Using Decorator @-Notation with Dask in Python 2026

Using Decorator @-Notation with Dask in Python 2026 – Best Practices

The @ notation (syntactic sugar for decorators) makes code much cleaner and more readable. When working with Dask, using the @ notation correctly with dask.delayed, custom decorators, and Dask collections is a key skill for writing elegant parallel code in 2026.

TL;DR — How to Use @-Notation Effectively

• @decorator is equivalent to func = decorator(func)
• Order matters when stacking multiple decorators
• Always combine with @wraps for metadata preservation
• Works beautifully with @delayed and custom timing/retry decorators

1. Basic @-Notation with Dask

from dask import delayed
from functools import wraps
import time

def timer(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        print(f"⏱️  {func.__name__}() took {time.perf_counter() - start:.4f}s")
        return result
    return wrapper

# Clean @-notation usage
@delayed
@timer
def heavy_computation(n: int):
    """Simulate heavy calculation."""
    return sum(i**2 for i in range(n))

# Usage
result = heavy_computation(50_000).compute()

2. Stacked Decorators with Proper Order

@delayed                     # Bottom decorator runs first
@timer                       # Middle
@retry(max_attempts=3)       # Top decorator runs last
def fetch_data(url: str):
    """Fetch data from external API."""
    import requests
    response = requests.get(url, timeout=5)
    response.raise_for_status()
    return response.json()

# Execution order: fetch_data → retry → timer → delayed
result = fetch_data("https://api.example.com/data").compute()

3. Custom Decorator Factory with @-Notation

def run_n_times(n: int = 2):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for i in range(n):
                print(f"Run {i+1}/{n} of {func.__name__}")
                result = func(*args, **kwargs)
            return result
        return wrapper
    return decorator

@delayed
@run_n_times(3)
def simulate_experiment():
    return "experiment completed"

result = simulate_experiment.compute()

4. Best Practices for @-Notation with Dask in 2026

• Always place @wraps(func) in your custom decorators
• Understand decorator execution order: bottom-to-top
• Combine @delayed with other decorators for clean parallel pipelines
• Use meaningful decorator names (e.g., @timer, @retry, @cache)
• Keep the number of stacked decorators reasonable (usually 2–4 max)
• Use the Dask Dashboard to verify that your decorated functions appear with correct names

Conclusion

The @ notation makes Dask code significantly cleaner and more Pythonic. In 2026, combining the @ syntax with dask.delayed and well-written custom decorators is the standard way to build readable, maintainable, and high-performance parallel applications.

Next steps:

• Review your current Dask code and convert manual decorator application to clean @ notation
• Related articles: Parallel Programming with Dask in Python 2026 • Decorators in Python 2026 • Deferring Computation with `delayed` in Dask – Python 2026 Best Practices