“As every company become a software company,
Security vulnerabilities are the new oil spills”
@jbaruch
#LiquidSoftware
#NashDevOps
http://jfrog.com/shownotes
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
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
Slide 31
The problem is not the code, it’s the data. Big data.
@jbaruch
#LiquidSoftware
#NashDevOps
http://jfrog.com/shownotes
Slide 32
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
Slide 33
Slide 34
Features that we want
@jbaruch
#LiquidSoftware
Acceptance tests costs
#NashDevOps
http://jfrog.com/shownotes
Slide 35
Slide 36
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)
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
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
Slide 46
continuous OTA updates are like normal OTA updates,
but better
@jbaruch
#LiquidSoftware
#NashDevOps
http://jfrog.com/shownotes
Slide 47
Slide 48
Slide 49
Slide 50
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
Slide 51
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
Slide 52
Slide 53
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
Slide 54
Continuous updates pattern: Automated deployment
@jbaruch
#LiquidSoftware
Problem: People suck at repetitive tasks. Solution: Automate everything.
#NashDevOps
http://jfrog.com/shownotes
Slide 55
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
Slide 56
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
Slide 57
Slide 58
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
Slide 59
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
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
Slide 62
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
Slide 63
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
Slide 64
Slide 65
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
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
Slide 68
”
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