Python Programming Questions

Project layout

• mypackage/
  • src/mypackage/init.py # package implementation
  • src/mypackage/module.py
  • tests/ # unit tests
    • test_module.py
  • pyproject.toml / setup.cfg

Where to place tests
Keep tests outside package source (e.g., tests/ at repo root). This prevents accidental import of tests and mirrors CI test discovery.

init.py & public API
Use init.py to expose a stable public API with explicit imports and all. Keep implementation modules internal (e.g., mypackage._internal).

A concrete worked example of what that __init__.py and its module actually contain:

# src/mypackage/module.py
def process(data):
    return [x * 2 for x in data]

def _internal_helper():   # leading underscore: not part of the public API
    pass

# src/mypackage/__init__.py
from .module import process

__all__ = ["process"]

A caller then does from mypackage import process, process is the stable public name, while mypackage.module._internal_helper stays a private implementation detail nobody outside the package should import directly; __all__ also controls exactly what from mypackage import * pulls in.

Testing in CI

• Use pytest, run in CI (GitHub Actions) with matrix for Python versions. Run mypy (checks your type hints without actually running the code) and unit tests; fail on coverage thresholds.

A minimal CI job doing exactly that:

# .github/workflows/test.yml
jobs:
  test:
    strategy:
      matrix:
        python-version: ["3.11", "3.12"]
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      - run: pip install -e .[dev]
      - run: pytest --cov=mypackage --cov-fail-under=80
      - run: mypy src/mypackage

Type stubs & docs

• Add type hints in code; supply .pyi stubs (a .pyi file is a type-hint-only file, holding no real code, for code Python itself cannot read type hints from directly, such as a compiled C extension) for compiled extensions.
• Use Sphinx or MkDocs (both turn your docstrings into a browsable documentation website) for docs; generate API reference from docstrings. Include CI job to build docs.

Which of these matter most for a first working setup versus are polish added later: the src/ layout, an explicit __init__.py public API, and pytest running in CI are the essentials, without them the package is neither testable nor safely importable by other code. mypy, coverage thresholds, .pyi stubs, and generated documentation sites are all valuable, genuinely worth doing, but they are follow-up hardening once the basic package structure and test suite already exist, not blockers to a first working, publishable package.

Best practices: pin dev deps, run linters, and keep public surface minimal and documented.

Approach: schedule tasks, wait with timeout, cancel pending tasks after timeout, re-raise exceptions from completed tasks. (A coroutine is what you get back from calling an async def function; calling it does not run it yet, asyncio.create_task(c) is what actually schedules a coroutine to start running concurrently with everything else.)

Implementation:

import asyncio

async def gather_with_timeout(coros, timeout):
    tasks = [asyncio.create_task(c) for c in coros]
    done, pending = await asyncio.wait(tasks, timeout=timeout)
    # cancel pending
    for t in pending:
        t.cancel()
    # await cancellations to finish
    if pending:
        await asyncio.gather(*pending, return_exceptions=True)
    # propagate exceptions from done
    results = []
    for t in done:
        exc = t.exception()
        if exc:
            # re-raise first exception
            raise exc
        results.append(t.result())
    return tuple(results), tuple(pending)

# Usage example
async def main():
    async def work(n):
        await asyncio.sleep(n); return n
    done, pending = await gather_with_timeout([work(0.1), work(2)], timeout=1)
    print(done, pending)

# asyncio.run(main())

Notes: ensures pending tasks are cancelled and awaited to avoid warnings; exceptions from completed tasks propagate.

Running it: uncommenting asyncio.run(main()) and adding print(len(pending), pending[0].cancelled()) right after print(done, pending) shows what actually happens: work(0.1) finishes well inside the 1-second timeout and work(2) does not, so done prints as (0.1,) (the tuple of results from the finished work(0.1)) and the follow-up print shows 1 True, one pending task, and it reports cancelled() == True, confirming the still-running work(2) really was cancelled rather than left dangling.

1) Multiprocessing (multiprocessing module)

• Benefit: sidesteps GIL (Global Interpreter Lock: a lock inside CPython that only lets one thread execute Python bytecode at a time, which is why adding more threads alone does not speed up pure-Python CPU-bound work) by using multiple processes; good for CPU-bound tasks that can be partitioned.
• Limitations: IPC and data serialization overhead, higher memory usage per process.
• When: embarrassingly parallel workloads (map-style), batch processing across cores.

2) C-accelerated libraries (NumPy / vectorization)

• Benefit: move heavy numeric loops into C (SIMD, Single Instruction Multiple Data, a CPU feature applying one operation to many values in one step; contiguous memory), drastically faster for array math.
• Limitations: requires expressing work in array form; not helpful for complex control flow.
• When: numerical computations over arrays, matrix ops.

3) Just-in-time / ahead-of-time compilation (Numba or Cython)

• Numba: JIT-compiles (just-in-time compiles: instead of compiling ahead of time before the program ever runs, the function is compiled straight to machine code the first time it is actually called) Python functions to machine code with little code changes; excellent for loops on numeric data.
  • Benefit: low development cost, big speedups.
  • Limitation: not all Python features supported; first-call compilation overhead.
• Cython: compile Python to C for maximum control and speed; can produce greatest gains but requires typing and build step.
  • When: algorithmic hotspots that remain after vectorization or need fine-grained control.

