I’ve been asked about SRE a lot in the past year and have finally gathered enough of my thoughts to start writing about it. Let’s start with the basics.

Embrace Risk

In SRE, we accept that failure is part of running systems at scale. Instead of striving for zero downtime, we manage risk through concepts like error budgeting. This involves setting aside a certain amount of acceptable downtime or errors, which balances innovation with reliability.

At the heart of this principle are Service Level Objectives (SLOs), which define clear, measurable targets for service reliability. Derived from Service Level Agreements (SLAs), SLOs help us determine an acceptable level of risk and guide our operational decisions.

Define SLOs and Track SLIs

SLOs and Service Level Indicators (SLIs) are critical tools in the SRE toolkit. SLIs are metrics that reflect the performance and reliability of a service – think latency, error rates, and uptime. SLOs are the target values or ranges for these metrics that we aim to achieve.

By defining and tracking SLOs and SLIs, we can make informed decisions about where to focus our efforts and resources, ensuring we maintain a balance between development velocity and system reliability.

Eliminate Toil

Toil is the manual, repetitive operational work that doesn’t add lasting value. One of the primary goals of SRE is to eliminate toil through automation. By automating routine tasks, we free up time for SREs to focus on more strategic initiatives that drive improvements in system reliability and performance.

Efficient processes are also key. Streamlining workflows and reducing overhead can significantly boost productivity and system reliability.

Embrace Monitoring and Observability

Comprehensive monitoring and observability are essential for understanding and maintaining system health. Effective monitoring provides insights into system performance, while observability ensures that our systems are designed in a way that makes it easy to diagnose and troubleshoot issues.

By investing in robust monitoring tools and practices, we can detect and address issues before they impact users, ensuring a smoother and more reliable service experience.

Plan for Capacity

Scalability is a cornerstone of reliable systems. Capacity planning ensures that our systems can handle increasing loads without sacrificing performance. Regularly assessing and planning for future capacity needs based on usage trends and business growth is essential for maintaining system reliability.

Incident Response and Postmortems

When incidents do occur, having a solid incident management process in place is crucial. SRE practices emphasize effective incident response and blameless postmortems. Postmortems are conducted to identify the root causes of incidents and learn from them without assigning blame, fostering a culture of continuous improvement.

Manage Change Wisely

Change management is another critical aspect of SRE. Techniques like canary releases, blue-green deployments, and feature flags help minimize the risk associated with deploying changes. Strict version control and rollback capabilities ensure that changes can be managed safely, reducing the likelihood of disruptions.

Cultivate a Collaborative Culture

SRE is as much about culture as it is about technology. Promoting collaboration between development and operations teams is vital for enhancing system reliability and performance. A culture of continuous learning encourages ongoing improvement through training, experimentation, and knowledge sharing.

Reduce Organizational Silos

Breaking down silos within an organization is key to the success of SRE. By integrating SREs with development teams, we ensure shared responsibility for system reliability. Shared ownership fosters a culture where both development and operations teams are accountable for service uptime and reliability.

By adhering to these principles, organizations can build and maintain systems that not only meet but exceed user expectations. Embracing SRE practices transforms how we approach infrastructure and operations, ultimately leading to more resilient, scalable, and efficient systems.