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

type can also be used as part of a dimension or filter, described on the Dimension, filter, and parameter types documentation page.

type can also be used as part of a dimension group, described on the dimension_group parameter documentation page.

Usage

This page includes details about the various types that can be assigned to a measure. A measure can only have one type, and it defaults to string if no type is specified.

Some measure types have supporting parameters, which are described within the appropriate section.

Measure type categories

Each measure type falls into one of the following categories. These categories determine whether the measure type performs aggregations, the type of fields that the measure type can reference, and whether you can filter the measure type using the filters parameter:

• Aggregate measures: Aggregate measure types perform aggregations, such as sum and average. Aggregate measures can reference only dimensions, not other measures. This is the only measure type that works with the filters parameter.
• Non-aggregate measures: Non-aggregate measures are, as the name suggests, measure types that do not perform aggregations, such as number and yesno. These measure types perform simple transformations, and since they do not perform aggregations, can reference only aggregate measures or previously-aggregated dimensions. You cannot use the filters parameter with these measure types.
• Post-SQL measures: Post-SQL measures are special measure types that perform specific calculations after Looker has generated query SQL. They can reference only numeric measures or numeric dimensions. You cannot use the filters parameter with these measure types.

List of type definitions

average

type: average averages the values in a given field. It is similar to SQL’s AVG function. However, unlike writing raw SQL, Looker will properly calculate averages even if your query’s joins contain fanouts.

The sql parameter for type: average measures can take any valid SQL expression that results in a numeric table column, LookML dimension, or combination of LookML dimensions.

type: average fields can be formatted by using the value_format or value_format_name parameters.

For example, the following LookML creates a field called avg_order by averaging the sales_price dimension, then displays it in a money format ($1,234.56):

average_distinct

type: average_distinct is for use with denormalized datasets. It averages the nonrepeated values in a given field, based on the unique values defined by the sql_distinct_key parameter.

This is an advanced concept which may be more clearly explained with an example. Consider a denormalized table like this:

In this situation you can see that there are multiple rows for each order. Consequently, if you added a simple type: average measure for the order_shipping column, you would get a value of 16.00, even though the actual average is 15.00.

To get an accurate result, you can explain to Looker how it should identify each unique entity (in this case, each unique order) by using the sql_distinct_key parameter. This will calculate the correct 15.00 amount:

Please note that every unique value of sql_distinct_key must have just one corresponding value in sql. In other words, the above example works because every row with an order_id of 1 has the same order_shipping of 10.00, every row with an order_id of 2 has the same order_shipping of 20.00, and so on.

type: average_distinct fields can be formatted by using the value_format or value_format_name parameters.

count

type: count performs a table count, similar to SQL’s COUNT function. However, unlike writing raw SQL, Looker will properly calculate counts even if your query’s joins contain fanouts.

type: count measures do not support the sql parameter, as a type: count measure performs table counts based on the table’s primary key. If you want to perform a table count on a field other than the table’s primary key, use a type: count_distinct measure.

For example, the following LookML creates a field number_of_products:

It is very common to provide a drill_fields (for fields) parameter when defining a type: count measure, so that users can see the individual records that make up a count when they click on it.

When you use a measure of type: count in an Explore, the visualization labels the resulting values with the view name rather than the word “Count.” To avoid confusion, we recommend pluralizing your view name, selecting Show Full Field Name under Series in the visualization settings, or using a view_label with a pluralized version of your view name.

If you want to perform a COUNT (not a COUNT_DISTINCT) on a field that is not the primary key, you can do so using a measure of type: number. See the Help Center article The Difference Between count and count_distinct Measure Types for more information.

You can add a filter to a measure of type: count using the filters parameter.

count_distinct

type: count_distinct calculates the number of distinct values in a given field. It makes use of SQL’s COUNT DISTINCT function.

The sql parameter for type: count_distinct measures can take any valid SQL expression that results in a table column, LookML dimension, or combination of LookML dimensions.

For example, the following LookML creates a field number_of_unique_customers, which counts the number of unique customer IDs:

You can add a filter to a measure of type: count_distinct using the filters parameter.

date

type: date is used with fields that contain dates.

