>{$statsQuery.data.stats[0].totalJobs}</td>
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</tr>
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<tr>
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<th scope="col">Short Jobs (< 2m)</th>
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<td>{$statsQuery.data.stats[0].shortJobs}</td>
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</tr>
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<tr>
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<th scope="col">Total Walltime</th>
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<td>{$statsQuery.data.stats[0].totalWalltime}</td>
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</tr>
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<tr>
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<th scope="col">Total Core Hours</th>
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<td>{$statsQuery.data.stats[0].totalCoreHours}</td>
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</tr>
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</Table>
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</div>
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<div style="height: 60%;">
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{#key $statsQuery.data.topUsers}
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<h4>Top Users (by node hours)</h4>
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<Histogram
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width={colWidth - 25} height={300 * 0.5}
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data={$statsQuery.data.topUsers.sort((a, b) => b.count - a.count).map(({ count }, idx) => ({ count, value: idx }))}
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label={(x) => x < $statsQuery.data.topUsers.length ? $statsQuery.data.topUsers[Math.floor(x)].name : '0'} />
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{/key}
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</div>
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</div>
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<div class="col-3">
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{#key $statsQuery.data.stats[0].histDuration}
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<h4>Walltime Distribution</h4>
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<Histogram
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width={colWidth - 25} height={300}
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data={$statsQuery.data.stats[0].histDuration} />
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{/key}
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</div>
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<div class="col-3">
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{#key $statsQuery.data.stats[0].histNumNodes}
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<h4>Number of Nodes Distribution</h4>
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<Histogram
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width={colWidth - 25} height={300}
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data={$statsQuery.data.stats[0].histNumNodes} />
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{/key}
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</div>
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<div class="col-3">
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{#if $rooflineQuery.fetching}
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<Spinner />
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{:else if $rooflineQuery.error}
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<Card body color="danger">{$rooflineQuery.error.message}</Card>
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{:else if $rooflineQuery.data && cluster}
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{#key $rooflineQuery.data}
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<Roofline
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width={colWidth - 25} height={300}
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tiles={$rooflineQuery.data.rooflineHeatmap}
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cluster={cluster.subClusters.length == 1 ? cluster.subClusters[0] : null}
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maxY={rooflineMaxY} />
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{/key}
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{/if}
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</div>
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</Row>
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{/if}
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<br/>
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{#if $footprintsQuery.error}
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<Row>
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<Col>
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<Card body color="danger">{$footprintsQuery.error.message}</Card>
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</Col>
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</Row>
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{:else if $footprintsQuery.data && $initq.data}
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<Row>
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<Col>
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<Card body>
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These histograms show the distribution of the averages of all jobs matching the filters. Each job/average is weighted by its node hours.
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</Card>
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<br/>
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</Col>
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</Row>
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<Row>
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<Col>
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<PlotTable
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let:item
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let:width
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items={metricsInHistograms.map(metric => ({ metric, ...binsFromFootprint(
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$footprintsQuery.data.footprints.nodehours,
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$footprintsQuery.data.footprints.metrics.find(f => f.metric == metric).data, numBins) }))}
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itemsPerRow={ccconfig.plot_view_plotsPerRow}>
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<h4>{item.metric} [{metricConfig(cluster.name, item.metric)?.unit}]</h4>
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<Histogram
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width={width} height={250}
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min={item.min} max={item.max}
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data={item.bins} label={item.label} />
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</PlotTable>
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</Col>
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</Row>
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<br/>
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<Row>
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<Col>
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<Card body>
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Each circle represents one job. The size of a circle is proportional to its node hours. Darker circles mean multiple jobs have the same averages for the respective metrics.
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</Card>
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<br/>
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</Col>
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</Row>
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<Row>
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<Col>
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<PlotTable
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let:item
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let:width
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items={metricsInScatterplots.map(([m1, m2]) => ({
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m1, f1: $footprintsQuery.data.footprints.metrics.find(f => f.metric == m1).data,
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m2, f2: $footprintsQuery.data.footprints.metrics.find(f => f.metric == m2).data }))}
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itemsPerRow={ccconfig.plot_view_plotsPerRow}>
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<ScatterPlot
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width={width} height={250} color={"rgba(0, 102, 204, 0.33)"}
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xLabel={`${item.m1} [${metricConfig(cluster.name, item.m1)?.unit}]`}
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yLabel={`${item.m2} [${metricConfig(cluster.name, item.m2)?.unit}]`}
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X={item.f1} Y={item.f2} S={$footprintsQuery.data.footprints.nodehours} />
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</PlotTable>
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</Col>
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</Row>
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{/if}
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