Limiting program resources using the resource module 🛡️

import resource
import sys

# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024 
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))

# 2. Checking the protection's working
try:
    print("Trying to allocate a huge array of memory...")
    huge_list = [i for i in range(10_000_000)]
except MemoryError:
    print("The limit worked! The program didn't crash, but caught the error.")

# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")

Protecting the server from "greedy" code 🔧

When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.

— Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.

— File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.

— Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.

#Python #ResourceManagement #ServerSafety #Coding #DevOps #Linux

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Python can substitute an empty context manager without conditions inside!

It often happens that a resource needs to be opened via with, and sometimes the object is already ready and there's no need to open anything.

This usually leads to code duplication or conditions around with:

if need_open:
    f = open(...)
else:
    f = existing_file

`nullcontext(obj) behaves like an empty context manager and allows you to maintain a single execution flow.

This is especially useful for APIs, tests, optional resources, dependency injection, and functions that can accept both a path and a ready-made object.


with ctx as resource:
    process(resource)

But note that nullcontext() does not close the passed object — it simply passes it on further.

🔥 nullcontext() helps to unify scenarios with optional context managers and significantly simplifies the architecture of IO code.

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