Generators & yield Keyword Mechanics

1Concept

Generators are functions that preserve state across invocations using `yield`. When executed, they return a generator object. Code runs lazily up to the next `yield` statement, suspending the call frame in memory without consuming memory for entire collections.

2Architecture Diagram

def gen():
  yield 1 ---> [ Suspends Frame ] ---> Next request resumes execution ---> yield 2

3Code Example

import sys

def infinite_log_stream(limit: int):
    for i in range(1, limit + 1):
        yield f"[LOG-{i:04d}] Process Heartbeat Active"

stream = infinite_log_stream(1_000_000)
print(f"Generator Memory Footprint: {sys.getsizeof(stream)} bytes (Zero Heap bloat!)")
print(f"First item:  {next(stream)}")
print(f"Second item: {next(stream)}")

4Expected Output

Generator Memory Footprint: 200 bytes (Zero Heap bloat!)
First item:  [LOG-0001] Process Heartbeat Active
Second item: [LOG-0002] Process Heartbeat Active

5Key Takeaways

• ✓Generators provide O(1) memory streaming for gigabyte-scale datasets.
• ✓A generator expression `(x * 2 for x in data)` is the lazy equivalent of list comprehension.
• ✓Once consumed, a generator cannot be restarted; a new generator instance must be created.