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https://github.com/retspen/webvirtcloud
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Update chartjs for node statistics
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catborise
parent
455f239093
commit
9ab198bd8c
2 changed files with 120 additions and 91 deletions
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@ -80,7 +80,7 @@
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<h3 class="page-header">{% trans "Performance" %}</h3>
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<div class="panel panel-success">
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<div class="panel-heading">
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<h3 class="panel-title"><i class="fa fa-long-arrow-right"></i> {% trans "CPU utilization" %}</h3>
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<h3 class="panel-title"><i class="fa fa-long-arrow-right"></i> {% trans "CPU Utilization" %}</h3>
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</div>
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<div class="panel-body">
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<div class="flot-chart">
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@ -92,7 +92,7 @@
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</div>
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<div class="panel panel-info">
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<div class="panel-heading">
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<h3 class="panel-title"><i class="fa fa-long-arrow-right"></i> {% trans "RAM utilization" %}</h3>
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<h3 class="panel-title"><i class="fa fa-long-arrow-right"></i> {% trans "RAM Utilization" %}</h3>
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</div>
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<div class="panel-body">
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<div class="flot-chart">
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@ -106,66 +106,128 @@
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</div>
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{% endblock %}
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{% block script %}
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<script src="{% static "js/Chart.min.js" %}"></script>
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<script src="{% static "js/Chart.bundle.min.js" %}"></script>
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<script>
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var cpuLineData = {
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labels : [0, 0, 0, 0, 0],
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datasets : [
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{
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fillColor: "rgba(241,72,70,0.5)",
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strokeColor: "rgba(241,72,70,1)",
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pointColor : "rgba(241,72,70,1)",
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pointStrokeColor : "#fff",
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pointHighlightFill : "#fff",
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pointHighlightStroke : "rgba(220,220,220,1)",
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data : [0, 0, 0, 0, 0]
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}
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]
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}
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var cpu_ctx = document.getElementById("cpuChart").getContext("2d");
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var cpuChart = new Chart(cpu_ctx).Line(cpuLineData, {
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animation: false,
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pointDotRadius: 2,
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scaleLabel: "<%=value%> %",
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scaleOverride: true,
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scaleSteps: 5,
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scaleStepWidth: 20,
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scaleStartValue: 0,
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responsive: true
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var cpuChart = new Chart(cpu_ctx, {
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type: 'line',
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data: {
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datasets : [{
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label: 'Usage',
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backgroundColor: "rgba(241,72,70,0.5)",
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pointRadius: 2,
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}]
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},
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options: {
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responsive: true,
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legend: {
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display: false
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},
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scales: {
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xAxes:[{
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offset: false,
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ticks: {
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beginAtZero: false,
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autoSkip: true,
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maxTicksLimit: 10,
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maxRotation: 0,
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minRotation: 0,
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stepSize: 10,
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},
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}],
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yAxes: [{
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ticks: {
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max: 100,
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min: 0,
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stepSize: 20,
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callback: function(value, index, values) {
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return value + ' %';
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}
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},
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}],
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},
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tooltips: {
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callbacks: {
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label: function (tooltipItem, chart) {
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var label = chart.datasets[tooltipItem.datasetIndex].label || '';
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if (label) {
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label += ': ';
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}
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return label += tooltipItem.yLabel + ' %';
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}
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}
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}
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}
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});
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var memLineData = {
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labels : [0, 0, 0, 0, 0],
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datasets : [
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{
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fillColor : "rgba(249,134,33,0.5)",
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strokeColor : "rgba(249,134,33,1)",
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pointColor : "rgba(249,134,33,1)",
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pointStrokeColor : "#fff",
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pointHighlightFill : "#fff",
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pointHighlightStroke : "rgba(151,187,205,1)",
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data : [0, 0, 0, 0, 0]
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var mem_ctx = document.getElementById("memChart").getContext("2d");
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var memChart = new Chart(mem_ctx, {
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type: 'line',
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data: {
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datasets: [{
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pointRadius: 2,
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}]
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},
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options: {
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responsive: true,
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legend: {
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display: false
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},
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scales: {
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xAxes:[{
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offset: false,
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ticks: {
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beginAtZero: false,
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autoSkip: true,
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maxTicksLimit: 10,
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maxRotation: 0,
