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How to Calculate VPC Subnet CIDR Details in Python
Compute network address, broadcast address, address count, prefix length, and netmask for any IPv4 CIDR using the Python standard library's ipaddress module.
import ipaddress
def subnet_details(cidr: str) -> dict:
network = ipaddress.ip_network(cidr, strict=False)
return {
"network_address": str(network.network_address),
"broadcast_address": str(network.broadcast_address),
"num_addresses": network.num_addresses,
"prefix_length": ne…How to Mock ELB Target Health Status in Python
Simulate AWS Elastic Load Balancer target health checks with a Python dict that mutates status and healthy host counts.
from random import randint
def elb_target_mock_status(target_id, healthy=True):
targets = {
1: {"Id": "i-001", "Status": "healthy", "Port": 80, "HealthyHostCount": 1},
2: {"Id": "i-002", "Status": "unhealthy", "Port": 80, "HealthyHostCount": 0},
3: {"Id": "i-003", "Status": "healthy", "Por…How to Generate a Mock Rollbar Error Report in Python
Create a realistic fake Rollbar error report with random timestamps, levels, messages, and counts for testing and demos.
import json
import random
import time
from datetime import datetime, timedelta
def mock_rollbar_report(n_errors=5):
messages = [
"TypeError: unsupported operand type(s) for +: 'int' and 'str'",
"KeyError: 'user_id'",
"ValueError: invalid literal for int() with base 10: 'abc'",
"At…How to Use threading.Lock to Synchronize a Counter in Python
Safely increment a shared counter across multiple threads using threading.Lock as a mutex to prevent race conditions.
import threading
counter = 0
lock = threading.Lock()
def increment():
global counter
for _ in range(100000):
with lock:
counter += 1
threads = [threading.Thread(target=increment) for _ in range(5)]
for t in threads:
t.start()
for t in threads:
t.join()
print(f"Final counter valu…How to Test Hypotheses with Property-Based Check in Python
A Python search that checks an integer property (palindrome divisible by digit sum) and returns the first counterexample within a range, with exactly reproduced output from the code.
def is_property_satisfied(n):
"""
Demonstrates a mathematically inspired property:
checks whether n is both a palindrome and divisible by its digit sum.
"""
s = str(n)
if s != s[::-1]:
return False
digit_sum = sum(int(d) for d in s)
return digit_sum != 0 and n % digit_sum == 0
…How to Aggregate Mock API Routes by Method in Python
Groups mock API routes by path and method, collecting response bodies and counts into a nested dictionary structure.
from collections import defaultdict
def aggregate_mock_routes(routes):
"""Aggregate mock API routes by method and aggregate their response bodies."""
aggregated = defaultdict(lambda: defaultdict(list))
for route in routes:
method = route["method"]
path = route["path"]
response = …How to Take Periodic Snapshots of Aggregate State in Python
Build a Python class that accumulates values and periodically captures immutable snapshots of total, count, and average for later analysis.
import time
import random
from collections import defaultdict
class SnapshotAggregator:
def __init__(self):
self.total = 0
self.count = 0
self.history = []
def add(self, value):
self.total += value
self.count += 1
def snapshot(self):
avg = self.total / se…How to Build a Message Stream Queue in Python
A beginner-friendly MessageStream class built on deque that sends messages one at a time, tracks unread counts, and records sent items.
from collections import deque
import time
class MessageStream:
def __init__(self, messages):
self._queue = deque(messages)
self._sent = []
def send_next(self):
if not self._queue:
return None
message = self._queue.popleft()
self._sent.append(message)
…How to Implement a Tumbling Window Counter in Python
Count events that fall within a fixed-size sliding time window using a deque and pruning logic.
from collections import deque
import time
class TumblingWindowCounter:
def __init__(self, window_size_seconds):
self.window_size = window_size_seconds
self.window = deque()
def add_event(self, timestamp):
self.window.append(timestamp)
def count(self, current_time):
while…How to Mock Kafka Topic Partitions with a Python dict of lists
Mocks a Kafka topic and its partitions using a defaultdict of lists to simulate message production, consumption, and per-partition counts.
from collections import defaultdict
class KafkaTopicPartitionMock:
"""A simple mock for Kafka topic-partition assignment using dict of lists."""
def __init__(self, topic):
self.topic = topic
self.partitions = defaultdict(list) # partition_id -> list of messages
def produce(self, message…Redis INCR DECR Counter Mock in Python
Simulate Redis INCR and DECR commands with a Python class to test counter logic without a live Redis server.
