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How to Build a TTL Cache Dict in Python
Create a dictionary subclass that automatically expires keys after a fixed time-to-live using timestamps.
import time
class TTLDict(dict):
def __init__(self, ttl, *args, **kwargs):
self.ttl = ttl
self._expires = {}
super().__init__(*args, **kwargs)
def __setitem__(self, key, value):
super().__setitem__(key, value)
self._expires[key] = time.time() + self.ttl
def __geti…Build a Network Ping Monitor in Python
A Python script that continuously pings a remote host using subprocess and reports connectivity status with timestamps and latency.
import subprocess
import time
def ping_host(host, count=4):
"""Ping a host and return the results."""
try:
# Platform-independent ping command
cmd = ["ping", "-c", str(count), host]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=10)
return result.stdout, r…Track Internet Connectivity and Downtime Automatically in Python
Monitors internet connectivity by pinging a remote host and logs any downtime events with timestamps and duration.
import time
import subprocess
from datetime import datetime
def check_internet(host="8.8.8.8", timeout=3):
"""Returns True if internet is reachable via ping."""
try:
subprocess.run(
["ping", "-c", "1", "-W", str(timeout), host],
capture_output=True,
timeout=timeout …How to Count Events by Minute with a Tumbling Window in Python
Group timestamps into fixed 60-second tumbling windows and count events per bucket using a dict.
from collections import defaultdict
from datetime import datetime, timedelta
def tumbling_window_count(events, window_seconds=60):
buckets = defaultdict(int)
for event in events:
ts = datetime.fromisoformat(event["timestamp"])
bucket_start = ts - timedelta(seconds=ts.second % window_seconds,
…Normalize Timestamps to UTC DateTime in Python
Convert timestamps in multiple formats to UTC-aware datetime objects using datetime.strptime and astimezone.
from datetime import datetime, timezone
raw_timestamps = [
"2024-01-15 14:30:00+02:00",
"17/05/2024 09:15:00 -0500",
"2024-03-01T22:45:00Z",
"2024-06-20 08:00:00+09:30"
]
def parse_and_convert(ts: str) -> datetime:
normalized_ts = ts.strip().replace("Z", "+00:00")
formats = [
"%Y-%m-%…How to Stream Join Windowed Mock Topics in Python
Simulates two message topics and joins their events when timestamps fall within a sliding time window using Python generators and deques.
import itertools
import random
import time
from collections import deque
from dataclasses import dataclass, field
@dataclass
class Event:
key: str
value: int
timestamp: float = field(default_factory=time.time)
def generate_topic(prefix, keys, start_time):
while True:
yield Event(
…Mock Watermark Late Event Side Output in Python
Simulates watermarking in a streaming pipeline by classifying events as on-time or late using timestamps and delays.
from datetime import datetime, timedelta
from typing import List, Tuple
def watermark_mock(
events: List[Tuple[datetime, str]], watermark_delay: timedelta, max_delay: timedelta
) -> Tuple[List[Tuple[datetime, str]], List[Tuple[datetime, str]]]:
"""Simulate watermarking: events arriving on time vs. late by ch…Redis-inspired sliding window rate limiter in Python
A pure-Python sliding window rate limiter using a deque of timestamps, mock-ready for Redis-backed production limits.
import time
from collections import deque
class SlidingWindowRateLimiter:
def __init__(self, max_requests: int, window_seconds: int) -> None:
self.max_requests = max_requests
self.window_seconds = window_seconds
self.requests: dict[str, deque] = {}
def is_allowed(self, client_id: str…How to Implement a Sliding Window Log Rate Limiter in Python
Implements a sliding window log rate limiter in Python using a deque of timestamps to enforce a maximum request count within a rolling time window.
from collections import deque
from datetime import datetime, timedelta
from time import sleep
class SlidingWindowLog:
def __init__(self, window_seconds: int, max_requests: int):
self.window_seconds = window_seconds
self.max_requests = max_requests
self.timestamps = deque()
def allow_…How to implement rate limiting per API key in Python
A simple sliding-window rate limiter that tracks request timestamps per API key and rejects requests exceeding the configured limit.
import time
API_RATE_LIMITS = {"api_key_1": 5, "api_key_2": 3} # max requests per window
WINDOW_SECONDS = 10
class RateLimiter:
def __init__(self, limits, window):
self.limits = limits
self.window = window
self.requests = {key: [] for key in limits}
def allow(self, api_key):
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