Flexible aggregation functions

Use the group_aggregate function in ThoughtSpot to aggregate measures at different granularities than the dimensions used in the search columns.

How aggregation formulas work

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:

revenue ship mode

$ a

air

$ r

rail

$ t

truck

$ s

sea transport

The aggregation formulas are described in Overview of aggregate formulas.

About flexible aggregation

ThoughtSpot provides flexible aggregation with the group_* function. 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 less detailed than the original query, ThoughtSpot adds the result column to the result of original query. When the sub-query is at a finer detail level than the original query, ThoughtSpot re-aggregates to formula’s results to match the groupings of the original query.

To ensure that the level of aggregation of the sub-query is returned the sub-query grouping columns must be included in the search query and/or visualized chart columns.

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.

Best practices for flexible aggregations

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:

  • Wrap group_aggregate in an aggregate function, such as sum or average

  • The wrapping function must be the immediate preceding function, such as sum(group_aggregate(...))

Examples

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

group_aggregate(
  sum(revenue),
  {ship mode, year(commit date)},
  {}
)

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

group_aggregate(
  sum(revenue),
  query_groups() - {commit date} + {year(commit date)},
  {}
)

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.

When group formula results are finer-grained than the search

With the flexibility of groupings for group formulas, the computed column created by a formula can be finer or coarser grained than the search itself. Note that ThoughtSpot by default aggregates formula results to the level of detail defined in the search bar. To increase the level of detail in your results, you must add filters to your search as well as defining them in your formula.

For example, you can have a search that shows total yearly sales and a formula that computes total sales for each month (a finer-grained calculation than the search).

In such cases, if an additional aggregation is specified by the formula, the results get reaggregated.

Reaggregation can be applied in either of these ways:

  • You can add an aggregation keyword just before a formula column in a search. For example, in this search we’ve added the keyword min just before our formula for monthly_sales:

    sum revenue yearly min monthly_sales

    where, the monthly_sales formula is written as:

    group_aggregate(
  sum(revenue),
  {start_of_month(date)},
  {}
)

  • You can create a separate formula, such as in this search for:

    sum revenue yearly min_monthly_sales

    where, the min_monthly_sales formula is written as:

    min(monthly_sales)

If no aggregation is specified, then the search query aggregates to the level of detailed defined in the search bar. In the following search:

sum revenue yearly monthly_sales

the original query is computed at a yearly grain instead of monthly.

Reaggregation scenarios

Some scenarios require aggregation on an already aggregated result.

For example, computing minimum monthly sales per ship mode, requires two aggregations:

  • the first aggregation of sum to compute total monthly sales per ship mode.

  • the second aggregation of min to compute the minimum sale that happened for any given month for that ship mode.

An example of this is this search:

ship mode min monthly_sales

where the formula monthly_sales is written as:

group_aggregate(
  sum(revenue),
  query_groups() + {start_of_month(date)},
  {}
)

For more extensive examples of using the group-aggregate function, we encourage you to see Reaggregation scenarios in practice

Query groups and query filters

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.

Query filters

With query_filters()+{filter_condition} or query_filters()-{filter_condition}, users will be able to aggregate the results while including/excluding a filter condition.

Including 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:

sales by Customer ID and Category= group_aggregate (
  sum(Sales),
  {Category, Customer ID },
  query_filters()+{Ship Mode='air'}
  )

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

Subtracting or removing a filter condition

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:

sales by Customer ID and Category= group_aggregate (
  sum(Sales),
  {Customer ID, Category },
  query_filters()-{Ship Mode}
  )

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.

Related information

Related information

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