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How to Implement an Adaptive Rate Limiter in Python
Build an adaptive rate limiter that adjusts request intervals dynamically based on recent error rates, slowing down when failures spike.
import time
import random
class AdaptiveRateLimiter:
"""Simple adaptive rate limiter that reduces requests when error rate is high."""
def __init__(self, min_interval=0.1, max_interval=2.0, error_threshold=0.3):
self.min_interval = min_interval
self.max_interval = max_interval
sel…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…Implement a Circuit Breaker Pattern in Python
This code implements a simple circuit breaker that opens after a threshold of consecutive failures, causing subsequent calls to fail fast without invoking the underlying function.
class CircuitBreaker:
def __init__(self, failure_threshold=3):
self.failure_threshold = failure_threshold
self.failure_count = 0
self.open = False
def call(self, func, *args, **kwargs):
if self.open:
raise RuntimeError("Circuit is open - failing fast")
try:
…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.…Retry with Exponential Backoff and Jitter in Python
A decorator-style retry wrapper that retries a flaky function with exponential backoff plus random jitter, then raises after the last attempt fails.
import random
import time
def retry_with_backoff(func, max_retries=3, base_delay=0.5, max_jitter=0.1):
for attempt in range(max_retries + 1):
try:
return func()
except Exception as e:
if attempt == max_retries:
raise
delay = base_delay * (2 ** at…Saga Compensating Transaction Mock in Python
Simulates a distributed transaction using a saga pattern with compensating actions that roll back steps on failure.
import random
import time
class OrderService:
def __init__(self):
self.orders = {}
def create_order(self, order_id):
print(f"[Order] Creating order {order_id}...")
time.sleep(0.1)
if random.random() < 0.3: # 30% chance of failure
raise RuntimeError(f"Order {order…Bulkhead Thread Pool per Service Mock in Python
Simulates a bulkhead pattern with per-service thread pools and semaphore-based rejection to isolate failures between dependent services.
import threading
import time
import random
from concurrent.futures import ThreadPoolExecutor
class ServiceBulkhead:
def __init__(self, name, max_threads, max_queue):
self.name = name
self.executor = ThreadPoolExecutor(max_workers=max_threads)
self.semaphore = threading.Semaphore(max_thread…Fallback cached response mock in Python
Wraps a mock function with a fallback to a real service and caches results to mask transient failures.
import time
from functools import wraps
class CachedMock:
def __init__(self, cache_ttl=5):
self.cache = {}
self.cache_ttl = cache_ttl
def get(self, key):
cached = self.cache.get(key)
if cached and time.time() - cached["timestamp"] < self.cache_ttl:
return cached["v…How to Build an Anti-Corruption Layer in Python
Translate messy legacy system data into a clean domain model using an anti-corruption layer in Python.
class MockLegacySystem:
"""Simulates a legacy system with messy data formats."""
def get_user_data(self):
# Legacy format: fields are abbreviated and types are inconsistent
return {
"usr_id": "USR-123",
"usr_nm": "john_doe",
"email_addrs": "John.Doe@example.c…How to Implement a Two-Phase Commit Mock in Python
Simulate a distributed two-phase commit with prepare, commit, and abort phases, including deterministic failure injection for testing.
import random
from dataclasses import dataclass
from typing import Dict, List, Optional
@dataclass
class Transaction:
tx_id: int
data: Dict[str, str]
class TwoPhaseCommitMock:
"""Simple two-phase commit mock with prepare and commit phases."""
def __init__(self) -> None:
self.prepared: List…How to implement a circuit breaker in Python
A Python CircuitBreaker class that tracks failures, opens after a threshold, and retries after a timeout.
class CircuitBreaker:
def __init__(self, failure_threshold=3, timeout=5):
self.failure_threshold = failure_threshold
self.timeout = timeout
self.failure_count = 0
self.last_failure_time = None
self.state = "CLOSED"
def call(self, mock_downstream):
if self.state …Python Saga Compensating Steps Mock
Mock a distributed transaction saga with forward steps and compensating actions that reverse partial progress on failure.
