DevOps Patterns & Antipatterns for Continuous Software Updates

A presentation at Nashville DevOps Meetup June 2020 in June 2020 in Nashville, TN, USA by Baruch Sadogursky

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DevOps Patterns & Antipatterns for Continuous Software Updates “What can possibly go wrong?!”

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Why software updates?

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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“As every company become a software company, Security vulnerabilities are the new oil spills” @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Identify @jbaruch #LiquidSoftware Fix #NashDevOps Deploy http://jfrog.com/shownotes

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Identify Fix Deploy Immediately OS upgrade years

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Identify Fix Deploy 2 months Struts upgrade 2 months

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware Identify As fast as possible Fix As fast as possible Deploy As fast as possible #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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This is not a new idea! @jbaruch #LiquidSoftware XP: short feedback Scrum: reducing cycle time to absolute minimum TPS: Decide as late as possible and Deliver as fast as possible Kanban: Incremental change #NashDevOps http://jfrog.com/shownotes

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🎩 @jbaruch #dockercon jfrog.com/shownotes @ErinMeyerINSEAD’s “Culture Map”

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shownotes http://jfrog.com/shownotes Slides Video Links Comments, Ratings Raffle @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Update available Yes No Do we trust the update? Yes How about no Let’s update! Yes Are there any high risks? No Do we want it? No

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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number of artifacts as a symptom of complexity Today IoT Serverless Docker Microservices Infrastructure as Code Continuous Delivery Continuous Integration Agile 2000 @jbaruch @jfrog #LiquidSoftware www.liquidsoftware.com

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The problem is not the code, it’s the data. Big data. @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Update available Yes No Can we verify the update? No Yes Yes How about no Do we trust the update? Time consuming verification Let’s update! Yes Are there any high risks? No Do we want it? No

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Features that we want @jbaruch #LiquidSoftware Acceptance tests costs #NashDevOps http://jfrog.com/shownotes

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Your browser Twitter in your browser Twitter on your smartphone Your smartphone OS?! Update available Yes Are there any high risks? No Let’s update! Do we want it? No one asked you (auto update)

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What can possibly go wrong?

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: Local rollback @jbaruch #LiquidSoftware Problem: update went catastrophically wrong and an over the-air patch can’t reach the device Solution: Have a previous version saved on the device prior to update. Rollback in case problem occurred #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: OTA software updates @jbaruch #LiquidSoftware Problem: physical recalls are costly. Extremely costly. Also, you can’t force an upgrade. Solution: Implement over the air software updates, preferably, continuous updates. #NashDevOps http://jfrog.com/shownotes

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continuous OTA updates are like normal OTA updates, but better @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: continuous updates @jbaruch #LiquidSoftware Problem: In batch updates important features wait for non-important features. Solution: Implement continuous updates. #NashDevOps http://jfrog.com/shownotes

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You thought your problems are hard? Things under your control Server-side Updates IoT (Mobile, Automotive, Edge) Updates ✓ ✓ ✓ ✓ ✕ ✕ ✕ ✕ The availability of the target The state of the target The version on the target The access to the target @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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KNIGHT-MARE @jbaruch #LiquidSoftware New system reused old APIs 1 out of 8 servers was not updated New clients sent requests to machine contained old code Engineers undeployed working code from updated servers, increasing the load on the not-updated server No monitoring, no alerting, no debugging #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: Automated deployment @jbaruch #LiquidSoftware Problem: People suck at repetitive tasks. Solution: Automate everything. #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: frequent updates @jbaruch #LiquidSoftware Problem: Seldom deployments generate anxiety and stress, leading to errors. Solution: Update frequently to develop skill and habit. #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: state awareness @jbaruch #LiquidSoftware Problem: Target state can affect the update process and the behavior of the system after the update. Solution: Know and consider target state when updating. Reverting might require revering the state. #NashDevOps http://jfrog.com/shownotes

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Cloud-dark @jbaruch #LiquidSoftware New rules are deployed frequently to battle attacks Deployment of a single misconfigured rule Included regex to spike CPU to 100% “Affected region: Earth” #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: Progressive Delivery @jbaruch #LiquidSoftware Problem: Releasing a bug affects ALL the users. Solution: Release to a small number of users first effectively reducing the blast radius and observe. If a problem occurs, stop the release, revert or update the affected users. #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: observability @jbaruch #LiquidSoftware Problem: Some problems are hard to trace relying on user feedback only Solution: Implement tracing, monitoring and logging #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: Rollbacks @jbaruch #LiquidSoftware Problem: Fixes might take time, users suffer in a meanwhile Solution: Implement rollback, the ability to deploy a previous version without delay #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: feature flags @jbaruch #LiquidSoftware Problem: Rollbacks are not always supported by the deployment target platform Solution: Embed 2 versions of the features in the app itself and trigger them with API calls #NashDevOps http://jfrog.com/shownotes

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Continuous updates pattern: zero downtime updates @jbaruch #LiquidSoftware Problem: You will probably loose all your users if you shut down for 5 weeks to perform an update. Solution: Perform zerodowntime OTA small and fequent continuous updates. #NashDevOps http://jfrog.com/shownotes

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Continuous updates @jbaruch #LiquidSoftware Frequent Automatic Tested Progressively delivered State-aware Observability *Local Rollbacks #NashDevOps http://jfrog.com/shownotes

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Update available Yes Do we trust the update? Yes Do we want it? Are there any high risks? Sure, why not? (auto update) Yes Let’s update! No

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” Our goal is to transition from bulk and rare software updates to extremely tiny and extremely frequent software updates; so tiny and so frequent that they provide an illusion of software flowing from development to the update target. We call it the Liquid Software vision. @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Corner cases? @jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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@jbaruch #LiquidSoftware #NashDevOps http://jfrog.com/shownotes

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Q&A and twitter ads @jbaruch #LiquidSoftware #NashDevOps https://liquidsoftware.com https://jfrog.com/shownotes