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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 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.
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…How to Check Service Readiness Dependencies in Python
This code simulates a readiness check for external dependencies (database, cache, queue) with mock availability data and reports readiness status.
import sys
from datetime import datetime
def check_dependencies(config):
results = []
for dep, required in config.items():
available = mock_availability(dep)
status = "READY" if available >= required else "NOT READY"
results.append((dep, available, required, status))
return result…How to Compute SRE Metrics Like Error Rate and Availability in Python
Tracks log events in a sliding time window and calculates error rate per second and availability percentage using an easy-to-follow class.
from collections import deque
from datetime import datetime, timedelta
from typing import Dict, Deque
class LogMetrics:
"""Simple observability helper to track log events and calculate SRE metrics."""
def __init__(self, window_seconds: int = 60):
self.window_seconds = window_seconds
self.eve…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 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…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):
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