This is the story of transforming Microsoft to One Engineering System with a globally distributed 24x7x365 service on the public cloud. We’ll show you round the system that handles the load of some of the most demanding engineering teams in the world and share some stories about how they got there.

2. About Me
Sam Guckenheimer
Product Owner, Visual Studio Cloud Services
14 years Microsoft
30 years software industry
@SamGuckenheimer
https://visualstudio.com/devops

3. Microsoft Org Chart ~2011
Unintended
consequence:
No reuse
would go
unpunished.

4. There cannot be a more important thing for
an engineer, for a product team, than to
work on the systems that drive our
productivity.
So I would, any day of the week, trade off
features for our own productivity.
I want our best engineers to work on our
engineering systems, so that we can later
on come back and build all of the new
concepts we want.
4
Purpose of One Engineering System

5. An engineering north star…
…the source across the company is available to anyone
…any dev can offer improvements to anything in the company
…the IP the company has built up over the years is made of re-usable components
…anybody can find and potentially re-use components from anywhere else
…devs are rewarded for creating popular components
…there is zero lag from when a dev makes a change & when the rest of the company sees it
… build and test time is directly proportional to the change made
…devs can move to another team and already know how to work

6. Demo

7. 0
50
100
150
200
250
300
VSTS
3GB
Linux
0.6GB
Windows
270GB
57k files
1.7GB initial pack file
5.5MB index file
110k files
8GB initial pack file
16MB index file
500 users
3.5M files
86GB initial pack file
400MB index file
4k users
Git repo sizes

8. Git experience on Windows repo (with GVFS on TFS)
Git GVFS
Improve
ment
clone 12hrs 2.5mins 288x
checkout 3hrs 30secs 360x
status 8mins 2.3sec 209x
commit 30mins 6.9secs 261x

9. Live Site Culture and Engineering
Live Site Health
Time to Detect
Time to Communicate
Time To Mitigate
Customer Impact
Incident prevention items
Aging live site problems
Customer support metrics
SLA per customer account
(SLA, MPI, top drivers)
Engineering
Bug cap per engineer
Aging bugs in important
categories
Pass rate & coverage by
test level
Velocity
Time to build
Time to self test
Time to deploy
Time to learn
(Telemetry pipe)
Usage
Acquisition
Engagement
Dedication
Churn
Feature usage

10. Common
Telemetry
Pipeline
Combines
high volume ingestion
fast queries over very
large data sets
text search structured
and unstructured data

11. We Create Transparency

12. Is It Working?

13. Self-forming teams
https://aka.ms/SelfFormingTeams

14. Engineering System – 1ES Day

15. Is 1ES Working?
4x active user growth in 2 years to 75,000