Panels on Grafana Labs

Working with Grafana panels

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Working with Grafana panels

A Grafana panel is the user interface you use to define a data source query, and transform and format data that appears in visualizations.

A panel editor includes a query builder and a series of options that you can use to transform data and add information to your panels.

Query a data source

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Query a data source

Data source queries return data that appear in visualizations. The query language in which you write depends on the data source you query. Grafana supports connections with many types of data sources.

Modify visualization text and background colors

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Modify visualization text and background colors

Use thresholds to set the color of a visualization text and background.

Override field values

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Override field values

Apply field overrides when you want to format field values that appear in a visualization.

Transform data

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Transform data

Transformations are a powerful way to manipulate data returned by a query before the system applies a visualization.

With transformations, you can:

Rename fields
Join time series data
Perform mathematical operations across queries
Use the output of one transformation as the input to another transformation

This section includes the following topics:

Format data using value mapping

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Format data using value mapping

In addition to field overrides, value mapping is a technique that you can use to change the visual treatment of data that appears in a visualization.

Create reusable Grafana panels

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Create reusable Grafana panels

Library panels enable you to create reusable panels that you can add to any dashboard in your organization.

Enable template variables to add panels dynamically

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Enable template variables to add panels dynamically

Harness the power of Grafana by configuring the system to automatically and dynamically add panels based on criteria that you define.

Reference: Query options

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Reference: Query options

Click Query options next to the data source selector to see settings for your selected data source. Changes you make here affect only queries made in this panel.

Grafana sets defaults that are shown in dark gray text. Changes are displayed in white text. To return a field to the default setting, delete the white text from the field.

Panel data source query options:

Max data points - If the data source supports it, sets the maximum numbers of data points for each series returned. If the query returns more data points than the max data points setting, then the data source consolidates them (reduces the number of points returned by aggregating them together by average or max or other function).

There are two main reasons for limiting the number of points, performance and smoothing the line. The default value is the width (or number of pixels) of the graph as there is no point in having more data points than the graph panel can display.

With streaming data, the max data points value is used for the rolling buffer. (Streaming is a continuous flow of data and buffering is a way of dividing the stream into chunks). Loki streams data in the live tailing mode.
Min interval - Sets a minimum limit for the automatically calculated interval, typically the minimum scrape interval. If a data point is saved every 15 seconds, then there’s no point in having an interval lower than that. Another use case is to set it to a higher minimum than the scrape interval to get more coarse-grained, well-functioning queries.
Interval - The interval is a time span that you can use when aggregating or grouping data points by time.

Grafana automatically calculates an appropriate interval and it can be used as a variable in templated queries. The variable is either in seconds: $__interval or in milliseconds: $__interval_ms. It is typically used in aggregation functions like sum or average. For example, a Prometheus query using the interval variable: rate(http_requests_total[$__interval]).

This automatic interval is calculated based on the width of the graph. If the user zooms out a lot then the interval becomes greater, resulting in a more coarse grained aggregation whereas if the user zooms in then the interval decreases resulting in a more fine grained aggregation.

For more information, refer to Global variables.
Relative time - You can override the relative time range for individual panels, causing them to be different than what is selected in the dashboard time picker in the top right corner of the dashboard. This allows you to show metrics from different time periods or days on the same dashboard.
Time shift - The time shift function is another way to override the time range for individual panels. It only works with relative time ranges and allows you to adjust the time range.

For example, you could shift the time range for the panel to be two hours earlier than the dashboard time picker. For more information, refer to Time range controls.
Cache timeout - (This field is only visible if available in your data source.) If your time series store has a query cache, then this option can override the default cache timeout. Specified as a numeric value in seconds.

Examples:

Relative time:

Example	Relative time field
Last 5 minutes	`now-5m`
The day so far	`now/d`
Last 5 days	`now-5d/d`
This week so far	`now/w`
Last 2 years	`now-2y/y`

Time shift:

Example	Time shift field
Last entire week	`1w/w`
Two entire weeks ago	`2w/w`
Last entire month	`1M/M`
This entire year	`1d/y`
Last entire year	`1y/y`

Reference: Transformation functions

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Reference: Transformation functions

You can perform the following transformations on your data.

