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Yandex DataLens data types

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Updated at September 24, 2026
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DataLens uses various data sources whose fields may have different data types.

To streamline data operations, DataLens converts source data types to its native ones.
This optimizes data operations. For more information, see Database type and internal Yandex DataLens type mapping table.

You can change the field data type both in the dataset interface and in the wizard.

Warning

When you upload a CSV file as a data source, DataLens will try to automatically detect the types of data in its fields.
If DataLens fails to identify the data type, it will set the String type for the field.

You can use the dataset interface or the wizard to change the field data type.

Geopoint

A geopoint is a coordinate point defined by latitude and longitude. In DataLens, you can set it using the GEOPOINT function.

At input, the function accepts the String and Geopoint data types, or two values of the Float or String type.
If a single string is input, it must contain a list of two numbers in JSON format.

If the source data comes as a string in [55.75222,37.61556] format, you can use the dataset interface or wizard to change the field data type without using any formula.

Note

You can use ready-made geodata sets in DataLens format from Geointellect, our partner.

The archive contains the following data:

  • Countries (polygons and points).
  • Russian regions (polygons and points).
  • Russian cities (points).
  • Districts of million-plus cities (polygons).

Examples

GEOPOINT("[55.7912,37.6872]")

GEOPOINT("55.7912","37.6872")

GEOPOINT(55.7912, 37.6872)

Geopolygon

A geopolygon is a set of coordinate points defining a polygon on a map. In DataLens, you can set it using the GEOPOLYGON function.
DataLens fills the polygon using the Even-Odd algorithm. This allows creating polygons with cutouts.

At input, the function accepts a string in [[[v1,v1], [v2,v2]], ..., [[vN-1,vN-1], [vN,vN]]] format. If the source data is in this format, you can use the dataset interface or wizard to change the field data type without using any formula.

Note

You can use ready-made geodata sets in DataLens format from Geointellect, our partner.

The archive contains the following data:

  • Countries (polygons and points).
  • Russian regions (polygons and points).
  • Russian cities (points).
  • Districts of million-plus cities (polygons).

Examples

/* Polygon without a cutout */
GEOPOLYGON("[[[55.79421,37.65046],[55.79594,37.6513],[55.79642,37.65133],[55.7969, 37.65114],[55.79783, 37.65098],[55.78871,37.75101]]]")

/* Polygons with a cutout */
GEOPOLYGON("[[[55.75,37.52],[55.75,37.68],[55.65,37.60]],[[55.79,37.60],[55.76,37.57],[55.76,37.63]]]")
GEOPOLYGON("[[[55.75,37.50],[55.80,37.60],[55.75,37.70],[55.70,37.70],[55.70,37.50]],[[55.75,37.52],[55.75,37.68],[55.65,37.60]],[[55.79,37.60],[55.76,37.57],[55.76,37.63]]]")

Date

Date without time.

In formulas, use # (hash) for date, e.g., DATETRUNC(#2018-07-12#, "year", 5).

You can convert the source data type to Date using the DATE and DATE_PARSE functions.

Examples

#2018-01-18#
#2015-01-01#
DATETRUNC(#2018-07-12#, "year", 5)
DATEADD(#2018-01-12#, "day", 6)

Date and time (deprecated)

Date with time specified (time converted to UTC).

In formulas, use # (hash) for date and time, e.g., DATEADD(#2018-01-12 01:02:03#, "second", 6).

You can convert the source data type to Date and time (deprecated) using the DATETIME and DATETIME_PARSE functions.

Examples

#2018-01-12 01:08:03#
#2018-05-01T#
DATEADD(#2018-01-12 01:02:03#, "second", 6)
DATETRUNC(#2018-07-12 11:07:13#, "month", 4)

Discontinuing the date and time (deprecated)type

  • Starting September 6, 2022, you can use the new Date and time type, without conversion to UTC.

  • On October 12, 2022:

    • In all datasets, the fields of the Date and time (deprecated) type will be replaced with fields of the Date and time type.
    • Using # will no longer convert constant values to UTC.
    • The DATETIME and DATETIME_PARSE functions will no longer convert expressions to UTC.

Date and time

Date with time specified (without converting time to UTC).

Note

Field selectors of this type are always in line with the time zone of the data source regardless of the field time zone.

In formulas, use ## (double hash) for date and time, e.g., DATEADD(#2018-01-12 01:02:03#, "second", 6).

You can convert the source data type to Date and time using the DATETIME and DATETIME_PARSE functions.

Examples

##2018-01-12 01:08:03##
##2018-05-01T##
DATEADD(##2018-01-12 01:02:03##, "second", 6)
DATETRUNC(##2018-07-12 11:07:13##, "month", 4)

Floating-point number

This is a decimal number, with a period as a separator.

