/plushcap/analysis/datadog/establishing-service-level-objectives

Service level objectives 101: Establishing effective SLOs

What's this blog post about?

Service level objectives (SLOs) have become an essential part of site reliability engineering (SRE) practices, with Google pioneering best practices in this area. SLOs help organizations balance product development and operational work to ensure a positive end-user experience. In this three-part series, the fundamentals of SLOs, how to manage them using Datadog, and how to maximize their value will be discussed. Key terms related to SLOs include Service Level Indicators (SLIs), which measure service levels provided to end users; Service Level Objectives (SLOs), the targeted levels of service measured by SLIs; Service Level Agreements (SLAs), contractual agreements outlining expected service levels with consequences for non-compliance; and error budgets, acceptable unreliability levels. Setting SLOs involves considering end users' expectations, developers' priorities, and operations engineers' goals. Developers aim to add features, while ops engineers maintain stability. SLOs help align these teams by setting reliability targets and allowing them to objectively decide which projects or initiatives to prioritize based on error budgets. To create useful SLOs, organizations should understand how users interact with their applications, identify critical user journeys, and select appropriate SLIs within categories such as response/request, storage, and data pipeline. Good SLIs directly affect user satisfaction and are typically measured using data from components closest to the user. SLOs are created by setting a target value or range of values for an SLI over a specified period. Realistic targets should be set, with typical industry standards being expressed as a number of nines (e.g., 99.9 percent). Experimentation and refinement of SLOs is encouraged to find the most optimal values. In summary, picking the right SLIs and transforming them into well-defined SLOs can help organizations improve feature velocity and system reliability by focusing on what's truly important for end users.

Company
Datadog

Date published
June 22, 2020

Author(s)
Mark Azer, Kai Xin Tai

Word count
2079

Language
English

Hacker News points
None found.


By Matt Makai. 2021-2024.