SLI/SLO Framework — Preview

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📁 File Structure 27 files

sli-slo-framework/ LICENSE README.md index.html interactive.html guides/ error_budget_policy.md tests/ test_sli_slo.py __pycache__/ test_sli_slo.cpython-312-pytest-9.0.2.pyc test_sli_slo.cpython-312.pyc examples/ full_pipeline_demo.py __pycache__/ full_pipeline_demo.cpython-312.pyc configs/ grafana_dashboard.json slo_catalog.yaml src/ __init__.py alert_rules.py burn_rate.py error_budget.py reporting.py sli_metrics.py slo_calculator.py __pycache__/ __init__.cpython-312.pyc alert_rules.cpython-312.pyc burn_rate.cpython-312.pyc error_budget.cpython-312.pyc reporting.cpython-312.pyc sli_metrics.cpython-312.pyc slo_calculator.cpython-312.pyc

📖 Documentation Preview README excerpt

# SLI/SLO Framework A comprehensive Python toolkit for defining, measuring, and tracking Service Level Indicators (SLIs) and Service Level Objectives (SLOs). Implements Google's multi-window multi-burn-rate alerting methodology with error budget management. ## Features - **SLI Computation** — Calculate availability, latency, throughput, and quality indicators from raw metric data - **SLO Evaluation** — Define targets with rolling compliance windows and automated status determination - **Error Budget Tracking** — Real-time budget consumption monitoring with velocity-based exhaustion prediction - **Multi-Window Burn-Rate Alerting** — Generate Prometheus and Grafana alert rules following Google SRE best practices - **Executive Reporting** — Markdown, JSON, and Slack-formatted compliance reports with trend analysis - **Alert Rule Generation** — Auto-generate Prometheus rules, Grafana provisioning JSON, and Alertmanager routing - **Budget Policy Enforcement** — Configurable thresholds that map budget levels to organizational actions ## File Structure ``` sli-slo-framework/ ├── README.md # This file ├── LICENSE # MIT License ├── src/ │ ├── __init__.py # Package initialization │ ├── sli_metrics.py # SLI computation (availability, latency, quality, throughput) │ ├── slo_calculator.py # SLO definition, evaluation, and compliance checking │ ├── error_budget.py # Budget tracking, velocity, and policy enforcement │ ├── burn_rate.py # Multi-window burn-rate alerting engine │ ├── alert_rules.py # Prometheus/Grafana alert rule generation │ └── reporting.py # Executive and team reporting ├── tests/ │ └── test_sli_slo.py # Comprehensive unit tests ├── configs/ │ ├── slo_catalog.yaml # Sample SLO definitions catalog (12 SLOs) │ └── grafana_dashboard.json # Pre-b

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📄 Content Sample guide/01-capacity-planning-fundamentals.md

Chapter 1: Capacity Planning Fundamentals

Duration: 45-60 minutes | Difficulty: Intermediate | Prerequisites: Basic familiarity with Python, AWS/Azure/GCP APIs, Prometheus, Terraform

Learning Objectives

By the end of this chapter, you will be able to:

1. Define the core architectural patterns and principles

2. Design a reference architecture aligned to business requirements

3. Identify the appropriate building blocks for each layer

4. Evaluate trade-offs between different design approaches

5. Create an implementation roadmap from architecture to production

6. Apply Python patterns to production scenarios

1. Understanding the Fundamentals

Before diving into implementation, it is essential to establish a solid conceptual foundation. Capacity Planning Fundamentals forms a critical pillar of the Capacity Planning Guide framework, and getting the fundamentals right determines the success of everything built on top.

1.1 Core Concepts

The core concepts underlying this chapter are rooted in established industry patterns and best practices. Each concept builds on the previous one, creating a coherent framework for reasoning about complex systems.

Concept	Description	Application
Foundation Layer	The core primitives and building blocks	Establish base capabilities for all higher-level patterns
Integration Layer	Interfaces and connectors between components	Define clear boundaries and contracts between subsystems
Orchestration Layer	Coordination and workflow management	Manage multi-step processes with error handling
Observability Layer	Monitoring, logging, and tracing	Provide visibility into system behavior and performance
Governance Layer	Policies, controls, and compliance	Ensure consistent operation within organizational guardrails

1.2 Why This Matters

In production environments, getting this wrong has measurable consequences. Teams that implement these patterns correctly experience:

Reduced incident frequency by 40-60% through proactive detection
Faster mean-time-to-resolution through structured procedures
Higher team confidence through documented and tested runbooks
Lower operational overhead through automation and standardization

2. Implementation Walkthrough

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