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Python Code Samples

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9 matches
Reliability & rate limiting easy

How to Inject Random Latency for Chaos Testing in Python

Mock unreliable services by wrapping functions with a decorator that adds random network-like delays before execution.

chaos-engineering decorators latency
Python
import random
import time
from functools import wraps

def inject_latency(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        latency = random.uniform(0.1, 0.5)
        print(f"Injecting {latency:.3f}s latency...")
        time.sleep(latency)
        return func(*args, **kwargs)
    return wrapper

@inje…
13 0 Open
Observability & SRE easy

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.

sre synthetic-data metrics
Python
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…
14 0 Open
Observability & SRE easy

How to Calculate Apdex Score from Latency Data in Python

Generate simulated latency samples and compute the Apdex score to gauge user satisfaction with an application's performance.

apdex latency observability
Python
import random
import statistics

def generate_latencies(count=100, base=100, stddev=30):
    return [max(0, random.gauss(base, stddev)) for _ in range(count)]

def apdex(latencies, threshold=200):
    satisfied = sum(1 for lat in latencies if lat < threshold)
    tolerating = sum(1 for lat in latencies if lat >= thres…
15 0 Open
Observability & SRE easy

How to Calculate Percentile Latency in Python

Generate mock latency samples with occasional spikes and compute 50th, 90th, 95th, and 99th percentile values in milliseconds.

percentile latency slo
Python
import random
import statistics

def generate_latency_samples(n=1000):
    """Generate realistic mock latency data (ms) with occasional spikes."""
    samples = []
    for _ in range(n):
        # Normal case: ~50ms with jitter
        base = random.gauss(50, 5)
        # 2% spike chance: slow downstream or GC pause
 …
13 0 Open
Observability & SRE easy

How to Implement Tail Sampling in Python

Sample the slowest subset of calls (tail) for latency analysis using a deque with a random ratio gate.

sampling latency observability
Python
import random
import time
from collections import deque

class TailSampler:
    def __init__(self, tail_ratio=0.1, max_samples=100):
        self.tail_ratio = tail_ratio
        self.max_samples = max_samples
        self.samples = deque(maxlen=max_samples)
        self.total_calls = 0

    def record(self, latency_ms…
13 0 Open
Observability & SRE easy

How to Mock HTTP Client Latency in Python

Simulate outbound HTTP request latency with configurable ranges to test timeouts, retries, and SLO monitoring without external services.

latency mocking http-client
Python
import time
import random

def mock_latency(host: str, min_ms: int = 100, max_ms: int = 500) -> dict:
    """Simulate an outbound HTTP request with mock latency."""
    latency_ms = random.randint(min_ms, max_ms)
    start = time.perf_counter()
    time.sleep(latency_ms / 1000)
    elapsed_ms = (time.perf_counter() - …
14 0 Open
Observability & SRE easy

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.

sre metrics latency
Python
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):
   …
14 0 Open
Microservices patterns easy

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.

asyncio concurrency api
Python
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),
      …
15 0 Open
Production deployment patterns easy

How to Build a Synthetic Monitor Mock in Python

Simulates a synthetic monitoring system in Python that collects latency samples, averages them, and reports service status as UP or DEGRADED.

monitoring dataclass simulation
Python
import random
import time
from dataclasses import dataclass, field
from statistics import mean


@dataclass
class SyntheticMonitor:
    service: str
    endpoint: str
    latency_ms: list[float] = field(default_factory=list)

    def check(self) -> float:
        latency = random.uniform(50.0, 250.0)
        self.late…
13 0 Open

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