Reference library

Observability & SRE

Structured logging, metrics, tracing, health checks, and SLO-friendly instrumentation.

3 matches

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…

15 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 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.

metrics counter observability

Python

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("…

13 0 Open

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