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How to Mock Redis Pipeline Batch Commands in Python
Create a lightweight MockRedis class that simulates Redis pipeline batching with SET, GET, and DELETE operations for testing without a live server.
import redis
import time
class MockRedis:
def __init__(self):
self.data = {}
def pipeline(self):
return MockPipeline(self)
def execute(self, commands):
results = []
for cmd in commands:
op, args = cmd[0], cmd[1:]
if op == "SET":
se…How to use Redis MGET MSET pipeline in Python
Store multiple keys atomically and read them efficiently with Redis MSET/MGET, then batch commands with a pipeline to cut round trips.
import redis # v4.x+ required
r = redis.Redis(host="localhost", port=6379, db=0, decode_responses=True)
# Sample data to store
r.flushdb()
data = {"name": "Alice", "age": "30", "city": "Berlin"}
# MSET: store multiple key-value pairs in one command
r.mset(data)
# MGET: fetch multiple keys in one round trip
keys =…Mock Redis Lua Script Atomic Execution in Python
A MockRedis class that simulates atomic Lua script execution via EVALSHA with a simplified parser for basic commands.
import hashlib
class MockRedis:
def __init__(self):
self.data = {}
self.scripts = {}
def script_load(self, script):
sha = hashlib.sha1(script.encode()).hexdigest()
self.scripts[sha] = script
return sha
def evalsha(self, sha, keys, args):
if sha not in self…Refresh Proactive TTL Renewal in Python
This snippet implements a proactive TTL renewal pattern that refreshes a cache expiration before it lapses, using a mock counter to track renewals.
import time
from datetime import datetime, timezone
class TTLRenewer:
def __init__(self, ttl_seconds=10, renew_at=0.5):
self.ttl = ttl_seconds
self.last_renewed = time.time()
self.renew_threshold = ttl_seconds * renew_at
self.renewals = 0
def check_and_renew(self):
if …How to Implement a Token Bucket Rate Limiter per Client IP in Python
Implements a simple sliding-window rate limiter using a dictionary of timestamp lists per client IP to limit requests per window.
from time import time
from collections import defaultdict
class RateLimiter:
def __init__(self, max_requests: int, window_seconds: int):
self.max_requests = max_requests
self.window_seconds = window_seconds
self.clients = defaultdict(list)
def allow(self, ip: str) -> bool:
now…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")
…How to Build a Burn Rate Alert with Multiple Time Windows in Python
Track token consumption and trigger alerts when the burn rate exceeds a threshold across multiple time windows using deque and time-based sliding windows.
import time
from collections import deque
class BurnRateAlert:
def __init__(self, windows_seconds=(60, 300, 900), threshold_rate=0.8):
self.windows = {w: deque() for w in windows_seconds}
self.threshold_rate = threshold_rate
self.previous_tokens = None
def record_sample(self, current_…How to Create a Mock OpenTelemetry Trace in Python
Create a mock OpenTelemetry trace in memory to test span creation, attributes, and parent-child relationships without exporting to a backend.
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
def create_mock_trace():
tracer_provider = TracerProvider()
span_exporter =…How to Ship Logs to an Aggregator Endpoint in Python
Ship batched log entries to a mock HTTP aggregator endpoint with proper error handling and response status.
import json
import requests
from datetime import datetime, timezone
LOG_ENTRIES = [
{"timestamp": "2024-01-15T10:00:00Z", "level": "INFO", "message": "Server started"},
{"timestamp": "2024-01-15T10:00:05Z", "level": "WARN", "message": "High memory usage"},
{"timestamp": "2024-01-15T10:00:10Z", "level": "E…Rotate Log Files by Size in Python
A mock log rotation script that renames log files exceeding a size threshold, appending numbered backups.
import os
from pathlib import Path
def rotate_logs(directory: str, max_size: int = 100) -> None:
"""Rotate log files that exceed max_size bytes."""
log_dir = Path(directory)
for log_file in sorted(log_dir.glob("*.log"), key=lambda p: str(p)):
if log_file.stat().st_size > max_size:
for …BFF aggregation pattern: combine multiple service responses in Python
Mock three backend services and aggregate their responses into one unified payload — the BFF pattern every Python microservice gateway relies on.
from dataclasses import dataclass
from typing import Any
@dataclass
class Service:
name: str
data: dict[str, Any]
def get_user_service() -> Service:
return Service("user", {"id": 1, "name": "Alice"})
def get_orders_service() -> Service:
return Service("orders", {"total": 299.99, "count": 2})
de…How to Compose Parallel API Calls in Python with asyncio.gather
Compose multiple mock API responses in parallel using asyncio.gather with per-service simulated latency.