Add field from calculation

Use this transformation to add a new field calculated from two other fields. Each transformation allows you to add one new field.

Mode - Select a mode:
- Reduce row - Apply selected calculation on each row of selected fields independently.
- Binary option - Apply basic math operation(sum, multiply, etc) on values in a single row from two selected fields.
Field name - Select the names of fields you want to use in the calculation for the new field.
Calculation - If you select Reduce row mode, then the Calculation field appears. Click in the field to see a list of calculation choices you can use to create the new field. For information about available calculations, refer to Reference: Calculations.
Operation - If you select Binary option mode, then the Operation fields appear. These fields allow you to do basic math operations on values in a single row from two selected fields. You can also use numerical values for binary operations.
Alias - (Optional) Enter the name of your new field. If you leave this blank, then the field will be named to match the calculation.
Replace all fields - (Optional) Select this option if you want to hide all other fields and display only your calculated field in the visualization.

In the example below, I added two fields together and named them Sum.

Concatenate fields

This transformation combines all fields from all frames into one result. Consider:

Query A:

Temp	Uptime
15.4	1230233

Query B:

AQI	Errors
3.2	5

After you concatenate the fields, the data frame would be:

Temp	Uptime	AQI	Errors
15.4	1230233	3.2	5

Config from query results

This transformation allow you to select one query and from it extract standard options like Min, Max, Unit and Thresholds and apply it to other query results. This enables dynamic query driven visualization configuration.

If you want to extract a unique config for every row in the config query result then try the rows to fields transformation.

Convert field type

This transformation changes the field type of the specified field.

Field - Select from available fields
as - Select the FieldType to convert to
- Numeric - attempts to make the values numbers
- String - will make the values strings
- Time - attempts to parse the values as time
  - Will show an option to specify a DateFormat as input by a string like yyyy-mm-dd or DD MM YYYY hh:mm:ss
- Boolean - will make the values booleans

For example the following query could be modified by selecting the time field, as Time, and Date Format as YYYY.

Time	Mark	Value
2017-07-01	above	25
2018-08-02	below	22
2019-09-02	below	29
2020-10-04	above	22

The result:

Time	Mark	Value
2017-01-01 00:00:00	above	25
2018-01-01 00:00:00	below	22
2019-01-01 00:00:00	below	29
2020-01-01 00:00:00	above	22

Filter data by name

Use this transformation to remove portions of the query results.

Grafana displays the Identifier field, followed by the fields returned by your query.

You can apply filters in one of two ways:

Enter a regex expression.
Click a field to toggle filtering on that field. Filtered fields are displayed with dark gray text, unfiltered fields have white text.

In the example below, I removed the Min field from the results.

Here is the original query table. (This is streaming data, so numbers change over time and between screenshots.)

Here is the table after I applied the transformation to remove the Min field.

Here is the same query using a Stat visualization.

Filter data by query

Use this transformation in panels that have multiple queries, if you want to hide one or more of the queries.

Grafana displays the query identification letters in dark gray text. Click a query identifier to toggle filtering. If the query letter is white, then the results are displayed. If the query letter is dark, then the results are hidden.

In the example below, the panel has three queries (A, B, C). I removed the B query from the visualization.

Note: This transformation is not available for Graphite because this data source does not support correlating returned data with queries.

Filter data by value

This transformation allows you to filter your data directly in Grafana and remove some data points from your query result. You have the option to include or exclude data that match one or more conditions you define. The conditions are applied on a selected field.

This transformation is very useful if your data source does not natively filter by values. You might also use this to narrow values to display if you are using a shared query.

