# # Dependency graph

For accuracy and performance, HyperFormula needs to process cells in a correct and optimal order. For example: in formula `C1=A1+B1`, cells `A1` and `B1` need to be evaluated before `C1`.

To find the right order of processing cells, HyperFormula builds a dependency graph (opens new window) which captures relationships between cells.

## # Cells in the dependency graph

In the dependency graph, each spreadsheet cell is represented by a separate node.

Nodes `X` and `Y` are connected by a directed edge if and only if the formula in cell `X` includes the address of cell `Y`.

## # Ranges in the dependency graph

If formulas in the spreadsheet include ranges, each range is represented by a separate node. The dependency graph may also contain ranges that are not used by any formula, for better optimization.

Range nodes can be connected to cell nodes and to other range nodes. ### # Optimizations for large ranges

In many applications, you may want to use formulas that depend on a large range of cells. For example, the formula `SUM(A1:A100)+B5` depends on 101 cells, and it needs to be represented in the dependency graph accordingly.

An interesting optimization challenge arises when there are multiple cells that depend on large ranges. For example, consider the following use-case:

• `B1=SUM(A1:A1)`
• `B2=SUM(A1:A2)`
• `B3=SUM(A1:A3)`
• ...
• `B100=SUM(A1:A100)`

The problem is that there are `1+2+3+...+100 = 5050` dependencies for such a simple situation. In general, for `n` such rows, the engine would need to add `n*(n+1)/2 ≈ n²` arcs in the graph. This value grows much faster than the size of data, meaning the engine would not be able to handle large data sets efficiently.

A solution to this problem comes from the observation that there is a way to rewrite the above formulas to equivalent ones, which will be more compact to represent. Specifically, the following formulas would compute the same values as the ones provided previously:

• `B1=A1`
• `B2=B1+A2`
• `B3=B2+A3`
• ...
• `B100=B99+A100`

Whereas this example is too specialized to provide a useful rule for optimization, it shows the main idea behind efficient handling of multiple ranges: to represent a range as a composition of smaller ranges.

In the adopted implementation, every time the engine encounters a range, say `B5:D20`, it checks if it has already considered the range which is one row shorter. In this example, it would be `B5:D19`. If so, then it represents `B5:D20` as the composition of a range `B5:D19` and three cells in the last row: `B20`,`C20` and `D20`. More generally, the result of any associative operation is obtained as the result of operations for these small rows. There are many examples of such associative functions: `SUM`, `MAX`, `COUNT`, etc. As one range can be used in different formulas, we can reuse its node and avoid duplicating the work during computation.

## # Getting the immediate precedents of a cell or a range

To get the immediate precedents of a cell or a range (the in-neighbors of the cell node or the range node), use the `getCellPrecedents()` method:

``````const hfInstance = HyperFormula.buildFromArray( [ ['1', '=A1', '=A1+B1'] ] );

hfInstance.getCellPrecedents({ sheet: 0, col: 2, row: 0});
// returns [{ sheet: 0, col: 0, row: 0}, { sheet: 0, col: 1, row: 0}]
``````

## # Getting the immediate dependents of a cell or a range

To get the immediate dependents of a cell or a range (the out-neighbors of the cell node or the range node), use the `getCellDependents()` method:

``````const hfInstance = HyperFormula.buildFromArray( [ ['1', '=A1', '=A1+B1'] ] );

hfInstance.getCellDependents({ sheet: 0, col: 2, row: 0});
// returns [{ sheet: 0, col: 0, row: 0}, { sheet: 0, col: 1, row: 0}]
``````

## # Getting all precedents of a cell or a range

To get all precedents of a cell or a range (all precedent nodes reachable from the cell node or the range node), use the `getCellPrecedents()` method to implement a Breadth-first search (BFS) (opens new window) algorithm:

`````` 1      AllCellPrecedents={start}
2      let Q be an empty queue
4      Q.enqueue(start)
5      while Q is not empty do
6          cell := Q.dequeue()
7          S := getCellPrecedents(cell)
9          for all cells c in S do:
10              if c is not in AllCellPrecedents then:
11                  insert w to AllCellPrecedents
12                  Q.enqueue(c)
``````

## # Getting all dependents of a cell or a range

To get all dependents of a cell or a range (all dependent nodes reachable from the cell node or the range node), use the `getCellDependents()` method to implement a Breadth-first search (BFS) (opens new window) algorithm:

`````` 1      AllCellDependents={start}
2      let Q be an empty queue
4      Q.enqueue(start)
5      while Q is not empty do
6          cell := Q.dequeue()
7          S := getCellDependents(cell)
9          for all cells c in S do:
10              if c is not in AllCellDependents then:
11                  insert w to AllCellDependents
12                  Q.enqueue(c)
``````