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Run Background Tasks with asyncio.create_task in Python
Create background tasks in an asyncio event loop with asyncio.create_task and run them concurrently using asyncio.gather.
import asyncio
import time
async def background_worker(name, duration):
"""Simulates a long-running background task."""
print(f"{name} started at t={time.monotonic():.1f}")
await asyncio.sleep(duration)
print(f"{name} finished at t={time.monotonic():.1f}")
async def main():
print(f"Main starting …Synchronize Threads with a Barrier in Python
Demonstrates using threading.Barrier to synchronize multiple threads at phase boundaries, ensuring all workers wait for each other before proceeding.
import threading
import time
from random import randint
def worker(barrier, worker_id):
for phase in range(3):
time.sleep(randint(1, 3))
print(f"Worker {worker_id} finished phase {phase} at {time.time():.2f}")
barrier.wait()
print(f"Worker {worker_id}: all phases complete")
if __name_…Thread Pool Map for IO Bound Tasks in Python
Run IO-bound mock tasks concurrently with ThreadPoolExecutor.map and measure total elapsed time in Python.
import concurrent.futures
import time
from pathlib import Path
def mock_io_task(filename):
"""Simulate an IO-bound task by creating a small file and measuring its latency."""
path = Path(filename)
path.write_text("data")
time.sleep(0.1) # Simulate slow disk/network
return f"{filename} written in …Thread-Safe Producer Consumer Queue in Python
A producer-consumer pattern using thread-safe queue.Queue with two threads, demonstrating safe communication and synchronized task completion.
import queue
import threading
import time
import random
def producer(q, item_count):
for i in range(item_count):
item = random.randint(1, 100)
q.put(item)
print(f"Producer added: {item}")
time.sleep(0.1)
def consumer(q):
while True:
try:
item = q.get(time…asyncio Condition wait notify pattern in Python
Coordinate coroutines with asyncio.Condition: workers wait for notifications and the main task notifies one or all of them.
import asyncio
async def worker(condition, name):
async with condition:
print(f"{name} waiting...")
await condition.wait()
print(f"{name} notified!")
async def main():
condition = asyncio.Condition()
tasks = [asyncio.create_task(worker(condition, f"worker-{i}")) for i in range(3…asyncio sleep cooperative scheduling demo in Python
This demo shows how asyncio.sleep yields control between concurrent tasks, letting multiple workers interleave their ticks.
import asyncio
async def worker(name, delay):
for i in range(3):
print(f"{name}: tick {i}")
await asyncio.sleep(delay)
return f"{name} done"
async def main():
tasks = [
asyncio.create_task(worker("A", 0.1)),
asyncio.create_task(worker("B", 0.2)),
asyncio.create_tas…How to Build an Immutable Money Value Object in Python
Implement an immutable Money class with rounded decimal amounts, currency, safe equality, and hashing for use as a value object.
class Money:
def __init__(self, amount: float, currency: str):
object.__setattr__(self, "_amount", round(amount, 2))
object.__setattr__(self, "_currency", currency)
def __setattr__(self, name, value):
raise AttributeError(f"Money is immutable: cannot set '{name}'")
def __delattr__…How to Limit Concurrent Requests with a Semaphore in Python
Use threading.Semaphore with a ThreadPoolExecutor to cap how many worker threads run simultaneously, preventing resource overload.
import threading
import time
from concurrent.futures import ThreadPoolExecutor
def worker(name, semaphore, results):
with semaphore:
results.append(f"start {name}")
time.sleep(0.5) # simulate async work
results.append(f"done {name}")
def main():
sem = threading.Semaphore(2) # max 2 …Implement Bulkhead Thread Pool Isolation in Python
Create isolated thread pools with a bulkhead pattern to protect different services from cascading failures.
import threading
import time
import random
from concurrent.futures import ThreadPoolExecutor
class Bulkhead:
"""Simple bulkhead isolation: separate thread pools for different tasks."""
def __init__(self, max_workers):
self.executor = ThreadPoolExecutor(max_workers=max_workers)
self.active = …How to Build a WebSocket Echo Server in Python with asyncio
Create a simple WebSocket echo server using the websockets library and asyncio to handle concurrent connections.
import asyncio
import websockets
async def echo(websocket):
async for message in websocket:
await websocket.send(f"Echo: {message}")
async def main():
async with websockets.serve(echo, "localhost", 8765):
print("WebSocket server started on ws://localhost:8765")
await asyncio.Future() …How to Implement ETag Optimistic Concurrency in Python
Build a lightweight in-memory resource store that uses MD5 hash ETags to prevent lost updates via optimistic concurrency control.
import hashlib
import json
class ResourceStore:
def __init__(self):
self.data = {}
self.etags = {}
def get(self, resource_id):
if resource_id not in self.data:
return None, None
return self.data[resource_id], self.etags[resource_id]
def put(self, resource_id, …Implement If-Match Precondition Update in Python
A mock resource store that uses the If-Match header's ETag to guard updates, preventing overwrites from stale clients.
