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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 Redact Secrets from Log Messages in Python
Build a lightweight RedactingFormatter class that replaces sensitive tokens like passwords and API keys with [REDACTED] before log messages are printed.
class RedactingFormatter:
def __init__(self, secrets):
self.secrets = secrets
def redact(self, message):
for secret in self.secrets:
message = message.replace(secret, "[REDACTED]")
return message
def format(self, record):
message = record["message"]
ret…How to Route Alerts by Severity in Python
Map alert severity levels to routing targets and simulate dispatching alerts to on-call pages, email, Slack, or logs.
def main():
# Severity levels with corresponding alert routing targets
routing_map = {
"critical": "call_page",
"high": "call_page",
"medium": "email_team",
"low": "slack_channel",
"info": "log_only"
}
# Simulated alerts with severity
alerts = [
{"na…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…How to Simulate Trace Sampling Head in Python
Simulate head-based probabilistic trace sampling on mock trace data with a configurable sample rate and optional seed for reproducibility.
import random
def trace_sampling_head(mock_traces, sample_rate=0.5, seed=None):
"""Simulate probabilistic trace sampling (head-based) on mock data.
Args:
mock_traces: list of trace dictionaries with a unique 'trace_id'
sample_rate: float 0.0-1.0, probability of keeping a trace
see…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 Track Cache Hit Ratio in Python
Simulate an LRU cache with hit/miss tracking and compute a real-time hit ratio from random access patterns.
import random
import time
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity: int):
self.cache = OrderedDict()
self.capacity = capacity
self.hits = 0
self.misses = 0
def get(self, key):
if key in self.cache:
self.hits += 1
…How to Use Log Levels DEBUG INFO WARNING ERROR in Python
Demonstrates Python's logging levels (DEBUG, INFO, WARNING, ERROR) with basicConfig and a logger, showing how severity filtering controls output.
import logging
# Configure a mock logger to demonstrate log levels
logging.basicConfig(level=logging.DEBUG, format="%(levelname)s: %(message)s")
logger = logging.getLogger("mock_logger")
# Simulate events at each severity level
logger.debug("Detailed diagnostic info")
logger.info("General system operation")
logger.w…How to mock Prometheus alert rule thresholds in Python
Simulate a Prometheus alert rule with a configurable threshold and duration window, firing only when the metric exceeds the threshold long enough.
import time
import random
class MetricsStore:
def __init__(self):
self.metrics = {}
def set_metric(self, name, value, labels=None):
key = (name, tuple(sorted((labels or {}).items())))
self.metrics[key] = value
def get_metric(self, name, labels=None):
key = (name, tuple(s…How to mock SLI availability success ratio in Python
Simulate request outcomes with deterministic randomness and compute the SLI availability success ratio to check if a target is met.
import random
from collections import defaultdict
def mock_availability(num_requests=1000, target_ratio=0.995):
"""
Simulate request outcomes and compute the SLI availability success ratio.
Args:
num_requests: Total number of requests to simulate
target_ratio: Target availability rati…Mock Health Endpoint Liveness Check in Python
Simulate a liveness endpoint that reports service health with a configurable failure rate and uptime.
import time
import random
def liveness_check(service_name: str, failure_rate: float = 0.1) -> dict:
"""Mock health check that returns liveness status with a configurable failure rate."""
healthy = random.random() > failure_rate
response = {
"service": service_name,
"status": "alive" if he…Mocking a Metrics Gauge's set_value Method in Python
Demonstrates using unittest.mock.Mock with wraps to intercept a gauge's set_value call while verifying arguments and preserving real behavior.
from unittest.mock import Mock
class MetricsGauge:
def __init__(self, name):
self.name = name
self.value = 0.0
def set_value(self, new_value):
self.value = float(new_value)
return self.value
# Usage demonstration with a mock
gauge = MetricsGauge("cpu_usage")
gauge_mock = Mock…Python Observability Data Helper for Beginners
A beginner-friendly Python helper to log events, record metrics, summarize observability data, and export it as JSON.