The available conditions for all fields are:

Regex: Match a regex expression
Is Null: Match if the value is null
Is Not Null: Match if the value is not null
Equal: Match if the value is equal to the specified value
Different: match if the value is different than the specified value

The available conditions for number fields are:

Greater: Match if the value is greater than the specified value
Lower: Match if the value is lower than the specified value
Greater or equal: Match if the value is greater or equal
Lower or equal: Match if the value is lower or equal
Range: Match a range between a specified minimum and maximum, min and max included

Consider the following data set:

Time	Temperature	Altitude
2020-07-07 11:34:23	32	101
2020-07-07 11:34:22	28	125
2020-07-07 11:34:21	26	110
2020-07-07 11:34:20	23	98
2020-07-07 10:32:24	31	95
2020-07-07 10:31:22	20	85
2020-07-07 09:30:57	19	101

If you Include the data points that have a temperature below 30°C, the configuration will look as follows:

Filter Type: Include
Condition: Rows where Temperature matches Lower Than 30

And you will get the following result, where only the temperatures below 30°C are included:

Time	Temperature	Altitude
2020-07-07 11:34:22	28	125
2020-07-07 11:34:21	26	110
2020-07-07 11:34:20	23	98
2020-07-07 10:31:22	20	85
2020-07-07 09:30:57	19	101

You can add more than one condition to the filter. For example, you might want to include the data only if the altitude is greater than 100. To do so, add that condition to the following configuration:

Filter type: Include rows that Match All conditions
Condition 1: Rows where Temperature matches Lower than 30
Condition 2: Rows where Altitude matches Greater than 100

When you have more than one condition, you can choose if you want the action (include / exclude) to be applied on rows that Match all conditions or Match any of the conditions you added.

In the example above we chose Match all because we wanted to include the rows that have a temperature lower than 30 AND an altitude higher than 100. If we wanted to include the rows that have a temperature lower than 30 OR an altitude higher than 100 instead, then we would select Match any. This would include the first row in the original data, which has a temperature of 32°C (does not match the first condition) but an altitude of 101 (which matches the second condition), so it is included.

Conditions that are invalid or incompletely configured are ignored.

Group by

This transformation groups the data by a specified field (column) value and processes calculations on each group. Click to see a list of calculation choices. For information about available calculations, refer to the List of calculations.

Here’s an example of original data.

Time	Server ID	CPU Temperature	Server Status
2020-07-07 11:34:20	server 1	80	Shutdown
2020-07-07 11:34:20	server 3	62	OK
2020-07-07 10:32:20	server 2	90	Overload
2020-07-07 10:31:22	server 3	55	OK
2020-07-07 09:30:57	server 3	62	Rebooting
2020-07-07 09:30:05	server 2	88	OK
2020-07-07 09:28:06	server 1	80	OK
2020-07-07 09:25:05	server 2	88	OK
2020-07-07 09:23:07	server 1	86	OK

This transformation goes in two steps. First you specify one or multiple fields to group the data by. This will group all the same values of those fields together, as if you sorted them. For instance if we group by the Server ID field, then it would group the data this way:

Time	Server ID	CPU Temperature	Server Status
2020-07-07 11:34:20	server 1	80	Shutdown
2020-07-07 09:28:06	server 1	80	OK
2020-07-07 09:23:07	server 1	86	OK
2020-07-07 10:32:20	server 2	90	Overload
2020-07-07 09:30:05	server 2	88	OK
2020-07-07 09:25:05	server 2	88	OK
2020-07-07 11:34:20	*server 3*	62	OK
2020-07-07 10:31:22	*server 3*	55	OK
2020-07-07 09:30:57	*server 3*	62	Rebooting

All rows with the same value of Server ID are grouped together.

After choosing which field you want to group your data by, you can add various calculations on the other fields, and apply the calculation to each group of rows. For instance, we could want to calculate the average CPU temperature for each of those servers. So we can add the mean calculation applied on the CPU Temperature field to get the following:

Server ID	CPU Temperature (mean)
server 1	82
server 2	88.6
server 3	59.6

And we can add more than one calculation. For instance:

For field Time, we can calculate the Last value, to know when the last data point was received for each server
For field Server Status, we can calculate the Last value to know what is the last state value for each server
For field Temperature, we can also calculate the Last value to know what is the latest monitored temperature for each server

We would then get :

Server ID	CPU Temperature (mean)	CPU Temperature (last)	Time (last)	Server Status (last)
server 1	82	80	2020-07-07 11:34:20	Shutdown
server 2	88.6	90	2020-07-07 10:32:20	Overload
server 3	59.6	62	2020-07-07 11:34:20	OK

This transformation allows you to extract some key information out of your time series and display them in a convenient way.

