IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.
« Aggregation-based Make dashboards interactive »
Elastic Docs ›Kibana Guide [7.14] ›Dashboard ›Create panels with editors

Timelion

edit

Timelion

edit

To use Timelion, you define a graph by chaining functions together, using the Timelion-specific syntax. The syntax enables some features that classical point series charts don’t offer, such as pulling data from different indices or data sources into one graph.

Timelion is driven by a simple expression language that you use to:

  • Retrieve time series data from one or more indices
  • Perform math across two or more time series
  • Visualize the results

Timelion

Deprecated in 7.0.0.

Timelion is still supported. The Timelion app is deprecated in 7.0, replaced by dashboard features. In 7.16 and later, the Timelion app is removed from Kibana. To prepare for the removal of Timelion app, you must migrate Timelion app worksheets to a dashboard. For information on how to migrate Timelion app worksheets, refer to the 7.10.0 Release Notes.

Timelion expressions

edit

Timelion functions always start with a dot, followed by the function name, followed by parentheses containing all the parameters to the function.

The .es (or .elasticsearch if you are a fan of typing long words) function gathers data from Elasticsearch and draws it over time. By default the .es function will just count the number of documents, resulting in a graph showing the amount of documents over time.

Function parameters

edit

Functions can have multiple parameters, and so does the .es function. Each parameter has a name, that you can use inside the parentheses to set its value. The parameters also have an order, which is shown by the autocompletion or the documentation (using the Docs button in the top menu).

If you don’t specify the parameter name, timelion assigns the values to the parameters in the order, they are listed in the documentation.

The fist parameter of the .es function is the parameter q (for query), which is a Query String used to filter the data for this series. You can also explicitly reference this parameter by its name, and I would always recommend doing so as soon as you are passing more than one parameter to the function. The following two expressions are thus equivalent:

es(q=*)Multiple parameters are separated by comma. The .es function has another parameter called index, that can be used to specify an index pattern for this series, so the query won’t be executed again all indexes (or whatever you changed the above mentioned setting to).

es(q=, index=logstash-)If the value of your parameter contains spaces or commas you have to put the value in single or double quotes. You can omit these quotes otherwise.

.yaxis() function
edit

Kibana supports many y-axis scales and ranges for your data series.

The .yaxis() function supports the following parameters:

  • yaxis — The numbered y-axis to plot the series on. For example, use .yaxis(2) to display a second y-axis.
  • min — The minimum value for the y-axis range.
  • max — The maximum value for the y-axis range.
  • position — The location of the units. Values include left or right.
  • label — The label for the axis.
  • color — The color of the axis label.
  • units — The function to use for formatting the y-axis labels. Values include bits, bits/s, bytes, bytes/s, currency(:ISO 4217 currency code), percent, and custom(:prefix:suffix).
  • tickDecimals — The tick decimal precision.

Example:

.es(index= kibana_sample_data_logs,
    timefield='@timestamp',
    metric='avg:bytes')
  .label('Average Bytes for request')
  .title('Memory consumption over time in bytes').yaxis(1,units=bytes,position=left), 
.es(index= kibana_sample_data_logs,
    timefield='@timestamp',
    metric=avg:machine.ram)
  .label('Average Machine RAM amount').yaxis(2,units=bytes,position=right)

.yaxis(1,units=bytes,position=left) — Specifies the first y-axis for the first data series, and changes the units on the left.

.yaxis(2,units=bytes,position=left) — Specifies the second y-axis for the second data series, and changes the units on the right.

Tutorial: Create visualizations with Timelion

edit

You collected data from your operating system using Metricbeat, and you want to visualize and analyze the data on a dashboard. To create panels of the data, use Timelion to create a time series visualization,

Add the data and create the dashboard
edit

Set up Metricbeat, then create the dashboard.

  1. To set up Metricbeat, go to Metricbeat quick start: installation and configuration
  2. From Kibana, open the main menu, then click Dashboard.
  3. On the Dashboards page, click Create dashboard.
Open and set up Timelion
edit

Open Timelion and change the time range.

  1. On the dashboard, click All types > Aggregation based, then select Timelion.
  2. Make sure the time filter is Last 7 days.

Create a time series visualization

edit

To compare the real-time percentage of CPU time spent in user space to the results offset by one hour, create a time series visualization.

Define the functions

edit

To track the real-time percentage of CPU, enter the following in the Timelion Expression field, then click Update:

.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')

Compare the data

edit

To compare two data sets, add another series, and offset the data back by one hour, then click Update:

.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct'),
.es(offset=-1h,
    index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')

Add label names

edit

To easily distinguish between the two data sets, add label names, then click Update:

.es(offset=-1h,index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct').label('last hour'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct').label('current hour')

Add a title

edit

To make is easier for unfamiliar users to understand the purpose of the visualization, add a title, then click Update:

.es(offset=-1h,
    index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('last hour'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('current hour')
  .title('CPU usage over time')

Change the appearance of the chart lines

edit

To differentiate between the current hour and the last hour, change the appearance of the chart lines, then click Update:

.es(offset=-1h,
    index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('last hour')
  .lines(fill=1,width=0.5),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('current hour')
  .title('CPU usage over time')

Change the line colors

edit

Timelion supports standard color names, hexadecimal values, or a color schema for grouped data.

