Glossary: functions, garbage collection and generics — DedSec Project

Function

Meaning and constraints

A function packages a repeatable computation behind inputs and a return value. A good contract identifies accepted types, invalid inputs, side effects and guarantees. Calling a function does not imply it is pure: it may modify files, global state or a database.

A concrete use

def area(width: float, height: float) -> float:
    if width < 0 or height < 0: raise ValueError('negative dimension')
    return width * height
assert area(3, 4) == 12
assert area(0, 5) == 0

Challenge and misconception

Add tests for a negative dimension and a nonnumeric input. Decide whether a function annotated with float should validate runtime types at an external boundary; annotations alone do not enforce conversion.

Further reading: https://docs.python.org/3/tutorial/controlflow.html#defining-functions

Garbage collection

Meaning and constraints

Garbage collection reclaims memory for unreachable objects according to runtime rules. Python primarily uses reference counting with a cyclic collector, while other runtimes may use tracing collectors. Collection frees memory resources, but does not guarantee prompt release of file handles or locks.

A concrete use

from contextlib import closing
from io import StringIO
with closing(StringIO('test')) as stream:
    assert stream.read() == 'test'
assert stream.closed

Challenge and misconception

Explain why an explicit context manager is preferable for file handles. Sketch a two-object reference cycle and note that memory collection timing is not a portable API contract.

Further reading: https://docs.python.org/3/library/gc.html

Generic type

Meaning and constraints

A generic type is parameterized by another type so one abstraction can preserve information about several concrete kinds of data. In Python, list[int] communicates element expectations to type checkers, but does not reject wrong elements at runtime by itself.

A concrete use

from typing import TypeVar
T = TypeVar('T')
def first(items: list[T]) -> T | None:
    return items[0] if items else None
assert first([3,4]) == 3
assert first([]) is None

Challenge and misconception

Ask a type checker to infer the return type for list[str] and list[int]. Compare the compile-time checking with a runtime validator that must still inspect unknown JSON elements.

Further reading: https://docs.python.org/3/library/typing.html

Git

Meaning and constraints

Git is a distributed version-control system: a local clone usually has commits, branches and metadata independently of the remote server. fetch updates knowledge of remote refs, merge or rebase reconciles histories, and push publishes local commits if permitted.

A concrete use

git status --short --branch
git log --oneline -5
git remote -v
git fetch origin

Challenge and misconception

Use a disposable clone to compare git status before and after fetch. Confirm that fetch alone does not alter tracked working files. Do not force-push or reset production branches to compensate for confusion.

Further reading: https://git-scm.com/book/en/v2/Getting-Started-What-is-Git%3F

Hash function

Meaning and constraints

A hash function deterministically maps input bytes to a fixed-size digest. Cryptographic SHA-256 aims to make accidental and deliberate collision construction impractical, but a digest only checks equality relative to a trusted known value. A dictionary's internal hash has different security goals.

A concrete use

import hashlib
h = lambda b: hashlib.sha256(b).hexdigest()
assert h(b'abc') == 'ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad'
assert h(b'abc') != h(b'abd')

Challenge and misconception

Hash a file before and after changing one byte; record both digests. Explain why publishing a malicious binary and its matching digest on the same compromised page offers no identity guarantee.

Further reading: https://docs.python.org/3/library/hashlib.html