Rollup

Supported in: Batch

Performs the specified aggregations on the input dataset at different levels of granularity, providing both intermediate and super aggregates.

Transform categories: Aggregate

Declared arguments

Aggregations: List of aggregations to perform on the dataset.
List<Expression<AnyType>>
Dataset: Dataset to perform rollup on.
Table
Rollup columns: List of columns to rollup the dataset by when aggregating. If empty, no roll up is applied.
List<Column<AnyType>>

Examples

Example 1: Base case

Argument values:

Aggregations: [
alias(
alias: max_price,
expression:
max(
expression: price,
),
)]
Dataset: ri.foundry.main.dataset.rollupBaseCase
Rollup columns: [city]

Input:

city	model	price	store
London	new phone	900.0	MegaMart
London	new phone	850.75	AA
London	new phone	870.75	ABC Zone
San Francisco	new phone	1000.0	Prescos
San Francisco	new phone	950.25	XZY Force
San Francisco	new phone	1105.7	Phone Mart
London	forestX 20	750.1	MegaMart
London	forestX 20	690.0	AA
London	forestX 20	730.0	ABC Zone
San Francisco	forestX 20	890.4	Prescos
San Francisco	forestX 20	900.1	XZY Force
San Francisco	forestX 20	1050.75	Phone Mart

Output:

city	max_price
London	900.0
San Francisco	1105.7
null	1105.7

Example 2: Base case

Argument values:

Aggregations: [
alias(
alias: mean_price,
expression:
mean(
expression: price,
),
)]
Dataset: ri.foundry.main.dataset.rollupBaseCase
Rollup columns: [city, model]

Input:

city	model	price	store
London	new phone	900.0	MegaMart
London	new phone	850.75	AA
London	new phone	870.75	ABC Zone
San Francisco	new phone	1000.0	Prescos
San Francisco	new phone	950.25	XZY Force
San Francisco	new phone	1105.7	Phone Mart
London	forestX 20	750.1	MegaMart
London	forestX 20	690.0	AA
London	forestX 20	730.0	ABC Zone
San Francisco	forestX 20	890.4	Prescos
San Francisco	forestX 20	900.1	XZY Force
San Francisco	forestX 20	1050.75	Phone Mart

Output:

city	model	mean_price
London	new phone	873.8333333333334
London	forestX 20	723.3666666666667
London	null	798.6
San Francisco	new phone	1018.65
San Francisco	forestX 20	947.0833333333334
San Francisco	null	982.8666666666667
null	null	890.7333333333335

Example 3: Base case

Argument values:

Aggregations: [
alias(
alias: max_price,
expression:
max(
expression: plan_prices,
),
)]
Dataset: ri.foundry.main.dataset.rollupComplexCase
Rollup columns: [model]

Input:

city	model	plan_prices	stores
London	new phone	[ 900.0, 1080.23, 899.99 ]	MegaMart
London	new phone	[ 850.75, 800.78, 999.99 ]	AA
London	new phone	[ 870.75, 775.0, 804.48 ]	ABC Zone
San Francisco	new phone	[ 910.0, 1030.23, 1100.5 ]	Prescos
San Francisco	new phone	[ 1020.0, 989.99, 1130.0 ]	XZY Force
San Francisco	new phone	[ 1020.0, 1065.25, 1110.99 ]	Phone Mart
London	forestX 20	[ 738.5, 701.25, 834.0 ]	MegaMart
London	forestX 20	[ 703.75, 821.0, 712.5 ]	AA
London	forestX 20	[ 692.0, 787.5, 841.75 ]	ABC Zone
San Francisco	forestX 20	[ 1003.25, 997.75, 893.5 ]	Prescos
San Francisco	forestX 20	[ 981.5, 872.25, 1035.0 ]	XZY Force
San Francisco	forestX 20	[ 928.0, 995.25, 1098.5 ]	Phone Mart

Output:

model	max_price
new phone	[ 1020.0, 1065.25, 1110.99 ]
forestX 20	[ 1003.25, 997.75, 893.5 ]
null	[ 1020.0, 1065.25, 1110.99 ]

Example 4: Null case

Argument values:

Aggregations: [
alias(
alias: max_price,
expression:
max(
expression: price,
),
)]
Dataset: ri.foundry.main.dataset.rollupNullCase
Rollup columns: [city, model]

Input:

city	model	price	stores
London	new phone	null	MegaMart
London	new phone	850.75	AA
London	new phone	870.75	ABC Zone
San Francisco	new phone	null	Prescos
San Francisco	new phone	null	XZY Force
San Francisco	new phone	null	Phone Mart
London	forestX 20	750.1	MegaMart
London	forestX 20	690.0	AA
London	forestX 20	null	ABC Zone
San Francisco	forestX 20	890.4	Prescos
San Francisco	forestX 20	null	XZY Force
San Francisco	forestX 20	1050.75	Phone Mart

Output:

city	model	max_price
London	new phone	870.75
London	forestX 20	750.1
London	null	870.75
San Francisco	new phone	null
San Francisco	forestX 20	1050.75
San Francisco	null	1050.75
null	null	1050.75

Example 5: Edge case

Argument values:

Aggregations: [
alias(
alias: mean_price,
expression:
mean(
expression: price,
),
)]
Dataset: ri.foundry.main.dataset.rollupBaseCase
Rollup columns: []

Input:

city	model	price	store
London	new phone	900.0	MegaMart
London	new phone	850.75	AA
London	new phone	870.75	ABC Zone
San Francisco	new phone	1000.0	Prescos
San Francisco	new phone	950.25	XZY Force
San Francisco	new phone	1105.7	Phone Mart
London	forestX 20	750.1	MegaMart
London	forestX 20	690.0	AA
London	forestX 20	730.0	ABC Zone
San Francisco	forestX 20	890.4	Prescos
San Francisco	forestX 20	900.1	XZY Force
San Francisco	forestX 20	1050.75	Phone Mart

Output:

mean_price
890.7333333333335

Example 6: Edge case

Argument values:

Aggregations: [
alias(
alias: max_price,
expression:
max(
expression: price,
),
)]
Dataset: ri.foundry.main.dataset.rollupEmptyCase
Rollup columns: [city, model]

Input:

city	model	price	store

Output:

city	model	max_price

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