How do generators and iterators simplify concurrency in Python?

1 What are generators and iterators?

Generators and iterators are two types of objects that can produce a sequence of values on demand, without storing them all in memory. Generators are functions that use the yield keyword to pause and resume their execution, returning one value at a time. Iterators are objects that implement the __iter__ and __next__ methods, which allow them to be iterated over using a for loop or a next function. Both generators and iterators support lazy evaluation, which means they only compute the next value when it is needed, not in advance.

2 How do generators and iterators enable concurrency?

Generators and iterators enable concurrency by allowing you to write code that can suspend and resume its execution, without blocking the main thread or creating new threads. For example, you can use a generator to fetch data from a web service, yielding the result when it is available, and continuing with other tasks in the meantime. You can also use an iterator to process a large file, reading one line at a time, and avoiding loading the whole file into memory. Generators and iterators can also communicate with each other, using the send and throw methods, creating coroutines that can exchange data and control flow.

3 What is the async/await syntax?

The async/await syntax is a feature of Python 3.5 and above that makes writing concurrent code easier and more readable. It allows you to mark a function as async, which means it can use the await keyword to pause its execution until a coroutine, a generator, or an awaitable object is done. The await keyword also returns the value of the awaited expression, which can be assigned to a variable or used in an expression. The async/await syntax can be used with generators and iterators, as well as with built-in modules like asyncio, which provide tools for managing concurrent tasks, events, and loops.

4 How do you use generators and iterators with async/await?

To use generators and iterators with async/await, you need to follow some rules and conventions. First, you need to use the async def syntax to define an async generator function, which can yield values and use await expressions. Second, you need to use the async for syntax to iterate over an async generator or an async iterator, which can produce values asynchronously. Third, you need to use the async with syntax to manage context managers that support async operations, such as opening and closing files or sockets. Fourth, you need to use the await syntax to call other async functions or coroutines, which can return values or raise exceptions.

5 What are the benefits of using generators and iterators with async/await?

Using generators and iterators with async/await can bring many advantages to your concurrent Python programs. Writing concise and expressive code that looks like synchronous code but can handle multiple tasks simultaneously is one of the benefits. You can also avoid the overhead and complexity of creating and managing threads, locks, queues, or events by relying on the event loop to handle scheduling and execution. Furthermore, generators and iterators are powerful and flexible, producing infinite or finite sequences, handling exceptions, and interacting with other coroutines. Finally, there are existing modules and libraries that support async/await such as asyncio, aiohttp, or asyncpg which provide high-level abstractions and interfaces for common concurrent tasks.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?