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Find Zombie Processes on Linux with Python
Parse the output of `ps -eo pid,stat,comm` to detect processes in zombie state (Z) on a Linux system and report their PIDs and commands.
#!/usr/bin/env python3
import os
import subprocess
def find_zombie_processes():
"""Find zombie processes (state 'Z') running on Linux."""
try:
result = subprocess.run(['ps', '-eo', 'pid,stat,comm'], capture_output=True, text=True, check=True)
zombies = []
for line in result.stdout.stri…How to Detect Applications Consuming Excessive Memory in Python
Use psutil to list the top memory-using processes by RSS and print their names, PIDs, and memory usage in MB.
import psutil
def find_top_memory_processes(limit=5):
"""Return top `limit` processes by memory usage (RSS)."""
processes = []
for proc in psutil.process_iter(['pid', 'name', 'memory_info']):
try:
info = proc.info
mem = info['memory_info'].rss if info['memory_info'] else 0…How to Demonstrate the GIL with Python Threads vs Processes
Measure and compare wall-clock time for CPU-bound work using Python threads (limited by the GIL) versus multiprocessing (which bypasses the GIL).
import threading
import multiprocessing
import time
import os
def cpu_heavy(n):
return sum(i * i for i in range(n))
def run_threads(n):
threads = [threading.Thread(target=cpu_heavy, args=(n,)) for _ in range(2)]
start = time.perf_counter()
for t in threads:
t.start()
for t in threads:
…How to Share Memory Between Processes in Python with multiprocessing.Value and Array
Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.
import multiprocessing
def worker(shared_value, shared_array, index):
shared_value.value += 10
shared_array[index] = shared_array[index] * 2
if __name__ == "__main__":
shared_value = multiprocessing.Value("i", 5)
shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])
processes = []
for i…How to Share a Dict and List Between Processes with multiprocessing Manager in Python
This code demonstrates how to share a dictionary and a list between multiple processes using multiprocessing.Manager, enabling safe concurrent updates.
import multiprocessing as mp
def worker(shared_dict, shared_list, name):
shared_dict[name] = name.upper()
shared_list.append(name)
print(f"{name} added to shared structures")
def main():
with mp.Manager() as manager:
shared_dict = manager.dict()
shared_list = manager.list()
…How to Share a Queue Between Processes in Python
Use multiprocessing.Queue to pass work from a producer process to multiple consumer processes, coordinating with a sentinel stop message.
import multiprocessing
import time
def producer(queue, items):
for item in items:
queue.put(item)
time.sleep(0.1)
queue.put("STOP")
def consumer(queue, name):
while True:
item = queue.get()
if item == "STOP":
break
print(f"{name} processed: {item}")
…Batch Consume Process Commit Pattern in Python
A mock batch processor that accumulates items in a queue, processes full batches, commits successful or failed results, and flushes remaining items.
import random
import threading
import time
from collections import deque
class MockBatchProcessor:
def __init__(self, process_func, commit_func, batch_size=5):
self.queue = deque()
self.batch_size = batch_size
self.process_func = process_func
self.commit_func = commit_func
de…At Least Once with Idempotent Consumer in Python
Implements a thread-safe idempotent consumer that processes each unique message exactly once, even when a producer sends duplicates under an at-least-once delivery model.
import threading
import time
import uuid
from collections import Counter
class IdempotentConsumer:
def __init__(self):
self.processed = set()
self._lock = threading.Lock()
def consume(self, message_id, payload):
with self._lock:
if message_id in self.processed:
…How to Mock Spark Streaming Micro-Batches in Python
Simulate Spark's micro-batch streaming with a simple deque-based class that collects events over time and processes them in timed batches.
import time
from collections import deque
from datetime import datetime
class MicroBatchStream:
def __init__(self, batch_interval_sec=2):
self.batch_interval = batch_interval_sec
self.source = deque()
self.processed = []
def add_events(self, events):
self.source.extend(events…Browse by section
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