Dimensionality & hierarchies

Use this category when you need to control a formula’s output shape: which dimensions and levels the result has, and at what granularity values exist.

These functions let you aggregate to higher levels, remove detail, expand values to more detailed intersections, or distribute values across level values.

Start here if…

Your result is too aggregated and you lost more detail than expected
Your result is too granular and contains more detail than expected
Two nodes won’t combine cleanly because they have different dimensionality
You want to prepare a node for simulation by creating the full dimensional space

Not here if…

You only need a numeric transformation such as scaling, rounding, or ratios → see Math & numeric
You only need a subset of level values → see Filtering & data shaping

Mental model

Roll up: aggregate to higher levels
Drop: remove levels from a dimension to simplify the shape
Expand: add detail by creating combinations of level values
Distribute: allocate values down to more detailed level values using a distribution key
Rebook: move values between level values within the same level

Common patterns

Aggregate to a higher level

Use when you want values only at selected higher levels.

ROLLUP('Node', "Dimension", "Level")

Match another node’s shape

Use when you want one node aggregated to another node’s dimensionality.

ROLLUP_TO('Node', 'TargetShapeNode')

Remove unwanted granularity

Use when a node carries detail you don’t want in further calculations.

DROPLEVEL('Node', "Level")

Expand to more detail

Use when you need a value available at a more detailed level.

EXPAND('Node', "Level")

Expand only to specific level values

Use when you want targeted expansion without creating the full cross product.

EXPANDSINGLE('Node', "Level", ["A", "B"])

Create an empty simulation space

Use when you want to initialize a node with the required dimensionality.

EXPAND(0, "Level1", "Level2")

Allocate totals down to detail

Use when you want allocations that preserve totals but add detail.

DISTRIBUTE('OriginalNode', 'DistributionNode')

Move values between level values

Use when you need to reclassify values within the same level.

REBOOK('Node', "Level", "OldValue", "NewValue")

Functions in this category

Function	Description
EXPAND	Expands a node or a specified value by adding the full cross product of the specified levels.
EXPANDSINGLE	Expands a node or a value to the specified level values.
DROPLEVEL	Removes one or more levels (and any finer levels in the same dimension) from the node.
ROLLUP	Aggregates a node to only the specified levels.
ROLLUP_TO	Aggregates the first node to match the level dimensionality of the second node.
DISTRIBUTE	Distributes values of one node across levels based on another node’s value distribution.
REDISTRIBUTE	Redistributes a value within specified clusters, weighted by a distribution measure.
REBOOK	Rebooks numeric values from one level value to another within the same level.

Choosing between similar functions

ROLLUP vs DROPLEVEL

Use ROLLUP when you want an explicit aggregation target
Use DROPLEVEL when you want to remove specific granularity from a dimension

EXPAND vs EXPANDSINGLE

Use EXPAND when you want the full cross product for a level
Use EXPANDSINGLE when you only want specific level values

ROLLUP vs ROLLUP_TO

Use ROLLUP when you know the target levels
Use ROLLUP_TO when another node already defines the target shape

DISTRIBUTE vs REDISTRIBUTE

Use DISTRIBUTE to allocate values down using a distribution key
Use REDISTRIBUTE when allocation should happen only within defined clusters

REBOOK vs DISTRIBUTE

Use REBOOK when you want to move values from one level value to another
Use DISTRIBUTE or REDISTRIBUTE when you want to split totals across multiple level values using weights

Pitfalls & troubleshooting

Result is too aggregated: check whether + or - aligned to shared dimensions; make the intended shape explicit with ROLLUP or ROLLUP_TO
Result is too granular or too large: check whether * or / expanded across combined dimensions or whether an EXPAND added more detail than intended
Result is empty after reshaping: confirm referenced levels/level values exist and labels match exactly (spelling/case)
Unexpected allocation results: verify the distribution key node has values at the target detail and that totals behave as expected after distribution
Element-wise adjustment needed: use ADDEACH rather than relying on + constant behavior

Filtering & data shaping: subset selection and fill or pick helpers
Operators: alignment behavior and comparisons used inside formulas
Troubleshooting guide: wrong shape and missing values
Function catalog: full signatures, parameters, and examples