demo.py

import sys
from typing import Self, cast
import dataclasses

@dataclasses.dataclass(slots=False)
class Node:
    _parent: Self | None

MessagePump = Node
DOMNode = Node


def mk_nodes():
    node = Node(None)
    for i in range(1000000):
        node = Node(node)

    return node

def ancestors_with_self_traditional(self):
    nodes = []
    node: Node | None = self

    while node is not None:
        nodes.append(node)
        node = node._parent

    return nodes

def ancestors_with_self_traditional_while_true_invert_cond_return(self):
    nodes = []
    node: Node | None = self

    while True:
        if node is None:
            return nodes

        nodes.append(node)
        node = node._parent


def ancestors_with_self_traditional_while_true_invert_cond_break(self):
    nodes = []
    node: Node | None = self

    while True:
        if node is None:
            break

        nodes.append(node)
        node = node._parent

    return nodes


def ancestors_with_self_traditional_while_true_break(self):
    nodes = []
    node: Node | None = self

    while True:
        if node is not None:
            nodes.append(node)
            node = node._parent
        else:
            break

    return nodes

def ancestors_with_self_traditional_while_true_return(self):
    nodes = []
    node: Node | None = self

    while True:
        if node is not None:
            nodes.append(node)
            node = node._parent
        else:
            return nodes

    return nodes




import pyperf

def main():
    runner = pyperf.Runner()
    runner.timeit(name="flatten a linked list tradition",
                  stmt="demo.ancestors_with_self_traditional(nodes)",
                  setup="import demo; nodes = demo.mk_nodes()")
    runner.timeit(name="flatten a linked list tradition while true, invert cond (return)",
                  stmt="demo.ancestors_with_self_traditional_while_true_invert_cond_return(nodes)",
                  setup="import demo; nodes = demo.mk_nodes()")
    runner.timeit(name="flatten a linked list tradition while true, invert cond (break)",
                  stmt="demo.ancestors_with_self_traditional_while_true_invert_cond_break(nodes)",
                  setup="import demo; nodes = demo.mk_nodes()")
    runner.timeit(name="flatten a linked list tradition while true, (break)",
                  stmt="demo.ancestors_with_self_traditional_while_true_break(nodes)",
                  setup="import demo; nodes = demo.mk_nodes()")
    runner.timeit(name="flatten a linked list tradition while true, (return)",
                  stmt="demo.ancestors_with_self_traditional_while_true_return(nodes)",
                  setup="import demo; nodes = demo.mk_nodes()")


if __name__ == "__main__":
    sys.exit(main())

.....................
WARNING: the benchmark result may be unstable
* the standard deviation (7.81 ms) is 16% of the mean (48.2 ms)

Try to rerun the benchmark with more runs, values and/or loops.
Run 'python -m pyperf system tune' command to reduce the system jitter.
Use pyperf stats, pyperf dump and pyperf hist to analyze results.
Use --quiet option to hide these warnings.

flatten a linked list tradition: Mean +- std dev: 48.2 ms +- 7.8 ms
.....................
WARNING: the benchmark result may be unstable
* the standard deviation (9.82 ms) is 22% of the mean (45.2 ms)
* the maximum (89.3 ms) is 98% greater than the mean (45.2 ms)

Try to rerun the benchmark with more runs, values and/or loops.
Run 'python -m pyperf system tune' command to reduce the system jitter.
Use pyperf stats, pyperf dump and pyperf hist to analyze results.
Use --quiet option to hide these warnings.

flatten a linked list tradition while true, invert cond (return): Mean +- std dev: 45.2 ms +- 9.8 ms
.....................
WARNING: the benchmark result may be unstable
* the standard deviation (7.46 ms) is 17% of the mean (44.4 ms)

Try to rerun the benchmark with more runs, values and/or loops.
Run 'python -m pyperf system tune' command to reduce the system jitter.
Use pyperf stats, pyperf dump and pyperf hist to analyze results.
Use --quiet option to hide these warnings.

flatten a linked list tradition while true, invert cond (break): Mean +- std dev: 44.4 ms +- 7.5 ms
.....................
WARNING: the benchmark result may be unstable
* the standard deviation (6.77 ms) is 14% of the mean (49.3 ms)
* the maximum (75.4 ms) is 53% greater than the mean (49.3 ms)

Try to rerun the benchmark with more runs, values and/or loops.
Run 'python -m pyperf system tune' command to reduce the system jitter.
Use pyperf stats, pyperf dump and pyperf hist to analyze results.
Use --quiet option to hide these warnings.

flatten a linked list tradition while true, (break): Mean +- std dev: 49.3 ms +- 6.8 ms
.....................
WARNING: the benchmark result may be unstable
* the standard deviation (6.21 ms) is 13% of the mean (49.5 ms)

Try to rerun the benchmark with more runs, values and/or loops.
Run 'python -m pyperf system tune' command to reduce the system jitter.
Use pyperf stats, pyperf dump and pyperf hist to analyze results.
Use --quiet option to hide these warnings.

flatten a linked list tradition while true, (return): Mean +- std dev: 49.5 ms +- 6.2 ms