Join by field (outer join)

Use this transformation to join multiple time series from a result set by field.

This transformation is especially useful if you want to combine queries so that you can calculate results from the fields.

In the example below, I have a template query displaying time series data from multiple servers in a table visualization. I can only view the results of one query at a time.

I applied a transformation to join the query results using the time field. Now I can run calculations, combine, and organize the results in this new table.

Labels to fields

This transformation changes time series results that include labels or tags into a table where each label keys and values are included in the table result. The labels can be displayed either as columns or as row values.

Given a query result of two time series:

Series 1: labels Server=Server A, Datacenter=EU
Series 2: labels Server=Server B, Datacenter=EU

In “Columns” mode, the result looks like this:

Time	Server	Datacenter	Value
2020-07-07 11:34:20	Server A	EU	1
2020-07-07 11:34:20	Server B	EU	2

In “Rows” mode, the result has a table for each series and show each label value like this:

label	value
Server	Server A
Datacenter	EU

label	value
Server	Server B
Datacenter	EU

Merge

Use this transformation to combine the result from multiple queries into one single result. This is helpful when using the table panel visualization. Values that can be merged are combined into the same row. Values are mergeable if the shared fields contain the same data. For information, refer to Table panel.

In the example below, we have two queries returning table data. It is visualized as two separate tables before applying the transformation.

Query A:

Time	Job	Uptime
2020-07-07 11:34:20	node	25260122
2020-07-07 11:24:20	postgre	123001233

Query B:

Time	Job	Errors
2020-07-07 11:34:20	node	15
2020-07-07 11:24:20	postgre	5

Here is the result after applying the Merge transformation.

Time	Job	Errors	Uptime
2020-07-07 11:34:20	node	15	25260122
2020-07-07 11:24:20	postgre	5	123001233

Organize fields

Use this transformation to rename, reorder, or hide fields returned by the query.

Note: This transformation only works in panels with a single query. If your panel has multiple queries, then you must either apply an Outer join transformation or remove the extra queries.

Grafana displays a list of fields returned by the query. You can:

Change field order by hovering your cursor over a field. The cursor turns into a hand and then you can drag the field to its new place.
Hide or show a field by clicking the eye icon next to the field name.
Rename fields by typing a new name in the Rename box.

In the example below, I hid the value field and renamed Max and Min.

Value field name

If you selected Server as the Value field name, then you would get one field for every value of the Server label.

Time	Datacenter	Server A	Server B
2020-07-07 11:34:20	EU	1	2

Merging behavior

The labels to fields transformer is internally two separate transformations. The first acts on single series and extracts labels to fields. The second is the merge transformation that joins all the results into a single table. The merge transformation tries to join on all matching fields. This merge step is required and cannot be turned off.

To illustrate this, here is an example where you have two queries that return time series with no overlapping labels.

Series 1: labels Server=ServerA
Series 2: labels Datacenter=EU

This will first result in these two tables:

Time	Server	Value
2020-07-07 11:34:20	ServerA	10

Time	Datacenter	Value
2020-07-07 11:34:20	EU	20

After merge:

Time	Server	Value	Datacenter
2020-07-07 11:34:20	ServerA	10
2020-07-07 11:34:20		20	EU

Reduce

The Reduce transformation applies a calculation to each field in the frame and return a single value. Time fields are removed when applying this transformation.

Consider the input:

Query A:

Time	Temp	Uptime
2020-07-07 11:34:20	12.3	256122
2020-07-07 11:24:20	15.4	1230233

Query B:

Time	AQI	Errors
2020-07-07 11:34:20	6.5	15
2020-07-07 11:24:20	3.2	5

The reduce transformer has two modes:

Series to rows - Creates a row for each field and a column for each calculation.
Reduce fields - Keeps the existing frame structure, but collapses each field into a single value.

