Functional Programming
Function Composition
Combining simple functions into complex pipelines — compose, pipe, and building reusable data transformation chains.
Interview: Functional design pattern — shows ability to build complex logic from simple, testable pieces.
Function composition is the act of combining simple functions to build more complex ones. The output of one function becomes the input of the next. This creates readable, testable data transformation pipelines from small, focused pieces.
Basic Composition
compose(f, g)(x)=f(g(x))— right-to-left composition (mathematical convention)pipe(f, g)(x)=g(f(x))— left-to-right (more readable for data pipelines)- Each function takes one input and produces one output
- Python doesn't have built-in compose/pipe, but they're easy to implement
Pipeline Patterns
- Chain map/filter/reduce for data transformation
- Use generator expressions for lazy pipelines
- Build reusable pipeline functions that accept any data
Interview Insight
Be able to implement a compose/pipe function. Show how breaking complex logic into composable pieces improves testability — each piece can be tested independently.
Use Cases
Data ETL pipelines — extract, transform, load in composable steps
Text processing — strip, normalize, tokenize, filter chains
Validation chains — sequential checks with fail-fast behavior
Image/audio processing — applying filter sequences to media
API middleware chains — composing request/response transformations
Common Mistakes
Composing functions with different input/output types — types must chain correctly
Not handling exceptions in pipelines — one failing function breaks the chain
Using compose when pipe is more readable — left-to-right is more intuitive for data flow
Building one giant pipeline instead of reusable smaller functions
Not considering that eager pipelines build intermediate lists — use generators for laziness