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Branching factor

As explained in Data-Oriented Programming: Reduce software complexity

A branching factor is the number of child nodes or partitions produced at each level of a tree-structured data representation.

A branching factor is the number of child nodes produced at each tree level. Increasing it changes the practical cost of traversing persistent data structures without changing logarithmic growth. section 9.2

Two-way versus 32-way branching

For a persistent list of 100,000 elements, binary branching requires about log₂ 100,000, or 16.6, operations to access an element. A branching factor of 32 reduces this to about 3.3 operations because log₃₂ N is five times smaller than log₂ N. section 9.2

The useful trade-off is practical rather than asymptotic:

  • Access becomes more efficient as the tree becomes shallower. section 9.2
  • Updates also improve, although each level copies more elements; CPU architecture affects the practical result. section 9.2

How Data-Oriented Programming: Reduce software complexity covers it

How deep it goes
Explains logarithmic depth, concrete operation counts, and copying trade-offs, then points toward CPU architecture without detailing it.
Examples
Uses persistent lists of 100,000 elements, comparing binary branching with a branching factor of 32.
What it assumes
Assumes basic familiarity with trees, persistent lists, and logarithms.
What you can do after
Evaluate persistent-list access and update trade-offs using branching-factor and logarithmic-depth comparisons.
How it connects
Connects branching factor with persistent data structures, persistent lists, structural sharing, and logarithmic complexity.

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This explainer was generated by Manning's knowledge graph from the text of Data-Oriented Programming: Reduce software complexity. The section links point to the parts of the book it draws on.