For example, if you used the First and Last calculation with a Series to rows transformation, then the result would be:

Field	First	Last
Temp	12.3	15.4
Uptime	256122	1230233
AQI	6.5	3.2
Errors	15	5

The Reduce fields with the Last calculation, results in two frames, each with one row:

Query A:

Temp	Uptime
15.4	1230233

Query B:

AQI	Errors
3.2	5

Rename by regex

Use this transformation to rename parts of the query results using a regular expression and replacement pattern.

You can specify a regular expression, which is only applied to matches, along with a replacement pattern that support back references. For example, let’s imagine you’re visualizing CPU usage per host and you want to remove the domain name. You could set the regex to ([^\.]+)\..+ and the replacement pattern to $1, web-01.example.com would become web-01.

In the following example, we are stripping the prefix from event types. In the before image, you can see everything is prefixed with system.

With the transformation applied, you can see we are left with just the remainder of the string.

Rows to fields

The rows to fields transformation converts rows into separate fields. This can be useful as fields can be styled and configured individually. It can also use additional fields as sources for dynamic field configuration or map them to field labels. The additional labels can then be used to define better display names for the resulting fields.

This transformation includes a field table which lists all fields in the data returned by the config query. This table gives you control over what field should be mapped to each config property (the *Use as** option). You can also choose which value to select if there are multiple rows in the returned data.

This transformation requires:

One field to use as the source of field names.

By default, the transform uses the first string field as the source. You can override this default setting by selecting Field name in the Use as column for the field you want to use instead.
One field to use as the source of values.

By default, the transform uses the first number field as the source. But you can override this default setting by selecting Field value in the Use as column for the field you want to use instead.

Useful when visualizing data in:

Gauge
Stat
Pie chart

Map extra fields to labels

If a field does not map to config property Grafana will automatically use it as source for a label on the output field-

Example:

Name	DataCenter	Value
ServerA	US	100
ServerB	EU	200

Output:

ServerA (labels: DataCenter: US)	ServerB (labels: DataCenter: EU)
10	20

The extra labels can now be used in the field display name provide more complete field names.

If you want to extract config from one query and appply it to another you should use the config from query results transformation.

Example

Input:

Name	Value	Max
ServerA	10	100
ServerB	20	200
ServerC	30	300

Output:

ServerA (config: max=100)	ServerB (config: max=200)	ServerC (config: max=300)
10	20	30

As you can see each row in the source data becomes a separate field. Each field now also has a max config option set. Options like Min, Max, Unit and Thresholds are all part of field configuration and if set like this will be used by the visualization instead of any options manually configured in the panel editor options pane.

Prepare time series

Note: This transformation is available in Grafana 7.5.10+ and Grafana 8.0.6+.

Prepare time series transformation is useful when a data source returns time series data in a format that isn’t supported by the panel you want to use. For more information about data frame formats, refer to Data frames.

This transformation helps you resolve this issue by converting the time series data from either the wide format to the long format or the other way around.

Select the Multi-frame time series option to transform the time series data frame from the wide to the long format.

Select the Wide time series option to transform the time series data frame from the long to the wide format.

Series to rows

Note: This transformation is available in Grafana 7.1+.

Use this transformation to combine the result from multiple time series data queries into one single result. This is helpful when using the table panel visualization.

The result from this transformation will contain three columns: Time, Metric, and Value. The Metric column is added so you easily can see from which query the metric originates from. Customize this value by defining Label on the source query.

In the example below, we have two queries returning time series data. It is visualized as two separate tables before applying the transformation.

Query A:

Time	Temperature
2020-07-07 11:34:20	25
2020-07-07 10:31:22	22
2020-07-07 09:30:05	19

Query B:

Time	Humidity
2020-07-07 11:34:20	24
2020-07-07 10:32:20	29
2020-07-07 09:30:57	33

Here is the result after applying the Series to rows transformation.

Time	Metric	Value
2020-07-07 11:34:20	Temperature	25
2020-07-07 11:34:20	Humidity	22
2020-07-07 10:32:20	Humidity	29
2020-07-07 10:31:22	Temperature	22
2020-07-07 09:30:57	Humidity	33
2020-07-07 09:30:05	Temperature	19

Sort by

This transformation will sort each frame by the configured field, When reverse is checked, the values will return in the opposite order.

