Reaggregation scenarios in practice
The following scenarios showcase the use of the group_aggregate
function in the real world.
We provide them to demonstrate to you how the function works, and the scenarios where it proved useful.
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 assum
oraverage

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

Do not use with conditional operators. For example, the following expression does not reaggregate the data because the
if
precedesgroup_aggregate
:(if(group_aggregate(...)))
Scenario 1: Supplier tendering by job
We have a fact table at a job or supplier tender response aggregation level. There are many rows for each job, where each row is a single row from a supplier. A competitive tender is a situation when multiple suppliers bid on the same job.
Our objective is to determine what percentage of jobs had more than 1 supplier response. We want to see high numbers, which indicate that many suppliers bid on the job, so we can select the best response.
Valid solution
A valid query that meets our objective may look something like this:
sum(group_aggregate(if(sum(# trades tendered ) > 1) then 1 else 0, query_groups() + {claimid, packageid}, query_filters()))
Resolution

The
sum ( # trades tendered )
function aggregates to these attributes:
{claimid, packageid}
The joblevel identifier

query_groups( )
Adds any additional columns in the search to this aggregation. Here, this is the
datelogged
column at the yearly level. 
query_filters ( )
Applies any filters entered in the search. Here, there are no filters.


For each row in this virtual table, the conditional
if() then else
function applies. So, if the sum of tendered responses is greater than 1, then the result returns 1, or else it returns 0. 
The outer function,
sum()
, reaggregates the final output as a single row for eachdatelogged
yearly value.
This reaggregation is possible because the conditional statement is inside the
group_aggregate
function. 
Rather than return a row for each
{claimid,packageid}
, the function returns a single row fordatelogged yearly
. 
The default aggregation setting does not reaggregate the result set.

NonAggregated Result
We include the following result to provide contrast to an example where ThoughtSpot does not reaggregate the result set.
Reaggregation requires the aggregate function, sum
, to precede the group_aggregate
function.
In the following scenario, the next statement is the conditional if
clause.
Because of this, the overall expression does not reaggregate.
The returned result is a row for each {claimid,packageid}
.
sum(if(group_aggregate (sum (# trades tendered), query_groups() + {claimid, packageid}, query_filters ( ) )>1) then 1 else 0)
Scenario 2: Average rates of exchange
The Average rate of exchange calculates for the selected period. These average rates provide a mechanism to hedge the value of loans against price fluctuations in the selected period. We apply the average rate after the aggregation.
The pseudologic that governs the value of loans is sum(loans) * average(rate)
.
The data model has two tables: a primary fact table, and a dimension table for rates
.

The
loans
column is from the primary fact table. 
The
rate
column is from therates
table.
These tables are at different levels of aggregation:

The primary fact table uses a lower level of aggregation, on
product
,department
, orcustomer
. 
The
rates
dimension table use a higher level of aggregation, ondaily
,transaction currency
, orreporting currency
.
The two tables are joined through a relationship join on date
and transaction currency
.
To simplify the scenario, we only use a single reporting currency
.
The join ensures that a single rate value returns each day for each transaction currency.
Valid solution
A valid query that meets our objective may look something like this:
sum(group_aggregate (sum(loans)*average (rate), query_groups () + {transaction_currency}, query_filters () ))
The following search and resulting response returns the dollar value for each year, for each target reporting currency.
Note that the dataset contains both euro (€) and US dollars ($).
The $ Loans Avg.
Rate
calculates the average rate of exchange for the entire period.
The $ Loans Spot Rate
applies the rate of exchange on the day of the transaction.
Resolution

The
sum(loans)
function aggregates to these attributes:
{transaction_currency}
andquery_groups()
Add additional search columns to this aggregation. Here, this at the level of
reporting currency
andyear
. 
query_filters( )
Applies any filters entered in the search. Here, there are no filters.


Similarly, the
average(rate)
function aggregates to these attributes:
{transaction_currency}
andquery_groups()
Add additional search columns to this aggregation. Here, this at the level of
reporting currency
andyear
. 
query_filters( )
Applies any filters entered in the search. Here, there are no filters.


For each row in this virtual table, the exchange rate applies to the sum of loans:
sum(loans) * average(rate)
. 
The outer
sum()
function reaggregates the final output as a single row for each yearly reporting currency value.Note that the default aggregation setting does not reaggregate the result set.
NonAggregated Result
We include the following result to provide contrast to an example where ThoughtSpot does not reaggregate the result set.
Reaggregation requires the aggregate function, sum
, to precede the group_aggregate
function.
In the following scenario, the formula assumes that the default aggregation applies.
Here, the result returns 1 row for each transaction currency
.
group_aggregate (sum(loans )*average (rate ), query_groups() + {transaction_currency}, query_filters())
Scenario 3: Average period value for semiadditive numbers I
Semiadditive numbers may be aggregated across some, but not all, dimensions. They commonly apply to specific time positions. In this scenario, we have daily position values for home loans, and therefore cannot aggregate on the date dimension.
Valid solution
A valid query that meets our objective may look something like this:
average(group_aggregate(sum(loan balance), query_groups() + {date(balance date)}, query_filters()))
Resolution

The
sum(loan balance)
function aggregates to the following attributes:
{date(balance date)}
andquery_groups()
Add additional search columns to this aggregation. Here, this at the
yearly
level. 
query_filters ( )
Applies any filters entered in the search. Here, there are no filters.


The
sum(loan balance)
function returns a result for each row in this virtual table. 
The outer
average()
function reaggregates the final output as a single row for eachyear
value.
Scenario 4: Average period value for semiadditive numbers II
Semiadditive numbers may be aggregated across some, but not all, dimensions. They commonly apply to specific time positions. In this scenario, we have daily position values for home loans, and therefore cannot aggregate on the date dimension.
Here, we consider a somewhat different situation than in Scenario 3. In some financial circumstances, the average daily balance has to be calculated, even if the balance does not exist. For example, if a banking account was opened on the 15th of June, business requirements have to consider all the days in the same month, starting with the 1st of June. Importantly, we cannot add these ‘missing’ data rows to the data set; note that the solution used in Scenario 3 returns an average only for the period that has data, such as June 15th to 30th, not for the entire month of June. The challenge is to ensure that in the daily average formula, the denominator returns the total days in the selected period, not just the days that have transactions:
sum(loans) / sum(days_in_period)
To solve for this, consider the data model:

The fact table
transactions
reports the daily position for each account, and uses aloan
column. 
The dimension table
date
tracks information for each date, starting with the very first transaction, all the way through the most recent transaction. This table includes the expecteddate
column, anddays_in_period
column that has a value of 1 in each row. 
Worksheets use the
date
column with keywords such as weekly, monthly, yearly to change the selected period. 
When users run a search with the monthly keyword, the denominator must reflect the number of days in each month.
Valid solution
A valid query that meets our objective may look something like this:
The following code in the denominator definition returns the total number of days for the period, regardless whether there are transactions, or what filters apply:
group_aggregate (sum(days_in_period),{Date},{})
Resolution

The
sum(days_in_period)
function aggregates to:
{Date}
No other search columns appear.

{}
We require the entire period, so there are no filters.
Note that the
date
keywords yearly, quarterly, monthly, and weekly apply because we use the same column in both the search and the aggregation function. So, the function will result in the following output when it runs with the yearly keyword in search:Year Result 2016
366
2017
365
2018
365
2019
365
2020
366


This data is not reaggregated because we want to return the result at the appropriate
date
level.