Today’s systems are growing increasingly complex as organizations move more services to cloud-based models. This complexity is not only costly to administer, but it can also make it difficult for you to identify infrastructure weaknesses and failures that can significantly impact your users’ experiences. The solution? Enter site reliability engineering (SRE).
What is site reliability engineering?
Site reliability engineering (SRE) is a practice used to optimize the performance and reliability of systems. While commonly associated with DevOps, SRE fulfills a complementary role that takes a fundamentally different approach to optimization. Both DevOps and SRE are focused on improving system performance, reliability, and efficiency. While DevOps typically installs optimizations after deployment, the SRE approach takes a shift to the left, focusing on optimizing systems during testing and before they show up in deployment. In other words, the two approaches overlap and can enhance each other in your site deployment workflow.
In the context of SRE, the term “system” takes on a broader, more holistic meaning than a single hardware device. For SRE teams, a system refers to a collection of hardware, software, processes, and networks operating together within a service or application development workflow.
Why is site reliability engineering important?
Large service providers, such as Google, understand the key role SRE plays in ensuring their customers experience always-available, low-latency, and high-capacity services. Organizations of all sizes can benefit from using SRE to troubleshoot issues and optimize site operations.
SRE is particularly valuable in ensuring users have reliable continuous access as organizations scale out their services. As more organizations move services and operations to a cloud-centric model, enticed by the promise of lower hardware costs and simplified scale-outs, companies are finding that cloud-native services cannot eliminate all their scale-out challenges. System inefficiencies that might not have shown up on a small scale can balloon into serious problems when a service is scaled out. SRE can help prevent this from happening by making sure an organization’s infrastructure is optimized to support growth.
How does site reliability engineering work?
SRE combines software engineering principles with infrastructure and operations to create scalable and reliable systems. Both DevOps and SRE are used to automate routine tasks, reduce the need for manual intervention, and create robust systems that are resilient to failure.
Traditional DevOps focuses on improving systems post-deployment, usually with customized code. DevOps engineers are required to make a lot of assumptions when writing code, many of which might be unrealistic or untested. Examples include assuming that the deployment environment is well-structured, that networks are reliable, that services are available 24/7, and that the data analysis is accurate.
With SRE, you optimize the infrastructure earlier in your development workflow. By proactively addressing potential problems and inefficiencies during the testing phase, SRE can help organizations maintain system stability and efficiency even as they scale. SRE enables you to avoid having to make and rely on unrealistic assumptions. Instead, it uses metrics and monitoring to identify areas for improvement, helping to ensure that changes are based on empirical evidence rather than untested assumptions. SRE uses this information to enable you to design systems that are resilient against network failures, hardware conflicts, and other unpredictable factors before they are deployed, rather than after.
For example, a typical DevOps approach for resolving poor system performance would be writing software instructions that auto-restart a service every five minutes. The SRE shift-left approach seeks to redesign the system so that service’s auto-restarts simply aren’t necessary. By holistically integrating both practices into your site development workflow, you can achieve a highly efficient and reliable approach to ensuring system stability and efficiency in complex, real-world environments.
What are the industry drivers behind site reliability engineering?
The cloud-centric model is the major industry shift that has motivated organizations to incorporate SRE and DevOps into their site deployment workflows. Over the past decade, more and more organizations have moved their services to hybrid, private, and public clouds.
The need to optimize cloud-native services is why SRE is no longer merely cutting-edge thinking by industry leaders, but a must-have practice for organizations of all sizes. Because cloud-based services lend themselves so well to scale-outs, it is tempting to quickly expand services to meet rapidly growing demands. However, once a service is scaled out, issues can arise from previously insignificant inefficiencies. Not checking that your infrastructure is ready to support expanded services can result in frustrated users, inefficient operations, and major service outages.
What are the use cases for site reliability engineering?
Some common scenarios benefit greatly from an SRE approach:
Cloud-centric models: Organizations move their services and operations to a cloud-centric model to lower costs, simplify upgrades, and enhance agility. SRE helps ensure that users experience the same high availability and low latency using cloud-based services as they did with a physical infrastructure.