class RedisCounter:
def __init__(self):
self._store = {}
def incr(self, key: str, amount: int = 1) -> int:
if key not in self._store:
self._store[key] = 0
self._store[key] += amount
return self._store[key]
def decr(self, key: str, amount: int = 1) -> int:
…Fixed Window Counter Rate Limiting in Python
A simple fixed window counter rate limiter that allows a maximum number of requests per 60-second window, with a mock time simulation.
from collections import deque
from time import time
class FixedWindowCounter:
def __init__(self, max_requests):
self.max_requests = max_requests
self.window_start = int(time())
self.window_count = 0
def allow_request(self):
current_time = int(time())
if current_time >=…How to Implement a Dead Letter Queue Replay in Python
A mock Dead Letter Queue that stores failed messages with retry attempts and replays them with a simple retry counter.
import json
from collections import deque
class DeadLetterQueue:
def __init__(self):
self.messages = deque()
def add_message(self, message_id, payload, attempts=3):
"""Add a message to the DLQ with retry metadata."""
self.messages.append({
"id": message_id,
…How to Implement a Sliding Window Counter in Python
This code implements an approximate sliding window counter using a deque of time-based buckets to track event counts within a recent time window.
from collections import deque
from time import time
class SlidingWindowCounter:
def __init__(self, window_size, bucket_size=1):
self.window_size = window_size
self.bucket_size = bucket_size
self.buckets = deque()
def _evict_expired(self, now):
while self.buckets and self.buck…How to Mock Daily and Monthly Quota Counters in Python
Track daily and monthly API call usage with automatic resets, quota checks, and limits using a Python class.
import random
from datetime import datetime, timedelta
class QuotaCounter:
def __init__(self, daily_limit=1000, monthly_limit=20000):
self.daily_limit = daily_limit
self.monthly_limit = monthly_limit
self.daily_usage = 0
self.monthly_usage = 0
self.current_day = datetime.n…Rate Limit per User ID in Python with a Dict Mock
Implements a simple sliding window rate limiter using a defaultdict of timestamps per user ID, blocking requests that exceed a max count within a time window.
import time
from collections import defaultdict
class RateLimiter:
def __init__(self, max_requests, window_seconds):
self.max_requests = max_requests
self.window_seconds = window_seconds
self.user_timestamps = defaultdict(list)
def allow_request(self, user_id):
now = time.tim…Calculate Error Rate from Log Stream in Python
Parses a mock log stream to count errors and compute the error percentage using a rolling window of recent entries.
import re
from collections import deque
def error_rate_from_log_stream(message):
log_pattern = r'^\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\] (ERROR|INFO|DEBUG): (.*)$'
recent_entries = deque(maxlen=100)
error_count = 0
total_count = 0
for line in message.strip().split('\n'):
match = re.mat…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 Metrics Counter with Increment and Snapshot in Python
A simple dict-backed MetricsCounter class that increments named counters and returns a snapshot of the current values.
class MetricsCounter:
def __init__(self):
self._metrics = {}
def increment(self, key, delta=1):
self._metrics[key] = self._metrics.get(key, 0) + delta
def snapshot(self):
return dict(self._metrics)
if __name__ == "__main__":
counter = MetricsCounter()
counter.increment("…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…How to Implement an Exactly-Once Deduplication Store in Python
Implement a Python class that deduplicates keys exactly once, tracking first-seen timestamps and duplicate counts.
from datetime import datetime
from typing import Any, Hashable
class ExactlyOnceStore:
def __init__(self) -> None:
self._seen: set[Hashable] = set()
self._first_seen: dict[Hashable, datetime] = {}
self._counts: dict[Hashable, int] = {}
def add(self, key: Hashable, value: Any = None) …How to mock a SPIFFE workload identity in Python
Generate a mock SPIFFE ID and token for a workload using a trust domain, namespace, and service account.
import hashlib
import json
from dataclasses import dataclass, asdict
@dataclass
class SPIFFEIdentity:
trust_domain: str
namespace: str
service_account: str
@property
def id(self) -> str:
return f"spiffe://{self.trust_domain}/ns/{self.namespace}/sa/{self.service_account}"
def mock_workl…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"…Mock a Sidecar Logger with Python Metrics
Simulate a sidecar logger that tracks request counts, error rates, and endpoint hits, producing a metrics snapshot.
import random
import time
from collections import defaultdict
class SidecarLogger:
def __init__(self):
self.metrics = defaultdict(int)
self.total_requests = 0
self.error_count = 0
def log_request(self, endpoint, status_code):
"""Simulate logging a request and updating metrics…Browse by section
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