Flexible aggregation functions

Typically, the groupings and filters used in a formula use the same fields as columns returned in the search results. The concept of a grouping equates to an attribute column.

For example, in the search revenue ship mode, revenue is the measure, and ship mode is the attribute, or grouping. The result of this search shows total revenue for each ship mode:

The aggregation formulas are described in Overview of aggregate formulas.

ThoughtSpot provides flexible aggregation with the group_* functions. You can use group_* formulas when you want to specify columns and filters to include or ignore in your query.

The group_* formulas use a sub-query to perform these custom aggregations. If the sub-query is at a different level of detail than the original query, ThoughtSpot adds the result column to the result of the original query.

To use the groups and filters, specify them using the query_groups and query_filters keywords, respectively. You can also add or exclude groups or filters.

The group_aggregate function enables you to calculate a result at a specific aggregation level, and then returns it at a different aggregation level. For this reaggregation result to return correctly, follow these syntax guidelines:

For a search on revenue monthly ship mode, you can add a formula to calculate yearly revenue by ship mode:

The same formula can also be written using query_groups() and query_filters() as following:

This is helpful to include the main query groups that are not known at formula creation time. You can use +/- to modify the set of groups included from the query.

Flexible group aggregate formulas allow for flexibility in both groupings and filters. The formulas give you the ability to specify only groupings or only filters.

With query_groups()+ {attribute_column} or query_groups()-{attribute_column}, you can aggregate results while including/excluding a column from the original search.

The query_groups() function returns all attribute columns defined in the base search.

If, for example, you use the condition query_groups() - {region, sku, name}, this changes the level of detail for the group aggregate formula. In this scenario, region, sku, and name have been removed. If these columns are not included in the base search, then this definition is ignored.

Under the new logic, the {region, sku, name} condition fixes the level of detail for the group by columns to the columns defined.

You can combine options to both add and remove columns like in the following example:

query_groups()+ {region} - {sku, name}.

Regarding dates, when query_groups() is defined, the date period defined in the search will be passed into the group function. Assuming the search is monthly, then the group function will also be at the monthly grain. You can use date functions to change the grain, such as {start_of_year(transaction date)}.

With query_filters()+ {filter_condition} or query_filters()-{filter_condition}, you can aggregate the results while including/excluding a filter condition.

Filter condition: Ship Mode='car'

For a search on Category Customer ID sales by customer id and category Ship Mode='car’, you can add a formula to calculate sales by category for each customer as:

In this case, the results will be aggregated based on the dimensions: ‘Category’ and ‘Customer ID’ and filters: ‘air’ and ‘car’.

With query_filters()-{column}, users will be able to aggregate the results while removing any expression related to a column.

Filter condition: Ship Mode='car'

For a search on Customer ID sales by customer id and category Ship Mode='car', you can add a formula to calculate sales for each customer while ignoring the filter on a column as:

In this case, the results will be aggregated based on the dimensions in the search; Customer ID and any filter related to Ship Mode will not be considered while aggregating the results.