A metric is an expression which describes a computation that will be performed over the project's data. Metrics are written in a custom query language, which is fairly simple and doesn't require users to know SQL. A metric written in this language is then translated to SQL by a backend service and executed as multidimensional query on data warehouse.

A metric is visualised on the map using an indicator, in which it is referenced by URL.

All properties used in metrics are dataset properties, located in On these properties, a set of functions can be applied. They can be nested, and the results of these functions can be combined and filtered.


This is the simplest type of metric. It returns the sum (function_sum) of all basket amounts (property baskets.amount).

All available functions and operators are described below.


Additional syntax examples

This metric extends the metric from the first example by filtering. The filter defined in options.filterBy object filters the baskets.on_off_name property with an eq operator - so the metric computes the turnover of baskets ordered offline.

All metrics can have arbitrary number of filters.


Let's have a look at a more complex example. This metric computes the number of purchases per customer.

Consider this metric a fraction. On the top level, the aggregate function is function_divide, which represents a fraction bar. The numerator here is a function_count of the number of baskets, and the denominator is the count of all clients.


This metric computes the penetration of the market. Market penetration is computed as a number of customers, divided by the sum of the number of households. This example demonstrates the use of withoutFilters. In our view, we have defined a globalDate filter which filters the dim_dates.date_iso property. There is no way to link the dim_dates and demography_postcode datasets, yet they both appear in one metric. To evade an error of finding a non-existent join path between these datasets, we use withoutFilters on all properties of the dim_dates dataset.

Another thing to note here is the use of function_ifnull. If the result of the function_divide should be null (e.g. in case of division by zero), the result of the metric in that case will be 0.0.

This metric computes the population in areas where the turnover is greater than 10000 (given that the contents of the demography dataset are computed to the ward level).

This metric uses the inAttribute operator, which is a specific operator that allows you to filter the metric based on the result of another metric (query). This query is specified in the filterBy.query object. This functionality - filtering areas based on the result of a different query - is also available in the form of indicator filters defined in the view object.

Arrivals/departures metrics compute the number of, e.g. people which have arrived to or departed from a destination. Whether it is a country, a city, (polygon) or a shop (marker). The syntax of these two metrics is very similar. Apart from this specific syntax, a indicator.content.relations.reversedMetric must be specified in the corresponding indicator:

And vice versa for destinations_indicator.

Also, the data which these metrics work with must have a specific format - mirrored pairs with values for each source/destination node:

1USACanada20 0008 000
2CanadaUSA8 00020 000
3USAUnited Kingdom32 00018 500
4United KingdomUSA18 50032 000

This is a metric which uses metric variables. The resulting sum of an exposure index is simply multiplied by the value substituted in the index_variable. The variables are set in the variables filter in the view object.

Key description


typestringdwh query property, dwh query function, or a number

[dwh query functionproperty, number, variable]


array of dwh property definitions (see the content.content table)

optionsobjectfunction options, allows to specify filters


idstringchoose a custom string identifier for the query property(a-z0-9_-)
typestringdwh query property, dwh query function, or a number

[dwh query functionproperty, number, variable]





string identifier of a dataset property, which the function will be applied to (for type=function)

string with variable name (for type=variable)

long or decimal value (for type=number)




specifies datasets which the metric will be aggregated to

if specified, always null



specifies datasets which the metric is allowed to be aggregated to

array of dataset names, or name prefixes (with the * wildcard)

(warning) only one of acceptAggregateBy and dontAggregateBy keys can be specified




specifies datasets which the metric is not allowed to be aggregated to

array of dataset names, or name prefixes (with the * wildcard)

(warning) only one of acceptAggregateBy and dontAggregateBy keys can be specified




specifies dataset properties not to be explicitly joined into the final query

array of dataset properties

(warning) only one of withoutFilters and acceptFilters keys can be specified





specifies dataset properties to be explicitly joined into the final query

array of dataset properties

(warning) only one of withoutFilters and acceptFilters keys can be specified




filterByobjectobject specifying the filter of the metric


the number of places to round the metric result to

(warning) only for a metric with function_round

Detailed options description


Specifies datasets which the metric will be aggregated to, usually some administrative unit - district, ward, etc.

So far, only null is implemented. That means, do not aggregate to any datasets. For example, we'd like to see a metric that computes the turnover share of one administrative unit, in comparison to the total turnover. Syntax of this metric would be:

acceptAggregateBy and dontAggregateBy:

Specify datasets which the metric is allowed/not allowed to be aggregated to, usually some administrative unit - district, ward, etc.

Some metrics are not computable when aggregated to certain datasets. Say we have a 3-level hierarchical administrative units - districts, wards and postcodes. But the business data is available only up to the 2nd level - wards.

When aggregating to the 3rd level (selecting the postcodes granularity), the metric would not be computable and respective indicator would show the "N/A" result. To avoid this, use acceptAggregateBy or dontAggregateBy

Using these options, the indicator card will show an explanation of why is there no metric result.


withoutFilters has 3 use cases:

  • If the map is zoomed in on a certain level, the map window has its bounding box properties. In this case, withoutFilters is used to not apply the filter to the areas which are out of the current bounding box. This is a performance improvement.
  • If there is an strictly defined metric e.g. "total number of customers", as shown in the example below. After applying a filter, the result of this metric wouldn't make sense. So here, withoutFilters prevents these possible semantic issues.
  • In an indicator, two tables can have a relationship through a catchment area - e.g. the demography of, and the orders made in a certain county. These tables however, are not linked through foreign key, and thus cannot be explicitly joined. If we apply a filter to this indicator, using withoutFilters, we can prevent errors of not finding the join path between orders and demography. This is more of an error evasion technique.

