Functions the Pythonic Way

Common Decorator Patterns

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LearnStep 1 of 3

Essential Decorator Patterns

Learning Objectives

•Implement decorators for monitoring execution time and logging
•Optimize function performance using lru_cache
•Build resilience with retry logic and exponential backoff
•Enforce input constraints using validation decorators

Lesson Outline

LearnStep 1/3

Essential Decorator Patterns

Decorators allow you to separate cross-cutting concerns—like logging, error handling, and caching—from your core business logic. This leads to cleaner, more maintainable code.

1. Logging and Timing

A common use case is tracking how long a function takes to execute. This is useful for profiling and monitoring.

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2. Caching with lru_cache

Python's functools module provides the @lru_cache (Least Recently Used) decorator. It memoizes function return values based on arguments, saving time on expensive computations.

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3. Retry with Backoff

For network operations or flaky services, a retry decorator can automatically attempt the operation again if it fails, often waiting longer between each attempt (exponential backoff).

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4. Input Validation

Decorators can enforce preconditions on arguments before the function even runs, keeping the function body focused on logic.

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