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minRotation: 0
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}
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}],
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yAxes: [{
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ticks:{
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suggestedMin: 0,
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suggestedMax: 100,
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callback: function(value, index, values) {
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return value + ' MB';
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}
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},
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}],
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},
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tooltips: {
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callbacks: {
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label: function (tooltipItem, chart) {
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var label = chart.datasets[tooltipItem.datasetIndex].label || '';
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if (label) {
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label += ': ';
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}
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return label += tooltipItem.yLabel + ' MB';
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}
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}
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}
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]
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}
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var mem_ctx = $("#memChart").get(0).getContext("2d");
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var memChart = new Chart(mem_ctx).Line(memLineData, {
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animation: false,
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pointDotRadius: 2,
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scaleLabel: "<%=value%> Mb",
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responsive: true
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}
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});
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window.setInterval(function graph_usage() {
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$.getJSON('{% url 'compute_graph' compute_id %}', function (data) {
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cpuChart.scale.xLabels = data.timeline;
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memChart.scale.xLabels = data.timeline;
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for (var i = 0; i < 5; i++) {
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cpuChart.datasets[0].points[i].value = data.cpudata[i];
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memChart.datasets[0].points[i].value = data.memdata[i];
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cpuChart.data.labels.push(data.timeline);
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memChart.data.labels.push(data.timeline);
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cpuChart.data.datasets[0].data.push(data.cpudata);
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if (cpuChart.data.datasets[0].data.length > 10){
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cpuChart.data.labels.shift();
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cpuChart.data.datasets[0].data.shift();
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}
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memChart.options.scales.yAxes[0].ticks.max = parseInt(data.memdata.total / 1048576);
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memChart.options.scales.yAxes[0].ticks.stepSize = parseInt(data.memdata.total / (1048576 * 5));
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memChart.data.datasets[0].data.push(parseInt(data.memdata.usage / 1048576));
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if (memChart.data.datasets[0].data.length > 10){
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memChart.data.labels.shift();
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memChart.data.datasets[0].data.shift();
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}
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cpuChart.update();
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memChart.update();
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});
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@ -1,5 +1,6 @@
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import time
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import json
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from django.utils import timezone
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from django.http import HttpResponse, HttpResponseRedirect
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from django.core.urlresolvers import reverse
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from django.shortcuts import render, get_object_or_404
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@ -172,59 +173,25 @@ def compute_graph(request, compute_id):
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:param request:
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:return:
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"""
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points = 5
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datasets = {}
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cookies = {}
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compute = get_object_or_404(Compute, pk=compute_id)
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current_time = time.strftime("%H:%M:%S")
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try:
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conn = wvmHostDetails(compute.hostname,
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compute.login,
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compute.password,
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compute.type)
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current_time = timezone.now().strftime("%H:%M:%S")
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cpu_usage = conn.get_cpu_usage()
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mem_usage = conn.get_memory_usage()
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conn.close()
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except libvirtError:
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cpu_usage = 0
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mem_usage = 0
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cpu_usage = {'usage': 0}
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mem_usage = {'usage': 0}
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try:
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cookies['cpu'] = request.COOKIES['cpu']
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cookies['mem'] = request.COOKIES['mem']
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cookies['timer'] = request.COOKIES['timer']
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except KeyError:
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cookies['cpu'] = None
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cookies['mem'] = None
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if not cookies['cpu'] or not cookies['mem']:
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datasets['cpu'] = [0] * points
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datasets['mem'] = [0] * points
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datasets['timer'] = [0] * points
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else:
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datasets['cpu'] = eval(cookies['cpu'])
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datasets['mem'] = eval(cookies['mem'])
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datasets['timer'] = eval(cookies['timer'])
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datasets['timer'].append(current_time)
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datasets['cpu'].append(int(cpu_usage['usage']))
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datasets['mem'].append(int(mem_usage['usage']) / 1048576)
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if len(datasets['timer']) > points:
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datasets['timer'].pop(0)
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if len(datasets['cpu']) > points:
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datasets['cpu'].pop(0)
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if len(datasets['mem']) > points:
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datasets['mem'].pop(0)
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data = json.dumps({'cpudata': datasets['cpu'], 'memdata': datasets['mem'], 'timeline': datasets['timer']})
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data = json.dumps({'cpudata': cpu_usage['usage'],
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'memdata': mem_usage,
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'timeline': current_time})
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response = HttpResponse()
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response['Content-Type'] = "text/javascript"
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response.cookies['cpu'] = datasets['cpu']
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response.cookies['timer'] = datasets['timer']
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response.cookies['mem'] = datasets['mem']
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response.write(data)
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return response
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