Growth spurts: For organizations looking to move forward, SRE is a critical component of growth planning. To handle anticipated rapid growth, such as during a startup year or after an initial public offering (IPO), services must be scaled out quickly. An organization implementing SRE should test and optimize its infrastructure to make sure that services continue to be reliable and available after scale-out.
Service scale-outs: An organization that scales out “too fast,” without testing systems before deployment, might discover that some services that worked well on a small scale suffer from poor and unreliable performance when scaled out. Cross-functional SRE teams can quickly troubleshoot and resolve these problems.
What are the implementation challenges with site reliability engineering?
Because every organization has unique needs and goals, they also face unique challenges when implementing SRE. Your company’s main need could be ensuring seamless data mobility across multiple clouds—or you could be planning a major scale-out to support anticipated business growth.
There’s no golden pathway for organizing your SRE implementation. The team structures, job roles and responsibilities, and task prioritization will vary depending on your unique IT needs and existing organizational structure. SRE creates an engineering culture where system optimization is everyone’s responsibility and operational tasks are treated with the same care as software development. One common and effective strategy embeds SRE into the deployment workflow in the form of cross-functional teams. These teams work on solving specific problems and operational efficiencies involving multiple systems. They can also advise on solutions for business problems.
Nearly all companies face the ongoing challenge of balancing future growth plans with the optimal performance and reliability needed now. While this balancing act might not obstruct implementation, optimizing it should be a top priority for your SRE teams. SRE tasks should include planning, testing, and troubleshooting scale-outs before deployment.
Another challenge that can hamper SRE implementation is an organization’s technical debt. Most organizations build and upgrade their infrastructures on an ad-hoc basis as they grow from startup to hyper-growth and maturity. Legacy systems can burden SRE teams with the chore of fixing outdated software and hardware. Typically, the older the systems, the greater the technical debt, and the more time gets spent on old problems. In other words, this technical debt creates an IT time sink that takes away from innovation and forward thinking.
What features should you look for in site reliability engineering tools?
Tools can vary depending on your organization’s needs, but the more visibility your SRE teams have into your tech stack, the better. An optimal tool is capable of delivering the three pillars of observability; your SRE team needs these three components to gain a view of systems that is both comprehensive and granular:
Metrics: Metrics are numerical measurements that track a system’s performance and behavior over time. Examples include service-level indicators (SLIs), service-level objectives (SLOs), and service-level agreements (SLAs). These measurements help identify trends and anomalies, which are used to ensure services are delivered reliably and perform optimally at scale.
Logs: Logs are detailed records of events that occur within a system. They provide a chronological account of what happened, when it happened, and why it happened. Logs are invaluable for debugging and gaining insights into specific events and errors.
Traces: Traces represent the path that a request takes through various components of a system. They use visualizations to identify service interactions, potential bottlenecks, and system dependencies. Traces are particularly useful for diagnosing issues in microservices-based architectures.
Get started implementing site reliability engineering practices
SRE is a crucial approach for optimizing the stability, efficiency, and scalability of cloud-native infrastructures. Your organization can use SRE shift-left testing to proactively identify and resolve potential issues early in development for improved workflow efficiency and service scale-outs.
Learn more about SRE:
- Datadog 101: Site Reliability Engineer
- Datadog on Site Reliability Engineering: Explore how Datadog can support your SRE teams with tools designed to provide deep visibility, automate routine tasks, and integrate seamlessly with your existing workflows:
- Datadog Infrastructure Monitoring offers comprehensive metrics for your tech stack, including real-time monitoring and alerts.
- Datadog Application Performance Monitoring (APM) uses end-to-end tracing and analytics to help monitor and optimize application performance.
- Datadog Log Management analyzes logs from all your services, giving you optimization and troubleshooting insights.
- Datadog Observability Pipelines help you control log volumes, adopt security tools, and manage sensitive data.
