Python Code
Samples
Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Health Check Mark Unhealthy Stop Traffic Mock in Python
Simulates a health check with a 20% failure rate and automatically stops traffic when the service is unhealthy.
import time
import random
class HealthCheck:
def __init__(self):
self.is_healthy = True
self.stop_traffic = False
def check_health(self):
# Simulate health check with random failure rate (20% chance unhealthy)
self.is_healthy = random.random() > 0.2
return self.is_heal…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 Mock a Liveness Check and Restart a Process in Python
Simulate a failing process and restart it after a liveness check fails, using a mock class and a liveness loop.
import subprocess
import sys
import time
import os
class ProcessMock:
def __init__(self, name, fail_after_seconds=3):
self.name = name
self.fail_after = fail_after_seconds
self.start_time = None
self.is_running = False
def start(self):
self.start_time = time.time()
…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…Leaky Bucket Rate Limiter in Python: Smooth Burst Traffic
Implements a token-bucket-style leaky bucket rate limiter that smooths bursty traffic by draining at a fixed rate and dropping excess packets.
import time
import random
class LeakyBucket:
def __init__(self, capacity, drain_rate):
self.capacity = capacity
self.drain_rate = drain_rate
self.water = 0.0
self.last_time = time.time()
def allow(self, packet_size=1.0):
now = time.time()
elapsed = now - self.…Mock a Two-Phase Commit Coordinator in Python
Simulates a two-phase commit protocol where a coordinator asks participants to prepare, then commits or aborts based on unanimous readiness.
import random
import time
from typing import Dict, List
class TwoPhaseCommitCoordinator:
def __init__(self, participants: List[str]):
self.participants = participants
self.participant_state: Dict[str, bool] = {}
def prepare(self) -> bool:
print("[Coordinator] Phase 1: Prepare")
…Generate Synthetic SRE Metrics and Calculate Availability in Python
Create realistic service metrics with random latency, error rate, and request counts, then compute availability and summarize the stream for SLO checks.
from datetime import datetime, timedelta
import random
def generate_service_metrics(service_name: str, minutes: int = 30) -> list[dict]:
"""Generate synthetic SRE metrics for a service across recent minutes."""
metrics = []
now = datetime.now()
for i in range(minutes):
timestamp = now - t…How to Build a Consumer Lag Gauge in Python
Simulate Kafka consumer lag with a Python class that tracks lag over time and reports health and averages.
import time
import random
from collections import deque
class ConsumerLagGauge:
"""Mock consumer lag gauge measuring how far behind a consumer is."""
def __init__(self, producer_rate=10, consumer_rate=7, initial_lag=0):
self.producer_rate = producer_rate
self.consumer_rate = consumer_rate
…How to Calculate SLO Error Budget in Python
Simulate an SLO error budget by computing allowed downtime from a target availability percentage and mocking monthly incidents.
```python
import random
def calculate_error_budget(total_seconds: int, target_availability: float) -> float:
return (1.0 - target_availability) * total_seconds
def simulate_monthly_availability(seconds_in_month: int, budget_seconds: float) -> float:
# Mock: randomly consume a fraction of the error budget i…How to Mock Database Query Duration in Python
Simulate realistic database query durations with random jitter for testing dashboards, alerts, and SLO calculations.
import random
import time
def mock_query_duration(db_name, avg_ms, jitter_ms=5, runs=3):
"""Simulate database query durations with realistic variation."""
durations = []
for _ in range(runs):
# Base duration plus random jitter (can be negative)
duration = avg_ms + random.uniform(-jitter_m…How to Process System Metrics (RSS, CPU) in Python
Simulate and aggregate RSS and CPU system metrics to compute averages and maximums for monitoring dashboards.
import random
import time
from collections import namedtuple
Metric = namedtuple("Metric", ["name", "value", "unit"])
def generate_metrics(num_metrics: int = 5) -> list:
"""Simulate a batch of system metrics."""
metrics = []
for i in range(num_metrics):
rss = random.randint(50, 500) # MB
…How to Simulate Trace Sampling Head in Python
Simulate head-based probabilistic trace sampling on mock trace data with a configurable sample rate and optional seed for reproducibility.
import random
def trace_sampling_head(mock_traces, sample_rate=0.5, seed=None):
"""Simulate probabilistic trace sampling (head-based) on mock data.
