However, FP's benefits can be overstated, especially for complex real-world systems. These systems frequently have non-unidirectional dependencies that create challenges in FP. For example, when component A depends on B, but B also depends on a previous state of A, their interrelationship must be hoisted to the edges of the program. This approach reduces races and nondeterministic behavior, but it can make local code harder to understand. As a result, FP's emphasis on granular transformations can increase cognitive load, particularly when dealing with these intricate dependencies.
Effective codebases often blend functional and imperative styles. Imperative code can model circular dependencies and stateful interactions more intuitively. Thus, selectively applying FP techniques within an imperative framework may be more practical than wholesale FP adoption, especially for systems with complex interdependencies.