Reference library

Production deployment patterns

Graceful shutdown, prod config, rollouts, readiness probes, and ship-with-confidence checks.

6 matches

Auto Rollback on Error Rate Exceeded in Python

Simulate a service that monitors a rolling window of request errors and automatically rolls back when the error rate exceeds a threshold.

error-rate rollback rolling-window

Python

import random
import time


def simulate_requests(total_requests=1000, rollback_threshold=0.2):
    """
    Simulate a service that automatically rolls back when the error rate
    exceeds a threshold within a rolling window.
    """
    window_size = 100
    errors_seen = []
    rolled_back = False

    for req_num i…

15 0 Open

Production deployment patterns medium

Automate Semantic Versioning with Conventional Commits in Python

Automatically bump a semantic version based on conventional commit messages (feat, fix, BREAKING CHANGE) and write the new version to a file.

semantic-versioning conventional-commits automation

Python

import re
from pathlib import Path


def get_next_version(current: str, commit_messages: list[str]) -> str:
    """Return the next semantic version based on conventional commit messages."""
    major, minor, patch = map(int, current.split("."))
    if any(msg.startswith("BREAKING CHANGE") for msg in commit_messages):
…

14 0 Open

Production deployment patterns medium

How to Drain a Connection Pool Before Exit in Python

Gracefully close all pooled sockets using a thread-safe ConnectionPool that drains connections before program exit.

connection-pool sockets threading

Python

import socket
import threading
import time
import random

class ConnectionPool:
    def __init__(self, size=5):
        self.pool = []
        self.lock = threading.Lock()
        self.closed = False
        for _ in range(size):
            self.pool.append(self.create_connection())
    
    def create_connection(sel…

13 0 Open

Production deployment patterns easy

How to Implement a Data Helper Class in Python for Production Deployments

Build an environment-aware data helper in Python that loads config, extracts, transforms, and reports on JSON data using small, testable functions.

data-helper production json

Python

"""Production-style data helper for beginners.

Demonstrates:
- environment-aware config
- central data extraction
- small, testable functions
"""

import os
import json
from pathlib import Path
from typing import List, Dict, Any


def load_config(env: str = os.getenv("APP_ENV", "development")) -> Dict[str, Any]:
    …

12 0 Open

Production deployment patterns easy

How to Mock a Feature Flag Rollout Percentage in Python

Simulate a percentage-based feature flag rollout by hashing a user ID to deterministically enable features for a subset of users.

feature-flags rollout deterministic

Python

import random
from dataclasses import dataclass


@dataclass
class FeatureFlag:
    name: str
    rollout_percentage: int


def is_feature_enabled(feature_flag: FeatureFlag, user_id: str) -> bool:
    hashed_id = hash(user_id) % 100
    return hashed_id < feature_flag.rollout_percentage


if __name__ == "__main__":
  …

11 0 Open

Production deployment patterns easy

How to simulate GitLab CI stages in Python

Build a lightweight Python mock of GitLab CI pipeline stages to test job sequencing and output locally.

gitlab ci simulation

Python

def mock_gitlab_ci_stages():
    stages = ["build", "test", "deploy"]
    stage_status = {}

    for stage in stages:
        jobs = []

        if stage == "build":
            jobs = ["compile", "package"]
        elif stage == "test":
            jobs = ["unit", "integration", "e2e"]
        elif stage == "deploy":…

14 0 Open

Browse by section

Each section groups closely related Python snippets.