Reference: Calculation types

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Reference: Calculations

Refer to the following list of calculations you can perform in Grafana. You can find these calculations in the Transform tab and in the bar gauge, gauge, and stat visualizations.

Calculation	Description
All nulls	True when all values are null
All zeros	True when all values are 0
Change count	Number of times the field’s value changes
Count	Number of values in a field
Delta	Cumulative change in value, only counts increments
Difference	Difference between first and last value of a field
Difference percent	Percentage change between first and last value of a field
Distinct count	Number of unique values in a field
First (not null)	First, not null value in a field
Max	Maximum value of a field
Mean	Mean value of all values in a field
Min	Minimum value of a field
Min (above zero)	Minimum, positive value of a field
Range	Difference between maximum and minimum values of a field
Step	Minimal interval between values of a field
Total	Sum of all values in a field

Reference: Standard field definitions

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Reference: Standard field definitions

This section explains all available standard options. They are listed in alphabetical order.

You can apply standard options to most built-in Grafana panels. Some older panels and community panels that have not updated to the new panel and data model will be missing either all or some of these field options.

Most field options will not affect the visualization until you click outside of the field option box you are editing or press Enter.

Note: We are constantly working to add and expand options for all visualization, so all options might not be available for all visualizations.

Decimals

Number of decimals to render value with. Leave empty for Grafana to use the number of decimals provided by the data source.

To change this setting, type a number in the field and then click outside the field or press Enter.

Data links

Lets you control the URL to which a value or visualization link.

For more information and instructions, refer to Data links.

Display name

Lets you set the display title of all fields. You can use variables in the field title.

When multiple stats, fields, or series are shown, this field controls the title in each stat. You can use expressions like ${__field.name} to use only the series name or the field name in title.

Given a field with a name of Temp, and labels of {“Loc”=“PBI”, “Sensor”=“3”}

Expression syntax	Example	Renders to	Explanation
`${__field.displayName}`	Same as syntax	`Temp {Loc="PBI", Sensor="3"}`	Displays the field name, and labels in `{}` if they are present. If there is only one label key in the response, then for the label portion, Grafana displays the value of the label without the enclosing braces.
`${__field.name}`	Same as syntax	`Temp`	Displays the name of the field (without labels).
`${__field.labels}`	Same as syntax	`Loc="PBI", Sensor="3"`	Displays the labels without the name.
`${__field.labels.X}`	`${__field.labels.Loc}`	`PBI`	Displays the value of the specified label key.
`${__field.labels.__values}`	Same as Syntax	`PBI, 3`	Displays the values of the labels separated by a comma (without label keys).

If the value is an empty string after rendering the expression for a particular field, then the default display method is used.

Max

Lets you set the maximum value used in percentage threshold calculations. Leave blank for auto calculation based on all series and fields

Min

Lets you set the minimum value used in percentage threshold calculations. Leave blank for auto calculation based on all series and fields

No value

Enter what Grafana should display if the field value is empty or null. The default value is a hyphen (-).

Unit

Lets you choose what unit a field should use. Click in the Unit field, then drill down until you find the unit you want. The unit you select is applied to all fields except time.

Custom units

You can use the unit dropdown to also specify custom units, custom prefix or suffix and date time formats.

To select a custom unit enter the unit and select the last Custom: xxx option in the dropdown.

suffix:<suffix> for custom unit that should go after value.
time:<format> For custom date time formats type for example time:YYYY-MM-DD. See formats for the format syntax and options.
si:<base scale><unit characters> for custom SI units. For example: si: mF. This one is a bit more advanced as you can specify both a unit and the source data scale. So if your source data is represented as milli (thousands of) something prefix the unit with that SI scale character.
count:<unit> for a custom count unit.
currency:<unit> for custom a currency unit.

You can also paste a native emoji in the unit picker and pick it as a custom unit:

String units

Grafana can sometime be too aggressive in parsing strings and displaying them as numbers. To make Grafana show the original string create a field override and add a unit property with the string unit.