Both withoutFilters and acceptFilters may contain:

  • specific dataset properties "clients.client_id"
  • wildcard on all dataset properties "clients.*"
  • multiple datasets, e.g. "dim_dates*.*" (this would filter out all datasets from the can-dim-dates dimension)
  • all datasets "*.*"

The behaviour of the acceptFilters array is the opposite of withoutFilters. Because sometimes, it is much simpler to define a list of filters to be accepted than those to be ignored.

An example can be seen above in the "Market penetration metric object syntax" code excerpt.


filterBy is a versatile object that provides various ways of filtering the result of the metric.


identifier of a dataset property, which the filter will be applied to






value, by which the property will be filtered

this key is polymorphic - it doesn't have only one type

it can also be a single value, or an array:

  • for the in operator, specify an array of values
  • for all the other operators, use single value

(warning) only one of value and query keys can be specified


query, by which the property will be filtered (see the content.content table)

(warning) only one of value and query keys can be specified


the operator that will be used by the filter

see the available filterBy operators

filterBy examples

DWH query function list

Aggregate functions

Aggregate functions compute a single result from a set of input values. 

function_avgarithmetic average of all input valuesfunction_avg(42.0, 17.38, 87.2, 36.9) = 45,87
function_sumsum of all input valuesfunction_sum(42.0, 17.38, 87.2, 36.9) = 183,48
function_countdistinct count of all input values that are not nullfunction_count("apple", "banana", "apple", "orange", "banana") = 3
function_maxmaximum valuefunction_max(42.0, 17.38, 87.2, 36.9) = 87.2
function_minminimum valuefunction_min(42.0, 17.38, 87.2, 36.9) = 17.38
function_stddev_sampsample standard deviation of the input valuesfunction_stddev_samp(42.0, 17.38, 87.2, 36.9= 29.525620512813
function_stddev_poppopulation standard deviation of the input valuesfunction_stddev_pop(42.0, 17.38, 87.2, 36.9) = 25.569937426595
function_var_sampsample variance of the input valuesfunction_var_samp(42.0, 17.38, 87.2, 36.9) = 871.76226666667
function_var_poppopulation variance of the input values

function_var_pop(42.0, 17.38, 87.2, 36.9) = 653.8217

Window functions

Window functions provide the ability to perform calculations across sets of rows that are related to the current query row.



integer ranging from 1 to the argument value, dividing the partition as equally as possible

input: buckets = 3, values = [15, 73, 54, 33, 24, 80], argument = 73, sort = "asc"

input values are grouped into these buckets = ([15, 24], [33, 54], [73, 80])

result: 3 (argument 73 is in the 3th bucket)

function_rankrank of the current row with gaps

input: values = [15, 73, 54, 33, 24, 15], argument = 54, sort = "asc"

input values sorted = (15, 15, 33, 54, 73, 80)

result: 4 (argument 54 is the 4th element in sorted list of input values)

function_percentilevalue below which a given percentage of observations in a group of observations fall

input: values = [10, 75.2, 35.2, 21, 42.7, 61.1, 105.9], argument = 75.2, sort = "desc"

input values sorted = (105.9, 75.2, 61.1, 42.7, 35.2, 21, 10)

result: 83,3 (number 75.2 is higher than 83,3% of other input values)

function_row_numbera unique number for each row starting with 1. For rows that have duplicate values, numbers are arbitrary assigned.

input: values = [15, 73, 54, 33, 24, 15], argument = 54, sort = "asc"

input values sorted = (15, 15, 33, 54, 73, 80)

result: 4 (argument 54 is the 4th element in sorted list of input values)

Arithmetic functions

Basic arithmetic functions.

  • function_plus, function_minus and function_multiply accept 2 or more arguments
  • function_divide and function_modulo accept exactly 2 arguments


7 + 2 = 9


7 - 2 = 5


7 * 2 = 14

function_divide7 / 2 = 3.5
function_modulo7 mod 2 = 1

Mathematical functions

Basic mathematical functions.


a rounding function (round to specific number of places)

function_round(56.157094235, 1) = 56.2

Conditional functions



defines the default value to use if the result of aggregate function is null

number = 0.0, value = null

function_ifnull(null) = 0.0

Filter operators list


eqis equal

input values: ["apple","orange","banana"], eq = "orange"

result: ["orange"]


input values: ["apple","orange","banana"], ne = "orange"

result: ["apple","banana"]

inin range

input values: [1,2,3,4,5,6,7,8], in = [3,6]

result: [3,4,5,6]

ltlower than

input values: [1,2,3,4,5,6,7,8], lt = 5

result: [1,2,3,4]

ltelower than or equal

input values: [1,2,3,4,5,6,7,8], lte = 5

result: [1,2,3,4,5]

gtgreater than

input values: [1,2,3,4,5,6,7,8], gt = 5

result: [6,7,8]

gtegreater than or equal

input values: [1,2,3,4,5,6,7,8], gt = 5

result: [5,6,7,8]

isNullis null

input values: [1,2,3,4,null,6,7,8], isNull = 2

result: false

isNotNullis not null

input values: [1,2,3,4,null,6,7,8], isNotNull = 2

result: true

inAttributeis in attributesee the usage in a complete metric example above
notInAttributeis not in attributecomplementary to the inAttribute example 

Logical operators

Allow you to create advanced filters and to combine operators.

andlogical conjuction - takes at least two arguments
orlogical disjunction - takes at least two arguments
notnegation - takes one argument

Visual representation

Metrics do not have a visual representation in the application.