Reliability & rate limiting
Retries, exponential backoff, circuit breakers, token buckets, and idempotent handlers.
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.…How to Retry on Specific Exception Tuples in Python
A decorator-based retry pattern that retries a function only when it raises exceptions specified in a tuple, with configurable retries and delay.
import time
import random
from unittest.mock import patch
def retry_on_exceptions(retries=3, exceptions=(ValueError,), delay=0.1):
def decorator(func):
def wrapper(*args, **kwargs):
for attempt in range(retries):
try:
return func(*args, **kwargs)
…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 Simulate an Outbox Pattern with Reliable Retry in Python
This code implements a mock outbox pattern with records, delivery attempts, and retries to simulate reliable message publishing.
import time
import itertools
class Outbox:
def __init__(self):
self._records = []
self._seq = itertools.count(1)
def publish(self, topic, payload):
record = {
"id": next(self._seq),
"topic": topic,
"payload": payload,
"status": "pending"…How to Stop Receiving Requests Until Ready in Python
A mock server that refuses requests until a readiness gate is passed, simulating fail-stop behavior for production reliability.
import random
import time
class MockServer:
def __init__(self):
self.ready = False
self.requests_received = 0
def readiness_check(self):
"""Simulates a readiness probe. Returns True only when ready."""
if not self.ready:
return False
return True
def r…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 an idempotency key store in Python
Build an in-memory idempotency key store with TTL that processes a request once and reuses the cached result for duplicate calls.
import hashlib
import time
from typing import Dict, Optional
class IdempotencyStore:
"""Simple in-memory idempotency key store with mock processing."""
def __init__(self, ttl_seconds: int = 3600) -> None:
self.ttl = ttl_seconds
self._store: Dict[str, tuple[str, float]] = {}
def _is_expi…How to implement rate limiting in Python
Build a simple sliding-window rate limiter in Python that enforces a max number of calls per time period and formats data with timestamps.
import time
class RateLimiter:
def __init__(self, max_calls, period):
self.max_calls = max_calls
self.period = period
self.calls = []
def allow(self):
now = time.time()
# Remove calls older than the period window
self.calls = [t for t in self.calls if now -…How to implement rate limiting in Python
A beginner-friendly Python rate limiter that throttles API calls and retries parsing tasks with exponential backoff.
import time
import random
class RateLimiter:
def __init__(self, max_calls, per_seconds):
self.max_calls = max_calls
self.per_seconds = per_seconds
self.timestamps = []
def allow(self):
now = time.time()
self.timestamps = [t for t in self.timestamps if now - t < sel…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):
…How to mock a fallback return value in Python
Test a function that returns a default value on failure by mocking requests.get and its side effects.
from unittest.mock import Mock, patch
import requests
def fetch_data(url, default=None):
try:
response = requests.get(url)
response.raise_for_status()
return response.json()
except (requests.RequestException, ValueError):
return default
with patch("requests.get") as mock_get:
…How to retry idempotent operations with a mock in Python
Wrap a flaky idempotent operation in a retry loop with exponential backoff, and use unittest.mock to deterministically test the str's behavior.
import random
import time
from unittest.mock import Mock
def idempotent_operation(value):
"""Simulate an idempotent operation that sometimes fails."""
if random.random() < 0.6: # 60% failure rate
raise ConnectionError("Temporary failure")
return value * 2
def retry_with_backoff(operation, max_…Rate Limiting in Python with a Sliding Window
A beginner-friendly dataclass-based sliding window rate limiter that controls how many calls are allowed per time window.
import time
from dataclasses import dataclass
@dataclass
class RateLimiter:
max_calls: int
window_seconds: float = 1.0
def __post_init__(self):
self.calls = []
self._start = time.monotonic()
def _update(self, now):
self.calls = [t for t in self.calls if now - t < self.window…Browse by section
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Reliability & rate limiting — Python code examples
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