Aggregations in DataLens

This section describes how data aggregation and grouping work in DataLens, how to correctly formulate expressions with aggregations, and also gives examples of SQL queries with aggregations for ClickHouse®.

As source data, we will use the Selling.csv file with the data on sales in cities.

SQL basic concepts

What is aggregation?

Let's take a look at the Selling table with data on sales in cities:

#CityCategoryDateSalesProfitDay's discount
1DetroitOffice Supplies2014-01-021070.05
2PortlandOffice Supplies2014-04-0514100.00
3PortlandOffice Supplies2014-01-2120120.20
4San FranciscoOffice Supplies2014-03-11830.10
7San FranciscoTechnology2014-01-02730.10
8San FranciscoTechnology2014-01-171350.20

Aggregation is used to calculate resulting values. Aggregation process essentially means converting a large set of strings into a single value. In SQL, special aggregate functions are used for this. The most common functions include SUM, MIN, MAX, AVG, and COUNT. Each of these functions operates with column values in a given table, which results in a single value. For example, for the SUM function, this is the sum of all the column values, for AVG — the average value, for MAX — the highest value.

There are two ways to use aggregate functions:

Aggregation for a single group

In this case, aggregate functions calculate and return a single resulting value for all rows that are combined into a single group.

For example, to get the sum of values in the Sales column from the table Selling, you need to run the following query:

SELECT    sum("Sales")FROM "Selling"



Aggregation for multiple groups

In this case, the entire set of rows returned by the query is split into separate groups. The group is determined by the value of the column for which the grouping is performed. In this way, aggregate functions calculate and return the resulting value separately for each group.

In SQL, the columns for grouping are specified in the GROUP BY section. Grouping can be performed either by one or several columns.


Calculating the sales amount for each city:

SELECT    "City",    sum("Sales")FROM "Selling"GROUP BY "City"


San Francisco28

Calculating the sales amount for each city and product category:

SELECT    "City",    "Category",    sum("Sales")FROM "Selling"GROUP BY "City","Category"


DetroitOffice Supplies10
PortlandOffice Supplies34
San FranciscoOffice Supplies8
San FranciscoTechnology20


When aggregating with grouping by several groups, keep in mind the following limitations:

  • Specify the columns used for selection in the GROUP BY section:

    SELECT    "City",    sum("Sales")FROM "Selling"GROUP BY "City"
    SELECT    "City",    sum("Sales")FROM "Selling"GROUP BY "Category"
  • Aggregated and non-aggregated expressions cannot be used at the same query level:

    SELECT    "City",    sum("Sales") as "Detroit Sales"FROM "Selling"WHERE "City" = 'Detroit'GROUP BY "City"
    SELECT    if("City" = 'Detroit', sum("Sales"), 0) as "Detroit Sales"FROM "Selling"GROUP BY "Category"


You can apply filtering in queries with grouping and aggregate functions. You can filter both the original pre-aggregation set of rows and the resulting values calculated by aggregate functions.

The filtering conditions for the original set of rows are specified in the WHERE section:

SELECT    "City",    sum("Sales")FROM "Selling"WHERE "Category" = 'Furniture'GROUP BY "City"



To filter aggregated values, specify a condition in the HAVING section. In this case, the query returns only those rows in which the resulting value of the aggregate function satisfies the specified condition:

SELECT    "City",    "Category",    sum("Sales")FROM "Selling"GROUP BY "City","Category"HAVING sum("Sales") >= 10


DetroitOffice Supplies10
PortlandOffice Supplies34
San FranciscoTechnology20

Data aggregation in DataLens

Dimensions and measures

In DataLens, aggregation is performed using dimensions and measures.

Measure: A dataset field with a specified aggregation type (for example, sum, average, or quantity). In the dataset and in the wizard, measures are displayed in blue. Usually, a measure is a business metric scrutinized by different slices or groupings, such as revenue, number of customers, or average customer bill.

Dimension: A dataset field without the specified aggregation, such as a region, a product, or category. In the dataset and in the wizard, dimensions are displayed in green. Dimensions are used to group a query in the chart (the GROUP BY section in SQL). To group data in the chart, you need to drag the dimension to the desired section.

