Site Reliability SRE

At, our mission is to provide valuable insights and resources about Site Reliability Engineering (SRE) to help organizations improve the reliability, availability, and performance of their digital services. We aim to be the go-to destination for SRE professionals, engineers, and anyone interested in learning about this critical discipline. Our goal is to empower our readers with the knowledge and tools they need to build and maintain highly reliable systems that meet the needs of their users.

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Site Reliability Engineering (SRE) Cheatsheet

This cheatsheet is designed to provide a quick reference guide for anyone getting started with Site Reliability Engineering (SRE). It covers the key concepts, topics, and categories related to SRE, as well as best practices and tools for implementing SRE principles.

What is Site Reliability Engineering?

Site Reliability Engineering (SRE) is a discipline that combines software engineering and operations to improve the reliability, scalability, and performance of large-scale systems. SRE teams are responsible for ensuring that systems are available, reliable, and performant, while also managing the risks associated with system failures.

Key Concepts

Service Level Objectives (SLOs)

Service Level Objectives (SLOs) are a key concept in SRE that define the level of service that a system should provide to its users. SLOs are typically expressed as a percentage of uptime or availability, and are used to measure the reliability of a system. SLOs are important because they provide a clear target for system performance, and help teams prioritize their efforts to improve reliability.

Error Budgets

Error Budgets are another key concept in SRE that define the amount of downtime or errors that a system can tolerate before it fails to meet its SLO. Error budgets are used to balance the need for reliability with the need for innovation and feature development. If a system is performing well and has a surplus of error budget, the team can use that budget to experiment with new features or improvements. If a system is performing poorly and has exhausted its error budget, the team must focus on improving reliability before adding new features.

Incident Management

Incident Management is the process of responding to and resolving system failures or incidents. SRE teams use incident management processes to minimize the impact of failures on users, and to identify and address the root causes of failures. Incident management processes typically include incident response plans, incident triage, post-incident reviews, and incident follow-up.

Monitoring and Alerting

Monitoring and Alerting are critical components of SRE that enable teams to detect and respond to system failures in real-time. Monitoring involves collecting and analyzing system metrics and logs to identify potential issues or anomalies. Alerting involves notifying the appropriate team members when a system metric or log entry meets a predefined threshold or condition.

Capacity Planning

Capacity Planning is the process of forecasting and managing the resources required to support a system's performance and scalability. SRE teams use capacity planning to ensure that systems have sufficient resources to meet their SLOs, and to identify and address potential capacity constraints before they become critical.

Best Practices

Automate Everything

Automation is a key best practice in SRE that enables teams to scale their operations and reduce the risk of human error. SRE teams should automate as much of their infrastructure and operations as possible, including deployment, configuration management, monitoring, and incident response.

Embrace Failure

Failure is inevitable in complex systems, and SRE teams should embrace failure as an opportunity to learn and improve. SRE teams should use post-incident reviews to identify the root causes of failures, and to implement improvements that prevent similar failures from occurring in the future.

Measure Everything

Measurement is critical to SRE, as it enables teams to track system performance, identify potential issues, and make data-driven decisions. SRE teams should measure everything that is relevant to system performance, including uptime, latency, error rates, and resource utilization.

Practice Continuous Improvement

Continuous Improvement is a key principle of SRE that involves constantly evaluating and improving system performance. SRE teams should use data-driven decision-making and experimentation to identify and implement improvements that increase system reliability, scalability, and performance.

Tools and Technologies


Kubernetes is an open-source container orchestration platform that is widely used in SRE. Kubernetes enables teams to deploy, manage, and scale containerized applications, and provides built-in features for monitoring, logging, and alerting.


Prometheus is an open-source monitoring system that is widely used in SRE. Prometheus enables teams to collect and analyze system metrics, and provides built-in features for alerting and visualization.


Grafana is an open-source visualization platform that is widely used in SRE. Grafana enables teams to create custom dashboards and visualizations of system metrics, and provides built-in features for alerting and collaboration.


Terraform is an open-source infrastructure as code (IaC) tool that is widely used in SRE. Terraform enables teams to define and manage infrastructure resources in a declarative way, and provides built-in features for automation and collaboration.


Site Reliability Engineering (SRE) is a critical discipline for ensuring the reliability, scalability, and performance of large-scale systems. By understanding the key concepts, best practices, and tools related to SRE, teams can improve their ability to deliver high-quality services to their users. This cheatsheet provides a quick reference guide for anyone getting started with SRE, and should be used as a starting point for further exploration and learning.

Common Terms, Definitions and Jargon

1. Availability: The measure of how often a system is operational and accessible to users.
2. Capacity Planning: The process of determining the resources needed to support a system's current and future demands.
3. Change Management: The process of controlling changes to a system to minimize the risk of negative impacts.
4. Chaos Engineering: The practice of intentionally introducing failures into a system to test its resilience.
5. Circuit Breaker: A mechanism that automatically stops traffic to a failing service to prevent cascading failures.
6. Cloud Computing: The delivery of computing services over the internet, including storage, processing, and software.
7. Configuration Management: The process of managing and tracking changes to a system's configuration.
8. Containerization: The process of packaging an application and its dependencies into a single unit for deployment.
9. Continuous Deployment: The practice of automatically deploying code changes to production as soon as they are ready.
10. Continuous Integration: The practice of regularly integrating code changes into a shared repository to detect issues early.
11. Disaster Recovery: The process of restoring a system to a functional state after a catastrophic event.
12. Distributed Systems: A collection of independent components that work together to achieve a common goal.
13. Elasticity: The ability of a system to automatically scale resources up or down in response to changing demand.
14. Fault Tolerance: The ability of a system to continue operating in the event of a failure.
15. High Availability: The ability of a system to remain operational and accessible even in the face of failures.
16. Incident Management: The process of responding to and resolving incidents that impact system availability or performance.
17. Infrastructure as Code: The practice of managing infrastructure using code and automation tools.
18. Kubernetes: An open-source container orchestration platform for managing containerized applications.
19. Load Balancing: The process of distributing traffic across multiple servers to improve performance and availability.
20. Microservices: A software architecture pattern that structures an application as a collection of small, independent services.

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