SRE and GitOps for Building Robust Kubernetes Platforms.pdf
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In today's technology-driven landscape, ensuring the reliability and stability of systems is critical for organizations to deliver exceptional user experiences. Site Reliability Engineering (SRE) has emerged as a proven methodology to achieve operational excellence and elevate performance. By combining SRE and GitOps, organizations can leverage the benefits of both methodologies. GitOps provides a reliable and auditable approach to managing infrastructure and application changes, ensuring that all deployments are version-controlled and consistent across environments. This aligns with the SRE principle of implementing standardized and automated processes for maintaining system reliability. Join our live webinar as we introduce the fundamentals and significance of SRE and GitOps, and provide actionable strategies for implementation. We’ll also explore the features of Weave GitOps that integrate SRE and GitOps practices to streamline workflows to support system reliability and stability. You will learn: An overview and correlation of key SRE and GitOps best practices The 5 keys DORA metrics for measuring performance of software delivery. How to leverage continuous delivery and progressive delivery to enhance application stability. How Weave GitOps can reliably simplify the management of infrastructure and applications, with real-world customer examples illustrating their impact.
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SRE and GitOps for Building Robust Kubernetes Platforms.pdf
- 1. 1 Confidential do not distribute 1 Chris Lavery - Senior Site Reliability Engineer SRE and GitOps for Building Robust Kubernetes Platforms
- 2. 2 2 Webinar Platform - FAQs Using Zoom • You are in listen only mode • This webinar is being recorded • Q&A session will follow the presentation, please use the Q&A panel to submit questions • Hit escape to exit full screen • Please introduce yourself in the chat. Technical Issues - please visit Zoom Help https://support.zoom.us/hc/en-us/articles/206175806-Top-Questions
- 3. 3 Weaveworks’ is backed by solid investors Weaveworks created the GitOps methodology and tooling to solve our own Kubernetes management, scalability, and reliability requirements Weaveworks is a key partner with all the major infrastructure and Kubernetes vendors Weaveworks: the GitOps company Weaveworks is deeply committed to the Open Source Community
- 4. 4 Confidential do not distribute 4 Weaveworks Site Reliability Engineer Chris Lavery Belfast, Northern Ireland Chris Lavery is a Senior Site Reliability Engineer at Weaveworks (currently on secondment to Deutsche Telekom) where he champions continuous improvement through DevOps/GitOps practices, collaborating with multiple technical and non-technical stakeholders to achieve organisational goals effectively. Chris has experience around high performance computing and modern data center architectures, familiarity with different use cases and verticals (Telecoms, Fintech, Gaming). Outside of work Chris enjoys cycling, music and a neverending list of DIY tasks. https://www.linkedin.com/in/christopherlavery https://twitter.com/mrchrislavery github.com/fire-ant
- 5. 5 Introducing Site Reliability Engineering (SRE) A (basic) definition: ▪ Operations focused (Production Infrastructure, On-call work) ▪ Often Embedded within Development teams to facilitate better operational outcomes ▪ Applying Software Engineering Principles to Operational Problems.
- 6. 6 Introducing Site Reliability Engineering (SRE) Why SRE ? What has changed? ▪ DevOps has progressed the culture of collaboration between development and operations ▪ Cloud computing has standardized/commodified aspects of software businesses ie. infrastructure as a service ▪ Consumers/End Users have transitioned to digital services
- 7. 7 Data Driven Decisions Data Driven Decisions ▪ Modern Distributed Systems have more moving parts ▪ These systems may be a hybrid of managed cloud resources/services and bespoke custom logic ▪ Modern Application Architectures can also be distributed in nature ▪ Increasing Cardinality (more things per thing) can provide better granularity/visibility but at the cost of complexity
- 8. 8 Data Driven Decisions Observability: ▪ System/Service Metrics (Infrastructure) ▪ Logging (and Events) ▪ Tracing (and Spans) APM (Application Performance Monitoring)
- 9. 9 SLIs and SLOs: Dashboard Gauges, Minimum Indicators and Penalty points ▪ SLIs - Service Level Indicator ▪ A measurement derived from one or more underlying metrics to support an SLO ▪ For example: A Yield SLI (fraction of successful requests) can inform an availability SLA based on an SLO of 97.5%
- 10. 10 SLIs and SLOs: Dashboard Gauges, Minimum Indicators and Penalty points ▪ SLOs - Service Level Objective ▪ The defined threshold that a service should ideally operate above or within as per the terms of an SLA
- 11. 11 SLIs and SLOs: Dashboard Gauges, Minimum Indicators and Penalty points ▪ SLAs - Service Level Agreements ▪ An agreement contracted with a customer to set expectations and convey minimum levels of service
- 12. 12 SLIs and SLOs: Dashboard Gauges, Minimum Indicators and Penalty points ▪ Reliable enough, but no more reliable than it needs to be. ▪ Use your error budget wisely. ▪ Use it to take risks. ▪ An error budget is 1 minus the SLO of the service
- 13. 13 Uptime Traditional Focus is on Uptime ▪ If you were down for 1 second per day, you would exceed the 5 9’s SLA. ▪ This level of uptime is expensive and not often needed. ▪ Set your uptime to a reasonable level and no better Uptime Target Yearly allowed downtime 99% 3d 15h 39m 29s 99.9% 8h 45m 56s 99.99% 52m 35s 99.999% 5m 15s
- 14. 14 Service Levels Summary Service Level Agreements = Agreement with a Customer to provide a service or penalties are applied. Service Level Objectives = A promise that must be achieved, e.g. uptime, response time Service Level Indicators = The metric that corresponds to meeting an objective, e.g. measured uptime, latency, error rates etc.