The sql parameter for type: date measures can take any valid SQL expression that results in a date. In practice, this type is rarely used, because most SQL aggregate functions do not return dates. One common exception is a MIN or MAX of a date dimension.

For example, the following LookML creates a field most_recent_order_date by taking the maximum value from the order_date field:

In this example type: date could be omitted, and the value would be treated as a string, because string is the default value for type. However, you will get better filtering capability for users if you use type: date. The convert_tz parameter prevents double time zone conversion. This concept is described in more detail in this Help Center article.

You can use the datatype parameter with type: date to enhance query performance by specifying the type of date data your database table uses.

list

type: list creates a list of the distinct values in a given field. It is similar to MySQL’s GROUP_CONCAT function.

You do not need to include a sql parameter for type: list measures. Instead, you’ll use the list_field parameter to specify the dimension from which you want to create lists.

The usage is:

For example, the following LookML creates a measure name_list based on the name dimension:

Note the following for list:

• The list measure type does not support filtering. You cannot use the filters parameter on a type: list measure.
• The list measure type cannot be referenced using the substitution operator ($). You cannot use the ${} syntax to refer to a type: list measure.

Supported database dialects for list

For Looker to support type: list in your Looker project, your database dialect must also support it. The following table shows which dialects support type: list in Looker 22.6:

max

type: max finds the largest value in a given field. It makes use of SQL’s MAX function.

The sql parameter for type: max measures can take any valid SQL expression that results in a numeric table column, LookML dimension, or combination of LookML dimensions. It will not work with dates; instead, see the example in type: date above.

type: max fields can be formatted by using the value_format or value_format_name parameters.

For example, the following LookML creates a field called largest_order by looking at the sales_price dimension, then displays it in a money format ($1,234.56):

You cannot currently use type: max measures for strings or dates, but you can manually add the MAX function to create such a field, like this:

median

type: median returns the midpoint value for the values in a given field. This is especially useful when the data has a few very large or small outlier values that would skew a simple average (mean) of the data.

Consider a table like this:

For easy viewing, the table is sorted by cost but that does not affect the result. While the average type would return 42 (adding all the values and dividing by 5), the median type would return the midpoint value: 20.00.

If there is an even number of values, then the median value is calculated by taking the mean of the two values closest to the midpoint. Consider a table like this with an even number of rows:

The median, the middle value, is (20 + 80)/2 = 50.

The median is also equal to the value at the 50th percentile.

The sql parameter for type: median measures can take any valid SQL expression that results in a numeric table column, LookML dimension, or combination of LookML dimensions.

type: median fields can be formatted by using the value_format or value_format_name parameters.

Example

For example, the following LookML creates a field called median_order by averaging the sales_price dimension, then displays it in a money format ($1,234.56):

Things to consider for median

If you are using median for a field involved in a fanout, Looker will attempt to use median_distinct instead. However, medium_distinct is supported only for certain dialects. If median_distinct is not available for your dialect, Looker returns an error. Since the median can be considered the 50th percentile, the error states that the dialect does not support distinct percentiles.

Supported database dialects for median

For Looker to support the median type in your Looker project, your database dialect must also support it. The following table shows which dialects support the median type in Looker 22.6:

When there is a fanout involved in a query, Looker tries to convert the median into median_distinct. This is only successful in dialects that support median_distinct.

median_distinct

Use type: median_distinct when your join involves a fanout. It averages the nonrepeated values in a given field, based on the unique values defined by the sql_distinct_key parameter. If the measure does not have a sql_distinct_key parameter, then Looker tries to use the primary_key field.

Consider the result of a query joining the Order Item and Order tables:

In this situation you can see that there are multiple rows for each order. This query involved a fanout because each order maps to several order items. The median_distinct takes this into consideration and finds the median between the distinct values 10, 20, and 50 so you would get a value of 20.

