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How to Parametrize pytest Tests with Multiple Input Cases in Python
This code shows how to use pytest's @pytest.mark.parametrize decorator to run the same test function across multiple input-output combinations, checking that an add function behaves correctly for each case.
import pytest
def add(a, b):
return a + b
@pytest.mark.parametrize("a,b,expected", [
(1, 2, 3),
(5, 5, 10),
(-1, 1, 0),
(0, 0, 0),
(10, -3, 7),
])
def test_add(a, b, expected):
assert add(a, b) == expected
if __name__ == "__main__":
pytest.main([__file__, "-v"])How to Share Fixtures Across Tests with pytest conftest
Learn how to define pytest fixtures in conftest.py and control their scope (function, module, session) so every test in a directory reuses the same setup and teardown.
import pytest
@pytest.fixture
def sample_data():
"""Simple fixture available to all tests in this directory."""
return {"name": "Alice", "age": 30}
@pytest.fixture(scope="session")
def session_data():
"""Fixture created once per test session."""
return {"session_id": 12345}
@pytest.fixture(scope="mo…How to Test Environment Variables with pytest monkeypatch in Python
Shows how to use pytest's monkeypatch fixture to set and delete environment variables for isolated tests.
import os
import pytest
def get_database_url():
return os.getenv("DATABASE_URL", "postgres://default")
def test_database_url_with_env(monkeypatch):
monkeypatch.setenv("DATABASE_URL", "postgres://test-db")
assert get_database_url() == "postgres://test-db"
def test_database_url_default(monkeypatch):
m…How to Use mock.assert_called_with in Python
Verify that a MagicMock received a call with specific positional and keyword arguments using assert_called_with in unittest.
import unittest
from unittest.mock import MagicMock
class TestMockAssertions(unittest.TestCase):
def test_assert_called_with(self):
# Create a mock object
mock = MagicMock()
# Call the mock with specific arguments
mock.send_email("alice@example.com", subject="Greetings", body="Hel…How to Build a Simple Service Discovery Registry in Python
A lightweight in-memory service registry class using a dict — register, deregister, and discover services with host, port, and version.
class ServiceRegistry:
def __init__(self):
self._services = {}
def register(self, name, host, port, version="1.0"):
self._services[name] = {
"host": host,
"port": port,
"version": version
}
def deregister(self, name):
return self._servic…How to Build a Weighted Random Load Balancer in Python
A Python load balancer mock that distributes requests across servers based on configurable weights using a cumulative weighted random selection algorithm.
import random
from collections import Counter
SERVERS = {
"server-a": 50,
"server-b": 30,
"server-c": 20,
}
def weighted_random_server(servers: dict[str, int]) -> str:
"""Select a server based on its weight (higher weight = more likely)."""
total_weight = sum(servers.values())
rand = random.…How to Implement the Repository Pattern in Python with an In-Memory Dict
Stores, retrieves, updates, and deletes user records in memory using a Repository abstraction over a plain dict, isolating data access from business logic.
class UserRepository:
def __init__(self):
self._storage = {}
self._next_id = 1
def create(self, name, email):
user_id = self._next_id
self._next_id += 1
self._storage[user_id] = {"id": user_id, "name": name, "email": email}
return self._storage[user_id]
def…How to mock the domain center in an onion architecture in Python
Define a repository interface and an in-memory mock to test domain services without touching infrastructure.
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Dict, List, Optional
@dataclass
class Order:
id: int
customer: str
items: List[str]
total: float
class OrderRepository(ABC):
@abstractmethod
def find_by_id(self, order_id: int) -> Optional[Order]:
…Round Robin Load Balancer in Python
This code simulates round robin load balancing by distributing a list of requests evenly across a list of servers.
def round_robin_servers(requests: list[str], servers: list[str]) -> dict[str, list[str]]:
assignments = {server: [] for server in servers}
for idx, request in enumerate(requests):
server = servers[idx % len(servers)]
assignments[server].append(request)
return assignments
if __name__ == "_…How to Mock Content-Disposition and Extract Filename in Python
Parse and mock Content-Disposition headers in Python to extract filenames, handling both plain and RFC 5987 encoded values.
import os
from pathlib import Path
import re
from unittest.mock import patch
def get_filename_from_content_disposition(header_value):
"""
Extract filename from a Content-Disposition header value.
Supports both filename and filename* parameters (RFC 5987).
"""
if not header_value:
return No…How to Mock a GraphQL Query Type in Python
Create a lightweight mock of a GraphQL Query type to simulate repository lookups without a server.
import json
class Query:
def __init__(self):
self.starred_repos = [
{"id": 1, "name": "graphql", "owner": "graphql"}
]
def repository(self, name):
if name == "graphql":
return {"id": 1, "name": "graphql", "stargazerCount": 85000}
return None
if __name…How to mock a REST POST endpoint in Python
Create a simple mock REST server that responds to POST requests with a 201 status and a JSON body.
import json
from http.server import BaseHTTPRequestHandler, HTTPServer
class MockHandler(BaseHTTPRequestHandler):
def do_POST(self):
content_length = int(self.headers.get("Content-Length", 0))
body = self.rfile.read(content_length) if content_length else b"{}"
try:
data = json…Serve Swagger UI with Python's built-in HTTP server
Hosts a self-contained Swagger UI with a mock OpenAPI spec using only Python's standard library HTTP server.
