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How to Implement a Trampoline for Tail Recursion in Python
This code implements a trampoline decorator that converts tail-recursive functions into iterative loops, allowing deep recursion without hitting Python's recursion limit.
def trampoline(fn):
"""Convert a tail-recursive function into an iterative loop."""
def wrapper(*args, **kwargs):
result = fn(*args, **kwargs)
while callable(result):
result = result()
return result
return wrapper
@trampoline
def factorial(n, acc=1):
"""Tail-recursi…How to Simulate Timeout with Custom TimeoutError in Python
Run a function in a daemon thread and raise a custom TimeoutError if it exceeds a specified time limit.
import time
from typing import Callable, TypeVar
T = TypeVar("T")
class TimeoutError(Exception):
"""Raised when an operation exceeds its time limit."""
def __init__(self, message: str = "Operation timed out"):
self.message = message
super().__init__(self.message)
def run_with_timeout(func…Binary Search on Answer in Python: Koko Eating Bananas
Find the minimum eating speed so Koko finishes all banana piles within a given hour limit using binary search on the answer.
import math
def min_eating_speed(piles, h):
"""Return minimum integer eating speed K so Koko finishes within h hours."""
def hours_needed(speed):
return sum(math.ceil(p / speed) for p in piles)
low, high = 1, max(piles)
while low < high:
mid = (low + high) // 2
if hours_needed…How to Generate Primes with a Generator in Python
Generate prime numbers up to a limit using the Sieve of Eratosthenes wrapped in a generator expression for lazy evaluation.
def prime_generator(limit):
sieve = [True] * (limit + 1)
sieve[0] = sieve[1] = False
for i in range(2, int(limit ** 0.5) + 1):
if sieve[i]:
for j in range(i * i, limit + 1, i):
sieve[j] = False
return (num for num, is_prime in enumerate(sieve) if is_prime)
if __n…How to Retry LLM Calls on Rate Limit Errors in Python
Implement a retry mechanism with exponential backoff for LLM API calls that raises a custom RateLimitError, using a mock function to demonstrate the pattern.
import time
import random
def mock_llm_call():
"""Simulates an LLM API call that may raise a rate limit error."""
if random.random() < 0.4: # 40% chance of rate limit
raise RateLimitError("Rate limit exceeded. Try again later.")
return {"response": "Hello world from mock LLM"}
class RateLimitE…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 Implement a Token Bucket Rate Limiter with asyncio in Python
This code implements a thread-safe token bucket rate limiter for asyncio, allowing you to limit the rate of async tasks or API calls.
import asyncio
import time
class TokenBucket:
def __init__(self, rate_per_second, capacity):
self.rate = rate_per_second
self.capacity = capacity
self.tokens = capacity
self.last_refill = time.monotonic()
self.lock = asyncio.Lock()
async def acquire(self):
asy…Limit Concurrency with asyncio.Semaphore in Python
Use asyncio.Semaphore to cap how many async tasks run at once, throttling a batch of coroutines to a set concurrency limit.
import asyncio
import random
async def fetch_data(i: int, semaphore: asyncio.Semaphore) -> str:
async with semaphore:
print(f"Task {i} starts")
await asyncio.sleep(random.uniform(0.1, 0.5))
print(f"Task {i} finishes")
return f"Result {i}"
async def main() -> None:
semaphore …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 …Simulate a Leaky Bucket Rate Limiter in Python
This code implements a leaky bucket rate limiter that drains at a fixed rate and accepts or rejects incoming requests based on capacity.
import time
from collections import deque
class LeakyBucket:
"""Simulates a leaky bucket rate limiter with a fixed drain rate."""
def __init__(self, capacity, drain_rate_per_sec):
self.capacity = capacity
self.drain_rate = drain_rate_per_sec
self.water = 0.0
self.last_refill =…How to Handle Retry-After Header in Python
Parse the Retry-After header from rate-limited API responses and implement retry logic with proper delays in Python.
```python
import time
from datetime import datetime, timedelta
class RetryAfterHandler:
def __init__(self, max_retries=3):
self.max_retries = max_retries
def get_retry_after_seconds(self, response_headers):
retry_after_value = response_headers.get("Retry-After")
if retry_after_value …How to Mock X-RateLimit Headers in Python
This code creates a local HTTP server that mimics rate limit headers (X-RateLimit-Limit, Remaining, Reset, Update) and returns 429 responses when the limit is exceeded.
import time
import threading
from http.server import BaseHTTPRequestHandler, HTTPServer
class RateLimitHandler(BaseHTTPRequestHandler):
RATE_LIMIT = 5 # max requests allowed
WINDOW_SECONDS = 60 # per time window
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
…How to Build a Flow Control Credit Window in Python
A Python class that reserves, confirms, releases, and settles credit to limit message flow and prevent overload in streaming pipelines.