from dataclasses import dataclass
from typing import Optional
@dataclass
class Resource:
id: str
version: int = 1
data: str = ""
etag: str = "etag-1"
class MockResourceStore:
def __init__(self):
self.resources = {}
def update(self, resource_id: str, new_data: str, if_match: Optiona…Cache Stampede Prevention with SingleFlight in Python
Implements a SingleFlight pattern in Python to deduplicate concurrent cache-miss computations and prevent cache stampede.
import threading
import time
from functools import wraps
class SingleFlight:
def __init__(self):
self._lock = threading.Lock()
self._inflight = None
def do(self, key, fn):
with self._lock:
if self._inflight is not None:
return self._inflight[1]
…Coalescing duplicate in-flight requests: one shared result for concurrent callers
Runs identical concurrent requests through a single shared call, caching the result while it's in flight and returning the same value to all callers.
import time
import threading
from collections import defaultdict
class CoalescingExecutor:
def __init__(self):
self._locks = defaultdict(threading.Lock)
self._in_flight = {}
def execute(self, key, func):
with self._locks[key]:
if key in self._in_flight:
re…How to Implement a Write-Through Cache in Python with a Mock Database
A thread-safe write-through cache that updates both cache and mock database atomically, computing values only after a successful write to the database.
import threading
import time
import random
class WriteThroughCache:
def __init__(self):
self.cache = {}
self.db = {}
self.lock = threading.Lock()
def write(self, key, value):
with self.lock:
# Simulate slow database write
time.sleep(random.uniform(0.01…How to implement Redlock distributed lock in Python
Simulate Redis Redlock multi-instance locking to show how a distributed lock is acquired only when a majority of instances agree.
import time
import random
import threading
from dataclasses import dataclass
@dataclass
class MockRedisLock:
"""Simple mock of a Redis lock instance."""
name: str
key: str
ttl: int
acquired: bool = False
expires_at: float = 0.0
def acquire(self, sleep_fn=time.sleep):
"""Try to ac…Mock Redis Distributed Lock in Python with SET NX EX
A minimal in-memory mock of Redis SET NX EX distributed lock semantics for testing concurrent code without a real Redis server.
import time
import threading
import uuid
from typing import Optional
class RedisLockMock:
"""A minimal mock of Redis SET NX EX distributed lock semantics."""
def __init__(self):
self._store = {} # key -> (value, expiry_epoch)
def acquire(self, key: str, token: str, ttl_seconds: int) -> bool:
…Python Redis WATCH optimistic lock mock
A MockRedis class that simulates WATCH/MULTI/EXEC transactions with optimistic locking to detect concurrent modifications before committing.
import time
import threading
class MockRedis:
def __init__(self):
self.data = {}
self.watched = {}
self.lock = threading.Lock()
def get(self, key):
return self.data.get(key)
def set(self, key, value):
self.data[key] = value
def watch(self, *keys):
wi…How to Implement a Bulkhead Pattern with Threading in Python
Implement a bulkhead pattern in Python that isolates concurrent tasks with a bounded semaphore, limiting active workers to prevent resource exhaustion.
import threading
import time
import random
class Bulkhead:
def __init__(self, workers: int):
self._semaphore = threading.BoundedSemaphore(workers)
self._lock = threading.Lock()
self._active = 0
def run(self, task):
with self._semaphore:
with self._lock:
…How to Propagate Context Variables with asyncio in Python
Use Python's ContextVar with asyncio to carry deadline information across concurrent tasks and propagate context automatically.
import asyncio
from contextvars import ContextVar
from datetime import datetime
deadline = ContextVar("deadline", default=None)
async def worker(name):
current = deadline.get()
if current:
print(f"{name} sees deadline: {current}")
else:
print(f"{name} sees no deadline")
await asyncio.…Bulkhead Thread Pool per Service Mock in Python
Simulates a bulkhead pattern with per-service thread pools and semaphore-based rejection to isolate failures between dependent services.
import threading
import time
import random
from concurrent.futures import ThreadPoolExecutor
class ServiceBulkhead:
def __init__(self, name, max_threads, max_queue):
self.name = name
self.executor = ThreadPoolExecutor(max_workers=max_threads)
self.semaphore = threading.Semaphore(max_thread…How to Compose Parallel API Calls in Python with asyncio.gather
Compose multiple mock API responses in parallel using asyncio.gather with per-service simulated latency.
import asyncio
import random
import time
async def mock_api(name: str, delay: float) -> dict:
await asyncio.sleep(delay)
return {"service": name, "value": random.randint(1, 100)}
async def fetch_all():
services = {
"users": mock_api("users", 0.2),
"orders": mock_api("orders", 0.3),
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