To make the first data series stand out, change the line colors, then click Update:

.es(offset=-1h,
    index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('last hour')
  .lines(fill=1,width=0.5)
  .color(gray),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('current hour')
  .title('CPU usage over time')
  .color(#1E90FF)

Adjust the legend

edit

Move the legend to the north west position with two columns, then click Update:

.es(offset=-1h,
    index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('last hour')
  .lines(fill=1,width=0.5)
  .color(gray),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='avg:system.cpu.user.pct')
  .label('current hour')
  .title('CPU usage over time')
  .color(#1E90FF)
  .legend(columns=2, position=nw)
Final time series visualization

 

To save the panel, click Save and return in the toolbar.

Visualize the inbound and outbound network traffic

edit

To create a visualization for inbound and outbound network traffic, use mathematical functions.

Define the functions

edit

To start tracking the inbound and outbound network traffic, enter the following in the Timelion Expression field, then click Update:

.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.in.bytes)

Plot the rate of change

edit

To easily monitor the inbound traffic, plots the change in values over time, then click Update:

.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.in.bytes)
  .derivative()

Add a similar calculation for outbound traffic, then click Update:

.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.in.bytes)
  .derivative(),
.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.out.bytes)
  .derivative()
  .multiply(-1)

.multiply(-1) converts the outbound network traffic to a negative value since the outbound network traffic is leaving your machine. .multiply() multiplies the data series by a number, the result of a data series, or a list of data series.

Change the data metric

edit

To make the data easier to analyze, change the data metric from bytes to megabytes, then click Update:

.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.in.bytes)
  .derivative()
  .divide(1048576),
.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.out.bytes)
  .derivative()
  .multiply(-1)
  .divide(1048576)

.divide() accepts the same input as .multiply(), then divides the data series by the defined divisor.

Customize and format the visualization

edit

Customize and format the visualization using the following functions, then click Update:

.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.in.bytes)
  .derivative()
  .divide(1048576)
  .lines(fill=2, width=1)
  .color(green)
  .label("Inbound traffic")
  .title("Network traffic (MB/s)"),
.es(index=metricbeat*,
    timefield=@timestamp,
    metric=max:system.network.out.bytes)
  .derivative()
  .multiply(-1)
  .divide(1048576)
  .lines(fill=2, width=1)
  .color(blue)
  .label("Outbound traffic")
  .legend(columns=2, position=nw)
Final visualization that displays inbound and outbound network traffic

 

To save the panel, click Save and return in the toolbar.

Detect outliers and discover patterns over time

edit

To easily detect outliers and discover patterns over time, modify the time series data with conditional logic and create a trend with a moving average.

With Timelion conditional logic, you can use the following operator values to compare your data:

eq

equal

ne

not equal

lt

less than

lte

less than or equal to

gt

greater than

gte

greater than or equal to

Define the functions

edit

To chart the maximum value of system.memory.actual.used.bytes, enter the following in the Timelion Expression field, then click Update:

.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')

Track used memory

edit

To track the amount of memory used, create two thresholds, then click Update:

.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .if(gt,                             
      11300000000,                    
      .es(index=metricbeat-*,
          timefield='@timestamp',
          metric='max:system.memory.actual.used.bytes'),
      null)
    .label('warning')
    .color('#FFCC11'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .if(gt,
      11375000000,
      .es(index=metricbeat-*,
          timefield='@timestamp',
          metric='max:system.memory.actual.used.bytes'),
      null)
  .label('severe')
  .color('red')

if() compares each point to a number. When the condition is true, adjust the styling. When the condition is false, use the default styling.

Timelion conditional logic for the greater than operator. In this example, the warning threshold is 11.3GB (11300000000), and the severe threshold is 11.375GB (11375000000). If the threshold values are too high or low for your machine, adjust the values.

Determine the trend

edit

To determine the trend, create a new data series, then click Update:

.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .if(gt,11300000000,
      .es(index=metricbeat-*,
          timefield='@timestamp',
          metric='max:system.memory.actual.used.bytes'),
      null)
      .label('warning')
      .color('#FFCC11'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .if(gt,11375000000,
      .es(index=metricbeat-*,
          timefield='@timestamp',
          metric='max:system.memory.actual.used.bytes'),
      null).
      label('severe')
      .color('red'),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .mvavg(10)

mvavg() calculates the moving average over a specified period of time. In this example, .mvavg(10) creates a moving average with a window of 10 data points.

Customize and format the visualization

edit

Customize and format the visualization using the following functions, then click Update:

.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .label('max memory')                    
  .title('Memory consumption over time'), 
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .if(gt,
      11300000000,
      .es(index=metricbeat-*,
          timefield='@timestamp',
          metric='max:system.memory.actual.used.bytes'),
      null)
    .label('warning')
    .color('#FFCC11')                 
    .lines(width=5),                  
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .if(gt,
      11375000000,
      .es(index=metricbeat-*,
          timefield='@timestamp',
          metric='max:system.memory.actual.used.bytes'),
      null)
    .label('severe')
    .color('red')
    .lines(width=5),
.es(index=metricbeat-*,
    timefield='@timestamp',
    metric='max:system.memory.actual.used.bytes')
  .mvavg(10)
  .label('mvavg')
  .lines(width=2)
  .color(#5E5E5E)
  .legend(columns=4, position=nw)
Final visualization that displays outliers and patterns over time

 

To save the panel, click Save and return in the toolbar.

For more information about Timelion conditions, refer to I have but one .condition().

[[role="xpack"]]

« Aggregation-based Make dashboards interactive »