Time Chart Widget

The Time Chart widget is the most commonly used widget in Humio. It displays bucketed time series data on a timeline.

See in Figure 125, “Time Chart” an example of how this widget may look like.

Time Chart

Figure 125. Time Chart

Input Format

The Time Chart widget expects a very specific input format that is produced by its companion query function timeChart(). Read more about this query function at the timeChart() page of this documentation.

Charting Metric Data

Say you have a service that periodically writes metrics to its logs. This could be tools such as DropWizard or Micrometer or a system monitoring tool like MetricBeat.

In this case we will have JSON logs that could look something like this:

{ "type": "metrics", "id": "1", "ts": "2018-11-01T00:10:11.001", "disk0": 11.21, "disk1": 21.14, "disk2": 12.01  }
{ "type": "metrics", "id": "2", "ts": "2018-11-01T00:10:13.106", "disk0": 11.21, "disk1": 21.14, "disk2": 12.01  }
{ "type": "metrics", "id": "3", "ts": "2018-11-01T00:10:18.771", "disk0": 10.57, "disk1": 20.41, "disk2": 11.91  }
{ "type": "metrics", "id": "4", "ts": "2018-11-01T00:10:18.772", "disk0": 9.15, "disk1": 19.12, "disk2": 10.07  }

where disk0-2 represents some metrics that you would like to create a time chart for.

type = metrics |
timechart(function=[max(disk0, as="Disk 0"), max(disk1, as="Disk 1"), max(disk2, as="Disk 2")])

Notice that we provide several aggregate functions to the function parameter. This is because we want to work on several fields on each input event. In this example it creates three series in the resulting time chart — one for each metric. We used the max() function on each field. This means that when the timechart function buckets the data it uses the larger number within the bucket to represent the value of the series in the bucket. In other words, imagine that event id=3 and id=4 in JSON events above end up in the same bucket (which is not an unreasonable assumption since their timestamps are only 1 ms apart).

If we use max we will get the largest value of the field, max(disk0) of id=3 and id=4 would be 10.57 even though id=4 occurs later in the stream. Alternatively, we could have used avg() to get the average of the two values of disk0, in this case 9.86. Which aggregate function to use depends on what you want to visualize.

Charting Log Levels

If you have logs that contain log levels like INFO, ERROR, and WARN, it can be interesting to visualize them over time. Say you have logs like:

2018-10-10T01:10:11.322Z [INFO] User Logged in. userId=10, ...
2018-10-10T01:10:12.172Z [WARN] Invalid Login Attempt. userId=10, ...
2018-10-10T01:10:14.122Z [INFO] Database Query. timeMs=20 connection=12.10...
2018-10-10T01:10:15.312Z [INFO] Database Query. timeMs=10 connection=12.10...
2018-10-10T01:10:16.912Z [INFO] Database Query. timeMs=21 connection=12.10...

The result is a parser that extracts a field called loglevel from each line. You can do something like:


This will count the number of occurrences of events that have a field called loglevel and put them in a series in the time chart based on their value. Based on the example data above this would create a time chart with two series, INFO and WARN.

By default the count function is used to calculate the value of each bucket, but you can easily plot other values by specifying other functions in the function property of the timeChart() function. For instance, if we use the avg() function on the field time:

timechart(loglevel, function=avg(time))
Timechart with Log Levels

Figure 126. Timechart with Log Levels

We can see the average time that a database query takes. The percentile() function is very useful as an aggregate function in time charts when you wish to visualize response times like this.

Charting Commits in GitHub

Humio can access the public github repository. From here, we can see the number of commits (type = PushEvent). The Y axis displays the total _count, and the X access displays the time value.

type = PushEvent | timechart()
Timechart with Commits to GitHub

Figure 127. Timechart with Commits to GitHub

Widget Properties

Use the widget's Edit Style panel to configure the following properties.

  • Title

    The title of the widget as displayed in the dashboard. As in the example Figure 125, “Time Chart”, it could be Errors Over Time.

  • Description

    The description of the time chart. This is free form text supporting markdown syntax.

    This same description appears in the dashboard as a tooltip by hovering over the question mark on top of the widget.

  • Plot

    • Plot type

      This is the plot type. Valid options are:

      • Area — a filled line chart representation of the data.

      • Line — a simple line plot of the data over time.

    • Stacking

      When set to Stack, places all the series on top of each other, so that the entire graph depicts the total of all data plotted. Overall, they are useful for comparing multiple variables changing over an interval.

      Valid options are:

      • Off — disables the feature.

      • Stack — enables the feature.

      • Normalize — converts the value of each series to a percentage of a whole. This makes it easier to see the relative difference between quantities in each group.