Args:
mock_traces: list of trace dictionaries with a unique 'trace_id'
sample_rate: float 0.0-1.0, probability of keeping a trace
see…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")
…How to mock SLI availability success ratio in Python
Simulate request outcomes with deterministic randomness and compute the SLI availability success ratio to check if a target is met.
import random
from collections import defaultdict
def mock_availability(num_requests=1000, target_ratio=0.995):
"""
Simulate request outcomes and compute the SLI availability success ratio.
Args:
num_requests: Total number of requests to simulate
target_ratio: Target availability rati…How to Mock Eventual Consistency UI Notes in Python
Simulates a UI note that shows local state until a pending server update is confirmed, mocking eventual consistency behavior in distributed systems.
class EventualConsistencyNote:
def __init__(self, entity_id, note):
self.entity_id = entity_id
self.note = note
self.confirmed = False
self.pending_updates = []
def add_pending_update(self, update):
self.pending_updates.append(update)
def confirm_update(self):
…How to Mock a Server-Side Load Balancer in Python
A simple Python class that mimics a server-side load balancer with round-robin, random, and least-connections selection strategies.
import itertools
import random
class LoadBalancer:
def __init__(self, servers=None):
self.servers = servers if servers else ["server1", "server2", "server3"]
self.counter = itertools.count(1)
def round_robin(self):
return next(self.counter) % len(self.servers)
def random_selectio…How to Mock a Service Mesh Sidecar Proxy in Python
Simulate a service mesh sidecar proxy with route registration, service discovery, and request proxying using a simple Python class.
class SidecarProxy:
def __init__(self, name):
self.name = name
self.routes = {}
self.services = {}
self.requests_processed = 0
def register_service(self, service_name, address, port):
self.services[service_name] = f"{address}:{port}"
def add_route(self, path, servi…How to Mock Spark Streaming Micro-Batches in Python
Simulate Spark's micro-batch streaming with a simple deque-based class that collects events over time and processes them in timed batches.
import time
from collections import deque
from datetime import datetime
class MicroBatchStream:
def __init__(self, batch_interval_sec=2):
self.batch_interval = batch_interval_sec
self.source = deque()
self.processed = []
def add_events(self, events):
self.source.extend(events…Mock RDD in Python: Simulate Spark RDD Lazy Transformations
Simulate Apache Spark RDD behavior in Python with lazy maps, filters, partitions, and a collect action.
import random
def mock_rdd(data, num_slices=2):
"""
A simple simulation of Spark RDD behavior with lazy evaluation,
transformations, and an action.
"""
class SimpleRDD:
def __init__(self, data, num_slices=2):
self.data = data
self.num_slices = num_slices
…Sliding Window Streaming Mock in Python
A simple Python class that maintains a sliding window of recent streaming values and computes the running average.
import time
import random
class StreamingMock:
"""Produces a stream of numbers using a sliding window."""
def __init__(self, window_size=5):
self.window = []
self.window_size = window_size
def push(self, value):
"""Add a value, sliding the window forward."""
s…Champion Challenger Deployment Mock in Python
Simulates an A/B champion-challenger ML deployment workflow — comparing two mock model accuracies and deciding which to promote to production.
import random
import time
class ModelMocker:
def __init__(self, name="Model", accuracy=0.85):
self.name = name
self.accuracy = accuracy
def predict(self, data):
"""Simulate prediction with some randomness."""
time.sleep(0.005) # simulate compute time
return 1 if rando…How to Mock Shadow Mode Inference in Python
Simulates running multiple candidate models in shadow mode by adding randomized delays and returning their outputs alongside a primary model's output.
import random
import time
def shadow_mode_inference(candidates, mock_delay=0.1):
"""
Simulates running multiple candidate models in 'shadow mode'
by adding tiny randomized delays and returning their outputs
alongside the primary model's output.
"""
primary_output = "primary: answer"
shado…How to Simulate an Airflow ML Pipeline in Python
Mock an Airflow ML pipeline in plain Python by defining steps, simulating their execution with delays, and returning a success summary.
from datetime import datetime, timedelta
import time
class MLPipeline:
def __init__(self, pipeline_name):
self.pipeline_name = pipeline_name
self.steps = []
def add_step(self, step_name, duration_seconds):
self.steps.append({"name": step_name, "duration": duration_seconds})
def …Check Sample Ratio Mismatch in Python
Estimates the probability that a simple random sample's proportion differs from the population proportion by more than 10% using simulation.
import random
def sample_ratio_mismatch(population_size: int, sample_size: int, p: float) -> float:
"""
Estimate the probability that a simple random sample's proportion
differs from the population proportion by more than 10%.
"""
total_counts = [0, 0]
for _ in range(10000):
sample = …Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
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.