Methods to create measures

You can add measures both at the dataset and the chart level. We recommend adding dimensions at the dataset level. This allows you to reuse them in different charts and speed up chart rendering.

Creating measures at the dataset level

You can add a measure at the dataset level in the following ways:

  • In the dataset creation interface, open the Fields tab and select the aggregation type for the field in the Aggregation column.


  • In the data creation interface, add a calculated field using aggregate functions. For more information, see How to create a calculable field. In the formula of the calculated field, you can substitute other measures.


    When you create a calculated field using an aggregate function, it is assigned the Auto aggregation type, which cannot be changed.

Creating measures at the chart level

You can add a measure at the chart level in the following ways:

Measures can consist of more than one aggregate function and have more complex expressions. For example, in this chart, to calculate the average sales amount for the day, we use the Sales per day measure calculated using the SUM([Sales])/COUNTD([Date]) formula.


Using dimensions and measures in charts

When building any chart in DataLens, data is grouped and aggregated.
Let's look at the Selling table, where we need to calculate the sales amount (Sales) for all dates (Date) separately for each city (City). To do this, you need to group the data by the City field. When grouping, rows are combined in such a way that each City value occupies one row. All source rows where the City values match and are equal, form a group of rows. As a result, there are three groups for which the Sales value will be summed up:

  • Rows 1 and 5 will be added to the Detroit group.
  • Rows 2, 3, and 6, to Portland.
  • Rows 4, 7, and 8, to San Francisco.

For example, in the Column chart, the result will be as follows:


You can group by several fields rather than one. In this case, each row is defined by a set of values of all fields by which grouping is performed. There will be as many rows in the final result as there are unique sets of such values.
For example, when adding the Category field to the Colors section, it will affect grouping. The chart will look as follows:



Measures in the Colors section also affect data grouping.

In some chart sections, you can drag only a dimension or only a measure. This depends on the chart type. For example, in the Y section of the Column chart, you can only drag a measure. If you drag a dimension to this section, it will be automatically converted to a measure as a result of the Number of unique aggregation.


Expression limitations

Like in SQL, in DataLens, you cannot use aggregated and non-aggregated values in the same expression.

For example, in the chart with groupings by the City and Category dimensions, you cannot add the SUM([Sales]) * (1 - [Day's discount]) measure to calculate the sales amount with discounts. In this case, the City and Category dimensions determine group breakdown, and therefore have fixed values in each group. For each group, you can calculate the SUM([Sales]) value. However, the Day's discount field is neither an aggregation nor a measure within the group. It does not have a fixed value and may vary from row to row in the group. Therefore, it is impossible to determine what specific value of the Day's discount field needs to be selected to calculate the SUM([Sales]) * (1 - [Day's discount]) measure for each group. Thus, the SUM([Sales]) * (1 - [Day's discount]) expression cannot be calculated. In DataLens, such cases result in the Inconsistent aggregation among operands error.


You can prevent this error in different ways:

  • Add the Day's discount field to the dimension section. In this case, data is grouped by the City, Category, and Day's discount dimensions, so the fixed value of the Day's discount field is used for each group to calculate the value of the SUM([Sales]) * (1 - [Day's discount]) measure.


  • Specify the aggregation type for the Day's discount field. In this case, this field will become a measure and the original formula will be correct.


Filtering dimensions and measures

In charts, you can filter the values of dimensions and measures. To do this, drag a dimension or a measure to Filters and set filtering conditions:



Sales by city in the Furniture category:


Sales by city and category, where the SUM([Sales]) measure is greater than or equal to 10:


Substituting fields

When creating calculated fields in a formula, you can use pre-existing measures. These measures can be set either using a formula or the dataset creation interface. The created calculated field is assigned the Auto aggregation type.

Example 1

The [TotalSales] field is set using the SUM([Sales]) aggregate function. Then the [TotalSales]/10 calculated field is assigned the Auto aggregation type.

If the measure set using the dataset creation interface is substituted in the calculated field, you can redefine the aggregation type. To do this, use a function with a different aggregation type in the formula.

Example 2

For the [Sales] field, the Amount aggregation type is set in the dataset creation interface. Then the AVG([Sales]) calculated field is assigned the Auto aggregation type and calculated as an average. The Amount aggregation will be ignored.

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