To get an accurate result, you can explain to Looker how it should identify each unique entity (in this case, each unique order) by using the sql_distinct_key parameter. This will calculate the correct amount:

Please note that every unique value of sql_distinct_key must have just one corresponding value in the measure’s sql parameter. In other words, the above example works because every row with an order_id of 1 has the same order_shipping of 10, every row with an order_id of 2 has the same order_shipping of 20, and so on.

type: median_distinct fields can be formatted by using the value_format or value_format_name parameters.

Things to consider for median_distinct

The medium_distinct measure type is supported only for certain dialects. If median_distinct is not available for the dialect, Looker returns an error. Since the median can be considered the 50th percentile, the error states that the dialect does not support distinct percentiles.

Supported database dialects for median_distinct

For Looker to support the median_distinct type in your Looker project, your database dialect must also support it. The following table shows which dialects support the median_distinct type in Looker 22.6:

min

type: min finds the smallest value in a given field. It makes use of SQL’s MIN function.

The sql parameter for type: min measures can take any valid SQL expression that results in a numeric table column, LookML dimension, or combination of LookML dimensions. It will not work with dates; instead, see the example in type: date above.

type: min fields can be formatted by using the value_format or value_format_name parameters.

For example, the following LookML creates a field called smallest_order by looking at the sales_price dimension, then displays it in a money format ($1,234.56):

You cannot currently use type: min measures for strings or dates, but you can manually add the MIN function to create such a field, like this:

number

type: number is used with numbers or integers. A measure of type: number does not perform any aggregation, and is meant to perform simple transformations on other measures. If you are defining a measure based on another measure, the new measure must be of type: number to avoid nested-aggregation errors.

The sql parameter for type: number measures can take any valid SQL expression that results in a number or an integer.

type: number fields can be formatted by using the value_format or value_format_name parameters.

For example, the following LookML creates a measure called total_gross_margin_percentage based on the total_sale_price and total_gross_margin aggregate measures, then displays it in a percentage format with two decimals (12.34%):

The example above also uses the NULLIF() SQL function to remove the possibility of division-by-zero errors.

Things to consider for type: number

There are several important things to keep in mind when using type: number measures:

• A measure of type: number can perform arithmetic only on other measures, not on other dimensions.
• Looker’s symmetric aggregates will not protect aggregate functions in the SQL of a measure type: number when computed across a join.
• The filters parameter cannot be used with type: number measures, but the filters documentation explains a workaround.
• type: number measures will not provide suggestions to users.

percent_of_previous

type: percent_of_previous calculates the percent difference between a cell and the previous cell in its column.

The sql parameter for type: percent_of_previous measures must reference another numeric measure.

type: percent_of_previous fields can be formatted by using the value_format or value_format_name parameters. However, the percentage formats of the value_format_name parameter do not work with type: percent_of_previous measures. These percentage formats multiply values by 100, which skews results of a percent of previous calculation.

In the following example, this LookML creates a measure count_growth based on the count measure:

In the Looker UI, this would look like:

Note that percent_of_previous values depend on sort order. If you change the sort, you must rerun the query to recalculate the percent_of_previous values. In cases where a query is pivoted, percent_of_previous runs across the row instead of down the column. You cannot currently change this behavior.

Additionally, percent_of_previous measures are calculated after data is returned from your database. This means that you should not reference a percent_of_previous measure within another measure; since they might be calculated at different times, you may not get accurate results. It also means that percent_of_previous measures cannot be filtered on.

percent_of_total

type: percent_of_total calculates a cell’s portion of the column total. The percentage is calculated against the total of the rows returned by your query, and not the total of all possible rows. However, if the data returned by your query exceeds a row limit, the field’s values will appear as nulls, since it needs the full results to calculate the percent of total.

The sql parameter for type: percent_of_total measures must reference another numeric measure.

type: percent_of_total fields can be formatted by using the value_format or value_format_name parameters. However, the percentage formats of the value_format_name parameter do not work with type: percent_of_total measures. These percentage formats multiply values by 100, which skews results of a percent_of_total calculation.

In the following example, this LookML creates a measure percent_of_total_gross_margin based on the total_gross_margin measure:

In the Looker UI, this would look like:

In cases where a query is pivoted, percent_of_total runs across the row instead of down the column. If this is not desired, add direction: "column" to the measure definition.