from http.server import HTTPServer, SimpleHTTPRequestHandler
import os
import tempfile
SWAGGER_HTML = """<!DOCTYPE html>
<html>
<head>
<title>Mock Swagger UI</title>
<link rel="stylesheet" href="https://unpkg.com/swagger-ui-dist@4/swagger-ui.css">
</head>
<body>
<div id="swagger-ui"></div>
<script src…At Most Once Fire-and-Forget Mock in Python
A Python mock that enforces send() is called at most once and records the arguments for verification.
class FireForgetMock:
def __init__(self):
self._calls = 0
self._last_args = None
self._last_kwargs = None
def send(self, *args, **kwargs):
if self._calls > 0:
raise RuntimeError("send() called more than once")
self._calls += 1
self._last_args = args
…Dedupe processed message IDs in Python
Filters an inbox of messages by removing items whose IDs have already been processed, using a set for fast lookups.
from pathlib import Path
import json
def dedupe_processed_ids(inbox_file: Path, processed_file: Path) -> list:
processed = set(json.loads(processed_file.read_text()))
inbox = json.loads(inbox_file.read_text())
deduped = [item for item in inbox if item["id"] not in processed]
return deduped
if __nam…Event Envelope with Schema Version Field in Python
Build a typed event envelope dataclass with an explicit schema version field for mock streaming scenarios.
from dataclasses import dataclass, field
from datetime import datetime
import uuid
@dataclass
class Event:
event_id: str = field(default_factory=lambda: str(uuid.uuid4()))
event_type: str = "user.created"
version: str = "1.0.0"
created_at: str = field(default_factory=lambda: datetime.utcnow().isoform…How to Build a Mock Change Data Capture Event Stream in Python
Generate a deterministic list of mock CDC events with event IDs, stream positions, payloads, and timestamps for testing streaming pipelines.
from itertools import count
from random import choice, randint, seed
from datetime import datetime, timedelta
seed(42) # Make output deterministic
event_types = ["INSERT", "UPDATE", "DELETE"]
table_names = ["users", "orders", "products", "payments"]
counter = count(1)
def mock_cdc_event(stream_index: int) -> dict:
…How to Mock MQTT Topic Subscriptions with QoS in Python
Build a lightweight MQTT client mock that tracks topic subscriptions with QoS levels and simulates wildcard message delivery.
import time
from collections import defaultdict
class MockMQTTClient:
def __init__(self):
self.subscriptions = defaultdict(list)
self.messages = []
def subscribe(self, topic, qos=0):
self.subscriptions[topic].append(qos)
print(f"Subscribed to '{topic}' with QoS {qos}")
…Chaos Inject Random Failures in Python
Simulate random failures in a Python function to test error handling and resilience, using random thresholds and controllable success rates.
import random
def unreliable_function(success_rate: float = 0.7) -> str:
"""Simulate a function that sometimes fails."""
if random.random() > success_rate:
raise ConnectionError("Simulated network failure")
return "Operation completed successfully"
if __name__ == "__main__":
random.seed(42)…How to Inject Random Latency for Chaos Testing in Python
Mock unreliable services by wrapping functions with a decorator that adds random network-like delays before execution.
import random
import time
from functools import wraps
def inject_latency(func):
@wraps(func)
def wrapper(*args, **kwargs):
latency = random.uniform(0.1, 0.5)
print(f"Injecting {latency:.3f}s latency...")
time.sleep(latency)
return func(*args, **kwargs)
return wrapper
@inje…Rate Limiting with a Simple Python RateLimiter Class
A beginner-friendly Python rate limiter that tracks call timestamps and enforces a maximum number of calls within a rolling time window, with a helper to validate positive integers.
import time
class RateLimiter:
def __init__(self, max_calls, period_seconds):
self.max_calls = max_calls
self.period_seconds = period_seconds
self.calls = []
def is_allowed(self):
now = time.time()
while self.calls and now - self.calls[0] >= self.period_seconds:
…Generate Mock CPU and Memory Metrics in Python
Build a mock_host_metrics() generator that outputs realistic CPU and memory usage percentages for monitoring demos and tests.
import time
import random
def mock_host_metrics():
"""Generate mock CPU and memory metrics for a host."""
cpu_percent = round(random.uniform(10.0, 95.0), 1)
memory_percent = round(random.uniform(20.0, 90.0), 1)
memory_used_mb = round(random.uniform(512, 8192), 1)
return {
"timestamp": in…Generate Prometheus Text Exposition Format in Python
Mock a Prometheus metrics endpoint by formatting metrics into the text exposition format with HELP, TYPE, and sample lines.
import time
from random import randint
# Mock a Prometheus metrics endpoint output
metrics = {
"http_requests_total": {
"help": "Total number of HTTP requests",
"type": "counter",
"samples": [
{"labels": {"method": "get", "code": "200"}, "value": randint(1000, 9999)},
…Generate Synthetic CPU Utilization Metrics in Python
Creates realistic time-series CPU utilization samples with timestamps, noise, and output as structured JSON for observability demos and testing.
from datetime import datetime, timedelta
import random
import json
def generate_metric_samples(base_value, noise, count=60, interval_minutes=1):
"""Generate realistic CPU utilization samples for a given time window."""
timestamps = []
values = []
now = datetime.utcnow()
start_time = now - timede…Browse by section
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