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How to Implement Message Visibility Timeout Renewal in Python
Simulate queue message visibility control with timeout renewal using a simple Python class that tracks received time and visibility state.
import time
import uuid
class Message:
def __init__(self, body, visibility_timeout=30):
self.body = body
self.visibility_timeout = visibility_timeout
self.receipt_handle = str(uuid.uuid4())
self.received_at = time.time()
self.deleted = False
def is_visible(self):
…
How to Implement a Dead Letter Queue Replay in Python
A mock Dead Letter Queue that stores failed messages with retry attempts and replays them with a simple retry counter.
import json
from collections import deque
class DeadLetterQueue:
def __init__(self):
self.messages = deque()
def add_message(self, message_id, payload, attempts=3):
"""Add a message to the DLQ with retry metadata."""
self.messages.append({
"id": message_id,
…
Rate Limiting with Queue Rejection in Python
Simulates a load shed pattern that rejects tasks when a queue fills up.
from collections import deque
import time
class RateLimiter:
def __init__(self, max_queue_size=3):
self.queue = deque()
self.max_queue_size = max_queue_size
self.rejected_count = 0
def submit(self, task_name):
if len(self.queue) >= self.max_queue_size:
self.reject…
How to Check Service Readiness Dependencies in Python
This code simulates a readiness check for external dependencies (database, cache, queue) with mock availability data and reports readiness status.
import sys
from datetime import datetime
def check_dependencies(config):
results = []
for dep, required in config.items():
available = mock_availability(dep)
status = "READY" if available >= required else "NOT READY"
results.append((dep, available, required, status))
return result…
How to Simulate a Queue Depth Gauge in Python
Simulate a queue depth over time using a random enqueue/dequeue process, returning depth values that can be used for monitoring or testing dashboards.
import collections
import random
import time
def simulate_queue_depth(max_depth=10, steps=20):
queue = collections.deque()
depth_history = []
for _ in range(steps):
# Randomly enqueue or dequeue
if random.random() < 0.6 and len(queue) < max_depth:
queue.append("task")
…
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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.