Additionally, percent_of_total measures are calculated after data is returned from your database. This means that you should not reference a percent_of_total measure within another measure; since they might be calculated at different times, you may not get accurate results. It also means that percent_of_total measures cannot be filtered on.

percentile

type: percentile returns the value at the specified percentile of values in a given field. For example, specifying the 75th percentile will return the value that is greater than 75% of the other values in the dataset.

To identify the value to return, Looker calculates the total number of data values and multiplies the specified percentile times the total number of data values. Regardless of how the data is actually sorted, Looker identifies the data values’ relative order in increasing value. The data value that Looker returns depends on whether the calculation results in an integer or not, as discussed below.

If the calculated value is not an integer

Looker rounds the calculated value up and uses it to identify the data value to return. In this example set of 19 test scores, the 75th percentile would be identified by 19 * .75 = 14.25, which means that 75% of the values are in the first 14 data values — below the 15th position. Thus, Looker returns the 15th data value (87) as being larger than 75% of the data values.

If the calculated value is an integer

In this slightly more complex case, Looker returns an average of the data value at that position and the following data value. To understand this, consider a set of 20 test scores, the 75th percentile would be identified by 20 * .75 = 15, which means that the data value at the 15th position is part of the 75th percentile and we need to return a value that is above 75% of the data values. By returning the average of the values at the 15th position (82) and the 16th position (87), Looker ensures that 75%. That average (84.5) does not exist in the set of data values but would be larger than 75% of the data values.

Required and optional parameters

Use the percentile: keyword to specify the fractional value, meaning the percent of the data that should be below the returned value. For example, use percentile: 75 to specify the value at the 75th percentile in the order of data, or percentile: 10 to return the value at the 10th percentile. If you want to find the value at the 50th percentile, you can specify percentile: 50 or simply use the median type.

The sql parameter for type: percentile measures can take any valid SQL expression that results in a numeric table column, LookML dimension, or combination of LookML dimensions.

type: percentile fields can be formatted by using the value_format or value_format_name parameters.

Example

For example, the following LookML creates a field called test_scores_75th_percentile which returns the value at the 75th percentile in the test_scores dimension:

Things to consider for percentile

If you are using percentile for a field involved in a fanout, Looker will attempt to use percentile_distinct instead. If percentile_distinct is not available for the dialect, Looker returns an error. For more information, see the supported dialects for percentile_distinct.

Supported database dialects for percentile

For Looker to support the percentile type in your Looker project, your database dialect must also support it. The following table shows which dialects support the percentile type in Looker 22.6:

percentile_distinct

The type: percentile_distinct is a specialized form of percentile and should be used when your join involves a fanout. It uses the nonrepeated values in a given field, based on the unique values defined by the sql_distinct_key parameter. If the measure does not have a sql_distinct_key parameter, then Looker tries to use the primary_key field.

Consider the result of a query joining the Order Item and Order tables:

In this situation you can see that there are multiple rows for each order. This query involved a fanout because each order maps to several order items. The percentile_distinct takes this into consideration and finds the percentile value using the distinct values 10, 20, 50, 70, and 110. The 25th percentile will return the second distinct value, or 20, while the 80th percentile will return the average of the fourth and fifth distinct values, or 90.

Required and optional parameters

Use the percentile: keyword to specify the fractional value. For example, use percentile: 75 to specify the value at the 75th percentile in the order of data, or percentile: 10 to return the value at the 10th percentile. If you are trying to find the value at the 50th percentile, you can use the median_distinct type instead.

To get an accurate result, specify how Looker should identify each unique entity (in this case, each unique order) by using the sql_distinct_key parameter.

Here’s an example of using percentile_distinct to return the value at the 90th percentile:

Please note that every unique value of sql_distinct_key must have just one corresponding value in the measure’s sql parameter. In other words, the above example works because every row with order_id of 1 has the same order_shipping of 10, every row with an order_id of 2 has the same order_shipping of 20, and so on.

type: percentile_distinct fields can be formatted by using the value_format or value_format_name parameters.