A concrete contrast (real values, not timing, since exact speed depends on hardware and is not something to hardcode here):

import numpy as np

def squares_loop(n):
    return [i * i for i in range(n)]

def squares_vectorized(n):
    return np.arange(n) ** 2

print(squares_loop(5))
# [0, 1, 4, 9, 16]
print(squares_vectorized(5))
# [ 0  1  4  9 16]

Both produce the identical five values; squares_loop pays Python's per-element interpreter overhead five separate times, while squares_vectorized dispatches once into a single compiled C loop, the same mechanism that makes strategy 2 (vectorization) and strategy 3 (Numba/Cython compilation) both faster than a plain interpreted loop: removing per-element interpreter overhead, not changing the underlying arithmetic.

Choose based on workload: use vectorization first, then Numba for loop-heavy numeric code, and multiprocessing when parallelism across cores is needed and data can be partitioned.

Approach summary
Use an atomic filesystem operation to create a lockfile and store owner PID. "Atomic" here means the operating system guarantees that checking whether the file already exists and creating it happen as a single, indivisible step: the kernel resolves any race internally, so it is impossible for two processes to both be told "it didn't exist, you just created it" for the same file. Concretely, os.open(path, O_CREAT|O_EXCL) either creates the file and returns a valid file descriptor (this process is now the owner), or, if the file already exists, raises OSError with errno.EEXIST and creates nothing, there is no window in between where a second process could sneak in and also succeed. On Unix use os.open with O_EXCL|O_CREAT; on Windows use msvcrt.locking or CreateFile with exclusive flags. Implement acquire(timeout) with retries and stale-lock detection via PID and age.

Implementation (Unix-first, cross-platform fallback)

import os, time, errno
from pathlib import Path

class FileLock:
    def __init__(self, path):
        self.path = Path(path)
    def acquire(self, timeout=10):
        end = time.time()+timeout
        while time.time()<end:
            try:
                fd = os.open(self.path, os.O_CREAT|os.O_EXCL|os.O_WRONLY)
                os.write(fd, str(os.getpid()).encode())
                os.close(fd)
                return True
            except OSError as e:
                if e.errno!=errno.EEXIST: raise
            time.sleep(0.1)
        return False
    def release(self):
        try: self.path.unlink()
        except FileNotFoundError: pass

Worked trace: two processes racing to acquire the same lock, simulated in one process for illustration (the real guarantee comes from the OS's atomic O_CREAT|O_EXCL, not from anything special in this simulation, but the sequence of return values is exactly what would happen with two real, separate processes):

lock_a = FileLock("/tmp/demo.lock")
lock_b = FileLock("/tmp/demo.lock")

print(lock_a.acquire(timeout=1))   # True: lock_a's os.open call wins the race, file now exists
print(lock_b.acquire(timeout=0.3)) # False: lock_b's os.open calls all hit EEXIST and it
                                    # gives up once the 0.3s timeout elapses
lock_a.release()
print(lock_b.acquire(timeout=1))   # True: now that lock_a released (deleted the file),
                                    # lock_b's next os.open call succeeds

The first acquire call's os.open(..., O_CREAT|O_EXCL) either wins outright (file did not exist, now it does, True) or loses outright (EEXIST, False after retries time out); there is no third outcome where both processes believe they created the file, which is exactly the race the atomic system call rules out by construction.

Platform notes

• Unix: O_EXCL is atomic across processes. Use fcntl.flock for advisory locks (a lock that only stops other processes if they also choose to check for it before touching the file; unlike a mandatory, OS-enforced lock, the OS itself does not block a process that simply ignores the lock and opens the file directly) when needed.
• Windows: O_EXCL behaves differently; prefer msvcrt.locking or pywin32 CreateFile for exclusive access.

Stale locks
Check PID from file and whether process exists; remove stale if safe. For production, add jitter, robust error handling, and optional directory-level locking for network filesystems.

Bottleneck reasoning

A bottleneck is the part of the system that dominates total runtime. If the report generator scans the same task records over and over, each new question forces another full pass through the data. That scales poorly as the team grows.

5 scans * 100000 rows = 500000 row visits
1 scan * 100000 rows = 100000 row visits

Intuition: one pass is 5x less work in this example.

What I would change
I would switch the data flow from repeated scans to one ingestion pass that builds summary structures as it goes:

• counters for totals by status, owner, or date
• maps indexed by the fields used in reports
• one reducer function that updates all needed aggregates together

Then each summary question reads from the precomputed aggregates instead of touching the raw records again. That keeps queries fast and the code maintainable because the aggregation logic lives in one place.

Tradeoff
The main cost is a bit more memory for the summary maps. I would accept that tradeoff because it is much cheaper than rescanning the whole dataset repeatedly.

Example
If ten report questions all need ticket counts by team, I would build that count once during ingestion, then reuse it for every report instead of looping over the task table ten times.