import json
from datetime import datetime
from collections import defaultdict
class ObservabilityDataHelper:
"""Helper for exploring basic observability data patterns."""
def __init__(self):
self.events = []
self.metrics = defaultdict(list)
def log_event(self, service, level, message):
…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 …Summary Quantile Mock Sketch in Python
Build a memory-efficient sketch that stores sorted bins of data points to answer approximate quantile queries like median without keeping all values in memory.
import random
import statistics
from collections import Counter
class SummaryQuantileSketch:
"""
A simple sketch that stores a fixed-size summary of data (min, max, deciles)
using sorted bins, then answers approximate quantile queries.
"""
def __init__(self, bins=10):
self.bins = bins
…Track Success Rates and Latency in Python: SRE Metrics Helper
A beginner-friendly Python class to record request outcomes and latencies, then report success rate, average latency, and p99.
import random
import time
from collections import defaultdict
class MetricsTracker:
"""Simple helper to track success rates and latencies for SRE beginners."""
def __init__(self):
self.successes = 0
self.failures = 0
self.latencies = []
def record(self, success, latency_ms):
…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…Backward Compatible Schema Evolution in Python
A mock schema validator that evolves JSON schemas while preserving backward compatibility by keeping old fields and validating required ones.
import json
from copy import deepcopy
class SchemaValidator:
def __init__(self, schema):
self.schema = schema
def evolve(self, new_schema):
"""Evolve mock schema while keeping backward compatibility."""
for field in self.schema:
if field not in new_schema:
…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…CQRS with Separate Read and Write Repositories in Python
Implement CQRS in Python with separate write and read repositories, using commands for mutations and frozen DTOs for queries.
from dataclasses import dataclass
from typing import Dict, List, Optional
# --- Write side: commands mutate state ---
@dataclass
class CreateUserCommand:
id: int
name: str
class UserWriteRepository:
def __init__(self) -> None:
self._store: Dict[int, Dict[str, object]] = {}
def create(self,…Cache-Aside Pattern in Python: Per-Service Mock
A Python mock of the cache-aside pattern for a single microservice—lazy-load from a database into an in-memory cache and invalidate on updates.
class ServiceCache:
def __init__(self):
self.database = {"user:1": "Alice", "user:2": "Bob", "user:3": "Charlie"}
self.cache = {}
def get_user(self, user_id):
cache_key = f"user:{user_id}"
if cache_key in self.cache:
print(f"CACHE HIT: {cache_key}")
retu…Consumer Driven Contract Pact Mock in Python
Define and verify consumer-driven contracts using Pact's Consumer and Provider classes, mocking the provider to assert expected interactions.
from pact import Consumer, Provider
pact = Consumer('OrderService').has_pact_with(Provider('InventoryService'))
@Pact.verify()
class TestInventoryContract:
def test_get_inventory(self):
expected = {"item": "widget", "quantity": 100}
(pact
.given('inventory exists for widget')
.u…Correlation ID HTTP header mock in Python
A lightweight HTTP server that echoes or generates correlation IDs to help test distributed systems.
import json
import uuid
from http.server import BaseHTTPRequestHandler, HTTPServer
class CorrelationHandler(BaseHTTPRequestHandler):
CORRELATION_HEADER = "X-Correlation-ID"
def do_GET(self):
correlation_id = self.headers.get(self.CORRELATION_HEADER) or str(uuid.uuid4())
response = {
…Distributed tracing with contextvars in Python
Propagate trace and span IDs across function calls using contextvars to mock distributed tracing in a single process.
import contextvars
import uuid
import time
_trace_context = contextvars.ContextVar("trace_context", default=None)
class TraceContext:
def __init__(self, trace_id, parent_span_id):
self.trace_id = trace_id
self.parent_span_id = parent_span_id
self.span_id = uuid.uuid4().hex[:16]
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