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
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 29
The problem is not the code, it’s the data. Big data.
@jbaruch
#LiquidSoftware
http://jfrog.com/shownotes
Slide 30
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 31
Slide 32
Features that we want
@jbaruch
Acceptance tests costs
#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
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
Slide 42
continuous OTA updates are like normal OTA updates,
but better
@jbaruch
#LiquidSoftware
http://jfrog.com/shownotes
Slide 43
Slide 44
Slide 45
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
Slide 46
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
Slide 47
Slide 48
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
Slide 49
Continuous updates pattern: Automated deployment
@jbaruch
Problem: People suck at repetitive tasks. Solution: Automate everything.
#LiquidSoftware
http://jfrog.com/shownotes
Slide 50
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
Slide 51
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
Slide 52
Slide 53
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
Slide 54
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
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
Slide 57
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
Slide 58
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
Slide 59
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 60
”
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