filters (for fields)

This page refers to the filters parameter that is part of a measure.

filters can also be used as part of a native derived table, described on the explore_source parameter documentation page.

filters can also be used as part of a dashboard, described on the Dashboard parameters documentation page.

filters can also be used as part of a dashboard element. A representative example of its usage is provided on the documentation page for column chart elements.

Usage

view: view_name {
  measure: field_name {
    filters: [dimension_name: "filter expression", dimension_name: "filter expression", ... ]
  }
}
Hierarchy
view
Field Type
filters
Possible Field Types
Measure

Accepts
A set of dimension names and corresponding Looker filter expressions

Definition

filters is an optional list of filter expressions that are applied to a measure calculation. It works only with the following measure types that perform aggregation:

  • type: count
  • type: count_distinct
  • type: sum
  • type: average

The syntax for filters is:

filters: [dimension_name_1: "Looker filter expression", dimension_name_2: "Looker filter expression", ...]

Refer to Looker's filter notation for details on how to write valid filter expressions. Place the entire filter expression in quotation marks, as shown in the following examples.

You can also use filters with Liquid parameters and templated filters to create dynamic filtered measures with a filter value that can change dynamically based on user input on a dashboard or Look. This approach allows users to select filter values that only apply to a specific measure, without filtering the entire query in a global WHERE clause. See the Creating a dynamic filtered measure section later on this page for reference.

Examples

There are many ways to use filters for measures. The examples below outline some solutions for common use cases:

Filtering on multiple fields

In this example, the measure will count distinct IDs that were created in the past seven days and were not from users with disabled accounts. This example also illustrates how to add multiple filters to a field. Each filter expression requires its own complete filters parameter:

measure: this_week_count {
  type: count_distinct
  sql: ${TABLE}.id ;;
  filters: [created_date: "7 days", user.status: "-disabled"]
  # Reference fields from other joined views with view_name.field_name syntax
  # Minus sign means "not" in this case, but check notation docs for details
}

Filtering on a yesno dimension

As another example, consider data that includes one row per person, who is either a regular employee or a contractor. There is a field, is_contractor, of type yesno, which contains yes if the person is a contractor. This measure will count only rows for contractors:

measure: contractor_count {
  type: count
  filters: [is_contractor: "yes"]
}

Adding multiple filter values

In this next example, the filters expression contains multiple values. The measure will count all rows that match any of the four values in the state field:

measure: customers_by_state {
  type: count
  filters: [state: "California, Nevada, Washington, Oregon"]
}

Filtering NULL values

In this example, the measure will count distinct IDs that were created in the past seven days and include a value in the user.email_address field that is not NULL:

measure: this_week_count_with_email {
  type: count_distinct
  sql: ${TABLE}.id ;;
  filters: [created_date: "7 days", user.email_address: "-NULL"]
}

Because the field user.email_address is of type string, the "-NULL" syntax is used. See the Looker filter expressions documentation page for more information.

Adding numeric filter conditions

In this example, the filter is given a numeric value. The measure will add only orders that have a price of more than 100:

measure: total_amt_large_orders {
  type: sum
  filters: [orders.price: ">100"]
  sql: ${orders.price} ;;
}

Using advanced filter syntax

In this example, the filter is given a range of numeric values. The measure will total orders from customers in the 18-25 age group:

measure: sales_18_to_25 {
  type: sum
  filters: [customers.age: ">=18 AND <=25"]
  sql: ${orders.price} ;;
}

Creating a dynamic filtered measure

In this example, a filter gives a user the option to see a count of orders — filtered by the status the user chooses — on a Look or dashboard filter:

First, create a filter-only field that allows the user to select a value:

  filter: status_filter {
    type: string
    suggest_dimension: status
  }

Next, create a hidden dimension that leverages templated filters to capture the user selection from the previous filter-only field. This dimension will return yes whenever a value is selected in the filter field:

  dimension: status_satisfies_filter {
    type: yesno
    hidden: yes
    sql: {% condition status_filter %} ${status} {% endcondition %} ;;
  }

Lastly, create a measure that filters on the type: yesno dimension:

  measure: count_dynamic_status {
    type: count
    filters: [status_satisfies_filter: "yes"]
  }

If a user chooses not to select a value for the filter-only field, the SQL will default to 1=1, which will not affect the measure values.

Common challenges

Don't use filters with measures of type: number

Many users try to use filters with measures of type: number, which does not work:

# Will NOT work
measure: total_food_profit {
  type: number
  sql: ${total_revenue} - ${total_cost} ;;
  filters: [segment: "food"]
}
measure: total_revenue {
  type: sum
  sql: ${revenue} ;;
}
measure: total_cost {
  type: sum
  sql: ${cost} ;;
}

Instead, apply a filters parameter to any of the individual measures that make up the measure of type: number, as follows:

# Will work
measure: total_food_profit {
  type: number
  sql: ${total_food_revenue} - ${total_food_cost} ;;
}
measure: total_food_revenue {
  type: sum
  sql: ${revenue} ;;
  filters: [segment: "food"]
}
measure: total_food_cost {
  type: sum
  sql: ${cost} ;;
  filters: [segment: "food"]
}