A Layered Approach to Progressive Delivery
Progressive Delivery is the practice of decoupling deploy from release, allowing changes to be safely pushed all the way to production and verified there before releasing to users.
Selectively dialing up and down the exposure of code in production without a new deploy, rollback, or hotfix is the foundation of Progressive Delivery, but the higher-level benefits of safety and fast feedback come from layering practices on top of that.
Key Takeaways:
Whether you are new to Progressive Delivery or are already practicing some aspect of it, you'll learn/refresh on the basics and then come away with a powerful model for layering higher-value benefits on top of that foundation:
Decouple Deploy from Release
o Incremental Feature Development
o Testing In Production
o Kill Switch (big red button instead of hotfixes and rollbacks)
Automate Guardrails/Do No Harm Metrics
o Alert on Exception / Performance (early in rollout)
o “Limit The Blast Radius” (without manual heroics)
Measure Release Impact
o Boost Team Pride
o Avoid the Feature Factory Trap
Test To Learn (A/B Testing)
o Taking Bigger Risks Safely
o Learning Faster With Less Waste
Painted door experiments
Dynamic configuration
00:00 Start
00:14 The Three Questions
01:00 Dave's POV: Sustainable Software Delivery (More Impact, Less Burnout)
01:38 What's Ahead In This Talk
02:20 What Is Progressive Delivery?
02:46 Sam Guckenheimer (Azure DevOps) "Progressive Experimentation"
03:13 James Governor of RedMonk (@monkchips) Coins Progressive Delivery
03:43 Carlos Sanchez's Definition of Progressive Delivery
04:41 Progressive Delivery Role Models
04:56 Walmart EXPO Test to Learn + Test to Launch
05:50 LinkedIn LiX: Watching Guardrails Automatically
06:49 The Foundation: Decouple Deploy From Release
06:57 Four Ways To Decouple Deploy From Release + Related Benefits
07:49 Blue/Green Deployment
08:45 Canary Releases Using Containers
09:47 Feature Flags
10:47 Feature Flags + Data, Integrated
11:20 Quick Recap on Feature Flags and How They Work
13:04 Capabilities Unlocked by Feature Flags
13:59 The Upper Layers: Data-Informed Practices Automated
14:46 Why Automate Data-Informed Practices?
16:51 Can't We Just Change Things And Monitor What Happens?
17:23 Problem To Solve: Separating Signal From Noise (Like Noise Cancelling Headphones)
18:45 Problem To Solve: Seeing EARLY Signs of Trouble (See it at 100% or 5%?)
19:39 How a Stats Engine is Like Noise Cancelling Headphones For Your Metrics
20:49 Automating Guardrails/Do-No-Harm-Metrics
21:25 Measuring Release Impact
22:16 Test To Learn (A/B Test)
22:47 Comparing New to Status Quo
22:59 Trying Multiple Things Without Multiple Versions of Your Code
24:02 Painted Door Experiments
24:26 Taking Bigger Risks Safely: Imperfect Foods Signup Flow
25:25 This is What Sustainable Software Delivery Looks Like
26:07 How In-House Progressive Delivery Platforms Paved The Way For Split ___________________________________________________
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