class CreditWindow:
def __init__(self, max_credit=1000):
self.max_credit = max_credit
self.used_credit = 0
self.pending_credit = 0
def try_reserve(self, amount):
available = self.max_credit - self.used_credit - self.pending_credit
if available >= amount:
…Redis Leaky Bucket Rate Limiting Mock in Python
Simulates a Redis-backed leaky bucket rate limiter using a local class with continuous leaking and token capacity checks.
import time
from collections import deque
class LeakyBucket:
def __init__(self, capacity, leak_rate):
self.capacity = capacity
self.leak_rate = leak_rate
self.water = 0.0
self.timestamp = time.time()
self.history = deque()
def allow(self):
current = time.time(…Redis-inspired sliding window rate limiter in Python
A pure-Python sliding window rate limiter using a deque of timestamps, mock-ready for Redis-backed production limits.
import time
from collections import deque
class SlidingWindowRateLimiter:
def __init__(self, max_requests: int, window_seconds: int) -> None:
self.max_requests = max_requests
self.window_seconds = window_seconds
self.requests: dict[str, deque] = {}
def is_allowed(self, client_id: str…GCRA generic cell rate algorithm in Python
Mock implementation of the Generic Cell Rate Algorithm (GCRA) for traffic shaping and rate limiting.
from collections import deque
import time
class GCRA:
def __init__(self, rate, burst):
self.tau = burst
self.T = rate
self.t = 0
self.LCT = 0
def add_cell(self, arrival_time):
if arrival_time <= self.t:
return False
arrived_early = (arrival_time - s…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 Implement a Sliding Window Log Rate Limiter in Python
Implements a sliding window log rate limiter in Python using a deque of timestamps to enforce a maximum request count within a rolling time window.
from collections import deque
from datetime import datetime, timedelta
from time import sleep
class SlidingWindowLog:
def __init__(self, window_seconds: int, max_requests: int):
self.window_seconds = window_seconds
self.max_requests = max_requests
self.timestamps = deque()
def allow_…How to Implement a Token Bucket Rate Limiter per Client IP in Python
Implements a simple sliding-window rate limiter using a dictionary of timestamp lists per client IP to limit requests per window.
from time import time
from collections import defaultdict
class RateLimiter:
def __init__(self, max_requests: int, window_seconds: int):
self.max_requests = max_requests
self.window_seconds = window_seconds
self.clients = defaultdict(list)
def allow(self, ip: str) -> bool:
now…How to Implement an Adaptive Rate Limiter in Python
Build an adaptive rate limiter that adjusts request intervals dynamically based on recent error rates, slowing down when failures spike.
import time
import random
class AdaptiveRateLimiter:
"""Simple adaptive rate limiter that reduces requests when error rate is high."""
def __init__(self, min_interval=0.1, max_interval=2.0, error_threshold=0.3):
self.min_interval = min_interval
self.max_interval = max_interval
sel…How to Send Messages to a Dead Letter Queue in Python
Simulates a poison message queue that retries failed messages up to a limit before moving them to a dead letter queue.
import json
class PoisonMessageQueue:
def __init__(self, max_retries=3):
self.dlq = []
self.max_retries = max_retries
self.processed_count = 0
self.failed_count = 0
def process_message(self, message_body):
if "poison" in message_body:
self.failed_count += 1…How to implement a rate-limited shared counter in Python
Implements a thread-safe global counter that allows a maximum number of increments per second using a lock and time-based refill.
import threading
import time
import random
counter = 0
lock = threading.Lock()
MAX_CALLS_PER_SECOND = 3
last_refill = time.time()
def rate_limited_increment():
global counter, last_refill
with lock:
now = time.time()
if now - last_refill >= 1.0:
last_refill = now
count…How to implement rate limiting per API key in Python
A simple sliding-window rate limiter that tracks request timestamps per API key and rejects requests exceeding the configured limit.
import time
API_RATE_LIMITS = {"api_key_1": 5, "api_key_2": 3} # max requests per window
WINDOW_SECONDS = 10
class RateLimiter:
def __init__(self, limits, window):
self.limits = limits
self.window = window
self.requests = {key: [] for key in limits}
def allow(self, api_key):
…Leaky Bucket Rate Limiter in Python: Smooth Burst Traffic
Implements a token-bucket-style leaky bucket rate limiter that smooths bursty traffic by draining at a fixed rate and dropping excess packets.
import time
import random
class LeakyBucket:
def __init__(self, capacity, drain_rate):
self.capacity = capacity
self.drain_rate = drain_rate
self.water = 0.0
self.last_time = time.time()
def allow(self, packet_size=1.0):
now = time.time()
elapsed = now - self.…Browse by section
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