    • Gradient Area

      Applies gradient colors to the area fill.

    • Max Series Count

      Performs automatic roll-up of all lower series based on the cumulative sum. As the result do not include low series filtered during search (for example, when using the limit parameter to timechart), it adds a series called Other.

  • Legend

    • Show Legend

      Tick the box to show the legend in the chart.

    • Position

      Choose where you want the legend to appear in the chart. Valid options are:

      • Bottom

      • Right

    • Labels

      You have two options for displaying the labels:

      • Truncate — shortens the length of the series for a better visualization within the chart. It is used in case of long labels that would exceed the maximum space allowed in the chart. It is the default option. Hover the mouse over a label, then press and hold ALT to momentarily see the full series.

      • Show full — shows the full name of the series, that is, the entire value is displayed in the label or tooltip. In case of very long labels, it might affect their visibility within the chart. Hover the mouse over a label, then press and hold ALT to momentarily see the truncated series.

  • Interpolation

    Interpolation determines how the lines between the points are shown.

    The lines produced by the basis and the bundle interpolation methods are not guaranteed to actually pass through the data points.

    • Type

      The interpolation method to use. Valid options are:

      • Monotone — produces a smooth curve with continuous first-order derivatives that passes through any given set of data points without spurious oscillations.

      • Linear — produces a polyline through the specified points.

      • Step after — produces a piecewise constant function (a step function ) consisting of alternating horizontal and vertical lines. The y-value changes after the x-value.

      • Basis — produces a cubic basis spline using the specified control points. The first and last points are triplicated such that the spline starts at the first point and ends at the last point, and is tangent to the line between the first and second points, and to the line between the penultimate and last points.

      • Natural — produces a natural cubic spline with the second derivative of the spline set to zero at the endpoints.

      • Cardinal — produces a cubic cardinal spline using the specified control points, with one-sided differences used for the first and last piece. The default tension is 0.

      • Catmull-Rom — produces a Catmull-rom spline , which is a special case of the cardinal spline.

      • Bundle — produces a straightened cubic basis spline using the specified control points, with the spline straightened according to the curve’s beta, which defaults to 0.85.

    • Tension

      Different interpolators change the curvature of the line, which can be adjusted by playing with this setting. The info button next to this setting mentions the interpolation types supported in the given chart.

    • Handle Missing Values

      How to handle any gaps between the logs received in the time span. Valid options are:

      • Show Gaps — show gaps for any missing values.

      • Linear Interpolation — use linear interpolation to estimate missing values based on the nearest known values.

      • Replace by Mean Value — use the mean value of each series to replace missing values.

      • Replace by Zero — use '0' to replace missing values.

    • Show Data Points

      Tick the box to display data point values in the chart, represented by dots.

  • Trend line A line or curve that estimates the relationship between X and Y values. In some cases, a straight line is the best fit. But there might be cases where other types of line may better estimate the relationship.

    • Enable trend line checkbox.

      Tick the box to visualize the trend line.

    • Type

      When Enable trend line is checked, enables to set the type of regression to be visualized in the chart. Valid options are:

      • Linear — a straight line described by the formula y = ax +b

      • Logarithmicy = a + b * log(x)

      • Exponentialy = a + e^(b * x)

      • Powery = a * x^b

      • Quadraticy = a + b * x + c * x^2

      • Polynomialy = a + b * x + … + k * x^order

  • Bucket Behavior

    • Latest Bucket (Live)

      Shows whether the last bucket that is currently receiving live data is shown in the chart (highlighted by a vertical yellow bar) or not. For more information on bucket, see Bucket Storage.

      Valid options are:

      • Include

      • Exclude

  • X-Axis

    • X-Axis Title

      Gives a title to the X-Axis.

    • Show UTC Time

      Tick the box to show the UTC time in the chart.

  • Y-Axis

    • Title

      Gives a title to the Y-Axis.

    • Unit (Suffix)

      Sets the time unit.

    • Scale

      Valid options are:

      • Linear — quantitative scales that preserve proportional differences.

      • Logarithmic — quantitative scales particularly useful for plotting data that varies over multiple orders of magnitude.

    • Min Value

    • Max Value

      Here you can enter the desired minimum or maximum values, respectively, to be displayed in the chart.

  • Horizontal Line

    Draws a fixed reference line, used for example if you want to highlight a threshold in the chart associated with a certain value in the Y-axis.

    • Label

      Gives a name to the reference line.

    • Y-Value

      Specifies a value in the Y-axis corresponding to where the reference line should appear in the chart.

  • Series

    Change the color of each series and assign each field the title you want to see displayed in the chart.