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How to Build a Rate Limiter in Python
Implements a simple sliding-window rate limiter that caps the number of calls per period, used to throttle processing of a data list.
import time
class RateLimiter:
def __init__(self, max_calls, period):
self.max_calls = max_calls
self.period = period
self.timestamps = []
def allow(self):
now = time.time()
self.timestamps = [t for t in self.timestamps if now - t < self.period]
if len(self.tim…How to implement an idempotency key store in Python
Build an in-memory idempotency key store with TTL that processes a request once and reuses the cached result for duplicate calls.
import hashlib
import time
from typing import Dict, Optional
class IdempotencyStore:
"""Simple in-memory idempotency key store with mock processing."""
def __init__(self, ttl_seconds: int = 3600) -> None:
self.ttl = ttl_seconds
self._store: Dict[str, tuple[str, float]] = {}
def _is_expi…How to Flush Metrics on Graceful Shutdown in Python
Register an atexit handler to automatically flush collected metrics when a Python process exits gracefully.
import atexit
import time
import random
class MetricsCollector:
def __init__(self):
self._metrics = []
atexit.register(self.flush)
def record(self, name, value):
self._metrics.append((name, value, time.time()))
def flush(self):
print(f"Flushing {len(self._metrics)} metri…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 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 Check an External Gateway vs Use an Internal Mock in Python
This code checks whether an external network gateway is reachable using ping, then falls back to a deterministic internal mock for testing environments.
import subprocess
import sys
def check_external_gateway():
"""True if we can reach an external network target."""
try:
subprocess.run(
["ping", "-c", "1", "-W", "2", "8.8.8.8"],
capture_output=True,
timeout=3,
check=True,
)
return True
…How to Deduplicate Events in Python with SHA256 Hashing
Build an event deduplicator that identifies duplicate inbox messages using SHA256 hashes and tracks duplicate counts per event type.
```python
import hashlib
import json
from collections import defaultdict
class EventDeduplicator:
def __init__(self):
self.seen_hashes = set()
self.duplicate_counts = defaultdict(int)
def process_event(self, event):
event_key = f"{event['event_id']}:{event['timestamp']}"
even…Idempotent Consumer Event Processing in Python
Track processed event IDs to skip duplicates and count event types for a reliable, idempotent consumer.
import json
from collections import defaultdict
class EventProcessor:
def __init__(self):
self.processed_ids = set()
self.counts = defaultdict(int)
def process_event(self, event):
event_id = event["id"]
if event_id in self.processed_ids:
return {"status": "skipped"…How to Load, Save, and Split JSON Data in Python
Provides helper functions to load, save, and split JSON dictionary data for simple ML pipeline preprocessing.
import json
from pathlib import Path
def load_json_data(file_path):
"""Load JSON data from a file, returning an empty dict if missing."""
path = Path(file_path)
if path.exists():
with path.open("r", encoding="utf-8") as f:
return json.load(f)
return {}
def save_json_data(data, f…How to do feature selection with VarianceThreshold in Python
This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.
import numpy as np
from sklearn.feature_selection import VarianceThreshold
def main():
# Mock dataset: 4 samples, 5 features
X = np.array([
[0.1, 0.2, 1.0, 1.0, 0.5],
[0.2, 0.2, 0.0, 1.0, 0.4],
[0.1, 0.2, 1.0, 1.0, 0.6],
[0.3, 0.2, 1.0, 0.0, 0.5]
])
# Select features w…One Hot Encode Categories in Python
Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.
import numpy as np
categories = ["red", "green", "blue", "red", "blue", "green", "red"]
unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}
one_hot = []
for cat in categories:
row = [0] * len(unique)
row[lookup[cat]] = 1
one_hot.append(row)
print("Categories:", categories…StandardScaler mock in Python
A pure-Python StandarScaler class that standardizes features to zero mean and unit variance without sklearn.
import math
class StandardScaler:
def __init__(self):
self.mean_ = None
self.std_ = None
def fit(self, X):
n = len(X)
self.mean_ = [sum(col) / n for col in zip(*X)]
self.std_ = []
for col in zip(*X):
variance = sum((x - self.mean_[i]) ** 2 for i, x …How to Limit a Result Set to Top N Rows in Python
Sort a list of dictionaries by a numeric key and return only the top N results, formatted as a readable ranked list.
import random
def top_n_mock(limit: int = 5):
"""Return a formatted top-N result set as a mock example."""
# Simulated data source
scores = [
{"name": "Alice", "score": 87},
{"name": "Bob", "score": 92},
{"name": "Charlie", "score": 78},
{"name": "Diana", "score": 95},
…Docker healthcheck CMD mock in Python
Runs a subprocess to curl a health endpoint and returns exit code 0 when healthy, 1 when unhealthy, mimicking a Docker HEALTHCHECK command.
import subprocess
import sys
def run_healthcheck() -> int:
result = subprocess.run(["curl", "-fsS", "http://localhost:8080/health"], capture_output=True, text=True)
if result.returncode == 0:
print("healthy")
return 0
print("unhealthy", file=sys.stderr)
return 1
if __name__ == "__ma…How to Build a Data Helper for Production Deployment in Python
Build a reusable DataHelper class that loads configs, validates required keys, normalizes string values, and logs schema details — a production-ready data processing pattern.
import json
from pathlib import Path
from typing import Any, Dict
class DataHelper:
"""Common data processing patterns for production deployment."""
def __init__(self, config_path: str | Path):
self.config_path = Path(config_path)
self.config = self._load_config()
def _load_confi…How to Mock a Dockerfile Multi-Stage Build in Python
Simulate a Dockerfile multi-stage build process in Python using dataclasses to validate stage ordering and file availability before you write the real Dockerfile.
from dataclasses import dataclass
from pathlib import Path
@dataclass
class BuildStage:
name: str
base_image: str
files: list[str]
commands: list[str]
def run_build(stage: BuildStage, context_dir: Path):
print(f"=== Stage: {stage.name} (base: {stage.base_image}) ===")
for file in stage.file…How to Mock a SIGTERM Handler in Python
Create a graceful shutdown handler for SIGTERM and SIGINT signals, then test it by simulating a signal delivery without terminating the process.
import signal
import time
class Service:
def __init__(self):
self.running = True
def shutdown(self, signum, frame):
print(f"Received signal {signum}, shutting down gracefully...")
self.running = False
def run(self):
signal.signal(signal.SIGTERM, self.shutdown)
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