Projects: Module 01 — Introduction to Async Python¶

Project ideas sized for the concepts covered in this module (async def, await, asyncio.run(), asyncio.gather(), concurrency model selection). All projects can be completed using only the standard library — no external dependencies required.

Project 1: Async Countdown Clock¶

Difficulty: Beginner Estimated Time: 1–2 hours Concepts: async def, await asyncio.sleep(), asyncio.gather(), cooperative multitasking

Goal: Build a terminal program that runs multiple countdown timers simultaneously. Each timer prints its name and remaining time every second. All timers run concurrently on a single thread.

Requirements: - Define async def countdown(name: str, seconds: int) that prints "{name}: {n}s remaining" every second and then prints "{name}: done!" - Run at least 3 timers with different durations concurrently - The program should finish when the longest timer completes

Expected output (with timers of 3s, 5s, and 2s):

alpha: 3s remaining
beta: 5s remaining
gamma: 2s remaining
alpha: 2s remaining
beta: 4s remaining
gamma: 1s remaining
gamma: done!
alpha: 1s remaining
beta: 3s remaining
...

Project 2: Async Concurrency Benchmarker¶

Difficulty: Beginner–Intermediate Estimated Time: 2–3 hours Concepts: Concurrency model comparison, asyncio.gather(), ThreadPoolExecutor, ProcessPoolExecutor, timing

Goal: Write a benchmarking program that tests the same workload using all three Python concurrency models and prints a comparison table.

Two workloads to implement: 1. I/O-bound: sleep for 0.1 seconds × 50 operations 2. CPU-bound: compute sum(i**2 for i in range(100_000)) × 8 operations

For each workload, measure the time taken by: - Sequential execution - asyncio.gather() (for I/O-bound) - ThreadPoolExecutor - ProcessPoolExecutor

Print a comparison table showing which approach is fastest for each workload type.

This project will concretely validate the theory from the module: async and threads win for I/O; multiprocessing wins for CPU.

Project 3: Simple Async Task Runner CLI¶

Difficulty: Intermediate Estimated Time: 3–4 hours Concepts: asyncio.create_task(), asyncio.gather(), error handling, task naming

Goal: Build a command-line tool that reads a list of "jobs" from a YAML or JSON file, runs them as concurrent async tasks, and prints a status report when done.

Each "job" in the file describes a shell command to run as a subprocess.

Requirements: - Parse a jobs file with this structure:

[
  {"name": "lint", "command": "python -m flake8 src/"},
  {"name": "test", "command": "python -m pytest tests/"},
  {"name": "type-check", "command": "python -m mypy src/"}
]

Why this is interesting: asyncio.create_subprocess_exec() is the async-native way to run subprocesses — it does not block the event loop while the subprocess runs. This is a real-world use case for asyncio that does not require any third-party libraries.