from datetime import datetime
def make_payment(user_id, amount):
print(f"[{datetime.now():%H:%M:%S}] Payment of ${amount} processed for user {user_id}")
return {"step": "payment", "status": "ok", "details": f"${amount} charged"}
def deduct_inventory(order_id, items):
print(f"[{datetime.now():%H:%M:%S}]…Saga pattern orchestration with rollback in Python
Orchestrate a distributed transaction with Saga steps and automated compensation rollback on failure.
import time
import random
class SagaStep:
def __init__(self, name):
self.name = name
self.executed = False
def execute(self):
print(f"Executing {self.name}...")
time.sleep(0.2)
if random.random() < 0.3:
raise RuntimeError(f"{self.name} failed")
sel…Detect Concept Drift in Python with a Simple Statistical Test
Detect concept drift by comparing the mean of recent data against a reference distribution using a z-score-like threshold.
import random
import statistics
def detect_drift(recent, reference, threshold=1.5):
ref_mean = statistics.mean(reference)
ref_std = statistics.stdev(reference)
recent_mean = statistics.mean(recent)
drift_score = abs(recent_mean - ref_mean) / (ref_std if ref_std > 0 else 1)
drifted = drif…How to Build a Mock ML Pipeline with Prefect in Python
Create a lightweight Prefect flow with mock preprocessing, training, and evaluation tasks to prototype an ML pipeline end-to-end.
from prefect import task, flow
from datetime import datetime
@task
def preprocess_data(raw_value: float) -> float:
"""Mock preprocessing: normalize the input value."""
return raw_value / 100.0
@task
def train_model(features: float) -> dict:
"""Mock training: return a fake model artifact."""
return …How to Build an sklearn Pipeline with ColumnTransformer in Python
A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…How to Mock ROC AUC in Python
Compute ROC AUC from scratch in Python using pairwise comparisons between positive and negative score distributions, ideal for testing ML models without sklearn.
import random
from math import comb
def mock_roc_auc(scores, labels):
"""Compute mock ROC AUC by simulating a classifier's score distribution."""
random.seed(42)
n = len(labels)
pos_scores = [scores[i] for i in range(n) if labels[i] == 1]
neg_scores = [scores[i] for i in range(n) if labels[i] == …How to Mock a Kubeflow Pipeline in Python
Build a minimal in-memory mock of a Kubeflow pipeline DAG using dataclasses and OrderedDict to chain component functions.
from typing import Dict, Any
from dataclasses import dataclass, field
from collections import OrderedDict
@dataclass
class KubeflowPipelineMock:
"""A minimal mock of a Kubeflow pipeline DAG."""
name: str
components: OrderedDict[str, callable] = field(default_factory=OrderedDict)
def add_component(se…How to Train a Gradient Boosting Regressor in Python
Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
def train_gradient_boosting_mock():
# Toy regression dataset
np.random.seed(42)
X = np.random.rand(100, 3) * 10
y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…Train Logistic Regression From Scratch in Python
Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.
import numpy as np
# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])
# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]
# Initialize parameters
theta = np.zeros(X_b.shape[1])
# Hyperparameters
learning_rate = 0…Training Pipeline Orchestration Mock DAG in Python
Build a mock DAG orchestrator that runs ML pipeline stages in dependency order using topological sorting (Kahn's algorithm).
from collections import deque
from dataclasses import dataclass, field
@dataclass
class DAGNode:
name: str
task: callable
dependencies: list[str] = field(default_factory=list)
class MockDAG:
def __init__(self, nodes: list[DAGNode]):
self.nodes = {n.name: n for n in nodes}
self.execu…How to Explain SQLite Query Plans in Python
Build a Python function that runs EXPLAIN QUERY PLAN on SQLite in-memory tables and prints the optimizer's execution plan for any SELECT statement.
import sqlite3
def explain_query(sql: str) -> str:
"""Return the SQLite query plan for the given SQL statement."""
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
# Create sample data for a realistic plan
cursor.execute("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT)")
c…How to Mock a Cross-Shard Saga in Python
Simulate a distributed saga with compensating transactions across multiple database shards using a lightweight Python class that tracks executed steps and rolls them back in reverse on failure.
import json
class SagaState:
def __init__(self, saga_id):
self.saga_id = saga_id
self.executed_steps = []
self.compensations = []
def execute_step(self, shard, step_name, operation):
self.executed_steps.append((shard, step_name))
print(f"[Saga {self.saga_id}] Executin…Browse by section
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