import asyncio
import random
import time
async def mock_api(name: str, delay: float) -> dict:
await asyncio.sleep(delay)
return {"service": name, "value": random.randint(1, 100)}
async def fetch_all():
services = {
"users": mock_api("users", 0.2),
"orders": mock_api("orders", 0.3),
…How to Demonstrate the Shared Database Antipattern in Python
This code simulates a shared database where multiple services write and read the same SQLite table, illustrating tight coupling and its pitfalls.
import sqlite3
from pathlib import Path
def create_shared_db(db_path: Path) -> None:
"""Mock demonstrating the shared database antipattern where multiple
services access the same database, causing tight coupling."""
conn = sqlite3.connect(db_path)
cur = conn.cursor()
cur.execute("""
CREATE…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 Explode an Array Column in Python
This code demonstrates a mock explode operation that converts an array column into multiple rows, similar to Spark's explode function.
import json
def explode_array_column(data, column):
"""Mock explode: split array column into multiple rows."""
exploded = []
for row in data:
values = row.get(column, [])
for value in values:
new_row = dict(row)
new_row[column] = value
exploded.append(n…How to Implement MapReduce Word Count in Python Using a Dict
Simulate a MapReduce word count pipeline in Python with a mock dict, splitting text into words, shuffling, and reducing to frequency counts.
def map_reduce_word_count(text: str) -> dict:
"""Simulate a MapReduce pipeline to count word frequencies."""
# MAP phase: split into words and emit (word, 1) pairs
mapped = []
for word in text.lower().split():
# Clean word of punctuation
clean_word = ''.join(char for char in word if cha…How to Implement a Mock MapReduce for Word Count in Python
Simulates a MapReduce word count pipeline with mapper, shuffle, and reducer phases using Python dicts and standard library modules.
from collections import defaultdict
import re
def mapper(text):
"""Split text into words and emit (word, 1) pairs."""
words = re.findall(r'\b\w+\b', text.lower())
return [(word, 1) for word in words]
def reducer(pairs):
"""Group word-count pairs and sum counts."""
counts = defaultdict(int)
fo…How to Mock a User-Defined Function (UDF) in Python
Wrap a real UDF implementation with call logging to simulate and track invocations in a data pipeline.
from typing import Any, Callable
# Mock a user-defined function (UDF) that was previously complex or external
def mock_udf(name: str, implementation: Callable[..., Any], *, calls: list[Any]) -> Callable[..., Any]:
"""Wrap a real implementation with call logging to simulate a UDF."""
def wrapper(*args: Any, *…How to Simulate a MapReduce Mock with Combine Phase in Python
Simulates a MapReduce pipeline with a combiner that aggregates local counts per reducer to reduce network and compute overhead.
from collections import defaultdict
def map_phase(lines):
intermediate = defaultdict(list)
for line in lines:
for word in line.strip().lower().split():
intermediate[word].append(1)
return dict(intermediate)
def combine_phase(intermediate, num_reducers=3):
combined = defaultdict(li…How to Truncate Lineage Back to a Checkpoint in Python
Walks a linked list of lineage nodes upward to find the nearest checkpoint and returns that node, truncating the lineage.
class LineageNode:
def __init__(self, name, parent=None, checkpoint=None):
self.name = name
self.parent = parent
self.checkpoint = checkpoint
def truncate_at_checkpoint(self):
"""Truncate lineage back to the last checkpoint."""
current = self
while current.check…Lazy Evaluation Transform Lineage Mock in Python
Build a mock lineage tracker for data transforms using lazy evaluation and function wrappers in Python.
import functools
def lazy_transform(pipeline):
"""Build a mock lineage tracker using lazy evaluation."""
lineage = []
def wrap(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
lineage.append({"transform": func.__name__, "a…Modeling a Hive Metastore Table Schema in Python
A dataclass that mimics a Hive metastore table schema—columns, partition keys, storage format, and location—with helper methods for description and mutation.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class HiveTable:
"""Simple mock of a Hive metastore table schema."""
name: str
database: str = "default"
columns: List[Dict[str, str]] = field(default_factory=list)
partition_keys: List[Dict[str, str]] = f…Build a Data Helper Class in Python for ML Pipelines
A beginner-friendly Python class that summarizes, filters, and exports ML dataset rows as JSON.
from typing import List, Dict, Any
import json
class DataHelper:
"""Beginner-friendly helpers for ML data pipelines."""
def __init__(self, data: List[Dict[str, Any]]):
self.data = data
self.keys = list(data[0].keys()) if data else []
def summary(self) -> Dict[str, Any]:
"…Compare Model A vs Model B Metrics in Python
A script that simulates and compares metrics between two ML models, showing a formatted diff table for quick insight.
import random
def compare_a_b(samples=5):
"""Mock comparison of model A vs model B predictions."""
metrics = ["accuracy", "precision", "recall", "f1"]
print(f"{'Metric':<12}{'Model A':>10}{'Model B':>10}{'Diff':>10}")
print("-" * 42)
random.seed(42)
for metric in metrics:
a = round(r…Browse by section
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