- 15. 15 DORA: DevOps Research and Assessment
- 16. 16 DORA: DevOps Research and Assessment ▪ Low Lead Time (minutes > hours > days) ▪ High Deployment Frequency (minutes < hours < days)
- 17. 17 DORA: DevOps Research and Assessment ▪ Low Lead Time (minutes > hours > days) ▪ High Deployment Frequency (minutes < hours < days) ▪ Change failure rate ▪ Time to restore
- 18. 18 DORA: DevOps Research and Assessment ▪ Lead Time + Deployment Frequency = Throughput ▪ Change failure rate + Time to restore = Stability
- 19. 19 DORA: DevOps Research and Assessment
- 20. 20 Confidential do not distribute 20 GitOps SRE
- 21. 21 Confidential do not distribute 21 The entire system is described declaratively The canonical desired system state is versioned in git Software agents ensure correctness and perform actions on divergence in a closed loop The Principles of GitOps Approved changes can be automatically applied to the system
- 22. Weave GitOps Continuous delivery and operations for Kubernetes 22
- 23. 23 Embrace Risk Traditional systems focus on Availability such as Uptime, but ignores velocity of deploying new features. An SRE balances both availability and velocity of features. So risk is an acceptable part of the system and should not be avoided, but it should be managed.
- 24. 24 Progressive Delivery: Overview ● Progressive delivery is the practice of limiting the audience for your code changes or new feature releases ● It is done to restrict the exposure area to a minimum in case of any risk instances ● Progressive delivery can be implemented through a number of strategies from A/B testing to canary, Blue/Green, Rolling/Immutable upgrades or Feature flag management ● Make Deployments boring!
- 25. 25 Progressive Delivery: SRE orientation ● Leverage the data emitted from an instrumented and observable system to reason about whether to proceed or rollback ● Configure the application infrastructure (service meshes, key metrics), SLA (overall downtime or service degradation) and automated operations (rollback/revert strategy) ● Foster higher collaboration and enables higher velocity through early feedback and deeper insight. Transferable approach which can be standardised with components and configurations within an organisation
- 26. 26 Progressive Delivery: Flagger ● Cloud Native Open source Progressive delivery Operator ● Broad support for all popular Service meshes, Ingresses and K8s Gateway API ● Designed with GitOps Methodologies in mind and a complementary component to FluxCD ● Commercial UI Integration in Weave GitOps ● Adopters include CNCF projects right through to Enterprise users
- 27. 27 Progressive Delivery: How it works
- 32. 32 Progressive Delivery: Takeaways ● Progressive delivery is the practice of limiting the audience for your code changes or new feature releases ● It is done to restrict the exposure area to a minimum in case of any risk instances ● Progressive delivery can be implemented through a number of strategies from A/B testing to canary, Blue/Green or Feature flag management
- 33. 33 Progressive Delivery: Business Value ● Deployment Frequency is higher with GitOps & Progressive Delivery ● Lead times are shorter with Progressive Delivery ● Change Failure Rate is lower with Progressive Delivery ● Mean Time to Recovery is shorter with GitOps
- 34. 34 34 Whitepaper: Progressive Delivery with GitOps https://bit.ly/3K8oZwU Learn about Weave GitOps Assured www.weave.works/product/gitops/ Learn more about Weave GitOps Enterprise www.weave.works/enterprise and a 5 min demo https://youtu.be/aqJaHNCz2lM Request a personal demo www.weave.works/contact More information
- 35. 35 Confidential do not distribute 3 5 You Thank Join our Community https://slack.weave.works Contact Us [email protected] Our products & services www.weave.works
- 36. 36 Progressive Delivery: Deployment Strategies Strategy Positive Negative Rolling Zero Downtime Longer Deployments Canary Low Risk/Early Feedback Complex Monitoring/Testing Blue/Green Zero Downtime + Fast Rollback Cost (2 active instances) Feature Flags Controlled Release + test in Production Increased complexity in app management Immutable Predictable and Simple process Increase cost, Data persistence