Reliability & rate limiting
Retries, exponential backoff, circuit breakers, token buckets, and idempotent handlers.
How to Deduplicate Messages in Python by ID
This code consumes a mock inbox of JSON messages and deduplicates them by message ID, keeping either the first or last occurrence.
import json
from collections import OrderedDict
mock_inbox = [
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 2, "message": "world", "timestamp": "2024-01-01T10:01:00Z"},
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 3, "message": "test", "times…Implementing Fallback with Cached Stale Data in Python
This code demonstrates a resilient data-fetching pattern that caches successful responses, falls back to cached data when the external API fails, and returns stale data as a last-resort fallback.
import random
import time
# Simulated cache dictionary: key -> (value, timestamp)
_cache = {}
_CACHE_TTL = 3 # seconds
# Mock data source (simulates an unreliable external API)
def fetch_mock_data(key):
failure = random.random() < 0.4 # 40% chance of failure
if failure:
raise ConnectionError("Mock …Browse by section
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Reliability & rate limiting — Python code examples
What you will find here
This page collects reliability & rate limiting snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.