Things to consider for percentile_distinct

If percentile_distinct is not available for the dialect, Looker returns an error. For more information, see the supported dialects for percentile_distinct.

Supported database dialects for percentile_distinct

For Looker to support the percentile_distinct type in your Looker project, your database dialect must also support it. The following table shows which dialects support the percentile_distinct type in Looker 22.6:

running_total

type: running_total calculates a cumulative sum of the cells along a column. It cannot be used to calculate sums along a row, unless the row has resulted from a pivot.

The sql parameter for type: running_total measures must reference another numeric measure.

type: running_total fields can be formatted by using the value_format or value_format_name parameters.

For example, the following LookML creates a measure cumulative_total_revenue based on the total_sale_price measure:

In the Looker UI, this would look like:

Note that running_total values depend on sort order. If you change the sort, you must re-run the query to re-calculate the running_total values. In cases where a query is pivoted, running_total runs across the row instead of down the column. If this is not desired, add direction: "column" to the measure definition.

Additionally, running_total measures are calculated after data is returned from your database. This means that you should not reference a running_total measure within another measure; since they might be calculated at different times, you may not get accurate results. It also means that running_total measures cannot be filtered on.

string

type: string is used with fields that contain letters or special characters.

The sql parameter for type: string measures can take any valid SQL expression that results in a string. In practice, this type is rarely used, because most SQL aggregate functions do not return strings. One common exception is MySQL’s GROUP_CONCAT function, although Looker provides type: list for that use case.

For example, the following LookML creates a field category_list by combining the unique values of a field called category:

In this example type: string could be omitted, because string is the default value for type.

sum

type: sum adds up the values in a given field. It is similar to SQL’s SUM function. However, unlike writing raw SQL, Looker will properly calculate sums even if your query’s joins contain fanouts.

The sql parameter for type: sum measures can take any valid SQL expression that results in a numeric table column, LookML dimension, or combination of LookML dimensions.

type: sum fields can be formatted by using the value_format or value_format_name parameters.

For example, the following LookML creates a field called total_revenue by adding up the sales_price dimension, then displays it in a money format ($1,234.56):

sum_distinct

type: sum_distinct is for use with denormalized datasets. It adds up the nonrepeated values in a given field, based on the unique values defined by the sql_distinct_key parameter.

This is an advanced concept which may be more clearly explained with an example. Consider a denormalized table like this:

In this situation you can see that there are multiple rows for each order. Consequently, if you added a simple type: sum measure for the order_shipping column, you would get a total of 80.00, even though the total shipping collected is actually 30.00.

To get an accurate result, you can explain to Looker how it should identify each unique entity (in this case, each unique order) by using the sql_distinct_key parameter. This will calculate the correct 30.00 amount:

Please note that every unique value of sql_distinct_key must have just one corresponding value in sql. In other words, the above example works because every row with an order_id of 1 has the same order_shipping of 10.00, every row with an order_id of 2 has the same order_shipping of 20.00, and so on.

type: sum_distinct fields can be formatted by using the value_format or value_format_name parameters.

yesno

type: yesno creates a field that indicates if something is true or false. The values appear as Yes and No in the Explore UI.

The sql parameter for a type: yesno measure takes a valid SQL expression that evaluates to TRUE or FALSE. If the condition evaluates to TRUE, Yes is displayed to the user; otherwise, No is displayed.

The SQL expression for type: yesno measures must include only aggregations, which means SQL aggregations or references to LookML measures. If you want to create a yesno field that includes a reference to a LookML dimension or a SQL expression that is not an aggregation, use a dimension with type: yesno, not a measure.

Similar to measures with type: number, a measure with type: yesno doesn’t do any aggregations; it just references other aggregations.

For example, the total_sale_price measure below is a sum of the total sale price of order items in an order. A second measure called is_large_total is type: yesno. The is_large_total measure has a sql parameter that evaluates whether the total_sale_price value is greater than $1,000.

If you want to reference a type: yesno field in another field, you should treat the type: yesno field as a boolean (in other words, as if it contains a true or false value already). For example: