Reference library

Observability & SRE

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

3 matches

Generate Synthetic CPU Utilization Metrics in Python

Creates realistic time-series CPU utilization samples with timestamps, noise, and output as structured JSON for observability demos and testing.

observability metrics time-series

Python

from datetime import datetime, timedelta
import random
import json


def generate_metric_samples(base_value, noise, count=60, interval_minutes=1):
    """Generate realistic CPU utilization samples for a given time window."""
    timestamps = []
    values = []

    now = datetime.utcnow()
    start_time = now - timede…

14 0 Open

Observability & SRE easy

How to Do Structured JSON Line Logging in Python

Create a simple JSON-lines logger that writes one JSON object per line to stdout with timestamp, level, message, and custom context fields.

logging json observability

Python

import json
import sys
from datetime import datetime

class JsonLineLogger:
    def __init__(self, stream=sys.stdout):
        self.stream = stream

    def log(self, level, message, **context):
        record = {
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "level": level,
            "me…

15 0 Open

Observability & SRE easy

How to Do Structured JSON Logging in Python

Create a custom logging formatter that outputs each log entry as a single JSON line with timestamp, level, logger name, and message.

logging json observability

Python

import json
import logging
from datetime import datetime


class JsonFormatter(logging.Formatter):
    def format(self, record):
        log_entry = {
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "level": record.levelname,
            "logger": record.name,
            "message": record.ge…

14 0 Open

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