Passing dynamic parameters in a query

In some cases we may want to let a user select a filter value and be able to use that value in calculations without filtering the entire query.

Use case

In some cases we may want to let a user select a filter value and be able to use that value in calculations without filtering the entire query.

In this example, we want to know the ratio between the number of people in a particular city and the total number of women in the country. The user can specify the city for the filter. The trick is to get the value of the city from the user and use it in the calculation. In the recipe below, we can learn how to join the data table with itself and reshape the dataset!

This pattern only allows users to choose from values that already exist in the data set. Rather than injecting arbitrary user input into the query, this method involves filtering the data based on the user’s input and utilizing a single value result in a calculation.

Data modeling

Essentially what we will be doing is allowing the user to select a specific city value, then cross joining that value with the rows in the data table. This will maintain the orginal number of rows in the dataset while adding a new column that has the value that the user chose. This will allow us to use that value in our calculations. In this case, we will use that value to filter a single metric so that we can compare that metric with the whole population.

Let’s explore the users cube data that contains various information about users, including city and gender:

id	city	gender	name
1	Seattle	female	Wendell Hamill
2	Chicago	male	Rahsaan Collins
3	New York	female	Megane O’Kon
…	…	…	…

To calculate the ratio between the number of women in a particular city and the total number of people in the country, we need to define three measures, one of which uses the city value that the user chose.

In order to prevent filtering the whole dataset with the user-selected value, we will need to define a new dimension that, when filtered on, only filters a specific part of the query. We will use this new filter field along with the FILTER_PARAMS parameter in the sql of the cube. This will allow us to apply to the filter to a subquery rather than the whole query so that it doesn’t affect other calculations. In this use case, we can join the data table with itself to create a new city_filter column with a single value that the user chose so that we can use it in other calculations.

YAML

cubes:
  - name: users
    sql: |
      WITH data AS (
        SELECT
          users.id AS id,
          users.city AS city,
          users.gender AS gender
        FROM public.users
      ),
      cities AS (
        SELECT city
        FROM data
        WHERE {FILTER_PARAMS.users.city.filter('city')}
      ),
      grouped AS (
        SELECT
          cities.city AS city_filter,
          data.id AS id,
          data.city AS city,
          data.gender AS gender
        FROM cities, data
        GROUP BY 1, 2, 3, 4
      )
      SELECT *
      FROM grouped

    measures:
      - name: total_number_of_women
        sql: id
        type: count
        filters:
          - sql: "gender = 'female'"

      - name: number_of_people_of_any_gender_in_the_city:
        sql: id
        type: count
        filters:
          - sql: "city = city_filter"

      - name: ratio
        title: Ratio Women in the City to Total Number of People
        sql: |
          1.0 * {number_of_people_of_any_gender_in_the_city} /
          {total_number_of_women}
        type: number

    dimensions:
      - name: city_filter
        sql: city_filter
        type: string

The above code shows very clearly what is happening, but it is even simplier to define the sql parameter in the following way:

YAML

cubes:
  - name: users
    sql: |
      WITH 
      city AS (
        SELECT DISTINCT city AS city_filter
        FROM public.users
        WHERE {FILTER_PARAMS.users.city.filter('city')}
        )
      SELECT city.city_filter, users.*
      FROM city, public.users

Result

By joining the data table with itself and filtering on city_filter, we get the ratio for the chosen city without affecting the total denominator. For example, when filtering for Seattle:

total_number_of_women	number_of_people_in_city	ratio
259	99	38.22%