impala/docker/timeline.html.template

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<!--

Template/header for a timeline visualization of a multi-container build.
The timelines represent interesting log lines, with one row per container.
The charts represent CPU usage within those containers.

To use this, concatenate this with a '<script>' block defining
a global variable named data.

The expected format of data is exemplified by the following,
and is tightly coupled with the implementation generating
it in monitor.py. The intention of this unfriendly file format
is to do as much munging as plausible in Python.

To make the visualization relative to the start time (i.e., to say that all
builds start at 00:00), the timestamps are all seconds since the build began.
To make the visualization work with them, the timestamps are then converted
into local time, and get displayed reasonably. This is a workaround to the fact
that the visualization library for the timelines does not accept any data types
that represent duration, but we still want timestamp-style formatting.

var data = {
  // max timestamp seen, in seconds since the epoch
  "max_ts": 8153.0,
  // map of container name to an array of metrics
  "metrics": {
    "i-20180312-140548-ee-test-serial": [
      // a single metric point is an array of timestamp, user CPU, system CPU
      // CPU is the percent of 1 CPU used since the previous timestamp.
      [
        4572.0,
        0.11,
        0.07
      ]
    ]
  },
  // Array of timelines
  "timeline": [
    // a timeline entry contains a name (for the entire row of the timeline),
    // the message (for a segment of the timeline), and start and end timestamps
    // for the segment.
    [
      "i-20180312-140548",
      "+ echo '>>> build' '4266 (begin)'",
      0.0,
      0.0
    ]
  ]
}
-->

<script type="text/javascript" src="https://www.gstatic.com/charts/loader.js"></script>

<script type="text/javascript">
google.charts.load("current", {packages:["timeline", "corechart"]});
google.charts.setOnLoadCallback(drawChart);

function ts_to_hms(secs) {
  var s = secs % 60;
  var m = Math.floor(secs / 60) % 60;
  var h = Math.floor(secs / (60 * 60));
  return [h, m, s];
}

/* Returns a Date object corresponding to secs seconds since the epoch, in
 * localtime. Date(x) and Date(0, 0, 0, 0, 0, 0, 0, x) differ in that the
 * former returns UTC whereas the latter returns the browser local time.
 * For consistent handling within this visualization, we use localtime.
 *
 * Beware that local time can be discontinuous around time changes.
 */
function ts_to_date(secs) {
  // secs may be a float, so we use millis as a common denominator unit
  var millis = 1000 * secs;
  return new Date(1970 /* yr; beginning of unix epoch */, 0 /* mo */, 0 /* d */,
      0 /* hr */, 0 /* min */, 0 /* sec */, millis);
}

function drawChart() {
  var container = document.getElementById('container');
  var timelineContainer = document.createElement("div");
  container.appendChild(timelineContainer);
  var chart = new google.visualization.Timeline(timelineContainer);
  var dataTable = new google.visualization.DataTable();
  dataTable.addColumn({ type: 'string', id: 'Position' });
  dataTable.addColumn({ type: 'string', id: 'Name' });
  // timeofday isn't supported here
  dataTable.addColumn({ type: 'datetime', id: 'Start' });
  dataTable.addColumn({ type: 'datetime', id: 'End' });
  // Timeline
  for (i = 0; i < data.timeline.length; ++i) {
    var row = data.timeline[i];
    dataTable.addRow([ row[0], row[1], ts_to_date(row[2]), ts_to_date(row[3]) ]);
  }
  chart.draw(dataTable, { height: "400px" } );

  for (const k of Object.keys(data.metrics)) {
    var lineChart = document.createElement("div");
    container.appendChild(lineChart);

    var dataTable = new google.visualization.DataTable();
    dataTable.addColumn({ type: 'timeofday', id: 'Time' });
    dataTable.addColumn({ type: 'number', id: 'User' });
    dataTable.addColumn({ type: 'number', id: 'System' });

    for (const row of data.metrics[k]) {
      dataTable.addRow([ ts_to_hms(row[0]), row[1], row[2] ]);
    }
    var options = {
      title: 'CPU',
      legend: { position: 'bottom' },
      hAxis: {
        minValue: [0, 0, 0],
        maxValue: ts_to_hms(data.max_ts)
      }
    };

    var chart = new google.visualization.LineChart(lineChart);
    chart.draw(dataTable, options);
  }
}
</script>
<div id="container" style="height: 200px;"></div>