Reference library

Observability & SRE

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

3 matches

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.

observability sre metrics

Python

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…

14 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 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.

queue simulation monitoring

Python

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")
       …

13 0 Open

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