perf: optimize get_all_nested_components with stack iteration - #1039
saquibsaifee wants to merge 1 commit into
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The old recursive By replacing the recursion with an iterative stack-based approach, we entirely avoid function call overhead and recursion limits (which protects against As verified by tests, this retains the exact same functionality while delivering a significant (55% measured) performance boost and better safety guarantees. |
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 0 |
| Duplication | 0 |
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Signed-off-by: saquibsaifee <saquibsaifee2@gmail.com>
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i see, the performance of the function is just bad, mostly due to the costly maybe this is even more performant: def iter_all_nested_components(self, include_self: bool = False) -> Iterator['Component']:
"""
Iterate over this component and all its nested components (assemblies), recursively.
Components are deduplicated by object identity — NOT by equality.
This never calls the (costly) ``Component.__hash__``/``__eq__``,
which makes it dramatically faster than :func:`get_all_nested_components`
for large component trees. Safe against cyclic component graphs.
Order of iteration is not guaranteed.
"""
seen: dict[int, 'Component'] = {}
stack: list['Component'] = [self] if include_self else list(self.components)
while stack:
current = stack.pop()
current_id = id(current)
if current_id not in seen:
seen[current_id] = current
stack.extend(current.components)
return iter(seen.values())
def get_all_nested_components(self, include_self: bool = False) -> set['Component']:
return set(self.iter_all_nested_components(include_self=include_self))and then, lets replace all aplicable internal calls on What do you think, @saquibsaifee ? |
Description
💡 What: Replaced the recursive function with an iterative stack-based approach.
🎯 Why: To avoid intermediate set allocations and recursive call overhead which was impacting performance for deep component trees.
📊 Measured Improvement: Improved execution time from ~123 seconds to ~55 seconds for 100 iterations of a 6-deep, 4-broad component tree (a >50% improvement).
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