DevOps Patterns & Antipatterns for Continuous Software Updates

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

Why software updates?

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

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

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

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

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

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This is not a new idea! @jbaruch 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 #LiquidSoftware http://jfrog.com/shownotes

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

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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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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

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

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

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

What can possibly go wrong?

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

Continuous updates pattern: Local rollback @jbaruch 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 #LiquidSoftware http://jfrog.com/shownotes

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

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

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

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

KNIGHT-MARE @jbaruch 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 #LiquidSoftware http://jfrog.com/shownotes

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

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

Continuous updates pattern: state awareness @jbaruch 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. #LiquidSoftware http://jfrog.com/shownotes

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

Continuous updates pattern: Progressive Delivery @jbaruch 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. #LiquidSoftware http://jfrog.com/shownotes

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

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

Continuous updates pattern: feature flags @jbaruch 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 #LiquidSoftware http://jfrog.com/shownotes

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

” 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 http://jfrog.com/shownotes

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

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

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