You can convert the source data type to Float using the FLOAT function.

Warning

When you convert the decimal type to Float, you may lose accuracy.

Examples

1.47113
0.62024
FLOAT("34.567")

Boolean

The boolean type can take one of two possible values: TRUE or FALSE.

You can convert the source data type to Boolean using the BOOL function.

Examples

FALSE

String

This is a string containing text, enclosed in single or double quotes.
If you enclose your string in one type of quotes, you can use the second quote type inside the string without escaping.
For example, 'Double quoted "example" and one " double quote.'

You can use the following characters in strings:

  • \n: Line feed (LF)
  • \r: Carriage return (CR)
  • \t: Tab
  • \": Double quote
  • \': Single quote
  • \\: Backslash

You can convert the source data type to String using the STR function.

Examples

"String"
'String with a "substring"'

Integer

This is a number without a fraction.

You can convert the source data type to Integer using the INT function.

In DataLens, the maximum size of the Integer type is limited by the maximum integer for the Number JavaScript type: 253 == 9,007,199,254,740,991.

Examples

42
157

Array

This is a structure made up of a series of elements of the same type.

DataLens supports processing columns in one-dimensional arrays for ClickHouse® and PostgreSQL. Array elements can have the following types:

  • Integer
  • Float
  • String

You can define an array in DataLens using the ARRAY function.

Examples

[1, 2, 3]
[1.1, 1.2, 1.3]
["Cheese", "Bread"]

Tree of strings

DataLens allows you to create tree hierarchies. To do this, use the Tree of strings field type. Data is stored as an array with a full path for each node. The sequence of array values determines the order the tree is expanded.

Note

You can only use a tree in a Table chart.

Creating a tree

To create a tree, add a calculated field at the dataset or chart level using the TREE(ARRAY([lev_1],[lev_2],[lev_3],[lev_n])) formula, where [lev_1], [lev_2], [lev_3], and [lev_n] are dataset fields defining the tree hierarchy.

Example of creating a tree for a source database containing no data array

  1. Prepare data in the source:

    1. Create a table with columns containing hierarchy values.

      Example of creating a table in PostgreSQL
      CREATE TABLE table_without_tree (
   id serial primary key,
   country text,
   region text,
   city text
);

    2. Add data with a full path for each tree node to the table.

      Example of adding data in PostgreSQL
      INSERT INTO table_without_tree (country, region, city)
VALUES('Russia', 'Altay', 'Barnaul');

INSERT INTO table_without_tree (country, region, city)
VALUES('Russia', 'Altay', 'Biysk');

INSERT INTO table_without_tree (country, region, city)
VALUES('Russia', 'Altay', 'Aleisk');

  2. Create the following in the dataset:

    • Array of strings type calculated field describing the tree, e.g., the position field with the ARRAY([country], [region], [city]) formula.

    • Tree of strings type calculated field, e.g., the hierarchy field with the TREE([position]) formula, where position is an Array of strings type field describing the tree.

      Tip

      You can combine creating an array and tree of strings in the same field with the TREE(ARRAY([country], [region], [city])) formula.

Example of creating a tree for a source database containing an array of data

  1. Prepare data in the source:

    1. Create a table with an array of strings.

      Example of creating a table in PostgreSQL
      CREATE TABLE table_with_tree (
  id serial primary key,
  position text[]
);

    2. Add data as an array with a full path for each tree node to the table.

      Example of adding data in PostgreSQL
      INSERT INTO table_with_tree (position)
VALUES('{"Russia","Altay","Barnaul"}');

INSERT INTO table_with_tree (position)
VALUES('{"Russia","Altay","Biysk"}');

INSERT INTO table_with_tree (position)
VALUES('{"Russia","Altay","Aleisk"}');

  2. In the dataset, create a Tree of strings type calculated field, e.g., the hierarchy field with the TREE([position]) formula, where position is an Array of strings type field describing the tree.

Using trees in charts

You can use the prepared source data to create a tree in a Table chart:

  1. Create a Table chart.

  2. Drag a dimension with the Tree of strings type to the Columns section. You will see a tree hierarchy in the visualization area. Expand or collapse the tree using + or -, respectively.

    Note

    You can only use tree dimensions in the chart.

  3. Add the required measures to the Columns section.

    image

Limitations

Currently, tree hierarchies have the following limitations:

  • You can use a single tree or hierarchy per chart.
  • You cannot use the Pagination option and Total row in a tree table.
  • The tree expansion state is not remembered.
  • Selectors do not support trees.
  • The source should store intermediate tree nodes, even those with no data available.

ClickHouse® is a registered trademark of ClickHouse, Inc.

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