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

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

Use this transformation to combine all fields from all frames into one result.

For example, if you have separate queries retrieving temperature and uptime data (Query A) and air quality index and error information (Query B), applying the concatenate transformation yields a consolidated data frame with all relevant information in one view.

Consider the following:

Query A:

Query B:

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

This transformation simplifies the process of merging data from different sources, providing a comprehensive view for analysis and visualization.

Use this transformation to select a query and extract standard options, such as Min, Max, Unit, and Thresholds, and apply them to other query results. This feature enables dynamic visualization configuration based on the data returned by a specific query.

Use this transformation to modify the field type of a specified field.

This transformation has the following options:

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

Use this transformation to select a source of data and extract content from it in different formats. This transformation has the following fields:

Consider the following dataset:

Use this transformation to enrich a field value by looking up additional fields from an external source.

This transformation has the following fields:

This transformation currently supports spatial data.

For example, if you have this data:

Use this transformation to hide one or more queries in panels that have multiple 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). We removed the B query from the visualization.

Use this transformation to selectively filter data points directly within your visualization. This transformation provides options to include or exclude data based on one or more conditions applied to 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:

The available conditions for number fields are:

Consider the following dataset:

Use this transformation to selectively remove parts of your query results. There are three ways to filter field names:

Use this transformation to customize the output of a string field. This transformation has the following fields:

This transformation provides a convenient way to standardize and tailor the presentation of string data for better visualization and analysis.

Use this transformation to customize the output of a time field. Output can be formatted using Moment.js format strings. For example, if you want to display only the year of a time field, the format string ‘YYYY’ can be used to show the calendar year (for example, 1999 or 2012).

Before Transformation:

After applying ‘YYYY-MM-DD HH:mm:ss’:

This transformation lets you tailor the time representation in your visualizations, providing flexibility and precision in displaying temporal data.

Use this transformation to group the data by a specified field (column) value and process calculations on each group. Click to see a list of calculation choices. For information about available calculations, refer to ref:calculation-types[Calculation types].

Here’s an example of original data.

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:

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:

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

We would then get:

This transformation allows you to extract essential information from your time series and present it conveniently.

Use this transformation to combine three fields—which are used as input for the Column, Row, and Cell value fields from the query output—and generate a matrix. The matrix is calculated as follows:

Original data

We can generate a matrix using the values of ‘Server Status’ as column names, the ‘Server ID’ values as row names, and the ‘CPU Temperature’ as content of each cell. The content of each cell will appear for the existing column (‘Server Status’) and row combination (‘Server ID’). For the rest of the cells, you can select which value to display between: Null, True, False, or Empty.

Output

Use this transformation to construct a matrix by specifying fields from your query results. The matrix output reflects the relationships between the unique values in these fields. This helps you present complex relationships in a clear and structured matrix format.

Use this transformation to prepare histogram data for visualizing trends over time. Similar to the heatmap visualization, this transformation converts histogram metrics into temporal buckets.

Use this transformation to generate a histogram based on input data, allowing you to visualize the distribution of values.

Original data

Series 1:

Series 2:

Output

Visualize the distribution of values using the generated histogram, providing insights into the data’s spread and density.

Use this transformation to merge multiple results into a single table, enabling the consolidation of data from different queries.

This is especially useful for converting multiple time series results into a single wide table with a shared time field.

Use this transformation to join multiple results into a single table.

This is especially useful for converting multiple time series results into a single wide table with a shared Label field.

Use this transformation to convert time series results with labels or tags into a table, including each label’s keys and values in the result. Display labels as either columns or row values for enhanced data visualization.

Given a query result of two time series:

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

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

Use this transformation to restrict the number of rows displayed, providing a more focused view of your data. This is particularly useful when dealing with large datasets.

Below is an example illustrating the impact of the Limit transformation on a response from a data source:

Here is the result after adding a Limit transformation with a value of ‘3’:

This transformation helps you tailor the visual presentation of your data to focus on the most relevant information.

