Data types

DataLens uses different data sources with various field data types.

To streamline the data operations, DataLens converts the source data types to its own types.
This optimizes data operations. Learn more in Data type matching table.

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

Geopoint

A coordinate point defined by latitude and longitude. In DataLens, it's set using the function GEOPOINT.

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

If the source data is a string like "[55.75222,37.61556]", you can use the dataset interface or wizard to change the field data type without a formula.

Record example

GEOPOINT("[55.7912,37.6872]")
GEOPOINT("[55.8538,37.6312]")

Geopolygon

Several coordinate points defining the polygon on the map. You can define a polygon in DataLens using the function GEOPOLYGON.
The Even-Odd algorithm is used to fill in the polygon. This way you can create polygons with holes.

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

Record example

/* Polygon without a hole */
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 hole */
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 specified time.

When you use date in formulas, make sure to use hash # around it. For example, DATETRUNC(#2018-07-12#, "year", 5).

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

Record example

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

Date and time

Date with specified time.

When you use date and time in formulas, make sure to use a hash # around it. For example, DATEADD(#2018-01-12 01:02:03#, "second", 6).

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

Record example

#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)

Fractional number

Decimal number. The dot is used as a decimal separator.

You can convert the source data type to a Fractional number by using the function FLOAT.

Record example

1.47113
0.62024
FLOAT("34.567")

Logical

A logical type that can be either TRUE or FALSE.

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

Record example

FALSE

String

A string containing text. It is 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 inside 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 by using the function STR.

Record example

"String"
'String with a "substring"'

Integer

A number with no no fractional part.

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

Record example

42
157

Data type matching table

When you create a dataset, DataLens converts the source data to its own type for streamlined data processing.
This helps DataLens unify the operations with data from different sources.

The table below shows how data types of different databases and DataLens internal data types correspond to one another.

^* You may lose accuracy when converting data.