In practice, what are the main uses for the "yield from" syntax

ghz 3months ago ⋅ 86 views

In practice, what are the main uses for the "yield from" syntax in Python 3.3?

I'm having a hard time wrapping my brain around PEP 380.

  1. What are the situations where yield from is useful?
  2. What is the classic use case?
  3. Why is it compared to micro-threads?

So far I have used generators, but never really used coroutines (introduced by PEP-342). Despite some similarities, generators and coroutines are basically two different concepts. Understanding coroutines (not only generators) is the key to understanding the new syntax.

IMHO coroutines are the most obscure Python feature, most books make it look useless and uninteresting.

Answers

PEP 380, which introduces the yield from syntax, extends the behavior of Python generators to allow for delegation to sub-generators or coroutines. This enhancement makes it easier to compose generators and enables powerful coroutine-like behavior. To fully understand yield from, you need to grasp both its utility in generators and its role in coroutines.

1. Situations Where yield from is Useful

yield from simplifies the delegation of part of a generator's work to another generator or iterator. Without yield from, manually yielding values from a sub-generator in a parent generator would require boilerplate code to loop over the sub-generator and yield each value one by one.

Key scenarios where yield from is useful:

  • Delegating iteration to another generator or iterator: When you want a generator to yield all values from a sub-generator.
  • Simplifying recursive generator delegation: Especially useful in scenarios involving recursive generators.
  • Managing coroutines with simplified control flow: yield from allows coroutines to return values and propagate exceptions directly between coroutines.

2. Classic Use Case of yield from

The classic use case of yield from involves refactoring a generator that delegates part of its functionality to another generator. Consider a case where you have a generator that needs to yield values from a sub-generator.

Without yield from:

def gen1():
    yield 1
    yield 2

def gen2():
    yield 'a'
    for value in gen1():
        yield value
    yield 'b'

for v in gen2():
    print(v)

In this example, the gen2 function has to manually iterate through gen1 and yield each value. The output is:

a
1
2
b

With yield from:

def gen2():
    yield 'a'
    yield from gen1()  # Delegates to gen1
    yield 'b'

for v in gen2():
    print(v)

The result is the same, but yield from eliminates the need to manually loop through gen1. This is both simpler and more readable.

3. Why yield from is Compared to Micro-threads

Micro-threads (also called fibers or green threads) are lightweight threads that allow concurrency without the overhead of traditional OS-level threads. The comparison with yield from comes from its ability to:

  • Delegate control between different coroutines: Coroutines can yield control back and forth using yield from, which can mimic the behavior of cooperative multitasking like micro-threads.
  • Enable cooperative multitasking: Coroutines can be used to create non-blocking functions that yield at certain points and later resume. This is analogous to how micro-threads yield control to other threads.

When you combine yield from with coroutines, you can build complex asynchronous systems with minimal boilerplate, where one coroutine can yield control to another. This resembles the behavior of micro-threads, where each thread can pause and resume execution cooperatively.

Understanding Coroutines and yield from

Coroutines, introduced by PEP 342, are generalizations of generators. While generators are focused on producing values using yield, coroutines can both produce values and receive input using send(). This makes them useful for cooperative multitasking and handling asynchronous operations.

With PEP 380, the yield from statement can delegate part of a coroutine's execution to another coroutine, which simplifies coroutine-based asynchronous programming.

Example of Coroutine Chaining with yield from:

def coroutine1():
    result = yield from coroutine2()  # Delegates to another coroutine
    print("Result from coroutine2:", result)

def coroutine2():
    yield 1
    yield 2
    return "done"  # Return value can be caught by the delegating coroutine

coro = coroutine1()
print(next(coro))  # Starts coroutine1, yields from coroutine2
print(next(coro))  # Proceeds with coroutine2's second yield
print(next(coro))  # Ends coroutine2 and returns control to coroutine1

This shows how yield from handles yielding from multiple coroutines, making it easier to pass values and exceptions between them.

Conclusion

To summarize:

  1. yield from simplifies generator delegation: It allows one generator to yield values from another without boilerplate.
  2. The classic use case: Delegating part of a generator’s task to another generator or composing coroutines.
  3. Micro-thread comparison: Coroutines using yield from allow for cooperative multitasking similar to micro-threads, which can pause and resume execution.

Understanding yield from and coroutines unlocks powerful patterns for asynchronous programming in Python and helps reduce complexity when working with generators or coroutine chains.

#python