Use this transformation to combine the results from multiple queries into a single result, which is particularly useful when using the table panel visualization. This transformation merges values into the same row if the shared fields contain the same data.

Here’s an example illustrating the impact of the Merge series/tables transformation on two queries returning table data:

Query A:

Query B:

Here is the result after applying the Merge transformation.

This transformation combines values from Query A and Query B into a unified table, enhancing the presentation of data for better insights.

Use this transformation to provide the flexibility to rename, reorder, or hide fields returned by a single query in your panel. This transformation is applicable only to panels with a single query. If your panel has multiple queries, consider using an “Outer join” transformation or removing extra queries.

Use this transformation to streamline the process of graphing multiple series without the need for multiple queries with different ‘WHERE’ clauses.

This is particularly useful when dealing with a metrics SQL table, as illustrated below:

With the Partition by values transformation, you can issue a single query and split the results by unique values in one or more columns (fields) of your choosing. The following example uses ‘Region’:

‘SELECT Time, Region, Value FROM metrics WHERE Time > “2022-10-20”’

This transformation simplifies the process and enhances the flexibility of visualizing multiple series within the same time series visualization.

Use this transformation to address issues when a data source returns time series data in a format that isn’t compatible with the desired visualization. This transformation allows you to convert time series data between wide and long formats, providing flexibility in data frame structures.

Use this transformation to apply a calculation to each field in the data frame and return a single value. This transformation is particularly useful for consolidating multiple time series data into a more compact, summarized format. Time fields are removed when applying this transformation.

Consider the input:

Query A:

Query B:

The reduce transformer has two modes:

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

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

Query A:

Query B:

This flexible transformation simplifies the process of consolidating and summarizing data from multiple time series into a more manageable and organized format.

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 ‘A-’ prefix from field names. In the before image, you can see everything is prefixed with ‘A-’:

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

This transformation lets you to tailor your data to meet your visualization needs, making your dashboards more informative and user-friendly.

Use this transformation to convert rows into separate fields. This can be useful because 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 configuration query. This table gives you control over what field should be mapped to each configuration 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:

Useful when visualizing data in:

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:

Query B:

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

This transformation facilitates the consolidation of results from multiple time series queries, providing a streamlined and unified dataset for efficient analysis and visualization in a tabular format.

Use this transformation to sort each frame within a query result based on a specified field, making your data easier to understand and analyze. By configuring the desired field for sorting, you can control the order in which the data is presented in the table or visualization.

Use the Reverse switch to inversely order the values within the specified field. This functionality is particularly useful when you want to quickly toggle between ascending and descending order to suit your analytical needs.

For example, in a scenario where time-series data is retrieved from a data source, the Sort by transformation can be applied to arrange the data frames based on the timestamp, either in ascending or descending order, depending on the analytical requirements. This capability ensures that you can easily navigate and interpret time-series data, gaining valuable insights from the organized and visually coherent presentation.

Use this transformation to apply spatial operations to query results.

This transformation allows you to manipulate and analyze geospatial data, enabling operations such as creating lines between points, calculating spatial properties, and more.

Use this transformation to convert time series results into a table, transforming a time series data frame into a Trend field. The Trend field can then be rendered using the ref:sparkline-cell-type[sparkline cell type], generating an inline sparkline for each table row. If there are multiple time series queries, each will result in a separate table data frame. These can be joined using join or merge transforms to produce a single table with multiple sparklines per row.

For each generated Trend field value, a calculation function can be selected. The default is Last non-null value. This value is displayed next to the sparkline and used for sorting table rows.

Use this transformation to create a new data frame containing values predicted by a statistical model. This is useful for finding a trend in chaotic data. It works by fitting a mathematical function to the data, using either linear or polynomial regression. The data frame can then be used in a visualization to display a trendline.

There are two different models: