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THE DEVOPS
HANDBOOK
How to Create World-Class
Agility, Reliability, & Security in
Technology Organizations
By Gene Kim, Jez Humble, Patrick Debois, and John Willis
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IT Revolution Press, LLC
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Copyright © 2016 by Gene Kim, Jez Humble, Patrick Debois, and John Willis
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dierent thinkers and industry leaders herein are excerpted from informal conversations,
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that took place over the last six years during the development and writing of this book.
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THE DEVOPS HANDBOOK
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TABLE OF CONTENTS
Preface xi
Foreword xix
Imagine a World Where Dev and Ops Become DevOps:
An Introduction to The DevOps Handbook xxi
PART I—THE THREE WAYS 1
Part I Introduction 3
1 Agile, Continuous Delivery, and the Three Ways 7
2 The First Way: The Principles of Flow 15
3 The Second Way: The Principles of Feedback 27
4 The Third Way: The Principles of Continual Learning
and Experimentation 37
PART II—WHERE TO START 47
Part II Introduction 49
5 Selecting Which Value Stream to Start With 51
6 Understanding the Work in Our Value Stream, Making it Visible,
and Expanding it Across the Organization 61
7 How to Design Our Organization and Architecture
with Conway’s Law in Mind 77
8 How to Get Great Outcomes by Integrating Operations
into the Daily Work of Development 95
PART III—THE FIRST WAY:
THE TECHNICAL PRACTICES OF FLOW 107
Part III Introduction 109
9 Create the Foundations of Our Deployment Pipeline 111
10 Enable Fast and Reliable Automated Testing 123
11 Enable and Practice Continuous Integration 143
12 Automate and Enable Low-Risk Releases 153
13 Architect for Low-Risk Releases 179
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PART IV—THE SECOND WAY:
THE TECHNICAL PRACTICES OF FEEDBACK 191
Part IV Introduction 193
14 Create Telemetry to Enable Seeing and Solving Problems 195
15 Analyze Telemetry to Better Anticipate Problems
and Achieve Goals 215
16 Enable Feedback So Development and Operations
Can Safely Deploy Code 227
17 Integrate Hypothesis-Driven Development and
A/B Testing into Our Daily Work 241
18 Create Review and Coordination Processes to
Increase Quality of Our Current Work 249
PART V—THE THIRD WAY:
THE TECHNICAL PRACTICES OF CONTINUAL LEARNING
AND EXPERIMENTATION 267
Part V Introduction 269
19 Enable and Inject Learning into Daily Work 271
20 Convert Local Discoveries into Global Improvements 287
21 Reserve Time to Create Organizational Learning
and Improvement 299
PART VI—THE TECHNOLOGICAL PRACTICES OF
INTEGRATING INFORMATION SECURITY, CHANGE
MANAGEMENT, AND COMPLIANCE 309
Part VI Introduction 311
22 Information Security as Everyone’s Job, Every Day 313
23 Protecting the Deployment Pipeline, and Integrating into Change
Management and Other Security and Compliance Controls 333
Conclusion to the DevOps Handbook:
A Call to Action 347
ADDITIONAL MATERIAL 351
Appendices 353
Additional Resources 366
Endnotes 370
Index 409
Acknowledgments 435
Author Biographies 439
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THE DEVOPS
HANDBOOK
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Preface
Aha!
The journey to complete The DevOps Handbook has been a long one—it started
with weekly working Skype calls between the co-authors in February of 2011,
with the vision of creating a prescriptive guide that would serve as a companion
to the as-yet unnished book The Phoenix Project: A Novel About IT, DevOps,
and Helping Your Business Win.
More than ve years later, with over two thousand hours of work, The DevOps
Handbook is nally here. Completing this book has been an extremely long
process, although one that has been highly rewarding and full of incredible
learning, with a scope that is much broader than we originally envisioned.
Throughout the project, all the co-authors shared a belief that DevOps is
genuinely important, formed in a personal “aha” moment much earlier in
each of our professional careers, which I suspect many of our readers will
resonate with.
Gene Kim
I’ve had the privilege of studying high-performing technology orga-
nizations since 1999, and one of the earliest ndings was that bound-
ary-spanning between the dierent functional groups of IT Operations,
Information Security, and Development was critical to success. But I
still remember the rst time I saw the magnitude of the downward
spiral that would result when these functions worked toward op-
posing goals.
It was 2006, and I had the opportunity to spend a week with the group
who managed the outsourced IT Operations of a large airline reser-
vation service. They described the downstream consequences of their
large, annual software releases: each release would cause immense
chaos and disruption for the outsourcer, as well as customers; there
would be SLA (service level agreement) penalties, because of the
customer-impacting outages; there would be layos of the most
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xii • The DevOps Handbook
talented and experienced sta, because of the resulting prot short-
falls; there would be much unplanned work and reghting so that
the remaining sta couldn’t work on the ever-growing service request
backlogs coming from customers; the contract would be held together
by the heroics of middle management; and everyone felt that the
contract would be doomed to be put out for re-bid in three years.
The sense of hopelessness and futility that resulted created for me
the beginnings of a moral crusade. Development seemed to always
be viewed as strategic, but IT Operations was viewed as tactical, often
delegated away or outsourced entirely, only to return in ve years in
worse shape than it was rst handed over.
For many years, many of us knew that there must be a better way. I
remember seeing the talks coming out of the 2009 Velocity Conference,
describing amazing outcomes enabled by architecture, technical
practices, and cultural norms that we now know as DevOps. I was so
excited, because it clearly pointed to the better way that we had all
been searching for. And helping spread that word was one of my
personal motivations to co-author The Phoenix Project. You can imagine
how incredibly rewarding it was to see the broader community react
to that book, describing how it helped them achieve their own
“aha” moments.
Jez Humble
My DevOps “aha” moment was at a start-up in 2000—my rst job
after graduating. For some time, I was one of two technical sta. I did
everything: networking, programming, support, systems adminis-
tration. We deployed software to production by FTP directly from our
workstations.
Then in 2004 I got a job at ThoughtWorks, a consultancy where my
rst gig was working on a project involving about seventy people. I
was on a team of eight engineers whose full-time job was to deploy
our software into a production-like environment. In the beginning,
it was really stressful. But over a few months we went from manual
deployments that took two weeks to an automated deployment that
took one hour, where we could roll forward and back in milliseconds
using the blue-green deployment pattern during normal business hours.
That project inspired a lot of the ideas in both the Continuous Delivery
(Addison-Wesley, 2000) book and this one. A lot of what drives me
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Preface • xiii
and others working in this space is the knowledge that, whatever
your constraints, we can always do better, and the desire to help people
on their journey.
Patrick Debois
For me, it was a collection of moments. In 2007 I was working on a
data center migration project with some Agile teams. I was jealous
that they had such high productivity—able to get so much done in
so little time.
For my next assignment, I started experimenting with Kanban in
Operations and saw how the dynamic of the team changed. Later, at
the Agile Toronto 2008 conference I presented my IEEE paper on this,
but I felt it didn’t resonate widely in the Agile community.We started
an Agile system administration group, but I overlooked the human
side of things.
After seeing the 2009 Velocity Conference presentation “10 Deploys
per Day” by John Allspaw and Paul Hammond, I was convinced others
were thinking in a similar way. So I decided to organize the rst
DevOpsDays, accidently coining the term DevOps.
The energy at the event was unique and contagious. When people
started to thank me because it changed their life for the better, I
understood the impact. I haven’t stopped promoting DevOps since.
John Willis
In 2008, I had just sold a consulting business that focused on
large-scale, legacy IT operations practices around conguration
management and monitoring (Tivoli) when I rst met Luke Kanies
(the founder of Puppet Labs). Luke was giving a presentation on Puppet
at an O’Reilly open source conference on conguration management
(CM).
At rst I was just hanging out at the back of the room killing time and
thinking, “What could this twenty-year-old tell me about conguration
management?” After all, I had literally been working my entire life
at some of the largest enterprises in the world, helping them architect
CM and other operations management solutions. However, about
ve minutes into his session, I moved up to the rst row and realized
everything I had been doing for the last twenty years was wrong. Luke
was describing what I now call second generation CM.
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xiv • The DevOps Handbook
After his session I had an opportunity to sit down and have coee
with him. I was totally sold on what we now call infrastructure as
code. However, while we met for coee, Luke started going even
further, explaining his ideas. He started telling me he believed that
operations was going to have to start behaving like software developers.
They were going to have to keep their congurations in source control
and adopt CI/CD delivery patterns for their workow. Being the old
IT Operations person at the time, I think I replied to him with some-
thing like, “That idea is going to sink like Led Zeppelin with Ops folk.”
(I was clearly wrong.)
Then about a year later in 2009 at another O’Reilly conference, Velocity,
I saw Andrew Clay Shafer give a presentation on Agile Infrastructure.
In his presentation, Andrew showed this iconic picture of a wall
between developers and operations with a metaphorical depiction of
work being thrown over the wall. He coined this “the wall of confusion.”
The ideas he expressed in that presentation codied what Luke was
trying to tell me a year earlier. That was the light bulb for me. Later
that year, I was the only American invited to the original DevOpsDays
in Ghent. By the time that event was over, this thing we call DevOps
was clearly in my blood.
Clearly, the co-authors of this book all came to a similar epiphany, even if they
came there from very dierent directions. But there is now an overwhelming
weight of evidence that the problems described above happen almost every-
where, and that the solutions associated with DevOps are nearly universally
applicable.
The goal of writing this book is to describe how to replicate the DevOps
transformations we’ve been a part of or have observed, as well as dispel many
of the myths of why DevOps won’t work in certain situations. Below are some
of the most common myths we hear about DevOps.
Myth—DevOps is Only for Startups: While DevOps practices have been pioneered
by the web-scale, Internet “unicorn” companies such as Google, Amazon,
Netix, and Etsy, each of these organizations has, at some point in their history,
risked going out of business because of the problems associated with more
traditional “horse” organizations: highly dangerous code releases that were
prone to catastrophic failure, inability to release features fast enough to beat
the competition, compliance concerns, an inability to scale, high levels of
distrust between Development and Operations, and so forth.
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Preface • xv
However, each of these organizations was able to transform their architecture,
technical practices, and culture to create the amazing outcomes that we
associate with DevOps. As Dr. Branden Williams, an information security
executive, quipped, “Let there be no more talk of DevOps unicorns or horses
but only thoroughbreds and horses heading to the glue factory.”
Myth—DevOps Replaces Agile: DevOps principles and practices are compatible
with Agile, with many observing that DevOps is a logical continuation of the
Agile journey that started in 2001. Agile often serves as an eective enabler
of DevOps, because of its focus on small teams continually delivering high
quality code to customers.
Many DevOps practices emerge if we continue to manage our work beyond
the goal of “potentially shippable code” at the end of each iteration, extending
it to having our code always in a deployable state, with developers checking
into trunk daily, and that we demonstrate our features in production-like
environments.
Myth—DevOps is incompatible with ITIL: Many view DevOps as a backlash to
ITIL or ITSM (IT Service Management), which was originally published in
1989. ITIL has broadly inuenced multiple generations of Ops practitioners,
including one of the co-authors, and is an ever-evolving library of practices
intended to codify the processes and practices that underpin world-class IT
Operations, spanning service strategy, design, and support.
DevOps practices can be made compatible with ITIL process. However, to
support the shorter lead times and higher deployment frequencies associated
with DevOps, many areas of the ITIL processes become fully automated, solving
many problems associated with the conguration and release management
processes (e.g., keeping the conguration management database and denitive
software libraries up to date). And because DevOps requires fast detection
and recovery when service incidents occur, the ITIL disciplines of service
design, incident, and problem management remain as relevant as ever.
Myth—DevOps is Incompatible with Information Security and Compliance: The
absence of traditional controls (e.g., segregation of duty, change approval
processes, manual security reviews at the end of the project) may dismay
information security and compliance professionals.
However, that doesn’t mean that DevOps organizations don’t have eective
controls. Instead of security and compliance activities only being performed
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xvi • The DevOps Handbook
at the end of the project, controls are integrated into every stage of daily work
in the software development life cycle, resulting in better quality, security,
and compliance outcomes.
Myth—DevOps Means Eliminating IT Operations, or “NoOps:” Many misinterpret
DevOps as the complete elimination of the IT Operations function. However,
this is rarely the case. While the nature of IT Operations work may change, it
remains as important as ever. IT Operations collaborates far earlier in the
software life cycle with Development, who continues to work with IT Opera-
tions long after the code has been deployed into production.
Instead of IT Operations doing manual work that comes from work tickets, it
enables developer productivity through APIs and self-serviced platforms that
create environments, test and deploy code, monitor and display production
telemetry, and so forth. By doing this, IT Operations become more like Devel-
opment (as do QA and Infosec), engaged in product development, where the
product is the platform that developers use to safely, quickly, and securely
test, deploy, and run their IT services in production.
Myth—DevOps is Just “Infrastructure as Code” or Automation: While many of
the DevOps patterns shown in this book require automation, DevOps also
requires cultural norms and an architecture that allows for the shared goals
to be achieved throughout the IT value stream. This goes far beyond just
automation. As Christopher Little, a technology executive and one of the
earliest chroniclers of DevOps, wrote, “DevOps isn’t about automation, just
as astronomy isn’t about telescopes.”
Myth—DevOps is Only for Open Source Software: Although many DevOps
success stories take place in organizations using software such as the LAMP
stack (Linux, Apache, MySQL, PHP), achieving DevOps outcomes is indepen-
dent of the technology being used. Successes have been achieved with appli-
cations written in Microsoft.NET, COBOL, and mainframe assembly code, as
well as with SAP and even embedded systems (e.g., HP LaserJet rmware).
SPREADING THE AHA! MOMENT
Each of the authors has been inspired by the amazing innovations happening
in the DevOps community and the outcomes they are creating: they are creating
safe systems of work, and enabling small teams to quickly and independently
develop and validate code that can be safely deployed to customers. Given our
belief that DevOps is a manifestation of creating dynamic, learning organi-
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Preface • xvii
zations that continually reinforce high-trust cultural norms, it is inevitable
that these organizations will continue to innovate and win in the marketplace.
It is our sincere hope that The DevOps Handbook will serve as a valuable resource
for many people in dierent ways: a guide for planning and executing DevOps
transformations, a set of case studies to research and learn from, a chronicle
of the history of DevOps, a means to create a coalition that spans Product
Owners, Architecture, Development, QA, IT Operations, and Information
Security to achieve common goals, a way to get the highest levels of leadership
support for DevOps initiatives, as well as a moral imperative to change the
way we manage technology organizations to enable better eectiveness and
eciency, as well as enabling a happier and more humane work environment,
helping everyone become lifelong learners—this not only helps everyone
achieve their highest goals as human beings, but also helps their or-
ganizations win.
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Foreword
In the past, many elds of engineering have experienced a sort of notable
evolution, continually “leveling-up” its understanding of its own work. While
there are university curriculums and professional support organizations
situated within specic disciplines of engineering (civil, mechanical, electrical,
nuclear, etc.), the fact is, modern society needs all forms of engineering to
recognize the benets of and work in a multidisciplinary way.
Think about the design of a high-performance vehicle. Where does the work
of a mechanical engineer end and the work of an electrical engineer begin?
Where (and how, and when) should someone with domain knowledge of
aerodynamics (who certainly would have well-formed opinions on the shape,
size, and placement of windows) collaborate with an expert in passenger er-
gonomics? What about the chemical inuences of fuel mixture and oil on the
materials of the engine and transmission over the lifetime of the vehicle?
There are other questions we can ask about the design of an automobile, but
the end result is the same: success in modern technical endeavors absolutely
requires multiple perspectives and expertise to collaborate.
In order for a eld or discipline to progress and mature, it needs to reach a
point where it can thoughtfully reect on its origins, seek out a diverse set of
perspectives on those reections, and place that synthesis into a context that
is useful for how the community pictures the future.
This book represents such a synthesis and should be seen as a seminal col-
lection of perspectives on the (I will argue, still emerging and quickly evolving)
eld of software engineering and operations.
No matter what industry you are in, or what product or service your organi-
zation provides, this way of thinking is paramount and necessary for survival
for every business and technology leader.
—John Allspaw, CTO, Etsy
Brooklyn, NY, August 2016
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Imagine a World Where Dev
and Ops Become DevOps
An Introduction to The
DevOps Handbook
Imagine a world where product owners, Development, QA, IT Operations,
and Infosec work together, not only to help each other, but also to ensure that
the overall organization succeeds. By working toward a common goal, they
enable the fast ow of planned work into production (e.g., performing tens,
hundreds, or even thousands of code deploys per day), while achieving world-
class stability, reliability, availability, and security.
In this world, cross-functional teams rigorously test their hypotheses of which
features will most delight users and advance the organizational goals. They
care not just about implementing user features, but also actively ensure their
work ows smoothly and frequently through the entire value stream without
causing chaos and disruption to IT Operations or any other internal or external
customer.
Simultaneously, QA, IT Operations, and Infosec are always working on ways
to reduce friction for the team, creating the work systems that enable devel-
opers to be more productive and get better outcomes. By adding the expertise
of QA, IT Operations, and Infosec into delivery teams and automated self-service
tools and platforms, teams are able to use that expertise in their daily work
without being dependent on other teams.
This enables organizations to create a safe system of work, where small teams
are able to quickly and independently develop, test, and deploy code and value
quickly, safely, securely, and reliably to customers. This allows organizations
to maximize developer productivity, enable organizational learning, create
high employee satisfaction, and win in the marketplace.
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xxii • The DevOps Handbook
These are the outcomes that result from DevOps. For most of us, this is not
the world we live in. More often than not, the system we work in is broken,
resulting in extremely poor outcomes that fall well short of our true po-
tential. In our world, Development and IT Operations are adversaries;
testing and Infosec activities happen only at the end of a project, too late
to correct any problems found; and almost any critical activity requires
too much manual effort and too many handoffs, leaving us to always be
waiting. Not only does this contribute to extremely long lead times to get
anything done, but the quality of our work, especially production deploy-
ments, is also problematic and chaotic, resulting in negative impacts to
our customers and our business.
As a result, we fall far short of our goals, and the whole organization is dis-
satised with the performance of IT, resulting in budget reductions and
frustrated, unhappy employees who feel powerless to change the process
and its outcomes.† The solution? We need to change how we work; DevOps
shows us the best way forward.
To better understand the potential of the DevOps revolution, let us look at the
Manufacturing Revolution of the 1980s. By adopting Lean principles and
practices, manufacturing organizations dramatically improved plant produc-
tivity, customer lead times, product quality, and customer satisfaction, enabling
them to win in the marketplace.
Before the revolution, average manufacturing plant order lead times were six
weeks, with fewer than 70% of orders being shipped on time. By 2005, with
the widespread implementation of Lean practices, average product lead times
had dropped to less than three weeks, and more than 95% of orders were being
shipped on time. Organizations that did not implement Lean practices lost
market share, and many went out of business entirely.
Similarly, the bar has been raised for delivering technology products and
services—what was good enough in previous decades is not good enough
now. For each of the last four decades, the cost and time required to develop
and deploy strategic business capabilities and features has dropped by orders
of magnitude. During the 1970s and 1980s, most new features required one
to ve years to develop and deploy, often costing tens of millions of dollars.
By the 2000’s, because of advances in technology and the adoption of Agile
principles and practices, the time required to develop new functionality had
† This is just a small sample of the problems found in typical IT organizations.
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Introduction • xxiii
dropped to weeks or months, but deploying into production would still require
weeks or months, often with catastrophic outcomes.
And by 2010, with the introduction of DevOps and the neverending commod-
itization of hardware, software, and now the cloud, features (and even entire
startup companies) could be created in weeks, quickly being deployed into
production in just hours or minutes—for these organizations, deployment
nally became routine and low risk. These organizations are able to perform
experiments to test business ideas, discovering which ideas create the most
value for customers and the organization as a whole, which are then further
developed into features that can be rapidly and safely deployed into production.
Table 1. The ever accelerating trend toward faster, cheaper, low-risk delivery of software
1970s–1980s 1990s 2000s–Present
Era Mainframes Client/Server Commoditization
and Cloud
Representative
technology
of era
COBOL, DB2 on
MVS, etc.
C++, Oracle,
Solaris, etc.
Java, MySQL, Red
Hat, Ruby on
Rails, PHP, etc.
Cycle time 1–5 years 3–12 months 2–12 weeks
Cost $1M–$100M $100k–$10M $10k–$1M
At risk The whole company A product line or
division A product feature
Cost of failure
Bankruptcy, sell
the company,
massive layos
Revenue miss,
CIO’s job Negligible
(Source: Adrian Cockcroft, “Velocity and Volume (or Speed Wins),” presentation at
FlowCon, San Francisco, CA, November 2013.)
Today, organizations adopting DevOps principles and practices often deploy
changes hundreds or even thousands of times per day. In an age where com-
petitive advantage requires fast time to market and relentless experimentation,
organizations that are unable to replicate these outcomes are destined to lose
in the marketplace to more nimble competitors and could potentially go out
of business entirely, much like the manufacturing organizations that did not
adopt Lean principles.
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xxiv • The DevOps Handbook
These days, regardless of what industry we are competing in, the way we
acquire customers and deliver value to them is dependent on the technology
value stream. Put even more succinctly, as Jerey Immelt, CEO of General
Electric, stated, “Every industry and company that is not bringing software
to the core of their business will be disrupted.” Or as Jerey Snover, Technical
Fellow at Microsoft, said, “In previous economic eras, businesses created value
by moving atoms. Now they create value by moving bits.”
It’s dicult to overstate the enormity of this problem—it aects every orga-
nization, independent of the industry we operate in, the size of our organization,
whether we are prot or non-prot. Now more than ever, how technology
work is managed and performed predicts whether our organizations will win
in the marketplace, or even survive. In many cases, we will need to adopt
principles and practices that look very dierent from those that have success-
fully guided us over the past decades. (See Appendix 1.)
Now that we have established the urgency of the problem that DevOps solves,
let us take some time to explore in more detail the symptomatology of the
problem, why it occurs, and why, without dramatic intervention, the problem
worsens over time.
THE PROBLEM: SOMETHING IN YOUR ORGANIZATION
MUST NEED IMPROVEMENT OR YOU WOULDN’T BE
READING THIS BOOK
Most organizations are not able to deploy production changes in minutes or
hours, instead requiring weeks or months. Nor are they able to deploy hundreds
or thousands of changes into production per day; instead, they struggle to
deploy monthly or even quarterly. Nor are production deployments routine,
instead involving outages and chronic reghting and heroics.
In an age where competitive advantage requires fast time to market, high
service levels, and relentless experimentation, these organizations are at a
signicant competitive disadvantage. This is in large part due to their inability
to resolve a core, chronic conict within their technology organization.
THE CORE, CHRONIC CONFLICT
In almost every IT organization, there is an inherent conict between Devel-
opment and IT Operations which creates a downward spiral, resulting in
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Introduction • xxv
ever-slower time to market for new products and features, reduced quality,
increased outages, and, worst of all, an ever-increasing amount of technical debt.
The term “technical debt” was rst coined by Ward Cunningham. Analogous
to nancial debt, technical debt describes how decisions we make lead to
problems that get increasingly more dicult to x over time, continually
reducing our available options in the future—even when taken on judiciously,
we still incur interest.
One factor that contributes to this is the often competing goals of Development
and IT Operations. IT organizations are responsible for many things. Among
them are the two following goals, which must be pursued simultaneously:
• Respond to the rapidly changing competitive landscape
•
Provide stable, reliable, and secure service to the customer
Frequently, Development will take responsibility for responding to changes
in the market, deploying features and changes into production as quickly as
possible. IT Operations will take responsibility for providing customers with
IT service that is stable, reliable, and secure, making it dicult or even im-
possible for anyone to introduce production changes that could jeopardize
production. Congured this way, Development and IT Operations have dia-
metrically opposed goals and incentives.
Dr. Eliyahu M. Goldratt, one of the founders of the manufacturing management
movement, called these types of conguration “the core, chronic conict”—
when organizational measurements and incentives across dierent silos
prevent the achievement of global, organizational goals.†
This conict creates a downward spiral so powerful it prevents the achievement
of desired business outcomes, both inside and outside the IT organization.
These chronic conicts often put technology workers into situations that lead
to poor software and service quality, and bad customer outcomes, as well as
a daily need for workarounds, reghting, and heroics, whether in Product
† In the manufacturing realm, a similar core, chronic conict existed: the need to simultaneously
ensure on-time shipments to customers and control costs. How this core, chronic conict was
broken is described in Appendix 2.
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xxvi • The DevOps Handbook
Management, Development, QA, IT Operations, or Information Security. (See
Appendix 2.)
DOWNWARD SPIRAL IN THREE ACTS
The downward spiral in IT has three acts that are likely familiar to most
IT practitioners.
The rst act begins in IT Operations, where our goal is to keep applications
and infrastructure running so that our organization can deliver value to
customers. In our daily work, many of our problems are due to applications
and infrastructure that are complex, poorly documented, and incredibly
fragile. This is the technical debt and daily workarounds that we live with
constantly, always promising that we’ll x the mess when we have a little more
time. But that time never comes.
Alarmingly, our most fragile artifacts support either our most important
revenue-generating systems or our most critical projects. In other words, the
systems most prone to failure are also our most important and are at the
epicenter of our most urgent changes. When these changes fail, they jeopardize
our most important organizational promises, such as availability to customers,
revenue goals, security of customer data, accurate nancial reporting, and
so forth.
The second act begins when somebody has to compensate for the latest broken
promise—it could be a product manager promising a bigger, bolder feature
to dazzle customers with or a business executive setting an even larger revenue
target. Then, oblivious to what technology can or can’t do, or what factors led
to missing our earlier commitment, they commit the technology organization
to deliver upon this new promise.
As a result, Development is tasked with another urgent project that inevitably
requires solving new technical challenges and cutting corners to meet the
promised release date, further adding to our technical debt—made, of course,
with the promise that we’ll x any resulting problems when we have a little
more time.
This sets the stage for the third and nal act, where everything becomes just
a little more dicult, bit by bit—everybody gets a little busier, work takes a
little more time, communications become a little slower, and work queues
get a little longer. Our work becomes more tightly coupled, smaller actions
cause bigger failures, and we become more fearful and less tolerant of making
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Introduction • xxvii
changes. Work requires more communication, coordination, and approvals;
teams must wait just a little longer for their dependent work to get done; and
our quality keeps getting worse. The wheels begin grinding slower and require
more eort to keep turning. (See Appendix 3.)
Although it’s dicult to see in the moment, the downward spiral is obvious
when one takes a step back. We notice that production code deployments are
taking ever-longer to complete, moving from minutes to hours to days to
weeks. And worse, the deployment outcomes have become even more prob-
lematic, that resulting in an ever-increasing number of customer-impacting
outages that require more heroics and reghting in Operations, further
depriving them of their ability to pay down technical debt.
As a result, our product delivery cycles continue to move slower and slower,
fewer projects are undertaken, and those that are, are less ambitious. Fur-
thermore, the feedback on everyone’s work becomes slower and weaker,
especially the feedback signals from our customers. And, regardless of what
we try, things seem to get worse—we are no longer able to respond quickly
to our changing competitive landscape, nor are we able to provide stable,
reliable service to our customers. As a result, we ultimately lose in the
marketplace.
Time and time again, we learn that when IT fails, the entire organization fails.
As Steven J. Spear noted in his book The High-Velocity Edge, whether the
damages “unfold slowly like a wasting disease” or rapidly “like a ery crash...
the destruction can be just as complete.”
WHY DOES THIS DOWNWARD SPIRAL HAPPEN EVERYWHERE?
For over a decade, the authors of this book have observed this destructive
spiral occur in countless organizations of all types and sizes. We understand
better than ever why this downward spiral occurs and why it requires DevOps
principles to mitigate. First, as described earlier, every IT organization has
two opposing goals, and second, every company is a technology company,
whether they know it or not.
As Christopher Little, a software executive and one of the earliest chroniclers
of DevOps, said, “Every company is a technology company, regardless of what
business they think they’re in. A bank is just an IT company with a
banking license.Ӡ
† In 2013, the European bank HSBC employed more software developers than Google.
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xxviii • The DevOps Handbook
To convince ourselves that this is the case, consider that the vast majority of
capital projects have some reliance upon IT. As the saying goes, “It is virtually
impossible to make any business decision that doesn’t result in at least one
IT change.”
In the business and nance context, projects are critical because they serve
as the primary mechanism for change inside organizations. Projects are
typically what management needs to approve, budget for, and be held ac-
countable for; therefore, they are the mechanism that achieve the goals and
aspirations of the organization, whether it is to grow or even shrink.†
Projects are typically funded through capital spending (i.e., factories, equip-
ment, and major projects, and expenditures are capitalized when payback is
expected to take years), of which 50% is now technology related. This is even
true in “low tech” industry verticals with the lowest historical spending on
technology, such as energy, metal, resource extraction, automotive, and
construction. In other words, business leaders are far more reliant upon the
eective management of IT in order to achieve their goals than they think.‡
THE COSTS: HUMAN AND ECONOMIC
When people are trapped in this downward spiral for years, especially those
who are downstream of Development, they often feel stuck in a system that
pre-ordains failure and leaves them powerless to change the outcomes. This
powerlessness is often followed by burnout, with the associated feelings of
fatigue, cynicism, and even hopelessness and despair.
Many psychologists assert that creating systems that cause feelings of pow-
erlessness is one of the most damaging things we can do to fellow human
beings—we deprive other people of their ability to control their own outcomes
and even create a culture where people are afraid to do the right thing because
of fear of punishment, failure, or jeopardizing their livelihood. This can create
† For now, let us suspend the discussion of whether software should be funded as a “project” or
a “product.” This is discussed later in the book.
‡ For instance, Dr. Vernon Richardson and his colleagues published this astonishing nding.
They studied the 10-K SEC lings of 184 public corporations and divided them into three groups:
A) rms with material weaknesses with IT-related deciencies, B) rms with material weak-
nesses with no IT-related deciencies, and C) “clean rms” with no material weaknesses. Firms
in Group A saw eight times higher CEO turnover than Group C, and there was four times higher
CFO turnover in Group A than in Group C. Clearly, IT may matter far more than we typically
think.
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Introduction • xxix
the conditions of learned helplessness, where people become unwilling or
unable to act in a way that avoids the same problem in the future.
For our employees, it means long hours, working on weekends, and a decreased
quality of life, not just for the employee, but for everyone who depends on
them, including family and friends. It is not surprising that when this occurs,
we lose our best people (except for those that feel like they can’t leave, because
of a sense of duty or obligation).
In addition to the human suering that comes with the current way of working,
the opportunity cost of the value that we could be creating is staggering—the
authors believe that we are missing out on approximately $2.6 trillion of value
creation per year, which is, at the time of this writing, equivalent to the annual
economic output of France, the sixth largest economy in the world.
Consider the following calculation—both IDC and Gartner estimated that in
2011, approximately 5% of the worldwide gross domestic product($3.1 trillion)
was spent on IT (hardware, services, and telecom). If we estimate that 50% of
that $3.1 trillion was spent on operating costs and maintaining existing systems,
and that one-third of that 50% was spent on urgent and unplanned work or
rework, approximately $520 billion was wasted.
If adopting DevOps could enable us, through better management and increased
operational excellence, to halve that waste and redeploy that human potential
into something that’s ve times the value (a modest proposal), we could create
$2.6 trillion of value per year.
THE ETHICS OF DEVOPS: THERE IS A BETTER WAY
In the previous sections, we described the problems and the negative conse-
quences of the status quo due to the core, chronic conict, from the inability
to achieve organizational goals, to the damage we inict on fellow human
beings. By solving these problems, DevOps astonishingly enables us to simul-
taneously improve organizational performance, achieve the goals of all the
various functional technology roles (e.g., Development, QA, IT Operations,
Infosec), and improve the human condition.
This exciting and rare combination may explain why DevOps has generated
so much excitement and enthusiasm in so many in such a short time, including
technology leaders, engineers, and much of the software ecosystem we
reside in.
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xxx • The DevOps Handbook
BREAKING THE DOWNWARD SPIRAL WITH DEVOPS
Ideally, small teams of developers independently implement their features,
validate their correctness in production-like environments, and have their
code deployed into production quickly, safely and securely. Code deployments
are routine and predictable. Instead of starting deployments at midnight on
Friday and spending all weekend working to complete them, deployments
occur throughout the business day when everyone is already in the oce and
without our customers even noticing—except when they see new features
and bug xes that delight them. And, by deploying code in the middle of the
workday, for the rst time in decades IT Operations is working during normal
business hours like everyone else.
By creating fast feedback loops at every step of the process, everyone can
immediately see the eects of their actions. Whenever changes are committed
into version control, fast automated tests are run in production-like environ-
ments, giving continual assurance that the code and environments operate
as designed and are always in a secure and deployable state.
Automated testing helps developers discover their mistakes quickly (usually
within minutes), which enables faster xes as well as genuine learning—
learning that is impossible when mistakes are discovered six months later
during integration testing, when memories and the link between cause and
eect have long faded. Instead of accruing technical debt, problems are xed
as they are found, mobilizing the entire organization if needed, because global
goals outweigh local goals.
Pervasive production telemetry in both our code and production environments
ensure that problems are detected and corrected quickly, conrming that
everything is working as intended and customers are getting value from the
software we create.
In this scenario, everyone feels productive—the architecture allows small
teams to work safely and architecturally decoupled from the work of other
teams who use self-service platforms that leverage the collective experience
of Operations and Information Security. Instead of everyone waiting all the
time, with large amounts of late, urgent rework, teams work independently
and productively in small batches, quickly and frequently delivering new
value to customers.
Even high-prole product and feature releases become routine by using dark
launch techniques. Long before the launch date, we put all the required code
for the feature into production, invisible to everyone except internal employees
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Introduction • xxxi
and small cohorts of real users, allowing us to test and evolve the feature until
it achieves the desired business goal.
And, instead of reghting for days or weeks to make the new functionality
work, we merely change a feature toggle or conguration setting.This small
change makes the new feature visible to ever-larger segments of customers,
automatically rolling back if something goes wrong. As a result, our releases
are controlled, predictable, reversible, and low stress.
It’s not just feature releases that are calmer—all sorts of problems are being
found and xed early, when they are smaller, cheaper, and easier to correct.
With every x, we also generate organizational learnings, enabling us to
prevent the problem from recurring and enabling us to detect and correct
similar problems faster in the future.
Furthermore, everyone is constantly learning, fostering a hypothesis-driven
culture where the scientic method is used to ensure nothing is taken for
granted—we do nothing without measuring and treating product development
and process improvement as experiments.
Because we value everyone’s time, we don’t spend years building features that
our customers don’t want, deploying code that doesn’t work, or xing some-
thing that isn’t actually the cause of our problem.
Because we care about achieving goals, we create long-term teams that are
responsible for meeting them. Instead of project teams where developers are
reassigned and shued around after each release, never receiving feedback
on their work, we keep teams intact so they can keep iterating and improving,
using those learnings to better achieve their goals. This is equally true for the
product teams who are solving problems for our external customers, as well
as our internal platform teams who are helping other teams be more productive,
safe, and secure.
Instead of a culture of fear, we have a high-trust, collaborative culture, where
people are rewarded for taking risks. They are able to fearlessly talk about
problems as opposed to hiding them or putting them on the backburner—after
all, we must see problems in order to solve them.
And, because everyone fully owns the quality of their work, everyone builds
automated testing into their daily work and uses peer reviews to gain con-
dence that problems are addressed long before they can impact a customer.
These processes mitigate risk, as opposed to approvals from distant authorities,
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xxxii • The DevOps Handbook
allowing us to deliver value quickly, reliably, and securely—even proving to
skeptical auditors that we have an eective system of internal controls.
And when something does go wrong, we conduct blameless post-mortems, not
to punish anyone, but to better understand what caused the accident and how
to prevent it. This ritual reinforces our culture of learning. We also hold internal
technology conferences to elevate our skills and ensure that everyone is always
teaching and learning.
Because we care about quality, we even inject faults into our production en-
vironment so we can learn how our system fails in a planned manner. We
conduct planned exercises to practice large-scale failures, randomly kill
processes and compute servers in production, and inject network latencies
and other nefarious acts to ensure we grow ever more resilient. By doing
this, we enable better resilience, as well as organizational learning and
improvement.
In this world, everyone has ownership in their work, regardless of their role in
the technology organization They have condence that their work matters and
is meaningfully contributing to organizational goals, proven by their low-stress
work environment and their organization’s success in the marketplace. Their
proof is that the organization is indeed winning in the marketplace.
THE BUSINESS VALUE OF DEVOPS
We have decisive evidence of the business value of DevOps. From 2013 through
2016, as part of Puppet Labs’ State Of DevOps Report, to which authors Jez
Humble and Gene Kim contributed, we collected data from over twenty-ve
thousand technology professionals, with the goal of better understanding
the health and habits of organizations at all stages of DevOps adoption.
The rst surprise this data revealed was how much high performing organi-
zations using DevOps practices were outperforming their non–high performing
peers in the following areas:
• Throughput metrics
• Code and change deployments (thirty times more frequent)
•
Code and change deployment lead time (two hundred times faster)
• Reliability metrics
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Introduction • xxxiii
•
Production deployments (sixty times higher change success rate)
• Mean time to restore service (168 times faster)
• Organizational performance metrics
•
Productivity, market share, and protability goals (two times
more likely to exceed)
• Market capitalization growth (50% higher over three years)
In other words, high performers were both more agile and more reliable,
providing empirical evidence that DevOps enables us to break the core, chronic
conict. High performers deployed code thirty times more frequently, and
the time required to go from “code committed” to “successfully running in
production” was two hundred times faster—high performers had lead times
measured in minutes or hours, while low performers had lead times measured
in weeks, months, or even quarters.
Furthermore, high performers were twice as likely to exceed profitability,
market share, and productivity goals. And, for those organizations that
provided a stock ticker symbol, we found that high performers had 50%
higher market capitalization growth over three years. They also had higher
employee job satisfaction, lower rates of employee burnout, and their
employees were 2.2 times more likely to recommend their organization to
friends as a great place to work.† High performers also had better infor-
mation security outcomes. By integrating security objectives into all stages
of the development and operations processes, they spent 50% less time
remediating security issues.
DEVOPS HELPS SCALE DEVELOPER PRODUCTIVITY
When we increase the number of developers, individual developer productivity
often signicantly decreases due to communication, integration, and testing
overhead. This is highlighted in the famous book by Frederick Brook, The
Mythical Man-Month, where he explains that when projects are late, adding
† As measured by employee Net Promoter Score (eNPS). This is a signicant nding, as research
has shown that “companies with highly engaged workers grew revenues two and a half times
as much as those with low engagement levels. And [publicly traded] stocks of companies with
a high-trust work environment outperformed market indexes by a factor of three from 1997
through 2011.”
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xxxiv • The DevOps Handbook
more developers not only decreases individual developer productivity but
also decreases overall productivity.
On the other hand, DevOps shows us that when we have the right architecture,
the right technical practices, and the right cultural norms, small teams of
developers are able to quickly, safely, and independently develop, integrate,
test, and deploy changes into production. As Randy Shoup, formerly a director
of engineering at Google, observed, large organizations using DevOps “have
thousands of developers, but their architecture and practices enable small
teams to still be incredibly productive, as if they were a startup.”
The 2015 State of DevOps Report examined not only “deploys per day” but also
“deploys per day per developer.” We hypothesized that high performers would
be able to scale their number of deployments as team sizes grew.
deploys / day
# of developers
3
2.5
2
1.5
1
.5
0
10 100 1000
High (linear)
Medium
Low
Figure 1. Deployments/day vs. number of developers
(Source: Puppet Labs, 2015 State Of DevOps Report.)†
Indeed, this is what we found. Figure 1 shows that in low performers, deploys
per day per developer go down as team size increases, stays constant for
medium performers, and increases linearly for high performers.
In other words, organizations adopting DevOps are able to linearly increase
the number of deploys per day as they increase their number of developers,
just as Google, Amazon, and Netix have done.‡
† Only organizations that are deploying at least once per day are shown.
‡ Another more extreme example is Amazon. In 2011, Amazon was performing approximately
seven thousand deploys per day. By 2015, they were performing 130,000 deploys per day.
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Introduction • xxxv
THE UNIVERSALITY OF THE SOLUTION
One of the most inuential books in the Lean manufacturing movement is
The Goal: A Process of Ongoing Improvement written by Dr. Eliyahu M. Goldratt
in 1984. It inuenced an entire generation of professional plant managers
around the world. It was a novel about a plant manager who had to x his cost
and product due date issues in ninety days, otherwise his plant would be shut
down.
Later in his career, Dr. Goldratt described the letters he received in response
to The Goal. These letters would typically read, “You have obviously been hiding
in our factory, because you’ve described my life [as a plant manager] exactly…”
Most importantly, these letters showed people were able to replicate the
breakthroughs in performance that were described in the book in their own
work environments.
The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win,
written by Gene Kim, Kevin Behr, and George Spaord in 2013, was closely
modeled after The Goal. It is a novel that follows an IT leader who faces all
the typical problems that are endemic in IT organizations: an over-budget,
behind-schedule project that must get to market in order for the company
to survive. He experiences catastrophic deployments; problems with avail-
ability, security, and compliance; and so forth. Ultimately, he and his team
use DevOps principles and practices to overcome those challenges, helping
their organization win in the marketplace. In addition, the novel shows how
DevOps practices improved the workplace environment for the team, creating
lower stress and higher satisfaction because of greater practitioner involve-
ment throughout the process.
As with The Goal, there is tremendous evidence of the universality of the
problems and solutions described in The Phoenix Project. Consider some of
the statements found in the Amazon reviews: “I nd myself relating to the
characters in The Phoenix Project...I’ve probably met most of them over the
course of my career,” “If you have ever worked in any aspect of IT, DevOps, or
Infosec you will denitely be able to relate to this book,” or “There’s not a
character in The Phoenix Project that I don’t identify with myself or someone
I know in real life… not to mention the problems faced and overcome by
those characters.”
In the remainder of this book, we will describe how to replicate the transfor-
mation described in The Phoenix Project, as well provide many case studies of
how other organizations have used DevOps principles and practices to replicate
those outcomes.
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xxxvi • The DevOps Handbook
THE DEVOPS HANDBOOK: AN ESSENTIAL GUIDE
The purpose of the DevOps Handbook is to give you the theory, principles, and
practices you need to successfully start your DevOps initiative and achieve
your desired outcomes. This guidance is based on decades of sound manage-
ment theory, study of high performing technology organizations, work we
have done helping organizations transform, and research that validates the
eectiveness of the prescribed DevOps practices. As well as interviews with
relevant subject matter experts and analyses of nearly one hundred case
studies presented at the DevOps Enterprise Summit.
Broken into six parts, this book covers DevOps theories and principles using
the Three Ways, a specic view of the underpinning theory originally intro-
duced in The Phoenix Project. The DevOps Handbook is for everyone who performs
or inuences work in the technology value stream (which typically includes
Product Management, Development, QA, IT Operations, and Information
Security), as well as for business and marketing leadership, where most
technology initiatives originate.
The reader is not expected to have extensive knowledge of any of these
domains, or of DevOps, Agile, ITIL, Lean, or process improvement. Each of
these topics is introduced and explained in the book as it becomes necessary.
Our intent is to create a working knowledge of the critical concepts in each
of these domains, both to serve as a primer and to introduce the language
necessary to help practitioners work with all their peers across the entire IT
value stream, and to frame shared goals.
This book will be of value to business leaders and stakeholders who are in-
creasingly reliant upon the technology organization for the achievement of
their goals.
Furthermore, this book is intended for readers whose organizations might
not be experiencing all the problems described in the book (e.g., long deploy-
ment lead times or painful deployments).Even readers in this fortunate position
will benet from understanding DevOps principles, especially those relating
to shared goals, feedback, and continual learning.
In Part I, we present a brief history of DevOps and introduce the underpinning
theory and key themes from relevant bodies of knowledge that span over
decades. We then present the high level principles of the Three Ways: Flow,
Feedback, and Continual Learning and Experimentaion.
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Introduction • xxxvii
Part II describes how and where to start, and presents concepts such as value
streams, organizational design principles and patterns, organizational adoption
patterns, and case studies.
Part III describes how to accelerate Flow by building the foundations of our
deployment pipeline: enabling fast and eective automated testing, continuous
integration, continuous delivery, and architecting for low-risk releases.
Part IV discusses how to accelerate and amplify Feedback by creating eective
production telemetry to see and solve problems, better anticipate problems
and achieve goals, enable feedback so that Dev and Ops can safely deploy
changes, integrate A/B testing into our daily work, and create review and
coordination processes to increase the quality of our work.
Part V describes how we accelerate Continual Learning by establishing a just
culture, converting local discoveries into global improvements, and properly
reserving time to create organizational learning and improvements.
Finally, in Part VI we describe how to properly integrate security and com-
pliance into our daily work, by integrating preventative security controls
into shared source code repositories and services, integrating security into
our deployment pipeline, enhancing telemetry to better enable detection
and recovery, protecting the deployment pipeline, and achieving change
management objectives.
By codifying these practices, we hope to accelerate the adoption of DevOps
practices, increase the success of DevOps initiatives, and lower the activation
energy required for DevOps transformations.
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PART
The Three Ways
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Part
Introduction
In Part I of The DevOps Handbook, we will explore how the convergence of
several important movements in management and technology set the stage
for the DevOps movement. We describe value streams, how DevOps is the
result of applying Lean principles to the technology value stream, and the
Three Ways: Flow, Feedback, and Continual Learning and Experimentation.
Primary focuses within these chapters include:
•
The principles of Flow, which accelerate the delivery of work from
Development to Operations to our customers
• The principles of Feedback, which enable us to create ever safer
systems of work
•
The principles of Continual Learning and Experimentation foster
a high-trust culture and a scientic approach to organizational
improvement risk-taking as part of our daily work
A BRIEF HISTORY
DevOps and its resulting technical, architectural, and cultural practices rep-
resent a convergence of many philosophical and management movements.
While many organizations have developed these principles independently,
understanding that DevOps resulted from a broad stroke of movements, a
phenomenon described by John Willis (one of the co-authors of this book) as
the “convergence of DevOps,” shows an amazing progression of thinking and
improbable connections. There are decades of lessons learned from manu-
facturing, high reliability organization, high-trust management models, and
others that have brought us to the DevOps practices we know today.
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4 • Part I
DevOps is the outcome of applying the most trusted principles from the
domain of physical manufacturing and leadership to the IT value stream.
DevOps relies on bodies of knowledge from Lean, Theory of Constraints,
the Toyota Production System, resilience engineering, learning organiza-
tions, safety culture, human factors, and many others. Other valuable
contexts that DevOps draws from include high-trust management cultures,
servant leadership, and organizational change management. The result is
world-class quality, reliability, stability, and security at ever lower cost and
effort; and accelerated flow and reliability throughout the technology
value stream, including Product Management, Development, QA, IT Op-
erations, and Infosec.
While the foundation of DevOps can be seen as being derived from Lean, the
Theory of Constraints, and the Toyota Kata movement, many also view DevOps
as the logical continuation of the Agile software journey that began in 2001.
THE LEAN MOVEMENT
Techniques such as Value Stream Mapping, Kanban Boards, and Total Pro-
ductive Maintenance were codied for the Toyota Production System in the
1980s. In 1997, the Lean Enterprise Institute started researching applications
of Lean to other value streams, such as the service industry and healthcare.
Two of Lean’s major tenets include the deeply held belief that manufacturing
lead time required to convert raw materials into nished goods was the best
predictor of quality, customer satisfaction, and employee happiness, and that
one of the best predictors of short lead times was small batch sizes of work.
Lean principles focus on how to create value for the customer through systems
thinking by creating constancy of purpose, embracing scientic thinking,
creating ow and pull (versus push), assuring quality at the source, leading
with humility, and respecting every individual.
THE AGILE MANIFESTO
The Agile Manifesto was created in 2001 by seventeen of the leading thinkers
in software development. They wanted to create a lightweight set of values
and principles against heavyweight software development processes such
as waterfall development, and methodologies such as the Rational
Unied Process.
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Introduction • 5
One key principle was to “deliver working software frequently, from a
couple of weeks to a couple of months, with a preference to the shorter
timescale,” emphasizing the desire for small batch sizes, incremental re-
leases instead of large, waterfall releases. Other principles emphasized the
need for small, self-motivated teams, working in a high-trust management
model.
Agile is credited for dramatically increasing the productivity of many devel-
opment organizations. And interestingly, many of the key moments in DevOps
history also occurred within the Agile community or at Agile conferences, as
described below.
AGILE INFRASTRUCTURE AND VELOCITY MOVEMENT
At the 2008 Agile conference in Toronto, Canada, Patrick Debois and Andrew
Schafer held a “birds of a feather” session on applying Agile principles to
infrastructure as opposed to application code. Although they were the only
people who showed up, they rapidly gained a following of like-minded thinkers,
including co-author John Willis.
Later, at the 2009 Velocity conference, John Allspaw and Paul Hammond gave
the seminal “10 Deploys per Day: Dev and Ops Cooperation at Flickr” presen-
tation, where they described how they created shared goals between Dev and
Ops and used continuous integration practices to make deployment part of
everyone’s daily work. According to rst hand accounts, everyone attending
the presentation immediately knew they were in the presence of something
profound and of historic signicance.
Patrick Debois was not there, but was so excited by Allspaw and Hammond’s
idea that he created the rst DevOpsDays in Ghent, Belgium, (where he lived)
in 2009. There the term “DevOps” was coined.
THE CONTINUOUS DELIVERY MOVEMENT
Building upon the development discipline of continuous build, test, and
integration, Jez Humble and David Farley extended the concept to continuous
delivery, which dened the role of a “deployment pipeline” to ensure that
code and infrastructure are always in a deployable state, and that all code
checked in to trunk can be safely deployed into production. This idea was
rst presented at the 2006 Agile conference, and was also independently
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6 • Part I
developed in 2009 by Tim Fitz in a blog post on his website titled “Continuous
Deployment.Ӡ
TOYOTA KATA
In 2009, Mike Rother wrote Toyota Kata: Managing People for Improvement,
Adaptiveness and Superior Results, which framed his twenty-year journey to
understand and codify the Toyota Production System. He had been one of the
graduate students who ew with GM executives to visit Toyota plants and
helped develop the Lean toolkit, but he was puzzled when none of the com-
panies adopting these practices replicated the level of performance observed
at the Toyota plants.
He concluded that the Lean community missed the most important practice
of all, which he called the improvement kata. He explains that every organization
has work routines, and the improvement kata requires creating structure for
the daily, habitual practice of improvement work, because daily practice is
what improves outcomes. The constant cycle of establishing desired future
states, setting weekly target outcomes, and the continual improvement of
daily work is what guided improvement at Toyota.
The above describes the history of DevOps and relevant movements that it
draws upon. Throughout the rest of Part I, we look at value streams, how Lean
principles can be applied to the technology value stream, and the Three Ways
of Flow, Feedback, and Continual Learning and Experimentation.
† DevOps also extends and builds upon the practices of infrastructure as code, which was pioneered
by Dr. Mark Burgess, Luke Kanies, and Adam Jacob. In infrastructure as code, the work of
Operations is automated and treated like application code, so that modern development
practices can be applied to the entire development stream. This further enabled fast deployment
ow, including continuous integration (pioneered by Grady Booch and integrated as one of
the key 12 practices of Extreme Programming), continuous delivery (pioneered by Jez Humble
and David Farley), and continuous deployment (pioneered by Etsy, Wealthfront, and Eric Ries’s
work at IMVU).
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Agile, Continuous Delivery,
and the Three Ways
In this chapter, an introduction to the underpinning theory of Lean Manu-
facturing is presented, as well as the Three Ways, the principles from which
all of the observed DevOps behaviors can be derived.
Our focus here is primarily on theory and principles, describing many decades
of lessons learned from manufacturing, high-reliability organizations, high-
trust management models, and others, from which DevOps practices have
been derived. The resulting concrete principles and patterns, and their practical
application to the technology value stream, are presented in the remaining
chapters of the book.
THE MANUFACTURING VALUE STREAM
One of the fundamental concepts in Lean is the value stream.We will dene
it rst in the context of manufacturing and then extrapolate how it applies
to DevOps and the technology value stream.
Karen Martin and Mike Osterling dene value stream in their book Value
Stream Mapping: How to Visualize Work and Align Leadership for Organiza-
tional Transformation as “the sequence of activities an organization under-
takes to deliver upon a customer request,” or “the sequence of activities re-
quired to design, produce, and deliver a good or service to a customer, including
the dual ows of information and material.”
In manufacturing operations, the value stream is often easy to see and observe:
it starts when a customer order is received and the raw materials are released
onto the plant oor. To enable fast and predictable lead times in any value
stream, there is usually a relentless focus on creating a smooth and even ow
of work, using techniques such as small batch sizes, reducing work in process
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8 • Part I
(WIP), preventing rework to ensure we don’t pass defects to downstream work
centers, and constantly optimizing our system toward our global goals.
THE TECHNOLOGY VALUE STREAM
The same principles and patterns that enable the fast ow of work in physical
processes are equally applicable to technology work (and, for that matter, for
all knowledge work). In DevOps, we typically dene our technology value
stream as the process required to convert a business hypothesis into a technology-
enabled service that delivers value to the customer.
The input to our process is the formulation of a business objective, concept,
idea, or hypothesis, and starts when we accept the work in Development,
adding it to our committed backlog of work.
From there, Development teams that follow a typical Agile or iterative process
will likely transform that idea into user stories and some sort of feature
specication, which is then implemented in code into the application or
service being built. The code is then checked in to the version control repository,
where each change is integrated and tested with the rest of the software system.
Because value is created only when our services are running in production,
we must ensure that we are not only delivering fast ow, but that our deploy-
ments can also be performed without causing chaos and disruptions such as
service outages, service impairments, or security or compliance failures.
FOCUS ON DEPLOYMENT LEAD TIME
For the remainder of this book, our attention will be on deployment lead time,
a subset of the value stream described above. This value stream begins when
any engineer† in our value stream (which includes Development, QA, IT
Operations, and Infosec) checks a change into version control and ends when
that change is successfully running in production, providing value to the
customer and generating useful feedback and telemetry.
The rst phase of work that includes Design and Development is akin to Lean
Product Development and is highly variable and highly uncertain, often re-
quiring high degrees of creativity and work that may never be performed
again, resulting in high variability of process times. In contrast, the second
† Going forward, engineer refers to anyone working in our value stream, not just developers.
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Chapter 1 • 9
phase of work, which includes Testing and Operations, is akin to Lean Man-
ufacturing. It requires creativity and expertise, and strives to be predictable
and mechanistic, with the goal of achieving work outputs with minimized
variability (e.g., short and predictable lead times, near zero defects).
Instead of large batches of work being processed sequentially through the
design/development value stream and then through the test/operations value
stream (such as when we have a large batch waterfall process or long-lived
feature branches), our goal is to have testing and operations happening si-
multaneously with design/development, enabling fast ow and high quality.
This method succeeds when we work in small batches and build quality into
every part of our value stream.‡
Defining Lead Time vs. Processing Time
In the Lean community, lead time is one of two measures commonly used to
measure performance in value streams, with the other being processing time
(sometimes known as touch time or task time).§
Whereas the lead time clock starts when the request is made and ends when
it is fullled, the process time clock starts only when we begin work on the
customer request—specically, it omits the time that the work is in queue,
waiting to be processed (gure 2).
Ticket
Created
Work
Started
Work
Completed
Process Time
Lead Time
Figure 2. Lead time vs. process time of a deployment operation
Because lead time is what the customer experiences, we typically focus our
process improvement attention there instead of on process time. However,
the proportion of process time to lead time serves as an important measure
‡ In fact, with techniques such as test-driven development, testing occurs even before the rst
line of code is written.
§ In this book, the term process time will be favored for the same reason Karen Martin and Mike
Osterling cite: “To minimize confusion, we avoid using the term cycle time as it has several
denitions synonymous with processing time and pace or frequency of output, to name a few.”
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10 • Part I
of eciency—achieving fast ow and short lead times almost always requires
reducing the time our work is waiting in queues.
The Common Scenario: Deployment Lead Times Requiring Months
In business as usual, we often nd ourselves in situations where our deploy-
ment lead times require months. This is especially common in large,
complex organizations that are working with tightly-coupled, monolithic
applications, often with scarce integration test environments, long test and
production environment lead times, high reliance on manual testing, and
multiple required approval processes.When this occurs, our value stream may
look like gure 3:
Figure 3: A technology value stream with a deployment lead time of three months
(Source: Damon Edwards, “DevOps Kaizen,” 2015.)
When we have long deployment lead times, heroics are required at almost
every stage of the value stream. We may discover that nothing works at the
end of the project when we merge all the development team’s changes together,
resulting in code that no longer builds correctly or passes any of our tests.
Fixing each problem requires days or weeks of investigation to determine
who broke the code and how it can be xed, and still results in poor cus-
tomer outcomes.
Our DevOps Ideal: Deployment Lead Times of Minutes
In the DevOps ideal, developers receive fast, constant feedback on their work,
which enables them to quickly and independently implement, integrate, and
validate their code, and have the code deployed into the production environ-
ment (either by deploying the code themselves or by others).
We achieve this by continually checking small code changes into our version
control repository, performing automated and exploratory testing against it,
and deploying it into production. This enables us to have a high degree of
condence that our changes will operate as designed in production and that
any problems can be quickly detected and corrected.
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Chapter 1 • 11
This is most easily achieved when we have architecture that is modular, well
encapsulated, and loosely-coupled so that small teams are able to work with
high degrees of autonomy, with failures being small and contained, and
without causing global disruptions.
In this scenario, our deployment lead time is measured in minutes, or, in the
worst case, hours. Our resulting value stream map should look something
like gure 4:
Total cycle time: 25m
Automatic approval
Manual approval
5m
Production
deploy
10m
Exploratory
Test
10m
Automated
Test
Commit stage
(automated)
Figure 4: A technology value stream with a lead time of minutes
OBSERVING “%C/A” AS A MEASURE OF REWORK
In addition to lead times and process times, the third key metric in the tech-
nology value stream is percent complete and accurate (%C/A). This metric
reects the quality of the output of each step in our value stream. Karen Martin
and Mike Osterling state that “the %C/A can be obtained by asking downstream
customers what percentage of the time they receive work that is ‘usable as is,’
meaning that they can do their work without having to correct the information
that was provided, add missing information that should have been supplied,
or clarify information that should have and could have been clearer.”
THE THREE WAYS: THE PRINCIPLES
UNDERPINNING DEVOPS
The Phoenix Project presents the Three Ways as the set of underpinning prin-
ciples from which all the observed DevOps behaviors and patterns are derived
(gure 5).
The First Way enables fast left-to-right ow of work from Development to
Operations to the customer. In order to maximize ow, we need to make work
visible, reduce our batch sizes and intervals of work, build in quality by pre-
venting defects from being passed to downstream work centers, and constantly
optimize for the global goals.
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12 • Part I
Dev Ops
Dev Ops
Dev Ops
(Business) (Customer)
Figure 5: The Three Ways (Source: Gene Kim, “The Three Ways: The Principles Underpinning DevOps,” IT
Revolution Press blog, accessed August 9, 2016, http://itrevolution.com/
the-three-ways-principles-underpinning-devops/.)
By speeding up ow through the technology value stream, we reduce the lead
time required to fulll internal or customer requests, especially the time re-
quired to deploy code into the production environment. By doing this, we
increase the quality of work as well as our throughput, and boost our ability
to out-experiment the competition.
The resulting practices include continuous build, integration, test, and de-
ployment processes; creating environments on demand; limiting work in
process (WIP); and building systems and organizations that are safe to change.
The Second Way enables the fast and constant ow of feedback from right
to left at all stages of our value stream. It requires that we amplify feedback
to prevent problems from happening again, or enable faster detection and
recovery. By doing this, we create quality at the source and generate or embed
knowledge where it is needed—this allows us to create ever-safer systems
of work where problems are found and xed long before a catastrophic
failure occurs.
By seeing problems as they occur and swarming them until eective counter-
measures are in place, we continually shorten and amplify our feedback loops,
a core tenet of virtually all modern process improvement methodologies. This
maximizes the opportunities for our organization to learn and improve.
The Third Way enables the creation of a generative, high-trust culture that
supports a dynamic, disciplined, and scientic approach to experimentation
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Chapter 1 • 13
and risk-taking, facilitating the creation of organizational learning, both from
our successes and failures. Furthermore, by continually shortening and
amplifying our feedback loops, we create ever-safer systems of work and are
better able to take risks and perform experiments that help us learn faster
than our competition and win in the marketplace.
As part of the Third Way, we also design our system of work so that we can
multiply the eects of new knowledge, transforming local discoveries into
global improvements. Regardless of where someone performs work, they do
so with the cumulative and collective experience of everyone in the
organization.
CONCLUSION
In this chapter, we described the concepts of value streams, lead time as one
of the key measures of the eectiveness for both manufacturing and technology
value streams, and the high-level concepts behind each of the Three Ways,
the principles that underpin DevOps.
In the following chapters, the principles for each of the Three Ways are de-
scribed in greater detail. The rst of these principles is Flow, which is focused
on how we create the fast ow of work in any value stream, whether it’s in
manufacturing or technology work. The practices that enable fast ow are
described in Part III.
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The First Way:
The Principles of Flow
In the technology value stream, work typically ows from Development to
Operations, the functional areas between our business and our customers.
The First Way requires the fast and smooth ow of work from Development
to Operations, to deliver value to customers quickly. We optimize for this
global goal instead of local goals, such as Development feature completion
rates, test nd/x ratios, or Ops availability measures.
We increase ow by making work visible, by reducing batch sizes and intervals
of work, and by building quality in, preventing defects from being passed to
downstream work centers. By speeding up the ow through the technology
value stream, we reduce the lead time required to fulll internal and external
customer requests, further increasing the quality of our work while making
us more agile and able to out-experiment the competition.
Our goal is to decrease the amount of time required for changes to be deployed
into production and to increase the reliability and quality of those services.
Clues on how we do this in the technology value stream can be gleaned from
how the Lean principles were applied to the manufacturing value stream.
MAKE OUR WORK VISIBLE
A signicant dierence between technology and manufacturing value streams
is that our work is invisible. Unlike physical processes, in the technology value
stream we cannot easily see where ow is being impeded or when work is
piling up in front of constrained work centers. Transferring work between
work centers is usually highly visible and slow because inventory must be
physically moved.
However, in technology work the move can be done with a click of a button,
such as by re-assigning a work ticket to another team. Because it is so easy,
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16 • Part I
work can bounce between teams endlessly due to incomplete information,
or work can be passed onto downstream work centers with problems that
remain completely invisible until we are late delivering what we promised to
the customer or our application fails in the production environment.
To help us see where work is owing well and where work is queued or
stalled, we need to make our work as visible as possible. One of the best
methods of doing this is using visual work boards, such as kanban boards
or sprint planning boards, where we can represent work on physical or
electronic cards. Work originates on the left (often being pulled from a
backlog), is pulled from work center to work center (represented in columns),
and nishes when it reaches the right side of the board, usually in a column
labeled “done” or “in production.”
Ready
Expedite
Investigate
Development Ops
Doing Done Doing Done UAT Delivered
Figure 6: An example kanban board, spanning Requirements, Dev, Test, Staging, and
In Production (Source: David J. Andersen and Dominica DeGrandis,
Kanban for ITOps, training materials for workshop, 2012.)
Not only does our work become visible, we can also manage our work so that
it ows from left to right as quickly as possible. Furthermore, we can measure
lead time from when a card is placed on the board to when it is moved into
the “Done” column.
Ideally, our kanban board will span the entire value stream, dening work as
completed only when it reaches the right side of the board (gure 6). Work is
not done when Development completes the implementation of a feature—
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Chapter 2 • 17
rather, it is only done when our application is running successfully in pro-
duction, delivering value to the customer.
By putting all work for each work center in queues and making it visible, all
stakeholders can more easily prioritize work in the context of global goals.
Doing this enables each work center to single-task on the highest priority
work until it is completed, increasing throughput.
LIMIT WORK IN PROCESS WIP
In manufacturing, daily work is typically dictated by a production schedule
that is generated regularly (e.g., daily, weekly), establishing which jobs must
be run based on customer orders, order due dates, parts available, and
so forth.
In technology, our work is usually far more dynamic—this is especially the
case in shared services, where teams must satisfy the demands of many
dierent stakeholders. As a result, daily work becomes dominated by the
priority du jour, often with requests for urgent work coming in through every
communication mechanism possible, including ticketing systems, outage
calls, emails, phone calls, chat rooms, and management escalations.
Disruptions in manufacturing are also highly visible and costly, often requiring
breaking the current job and scrapping any incomplete work in process to
start the new job. This high level of eort discourages frequent disruptions.
However, interrupting technology workers is easy, because the consequences
are invisible to almost everyone, even though the negative impact to produc-
tivity may be far greater than in manufacturing. For instance, an engineer
assigned to multiple projects must switch between tasks, incurring all the
costs of having to re-establish context, as well as cognitive rules and goals.
Studies have shown that the time to complete even simple tasks, such as
sorting geometric shapes, signicantly degrades when multitasking. Of course,
because our work in the technology value stream is far more cognitively
complex than sorting geometric shapes, the eects of multitasking on process
time is much worse.
We can limit multitasking when we use a kanban board to manage our work,
such as by codifying and enforcing WIP (work in progress) limits for each
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18 • Part I
column or work center that puts an upper limit on the number of cards that
can be in a column.
For example, we may set a WIP limit of three cards for testing. When there
are already three cards in the test lane, no new cards can be added to the lane
unless a card is completed or removed from the “in work” column and put
back into queue (i.e., putting the card back to the column to the left). Nothing
can can be worked on until it is represented rst in a work card, reinforcing
that all work must be made visible.
Dominica DeGrandis, one of the leading experts on using kanbans in DevOps
value streams, notes that “controlling queue size [WIP] is an extremely powerful
management tool, as it is one of the few leading indicators of lead time—with
most work items, we don’t know how long it will take until it’s actually
completed.”
Limiting WIP also makes it easier to see problems that prevent the completion
of work.† For instance, when we limit WIP, we nd that we may have nothing
to do because we are waiting on someone else. Although it may be tempting
to start new work (i.e., “It’s better to be doing something than nothing”), a far
better action would be to nd out what is causing the delay and help x that
problem. Bad multitasking often occurs when people are assigned to multiple
projects, resulting in many prioritization problems.
In other words, as David J. Andersen, author of Kanban: Successful Evolutionary
Change for Your Technology Business, quipped, “Stop starting. Start nishing.”
REDUCE BATCH SIZES
Another key component to creating smooth and fast ow is performing work
in small batch sizes. Prior to the Lean manufacturing revolution, it was common
practice to manufacture in large batch sizes (or lot sizes), especially for oper-
ations where job setup or switching between jobs was time-consuming or
costly. For example, producing large car body panels requires setting large
and heavy dies onto metal stamping machines, a process that could take days.
When changeover cost is so expensive, we would often stamp as many panels
at a time as possible, creating large batches in order to reduce the number of
changeovers.
† Taiichi Ohno compared enforcing WIP limits to draining water from the river of inventory in
order to reveal all the problems that obstruct fast ow.
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Chapter 2 • 19
However, large batch sizes result in skyrocketing levels of WIP and high levels
of variability in ow that cascade through the entire manufacturing plant.
The result is long lead times and poor quality—if a problem is found in one
body panel, the entire batch has to be scrapped.
One of the key lessons in Lean is that in order to shrink lead times and increase
quality, we must strive to continually shrink batch sizes. The theoretical lower
limit for batch size is single-piece ow, where each operation is performed one
unit at a time.‡
The dramatic dierences between large and small batch sizes can be seen in
the simple newsletter mailing simulation described in Lean Thinking: Banish
Waste and Create Wealth in Your Corporation by James P. Womack and Daniel
T. Jones.
Suppose in our own example we have ten brochures to send and mailing each
brochure requires four steps: fold the paper, insert the paper into the envelope,
seal the envelope, and stamp the envelope.
The large batch strategy (i.e., “mass production”) would be to sequentially
perform one operation on each of the ten brochures. In other words, we would
rst fold all ten sheets of paper, then insert each of them into envelopes, then
seal all ten envelopes, and then stamp them.
On the other hand, in the small batch strategy (i.e., “single-piece ow”), all
the steps required to complete each brochure are performed sequentially
before starting on the next brochure. In other words, we fold one sheet of
paper, insert it into the envelope, seal it, and stamp it—only then do we start
the process over with the next sheet of paper.
The dierence between using large and small batch sizes is dramatic (see
gure 7). Suppose each of the four operations takes ten seconds for each of
the ten envelopes. With the large batch size strategy, the rst completed and
stamped envelope is produced only after 310 seconds.
Worse, suppose we discover during the envelope sealing operation that we
made an error in the rst step of folding—in this case, the earliest we would
discover the error is at two hundred seconds, and we have to refold and reinsert
all ten brochures in our batch again.
‡ Also known as “batch size of one” or “1x1 ow,” terms that refer to batch size and a WIP limit
of one.
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20 • Part I
Large Batches
WAITING First product ready
Single-Piece Flow
WAITING First product ready
Figure 7: Simulation of “envelope game” (fold, insert, seal, and stamp the envelope)
(Source: Stefan Luyten, “Single Piece Flow: Why mass production isn’t the most ecient way of doing ‘stu’,”
Medium.com, August 8, 2014, https://medium.com/@stefanluyten/single-piece-ow-5d2c2bec845b#.9o7sn74ns.)
In contrast, in the small batch strategy the rst completed stamped envelope
is produced in only forty seconds, eight times faster than the large batch
strategy. And, if we made an error in the rst step, we only have to redo the
one brochure in our batch. Small batch sizes result in less WIP, faster lead
times, faster detection of errors, and less rework.
The negative outcomes associated with large batch sizes are just as relevant
to the technology value stream as in manufacturing. Consider when we have
an annual schedule for software releases, where an entire year’s worth of code
that Development has worked on is released to production deployment.
Like in manufacturing, this large batch release creates sudden, high levels of
WIP and massive disruptions to all downstream work centers, resulting in
poor ow and poor quality outcomes. This validates our common experience
that the larger the change going into production, the more dicult the produc-
tion errors are to diagnose and x, and the longer they take to remediate.
In a post on Startup Lessons Learned, Eric Ries states, “The batch size is the unit
at which work-products move between stages in a development [or DevOps]
process. For software, the easiest batch to see is code. Every time an engineer
checks in code, they are batching up a certain amount of work. There are many
techniques for controlling these batches, ranging from the tiny batches needed
for continuous deployment to more traditional branch-based development,
where all of the code from multiple developers working for weeks or months
is batched up and integrated together.”
The equivalent to single piece ow in the technology value stream is realized
with continuous deployment, where each change committed to version control
is integrated, tested, and deployed into production. The practices that enable
this are described in Part IV.
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Chapter 2 • 21
REDUCE THE NUMBER OF HANDOFFS
In the technology value stream, whenever we have long deployment lead
times measured in months, it is often because there are hundreds (or even
thousands) of operations required to move our code from version control into
the production environment. To transmit code through the value stream
requires multiple departments to work on a variety of tasks, including func-
tional testing, integration testing, environment creation, server administration,
storage administration, networking, load balancing, and information security.
Each time the work passes from team to team, we require all sorts of com-
munication: requesting, specifying, signaling, coordinating, and often pri-
oritizing, scheduling, deconicting, testing, and verifying. This may require
using dierent ticketing or project management systems; writing technical
specication documents; communicating via meetings, emails, or phone
calls; and using le system shares, FTP servers, and Wiki pages.
Each of these steps is a potential queue where work will wait when we rely on
resources that are shared between dierent value streams (e.g., centralized
operations). The lead times for these requests are often so long that there is
constant escalation to have work performed within the needed timelines.
Even under the best circumstances, some knowledge is inevitably lost with
each hando. With enough handos, the work can completely lose the context
of the problem being solved or the organizational goal being supported. For
instance, a server administrator may see a newly created ticket requesting
that user accounts be created, without knowing what application or service
it’s for, why it needs to be created, what all the dependencies are, or whether
it’s actually recurring work.
To mitigate these types of problems, we strive to reduce the number of
handos, either by automating signicant portions of the work or by reorg-
anizing teams so they can deliver value to the customer themselves, instead
of having to be constantly dependent on others. As a result, we increase ow
by reducing the amount of time that our work spends waiting in queue, as
well as the amount of non–value-added time. (See Appendix 4.)
CONTINUALLY IDENTIFY AND ELEVATE OUR CONSTRAINTS
To reduce lead times and increase throughput, we need to continually identify
our system’s constraints and improve its work capacity. In Beyond the Goal,
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22 • Part I
Dr. Goldratt states, “In any value stream, there is always a direction of ow,
and there is always one and only constraint; any improvement not made at
that constraint is an illusion.” If we improve a work center that is positioned
before the constraint, work will merely pile up at the bottleneck even faster,
waiting for work to be performed by the bottlenecked work center.
On the other hand, if we improve a work center positioned after the bottleneck,
it remains starved, waiting for work to clear the bottleneck. As a solution, Dr.
Goldratt dened the “ve focusing steps:”
• Identify the system’s constraint.
• Decide how to exploit the system’s constraint.
• Subordinate everything else to the above decisions.
• Elevate the system’s constraint.
• If in the previous steps a constraint has been broken, go back to
step one, but do not allow inertia to cause a system constraint.
In typical DevOps transformations, as we progress from deployment lead
times measured in months or quarters to lead times measured in minutes,
the constraint usually follows this progression:
•
Environment creation: We cannot achieve deployments on-
demand if we always have to wait weeks or months for production
or test environments. The countermeasure is to create environ-
ments that are on demand and completely self-serviced, so that
they are always available when we need them.
•
Code deployment: We cannot achieve deployments on demand
if each of our production code deployments take weeks or months
to perform (i.e., each deployment requires 1,300 manual, error-
prone steps, involving up to three hundred engineers). The
countermeasure is to automate our deployments as much as
possible, with the goal of being completely automated so they
can be done self-service by any developer.
•
Test setup and run: We cannot achieve deployments on demand
if every code deployment requires two weeks to set up our test
environments and data sets, and another four weeks to manually
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Chapter 2 • 23
execute all our regression tests. The countermeasure is to auto-
mate our tests so we can execute deployments safely and to
parallelize them so the test rate can keep up with our code devel-
opment rate.
• Overly tight architecture: We cannot achieve deployments on
demand if overly tight architecture means that every time we
want to make a code change we have to send our engineers to
scores of committee meetings in order to get permission to make
our changes. Our countermeasure is to create more loosely coupled
architecture so that changes can be made safely and with more
autonomy, increasing developer productivity.
After all these constraints have been broken, our constraint will likely be
Development or the product owners. Because our goal is to enable small teams
of developers to independently develop, test, and deploy value to customers
quickly and reliably, this is where we want our constraint to be. High perform-
ers, regardless of whether an engineer is in Development, QA, Ops, or Infosec,
state that their goal is to help maximize developer productivity.
When the constraint is here, we are limited only by the number of good business
hypotheses we create and our ability to develop the code necessary to test
these hypotheses with real customers.
The progression of constraints listed above are generalizations of typical
transformations—techniques to identify the constraint in actual value streams,
such as through value stream mapping and measurements, are described
later in this book.
ELIMINATE HARDSHIPS AND WASTE IN THE
VALUE STREAM
Shigeo Shingo, one of the pioneers of the Toyota Production System, believed
that waste constituted the largest threat to business viability—the commonly
used denition in Lean is “the use of any material or resource beyond what
the customer requires and is willing to pay for.” He dened seven major types
of manufacturing waste: inventory, overproduction, extra processing, trans-
portation, waiting, motion, and defects.
More modern interpretations of Lean have noted that “eliminating waste” can
have a demeaning and dehumanizing context; instead, the goal is reframed
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24 • Part I
to reduce hardship and drudgery in our daily work through continual learning
in order to achieve the organization’s goals. For the remainder of this book,
the term waste will imply this more modern denition, as it more closely
matches the DevOps ideals and desired outcomes.
In the book Implementing Lean Software Development: From Concept to Cash,
Mary and Tom Poppendieck describe waste and hardship in the software
development stream as anything that causes delay for the customer, such as
activities that can be bypassed without aecting the result.
The following categories of waste and hardship come from Implementing Lean
Software Development unless otherwise noted:
•
Partially done work: This includes any work in the value stream
that has not been completed (e.g., requirement documents or
change orders not yet reviewed) and work that is sitting in queue
(e.g., waiting for QA review or server admin ticket). Partially done
work becomes obsolete and loses value as time progresses.
• Extra processes: Any additional work that is being performed
in a process that does not add value to the customer. This may
include documentation not used in a downstream work center,
or reviews or approvals that do not add value to the output. Extra
processes add eort and increase lead times.
•
Extra features: Features built into the service that are not needed
by the organization or the customer (e.g., “gold plating”).
Extra features add complexity and eort to testing and managing
functionality.
• Task switching: When people are assigned to multiple projects
and value streams, requiring them to context switch and manage
dependencies between work, adding additional eort and time
into the value stream.
• Waiting: Any delays between work requiring resources to wait
until they can complete the current work. Delays increase cycle
time and prevent the customer from getting value.
• Motion: The amount of eort to move information or materials
from one work center to another. Motion waste can be created
when people who need to communicate frequently are not
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Chapter 2 • 25
colocated. Handos also create motion waste and often require
additional communication to resolve ambiguities.
• Defects: Incorrect, missing, or unclear information, materials,
or products create waste, as eort is needed to resolve these issues.
The longer the time between defect creation and defect detection,
the more dicult it is to resolve the defect.
•
Nonstandard or manual work: Reliance on nonstandard or
manual work from others, such as using non-rebuilding servers,
test environments, and congurations. Ideally, any dependencies
on Operations should be automated, self-serviced, and available
on demand.
•
Heroics: In order for an organization to achieve goals, individuals
and teams are put in a position where they must perform unrea-
sonable acts, which may even become a part of their daily work
(e.g., nightly 2:00 a.m. problems in production, creating hundreds
of work tickets as part of every software release).†
Our goal is to make these wastes and hardships—anywhere heroics become
necessary—visible, and to systematically do what is needed to alleviate or
eliminate these burdens and hardships to achieve our goal of fast ow.
CONCLUSION
Improving ow through the technology value stream is essential to achieving
DevOps outcomes. We do this by making work visible, limiting WIP, reducing
batch sizes and the number of handos, continually identifying and evaluating
our constraints, and eliminating hardships in our daily work.
The specic practices that enable fast ow in the DevOps value stream are
presented in Part IV. In the next chapter, we present The Second Way: The
Principles of Feedback.
† Although heroics is not included in the Poppendieck categories of waste, it is included here
because of how often it occurs, especially in Operation shared services.
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The Second Way:
The Principles of Feedback
While the First Way describes the principles that enable the fast ow of work
from left to right, the Second Way describes the principles that enable the
reciprocal fast and constant feedback from right to left at all stages of the
value stream. Our goal is to create an ever safer and more resilient system of
work.
This is especially important when working in complex systems, when the
earliest opportunity to detect and correct errors is typically when a catastrophic
event is underway, such as a manufacturing worker being hurt on the job or
a nuclear reactor meltdown in progress.
In technology, our work happens almost entirely within complex systems
with a high risk of catastrophic consequences. As in manufacturing, we often
discover problems only when large failures are underway, such as a massive
production outage or a security breach resulting in the theft of customer data.
We make our system of work safer by creating fast, frequent, high quality
information ow throughout our value stream and our organization, which
includes feedback and feedforward loops. This allows us to detect and reme-
diate problems while they are smaller, cheaper, and easier to x; avert
problems before they cause catastrophe; and create organizational learning
that we integrate into future work. When failures and accidents occur, we
treat them as opportunities for learning, as opposed to a cause for punishment
and blame. To achieve all of the above, let us rst explore the nature of complex
systems and how they can be made safer.
WORKING SAFELY WITHIN COMPLEX SYSTEMS
One of the dening characteristics of a complex system is that it dees any
single person’s ability to see the system as a whole and understand how all
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28 • Part I
the pieces t together. Complex systems typically have a high degree of in-
terconnectedness of tightly coupled components, and system-level behavior
cannot be explained merely in terms of the behavior of the system components.
Dr. Charles Perrow studied the Three Mile Island crisis and observed that it
was impossible for anyone to understand how the reactor would behave in all
circumstances and how it might fail. When a problem was underway in one
component, it was dicult to isolate from the other components, quickly
owing through the paths of least resistance in unpredictable ways.
Dr. Sidney Dekker, who also codied some of the key elements of safety
culture, observed another characteristic of complex systems: doing the same
thing twice will not predictably or necessarily lead to the same result. It is this
characteristic that makes static checklists and best practices, while valuable,
insucient to prevent catastrophes from occurring. (See Appendix 5.)
Therefore, because failure is inherent and inevitable in complex systems, we
must design a safe system of work, whether in manufacturing or technology,
where we can perform work without fear, condent that any errors will be
detected quickly, long before they cause catastrophic outcomes, such as
worker injury, product defects, or negative customer impact.
After he decoded the causal mechanism behind the Toyota Product System
as part of his doctoral thesis at Harvard Business School, Dr. Steven Spear
stated that designing perfectly safe systems is likely beyond our abilities, but
we can make it safer to work in complex systems when the four following
conditions are met:†
• Complex work is managed so that problems in design and oper-
ations are revealed
•
Problems are swarmed and solved, resulting in quick construction
of new knowledge
•
New local knowledge is exploited globally throughout the
organization
• Leaders create other leaders who continually grow these types
of capabilities
† Dr. Spear extended his work to explain the long-lasting successes of other organizations, such
as the Toyota supplier network, Alcoa, and the US Navy’s Nuclear Power Propulsion Program.
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Chapter 3 • 29
Each of these capabilities are required to work safely in a complex system. In
the next sections, the rst two capabilities and their importance are described,
as well as how they have been created in other domains and what practices
enable them in the technology value stream. (The third and fourth capabilities
are described in chapter 4.)
SEE PROBLEMS AS THEY OCCUR
In a safe system of work, we must constantly test our design and operating
assumptions. Our goal is to increase information ow in our system from as
many areas as possible, sooner, faster, cheaper, and with as much clarity
between cause and eect as possible. The more assumptions we can invalidate,
the faster we can nd and x problems, increasing our resilience, agility, and
ability to learn and innovate.
We do this by creating feedback and feedforward loops into our system of
work. Dr. Peter Senge in his book The Fifth Discipline: The Art & Practice of the
Learning Organization described feedback loops as a critical part of learning
organizations and systems thinking. Feedback and feedforward loops cause
components within a system to reinforce or counteract each other.
In manufacturing, the absence of eective feedback often contribute to major
quality and safety problems. In one well-documented case at the General
Motors Fremont manufacturing plant, there were no eective procedures in
place to detect problems during the assembly process, nor were there explicit
procedures on what to do when problems were found. As a result, there were
instances of engines being put in backward, cars missing steering wheels or
tires, and cars even having to be towed o the assembly line because they
wouldn’t start.
In contrast, in high performing manufacturing operations there is fast,
frequent, and high quality information ow throughout the entire value
stream—every work operation is measured and monitored, and any defects
or signicant deviations are quickly found and acted upon. These are the
foundation of what enables quality, safety, and continual learning and
improvement.
In the technology value stream, we often get poor outcomes because of the
absence of fast feedback. For instance, in a waterfall software project, we may
develop code for an entire year and get no feedback on quality until we begin
the testing phase—or worse, when we release our software to customers.
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30 • Part I
When feedback is this delayed and infrequent, it is too slow to enable us to
prevent undesirable outcomes.
In contrast, our goal is to create fast feedback and fastforward loops wherever
work is performed, at all stages of the technology value stream, encompassing
Product Management, Development, QA, Infosec, and Operations. This includes
the creation of automated build, integration, and test processes, so that we
can immediately detect when a change has been introduced that takes us out
of a correctly functioning and deployable state.
We also create pervasive telemetry so we can see how all our system compo-
nents are operating in the production environment, so that we can quickly
detect when they are not operating as expected. Telemetry also allows us to
measure whether we are achieving our intended goals and, ideally, is radiated
to the entire value stream so we can see how our actions aect other portions
of the system as a whole.
Feedback loops not only enable quick detection and recovery of problems,
but they also inform us on how to prevent these problems from occurring
again in the future. Doing this increases the quality and safety of our system
of work, and creates organizational learning.
As Elisabeth Hendrickson, VP of Engineering at Pivotal Software, Inc. and
author of Explore It!: Reduce Risk and Increase Condence with Exploratory
Testing, said, “When I headed up quality engineering, I described my job as
‘creating feedback cycles.’ Feedback is critical because it is what allows us to
steer. We must constantly validate between customer needs, our intentions
and our implementations. Testing is merely one sort of feedback.”
SWARM AND SOLVE PROBLEMS TO BUILD
NEW KNOWLEDGE
Obviously, it is not sucient to merely detect when the unexpected occurs.
When problems occur, we must swarm them, mobilizing whoever is required
to solve the problem.
According to Dr. Spear, the goal of swarming is to contain problems before
they have a chance to spread, and to diagnose and treat the problem so that
it cannot recur. “In doing so,” he says, “they build ever-deeper knowledge
about how to manage the systems for doing our work, converting inevitable
up-front ignorance into knowledge.”
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Chapter 3 • 31
The paragon of this principle is the Toyota Andon cord. In a Toyota manufac-
turing plant, above every work center is a cord that every worker and manager
is trained to pull when something goes wrong; for example, when a part is
defective, when a required part is not available, or even when work takes
longer than documented.†
When the Andon cord is pulled, the team leader is alerted and immediately
works to resolve the problem. If the problem cannot be resolved within a
specied time (e.g., fty-ve seconds), the production line is halted so that
the entire organization can be mobilized to assist with problem resolution
until a successful countermeasure has been developed.
Instead of working around the problem or scheduling a x “when we have
more time,” we swarm to x it immediately—this is nearly the opposite of the
behavior at the GM Fremont plant described earlier. Swarming is necessary
for the following reasons:
•
It prevents the problem from progressing downstream, where
the cost and eort to repair it increases exponentially and technical
debt is allowed to accumulate.
• It prevents the work center from starting new work, which will
likely introduce new errors into the system.
•
If the problem is not addressed, the work center could potentially
have the same problem in the next operation (e.g., fty-ve
seconds later), requiring more xes and work. (See Appendix 6.)
This practice of swarming seems contrary to common management practice,
as we are deliberately allowing a local problem to disrupt operations globally.
However, swarming enables learning. It prevents the loss of critical infor-
mation due to fading memories or changing circumstances. This is especially
critical in complex systems, where many problems occur because of some
unexpected, idiosyncratic interaction of people, processes, products, places,
and circumstances—as time passes, it becomes impossible to reconstruct
exactly what was going on when the problem occurred.
As Dr. Spear notes, swarming is part of the “disciplined cycle of real-time
problem recognition, diagnosis,...and treatment (countermeasures or correc-
tive measures in manufacturing vernacular). It [is] the discipline of the
† In some of its plants, Toyota has moved to using an Andon button.
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32 • Part I
Shewhart cycle—plan, do, check, act—popularized by W. Edwards Deming,
but accelerated to warp speed.”
It is only through the swarming of ever smaller problems discovered ever
earlier in the life cycle that we can deect problems before a catastrophe
occurs. In other words, when the nuclear reactor melts down, it is already too
late to avert worst outcomes.
To enable fast feedback in the technology value stream, we must create the
equivalent of an Andon cord and the related swarming response. This requires
that we also create the culture that makes it safe, and even encouraged, to
pull the Andon cord when something goes wrong, whether it is when a pro-
duction incident occurs or when errors occur earlier in the value stream, such
as when someone introduces a change that breaks our continuous build or
test processes.
When conditions trigger an Andon cord pull, we swarm to solve the problem
and prevent the introduction of new work until the issue has been resolved.†
This provides fast feedback for everyone in the value stream (especially the
person who caused the system to fail), enables us to quickly isolate and diag-
nose the problem, and prevents further complicating factors that can obscure
cause and eect.
Preventing the introduction of new work enables continuous integration and
deployment, which is single-piece ow in the technology value stream. All
changes that pass our continuous build and integration tests are deployed
into production, and any changes that cause any tests to fail trigger our Andon
cord and are swarmed until resolved.
KEEP PUSHING QUALITY CLOSER TO THE SOURCE
We may inadvertently perpetuate unsafe systems of work due to the way we
respond to accidents and incidents. In complex systems, adding more inspec-
tion steps and approval processes actually increases the likelihood of future
failures. The eectiveness of approval processes decreases as we push decision-
making further away from where the work is performed. Doing so not only
lowers the quality of decisions but also increases our cycle time, thus decreasing
† Astonishingly, when the number of Andon cord pulls drop, plant managers will actually decrease
the tolerances to get an increase in the number of Andon cord pulls in order to continue to
enable more learnings and improvements and to detect ever-weaker failure signals.
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Chapter 3 • 33
the strength of the feedback between cause and eect, and reducing our ability
to learn from successes and failures.‡
This can be seen even in smaller and less complex systems.When top-down,
bureaucratic command and control systems become ineective, it is usually
because the variance between “who should do something” and “who is actually
doing something” is too large, due to insucient clarity and timeliness.
Examples of ineective quality controls include:
•
Requiring another team to complete tedious, error-prone, and
manual tasks that could be easily automated and run as needed
by the team who needs the work performed
• Requiring approvals from busy people who are distant from the
work, forcing them to make decisions without an adequate knowl-
edge of the work or the potential implications, or to merely rubber
stamp their approvals
• Creating large volumes of documentation of questionable detail
which become obsolete shortly after they are written
• Pushing large batches of work to teams and special committees
for approval and processing and then waiting for responses
Instead, we need everyone in our value stream to nd and x problems in
their area of control as part of our daily work. By doing this, we push quality
and safety responsibilities and decision-making to where the work is per
-
formed, instead of relying on approvals from distant executives.
We use peer reviews of our proposed changes to gain whatever assurance is
needed that our changes will operate as designed. We automate as much of
the quality checking typically performed by a QA or Information Security
department as possible. Instead of developers needing to request or schedule
‡ In the 1700s, the British government engaged in a spectacular example of top-down, bureau-
cratic command and control, which proved remarkably ineective. At the time, Georgia was
still a colony, and despite the fact that the British government was three thousand miles away
and lacked rsthand knowledge of local land chemistry, rockiness, topography, accessibility
to water, and other conditions, it tried to plan Georgia’s entire agricultural economy. The results
of the attempt were dismal and left Georgia with the lowest levels of prosperity and population
in the thirteen colonies.
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34 • Part I
a test to be run, these tests can be performed on demand, enabling developers
to quickly test their own code and even deploy those changes into production
themselves.
By doing this, we truly make quality everyone’s responsibility as opposed to
it being the sole responsibility of a separate department. Information security
is not just Information Security’s job, just as availability isn’t merely the job
of Operations.
Having developers share responsibility for the quality of the systems they
build not only improves outcomes but also accelerates learning.This is espe-
cially important for developers as they are typically the team that is furthest
removed from the customer. As Gary Gruver observes, “It’s impossible for a
developer to learn anything when someone yells at them for something they
broke six months ago—that’s why we need to provide feedback to everyone
as quickly as possible, in minutes, not months.”
ENABLE OPTIMIZING FOR DOWNSTREAM WORK CENTERS
In the 1980s, Designing for Manufacturability principles sought to design
parts and processes so that nished goods could be created with the lowest
cost, highest quality, and fastest ow. Examples include designing parts that
are wildly asymmetrical to prevent them from being put on backwards, and
designing screw fasteners so that they are impossible to over-tighten.
This was a departure from how design was typically done, which focused on
the external customers but overlooked internal stakeholders, such as the
people performing the manufacturing.
Lean denes two types of customers that we must design for: the external
customer (who most likely pays for the service we are delivering) and the
internal customer (who receives and processes the work immediately after
us). According to Lean, our most important customer is our next step down-
stream. Optimizing our work for them requires that we have empathy for
their problems in order to better identify the design problems that prevent
fast and smooth ow.
In the technology value stream, we optimize for downstream work centers by
designing for operations, where operational non-functional requirements
(e.g., architecture, performance, stability, testability, congurability, and
security) are prioritized as highly as user features.
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Chapter 3 • 35
By doing this, we create quality at the source, likely resulting in a set of codied
non-functional requirements that we can proactively integrate into every
service we build.
CONCLUSION
Creating fast feedback is critical to achieving quality, reliability, and safety in
the technology value stream. We do this by seeing problems as they occur,
swarming and solving problems to build new knowledge, pushing quality
closer to the source, and continually optimizing for downstream work centers.
The specic practices that enable fast ow in the DevOps value stream are
presented in Part IV. In the next chapter, we present the Third Way, the Prin-
ciples of Feedback
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The Third Way:
The Principles of
Continual Learning
and Experimentation
While the First Way addresses work ow from left to right and the Second
Way addresses the reciprocal fast and constant feedback from right to left,
the Third Way focuses on creating a culture of continual learning and exper-
imentation. These are the principles that enable constant creation of individual
knowledge, which is then turned into team and organizational knowledge.
In manufacturing operations with systemic quality and safety problems, work
is typically rigidly dened and enforced.For instance, in the GM Fremont
plant described in the previous chapter, workers had little ability to integrate
improvements and learnings into their daily work, with suggestions for
improvement “apt to meet a brick wall of indierence.”
In these environments, there is also often a culture of fear and low trust, where
workers who make mistakes are punished, and those who make suggestions
or point out problems are viewed as whistle-blowers and troublemakers.
When this occurs, leadership is actively suppressing, even punishing, learning
and improvement, perpetuating quality and safety problems.
In contrast, high-performing manufacturing operations require and actively
promote learning—instead of work being rigidly dened, the system of work
is dynamic, with line workers performing experiments in their daily work to
generate new improvements, enabled by rigorous standardization
of work procedures and documentation of the results.
In the technology value stream, our goal is to create a high-trust culture, re-
inforcing that we are all lifelong learners who must take risks in our daily
work. By applying a scientic approach to both process improvement and
product development, we learn from our successes and failures, identifying
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38 • Part I
which ideas don’t work and reinforcing those that do. Moreover, any local
learnings are rapidly turned into global improvements, so that new techniques
and practices can be used by the entire organization.
We reserve time for the improvement of daily work and to further accelerate
and ensure learning. We consistently introduce stress into our systems to
force continual improvement. We even simulate and inject failures in our
production services under controlled conditions to increase our resilience.
By creating this continual and dynamic system of learning, we enable teams
to rapidly and automatically adapt to an ever-changing environment, which
ultimately helps us win in the marketplace.
ENABLING ORGANIZATIONAL LEARNING AND A
SAFETY CULTURE
When we work within a complex system, by denition it is impossible for us
to perfectly predict all the outcomes for any action we take. This is what
contributes to unexpected, or even catastrophic, outcomes and accidents in
our daily work, even when we take precautions and work carefully.
When these accidents aect our customers, we seek to understand why it
happened. The root cause is often deemed to be human error,and the all too
common management response is to “name, blame, and shame” the person who
caused the problem.† And, either subtly or explicitly, management hints that
the person guilty of committing the error will be punished. They then create
more processes and approvals to prevent the error from happening again.
Dr. Sidney Dekker, who codied some of the key elements of safety culture
and coined the term just culture, wrote, “Responses to incidents and accidents
that are seen as unjust can impede safety investigations, promote fear rather
than mindfulness in people who do safety-critical work, make organizations
more bureaucratic rather than more careful, and cultivate professional secrecy,
evasion, and self-protection.”
These issues are especially problematic in the technology value stream—our
work is almost always performed within a complex system, and how man-
agement chooses to react to failures and accidents leads to a culture of fear,
† The “name, blame, shame” pattern is part of the Bad Apple Theory criticized by Dr. Sydney
Dekker and extensively discussed in his book The Field Guide to Understanding Human Error.
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Chapter 4 • 39
which then makes it unlikely that problems and failure signals are ever re-
ported. The result is that problems remain hidden until a catastrophe occurs.
Dr. Ron Westrum was one of the rst to observe the importance of organiza-
tional culture on safety and performance. He observed that in healthcare
organizations, the presence of “generative” cultures was one of the top pre-
dictors of patient safety. Dr. Westrum dened three types of culture:
•
Pathological organizations are characterized by large amounts
of fear and threat. People often hoard information, withhold it
for political reasons, or distort it to make themselves look better.
Failure is often hidden.
• Bureaucratic organizations are characterized by rules and pro-
cesses, often to help individual departments maintain their “turf.”
Failure is processed through a system of judgment, resulting in
either punishment or justice and mercy.
•
Generative organizations are characterized by actively seeking
and sharing information to better enable the organization to
achieve its mission. Responsibilities are shared throughout the
value stream, and failure results in reection and genuine
inquiry.
Pathological
Information is hidden
Messengers are “shot”
Responsibilities are shirked
Bridging between teams is
discouraged
Failure is covered up
New ideas are crushed
Bureaucratic
Information may be ignored
Messengers are tolerated
Responsibilities are compartmented
Bridging between teams is
allowed but discouraged
Organizatoin is just and merciful
New ideas create problems
Generative
Information is actively sought
Messengers are trained
Responsibilities are shared
Bridging between teams is
rewarded
Failure causes inquiry
New ideas are weclomed
Figure 8: The Westrum organizational typology model: how organizations
process information (Source: Ron Westrum, “A typology of organisation culture,” BMJ Quality & Safety 13,
no. 2 (2004), doi:10.1136/qshc.2003.009522.)
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40 • Part I
Just as Dr. Westrum found in healthcare organizations, a high-trust, generative
culture also predicted IT and organizational performance in technology
value streams.
In the technology value stream, we establish the foundations of a generative
culture by striving to create a safe system of work. When accidents and failures
occur, instead of looking for human error, we look for how we can redesign
the system to prevent the accident from happening again.
For instance, we may conduct a blameless post-mortem after every incident
to gain the best understanding of how the accident occurred and agree upon
what the best countermeasures are to improve the system, ideally preventing
the problem from occurring again and enabling faster detection and recovery.
By doing this, we create organizational learning. As Bethany Macri, an engineer
at Etsy who led the creation of the Morgue tool to help with recording of
post-mortems, stated, “By removing blame, you remove fear; by removing
fear, you enable honesty; and honesty enables prevention.”
Dr. Spear observes that the result of removing blame and putting organizational
learning in its place is that “organizations become ever more self-
diagnosing and self-improving, skilled at detecting problems [and] solving
them.”
Many of these attributes were also described by Dr. Senge as attributes of
learning organizations. In The Fifth Discipline, he wrote that these charac-
teristics help customers, ensure quality, create competitive advantage and an
energized and committed workforce, and uncover the truth.
INSTITUTIONALIZE THE IMPROVEMENT OF DAILY WORK
Teams are often not able or not willing to improve the processes they operate
within. The result is not only that they continue to suer from their current
problems, but their suering also grows worse over time. Mike Rother observed
in Toyota Kata that in the absence of improvements, processes don’t stay the
same—due to chaos and entropy, processes actually degrade over time.
In the technology value stream, when we avoid xing our problems, relying
on daily workarounds, our problems and technical debt accumulates until all
we are doing is performing workarounds, trying to avoid disaster, with no
cycles leftover for doing productive work. This is why Mike Orzen, author of
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Chapter 4 • 41
Lean IT, observed, “Even more important than daily work is the improvement
of daily work.”
We improve daily work by explicitly reserving time to pay down technical
debt, x defects, and refactor and improve problematic areas of our code and
environments—we do this by reserving cycles in each development interval,
or by scheduling kaizen blitzes, which are periods when engineers self-organize
into teams to work on xing any problem they want.
The result of these practices is that everyone nds and xes problems in their
area of control, all the time, as part of their daily work. When we nally x
the daily problems that we’ve worked around for months (or years), we can
eradicate from our system the less obvious problems. By detecting and re-
sponding to these ever-weaker failure signals, we x problems when it is not
only easier and cheaper but also when the consequences are smaller.
Consider the following example that improved workplace safety at Alcoa, an
aluminum manufacturer with $7.8 billion in revenue in 1987. Aluminum
manufacturing requires extremely high heat, high pressures, and corrosive
chemicals. In 1987, Alcoa had a frightening safety record, with 2% of the ninety
thousand employee workforce being injured each year—that’s seven injuries
per day. When Paul O’Neill started as CEO, his rst goal was to have zero injuries
to employees, contractors, and visitors.
O’Neill wanted to be notied within twenty-four hours of anyone being injured
on the job—not to punish, but to ensure and promote that learnings were
being generated and incorporated to create a safer workplace. Over the course
of ten years, Alcoa reduced their injury rate by 95%.
The reduction in injury rates allowed Alcoa to focus on smaller problems and
weaker failure signals—instead of notifying O’Neill only when injuries oc-
curred, they started reporting any close calls as well.† By doing this, they
improved workplace safety over the subsequent twenty years and have one
of the most enviable safety records in the industry.
As Dr. Spear writes, “Alcoans gradually stopped working around the diculties,
inconveniences, and impediments they experienced. Coping, re ghting,
and making do were gradually replaced throughout the organization by a
† It is astonishing, instructional, and truly moving to see the level of conviction and passion that
Paul O’Neill has about the moral responsibility leaders have to create workplace safety.
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42 • Part I
dynamic of identifying opportunities for process and product improvement.
As those opportunities were identied and the problems were investigated,
the pockets of ignorance that they reected were converted into nuggets of
knowledge.” This helped give the company a greater competitive advantage
in the market.
Similarly, in the technology value stream, as we make our system of work
safer, we nd and x problems from ever weaker failure signals. For example,
we may initially perform blameless post-mortems only for customer-impacting
incidents. Over time, we may perform them for lesser team-impacting incidents
and near misses as well.
TRANSFORM LOCAL DISCOVERIES INTO GLOBAL
IMPROVEMENTS
When new learnings are discovered locally, there must also be some mechanism
to enable the rest of the organization to use and benet from that knowledge.
In other words, when teams or individuals have experiences that create ex-
pertise, our goal is to convert that tacit knowledge (i.e., knowledge that is
dicult to transfer to another person by means of writing it down or verbal-
izing) into explicit, codied knowledge, which becomes someone else’s ex-
pertise through practice.
This ensures that when anyone else does similar work, they do so with the
cumulative and collective experience of everyone in the organization who
has ever done the same work. A remarkable example of turning local knowl-
edge into global knowledge is the US Navy’s Nuclear Power Propulsion
Program (also known as “NR” for “Naval Reactors”), which has over 5,700
reactor-years of operation without a single reactor-related casualty or escape
of radiation.
The NR is known for their intense commitment to scripted procedures and
standardized work, and the need for incident reports for any departure from
procedure or normal operations to accumulate learnings, no matter how
minor the failure signal—they constantly update procedures and system
designs based on these learnings.
The result is that when a new crew sets out to sea on their rst deployment,
they and their ocers benet from the collective knowledge of 5,700 acci-
dent-free reactor-years. Equally impressive is that their own experiences at
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Chapter 4 • 43
sea will be added to this collective knowledge, helping future crews safely
achieve their own missions.
In the technology value stream, we must create similar mechanisms to create
global knowledge, such as making all our blameless post-mortem reports
searchable by teams trying to solve similar problems, and by creating shared
source code repositories that span the entire organization, where shared code,
libraries, and congurations that embody the best collective knowledge of
the entire organization can be easily utilized. All these mechanisms help
convert individual expertise into artifacts that the rest of the organization
can use.
INJECT RESILIENCE PATTERNS INTO OUR DAILY WORK
Lower performing manufacturing organizations buer themselves from
disruptions in many ways—in other words, they bulk up or add ab. For in-
stance, to reduce the risk of a work center being idle (due to inventory arriving
late, inventory that had to be scrapped, etc.), managers may choose to
stockpile more inventory at each work center. However, that inventory buer
also increases WIP, which has all sorts of undesired outcomes, as pre-
viously discussed.
Similarly, to reduce the risk of a work center going down due to machinery
failure, managers may increase capacity by buying more capital equipment,
hiring more people, or even increasing oor space. All these options in-
crease costs.
In contrast, high performers achieve the same results (or better) by improving
daily operations, continually introducing tension to elevate performance, as
well as engineering more resilience into their system.
Consider a typical experiment at one of Aisin Seiki Global’s mattress factories,
one of Toyota’s top suppliers. Suppose they had two production lines, each
capable of producing one hundred units per day. On slow days, they would
send all production onto one line, experimenting with ways to increase capacity
and identify vulnerabilities in their process, knowing that if overloading the
line caused it to fail, they could send all production to the second line.
By relentless and constant experimentation in their daily work, they were
able to continually increase capacity, often without adding any new equipment
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44 • Part I
or hiring more people. The emergent pattern that results from these types of
improvement rituals not only improves performance but also improves resil-
ience, because the organization is always in a state of tension and change.
This process of applying stress to increase resilience was named antifragility
by author and risk analyst Nassim Nicholas Taleb.
In the technology value stream, we can introduce the same type of tension
into our systems by seeking to always reduce deployment lead times, increase
test coverage, decrease test execution times, and even by re-architecting if
necessary to increase developer productivity or increase reliability.
We may also perform game day exercises, where we rehearse large scale failures,
such as turning o entire data centers. Or we may inject ever-larger scale
faults into the production environment (such as the famous Netix “Chaos
Monkey” which randomly kills processes and compute servers in production)
to ensure that we’re as resilient as we want to be.
LEADERS REINFORCE A LEARNING CULTURE
Traditionally, leaders were expected to be responsible for setting objectives,
allocating resources for achieving those objectives, and establishing the right
combination of incentives. Leaders also establish the emotional tone for the
organizations they lead. In other words, leaders lead by “making all the
right decisions.”
However, there is signicant evidence that shows greatness is not achieved
by leaders making all the right decisions—instead, the leader’s role is to create
the conditions so their team can discover greatness in their daily work. In
other words, creating greatness requires both leaders and workers, each of
whom are mutually dependent upon each other.
Jim Womack, author of Gemba Walks, described the complementary working
relationship and mutual respect that must occur between leaders and frontline
workers. According to Womack, this relationship is necessary because neither
can solve problems alone—leaders are not close enough to the work, which
is required to solve any problem, and frontline workers do not have the broader
organizational context or the authority to make changes outside of their area
of work.†
† Leaders are responsible for the design and operation of processes at a higher level of aggregation
where others have less perspective and authority.
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Chapter 4 • 45
Leaders must elevate the value of learning and disciplined problem solving.
Mike Rother formalized these methods in what he calls the coaching kata. The
result is one that mirrors the scientic method, where we explicitly state our
True North goals, such as “sustain zero accidents” in the case of Alcoa, or
“double throughput within a year” in the case of Aisin.
These strategic goals then inform the creation of iterative, shorter term goals,
which are cascaded and then executed by establishing target conditions at
the value stream or work center level (e.g., “reduce lead time by 10% within
the next two weeks”).
These target conditions frame the scientic experiment: we explicitly state
the problem we are seeking to solve, our hypothesis of how our proposed
countermeasure will solve it, our methods for testing that hypothesis,
our interpretation of the results, and our use of learnings to inform the
next iteration.
The leader helps coach the person conducting the experiment with questions
that may include:
• What was your last step and what happened?
• What did you learn?
• What is your condition now?
• What is your next target condition?
• What obstacle are you working on now?
• What is your next step?
• What is your expected outcome?
• When can we check?
This problem-solving approach in which leaders help workers see and solve
problems in their daily work is at the core of the Toyota Production System,
of learning organizations, the Improvement Kata, and high-reliability or-
ganizations. Mike Rother observes that he sees Toyota “as an organization
dened primarily by the unique behavior routines it continually teaches to
all its members.”
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46 • Part I
In the technology value stream, this scientic approach and iterative method
guides all of our internal improvement processes, but also how we perform
experiments to ensure that the products we build actually help our internal
and external customers achieve their goals.
CONCLUSION
The principles of the Third Way address the need for valuing organizational
learning, enabling high trust and boundary-spanning between functions,
accepting that failures will always occur in complex systems, and making it
acceptable to talk about problems so we can create a safe system of work. It
also requires institutionalizing the improvement of daily work, converting
local learnings into global learnings that can be used by the entire organization,
as well as continually injecting tension into our daily work.
Although fostering a culture of continual learning and experimentation is
the principle of the Third Way, it is also interwoven into the First and Second
Ways. In other words, improving ow and feedback requires an iterative and
scientic approach that includes framing of a target condition, stating a hy-
pothesis of what will help us get there, designing and conducting experiments,
and evaluating the results.
The results are not only better performance but also increased resilience,
higher job satisfaction, and improved organization adaptability.
PART I CONCLUSION
In Part I of The DevOps Handbook we looked back at several movements in
history that helped lead to the development of DevOps. We also looked at the
three main principles that form the foundation for successful DevOps orga-
nizations: the principles of Flow, Feedback, and Continual Learning and Ex-
perimentation. In Part II, we will begin to look at how to start a DevOps
movement in your organization.
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PART
Where to Start
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Part
Introduction
How do we decide where to start a DevOps transformation in our organization?
Who needs to be involved? How should we organize our teams, protect their
work capacity, and maximize their chances of succeess? These are the questions
we aim to answer in Part II of The DevOps Handbook.
In the following chapters we will walk through the process of initiating a DevOps
transformation. We begin by evaluating the value streams in our organization,
locating a good place to start, and forming a strategy to create a dedicated
transformation team with specic improvement goals and eventual expansion.
For each value stream being transformed, we identify the work being performed
and then look at organizational design strategies and organizational archetypes
that best support the transformation goals.
Primary focuses in these chapters include:
• Selecting which value streams to start with
•
Understanding the work being done in our candidate value streams
•
Designing our organization and architecture with Conway’s Law
in mind
• Enabling market-oriented outcomes through more eective col-
laboration between functions throughout the value stream
• Protecting and enabling our teams
Beginning any transformation is full of uncertainty—we are charting a journey
to an ideal end state, but where virtually all the intermediate steps are unknown.
These next chapters are intended to provide a thought process to guide our
decisions, provide actionable steps we can take, and illustrate case studies
as examples.
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Selecting Which
Value Stream
to Start With
Choosing a value stream for DevOps transformation deserves careful consid-
eration. Not only does the value stream we choose dictate the diculty of our
transformation, but it also dictates who will be involved in the transformation.
It will aect how we need to organize into teams and how we can best enable
the teams and individuals in them.
Another challenge was noted by Michael Rembetsy, who helped lead the
DevOps transformation as the Director of Operations at Etsy in 2009. He
observed, “We must pick our transformation projects carefully—when we’re
in trouble, we don’t get very many shots. Therefore, we must carefully pick
and then protect those improvement projects that will most improve the state
of our organization.”
Let us examine how the Nordstrom team started their DevOps transformation
initiative in 2013, which Courtney Kissler, their VP of E-Commerce and Store
Technologies, described at the DevOps Enterprise Summit in 2014 and 2015.
Founded in 1901, Nordstrom is a leading fashion retailer that is focused on
delivering the best possible shopping experience to their customers. In 2015,
Nordstrom had annual revenue of $13.5 billion.
The stage for Nordstrom’s DevOps journey was likely set in 2011 during one
of their annual board of directors meetings. That year, one of the strategic
topics discussed was the need for online revenue growth. They studied the
plight of Blockbusters, Borders, and Barnes & Nobles, which demonstrated
the dire consequences when traditional retailers were late creating competitive
e-commerce capabilities—these organizations were clearly at risk of losing
their position in the marketplace or even going out of business entirely.†
† These organizations were sometimes known as the “Killer B’s that are Dying.”
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52 • Part II
At that time, Courtney Kissler was the senior director of Systems Delivery and
Selling Technology, responsible for a signicant portion of the technology
organization, including their in-store systems and online e-commerce site.
As Kissler described, “In 2011, the Nordstrom technology organization was
very much optimized for cost—we had outsourced many of our technology
functions, we had an annual planning cycle with large batch, ‘waterfall’
software releases. Even though we had a 97% success rate of hitting our
schedule, budget, and scope goals, we were ill-equipped to achieve what the
ve-year business strategy required from us, as Nordstrom started optimizing
for speed instead of merely optimizing for cost.”
Kissler and the Nordstrom technology management team had to decide where
to start their initial transformation eorts. They didn’t want to cause upheaval
in the whole system. Instead, they wanted to focus on very specic areas of
the business so that they could experiment and learn. Their goal was to
demonstrate early wins, which would give everyone condence that these
improvements could be replicated in other areas of the organization. How
exactly that would be achieved was still unknown.
They focused on three areas: the customer mobile application, their in-store
restaurant systems, and their digital properties. Each of these areas had business
goals that weren’t being met; thus, they were more receptive to considering
a dierent way of working. The stories of the rst two are described below.
The Nordstrom mobile application had experienced an inauspicious start. As
Kissler said, “Our customers were extremely frustrated with the product, and
we had uniformly negative reviews when we launched it in the App Store.
Worse, the existing structure and processes (aka “the system”) had designed
their processes so that they could only release updates twice per year.” In other
words, any xes to the application would have to wait months to reach the
customer.
Their rst goal was to enable faster or on-demand releases, providing faster
iteration and the ability to respond to customer feedback. They created a
dedicated product team that was solely dedicated to supporting the mobile
application, with the goal of enabling that team to be able to independently
implement, test, and deliver value to the customer. By doing this, they would
no longer have to depend on and coordinate with scores of other teams inside
Nordstrom. Furthermore, they moved from planning once per year to a con-
tinuous planning process. The result was a single prioritized backlog of work
for the mobile app based on customer need—gone were all the conicting
priorities when the team had to support multiple products.
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Chapter 5 • 53
Over the following year, they eliminated testing as a separate phase of work,
instead integrating it into everyone’s daily work.† They doubled the features
being delivered per month and halved the number of defects—creating a
successful outcome.
Their second area of focus was the systems supporting their in-store Café
Bistro restaurants. Unlike the mobile app value stream where the business
need was to reduce time to market and increase feature throughput, the
business need here was to decrease cost and increase quality. In 2013, Nord-
strom had completed eleven “restaurant re-concepts” which required changes
to the in-store applications, causing a number of customer-impacting incidents.
Disturbingly, they had planned forty-four more of these re-concepts for
2014—four times as many as in the previous year.
As Kissler stated, “One of our business leaders suggested that we triple our
team size to handle these new demands,but I proposed that we had to stop
throwing more bodies at the problem and instead improve the way we worked.”
They were able to identify problematic areas, such as in their work intake and
deployment processes, which is where they focused their improvement eorts.
They were able to reduce code deployment lead times by 60% and reduce the
number of production incidents 60% to 90%.
These successes gave the teams condence that DevOps principles and practices
were applicable to a wide variety of value streams. Kissler was promoted to
VP of E-Commerce and Store Technologies in 2014.
In 2015, Kissler said that in order for the selling or customer-facing technology
organization to enable the business to meet their goals, “…we needed to in-
crease productivity in all our technology value streams, not just in a few. At
the management level, we created an across-the-board mandate to reduce
cycle times by 20% for all customer-facing services.”
She continued, “This is an audacious challenge. We have many problems in
our current state—process and cycle times are not consistently measured
across teams, nor are they visible. Our rst target condition requires us to
help all our teams measure, make it visible, and perform experiments to start
reducing their process times, iteration by iteration.”
† The practice of relying on a stabilization phase or hardening phase at the end of a project often
has very poor outcomes, because it means problems are not being found and xed as part of
daily work and are left unaddressed, potentially snowballing into larger issues.
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54 • Part II
Kissler concluded, “From a high level perspective, we believe that techniques
such as value stream mapping, reducing our batch sizes toward single-piece
ow, as well as using continuous delivery and microservices will get us to our
desired state. However, while we are still learning, we are condent that we
are heading in the right direction, and everyone knows that this eort has
support from the highest levels of management.”
In this chapter, various models are presented that will enable us to replicate
the thought processes that the Nordstrom team used to decide which value
streams to start with. We will evaluate our candidate value streams in many
ways, including whether they are a greeneld or browneld service, a system of
engagement or a system of record. We will also estimate the risk/reward balance
of transforming and assess the likely level of resistance we may get from the
teams we would work with.
GREENFIELD VS. BROWNFIELD SERVICES
We often categorize our software services or products as either greeneld or
browneld. These terms were originally used for urban planning and building
projects. Greeneld development is when we build on undeveloped land.
Browneld development is when we build on land that was previously used
for industrial purposes, potentially contaminated with hazardous waste or
pollution. In urban development, many factors can make greeneld projects
simpler than browneld projects—there are no existing structures that need
to be demolished nor are there toxic materials that need to be removed.
In technology, a greeneld project is a new software project or initiative, likely
in the early stages of planning or implementation, where we build our appli-
cations and infrastructure anew, with few constraints. Starting with a
greeneld software project can be easier, especially if the project is already
funded and a team is either being created or is already in place. Furthermore,
because we are starting from scratch, we can worry less about existing code
bases, processes, and teams.
Greeneld DevOps projects are often pilots to demonstrate feasibility of public
or private clouds, piloting deployment automation, and similar tools. An
example of a greeneld DevOps project is the Hosted LabVIEW product in
2009 at National Instruments, a thirty-year-old organization with ve thousand
employees and $1 billion in annual revenue. To bring this product to market
quickly, a new team was created and allowed to operate outside of the existing
IT processes and explore the use of public clouds. The initial team included
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Chapter 5 • 55
an applications architect, a systems architect, two developers, a system auto-
mation developer, an operations lead, and two oshore operations sta. By
using DevOps practices, they were able to deliver Hosted LabVIEW to market
in half the time of their normal product introductions.
On the other end of the spectrum are browneld DevOps projects, these are
existing products or services that are already serving customers and have
potentially been in operation for years or even decades. Browneld projects
often come with signicant amounts of technical debt, such as having no test
automation or running on unsupported platforms. In the Nordstrom example
presented earlier in this chapter, both the in-store restaurant systems and
e-commerce systems were browneld projects.
Although many believe that DevOps is primarily for greeneld projects,
DevOps has been used to successfully transform browneld projects of all
sorts. In fact, over 60% of the transformation stories shared at the DevOps
Enterprise Summit in 2014 were for browneld projects. In these cases, there
was a large performance gap between what the customer needed and what
the organization was currently delivering, and the DevOps transformations
created tremendous business benet.
Indeed, one of the ndings in the 2015 State of DevOps Report validated that
the age of the application was not a signicant predictor of performance;
instead, what predicted performance was whether the application was archi-
tected (or could be re-architected) for testability and deployability.
Teams supporting browneld projects may be very receptive to experimenting
with DevOps, particularly when there is a widespread belief that traditional
methods are insucient to achieve their goals—and especially if there is a
strong sense of urgency around the need for improvement.†
When transforming browneld projects, we may face signicant impediments
and problems, especially when no automated testing exists or when there is
a tightly-coupled architecture that prevents small teams from developing,
testing, and deploying code independently. How we overcome these issues
are discussed throughout this book.
Examples of successful browneld transformations include:
† That the services that have the largest potential business benet are browneld systems
shouldn’t be surprising. After all, these are the systems that are most relied upon and have the
largest number of existing customers or highest amount of revenue depending upon them.
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56 • Part II
•
CSG (2013): In 2013, CSG International had $747 million in
revenue and over 3,500 employees, enabling over ninety thou-
sand customer service agents to provide billing operations and
customer care to over fty million video, voice, and data cus-
tomers, executing over six billion transactions, and printing
and mailing over seventy million paper bill statements every
month. Their initial scope of improvement was bill printing,
one of their primary businesses, and involved a COBOL main-
frame application and the twenty surrounding technology
platforms. As part of their transformation, they started per-
forming daily deployments into a production-like environment,
and doubled the frequency of customer releases from twice
annually to four times annually. As a result, they signicantly
increased the reliability of the application and reduced code
deployment lead times from two weeks to less than one day.
•
Etsy (2009): In 2009, Etsy had thirty-ve employees and was
generating $87 million in revenue, but after they “barely survived
the holiday retail season,” they started transforming virtually
every aspect of how the organization worked, eventually turning
the company into one of the most admired DevOps organizations
and set the stage for a successful 2015 IPO.
CONSIDER BOTH SYSTEMS OF RECORD AND SYSTEMS
OF ENGAGEMENT
The Gartner research rm has recently popularized the notion of bimodal IT,
referring to the wide spectrum of services that typical enterprises support.
Within bimodal IT there are systems of record, the ERP-like systems that run
our business (e.g., MRP, HR, nancial reporting systems), where the correctness
of the transactions and data are paramount; and systems of engagement, which
are customer-facing or employee-facing systems, such as e-commerce systems
and productivity applications.
Systems of record typically have a slower pace of change and often have
regulatory and compliance requirements (e.g., SOX). Gartner calls these types
of systems “Type 1,” where the organization focuses on “doing it right.”
Systems of engagement typically have a much higher pace of change to support
rapid feedback loops that enable them to conduct experimentation to discover
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Chapter 5 • 57
how to best meet customer needs. Gartner calls these types of systems “Type
2,” where the organization focuses on “doing it fast.”
It may be convenient to divide up our systems into these categories; however,
we know that the core, chronic conict between “doing it right” and “doing
it fast” can be broken with DevOps. The data from Puppet Labs’ State of DevOps
Reports—following the lessons of Lean manufacturing—shows that high
performing organizations are able to simultaneously deliver higher levels of
throughput and reliability.
Furthermore, because of how interdependent our systems are, our ability to
make changes to any of these systems is limited by the system that is most
dicult to safely change, which is almost always a system of record.
Scott Prugh, VP of Product Development at CSG, observed, “We’ve adopted a
philosophy that rejects bi-modal IT, because every one of our customers
deserve speed and quality. This means that we need technical excellence,
whether the team is supporting a 30 year old mainframe application, a Java
application, or a mobile application.”
Consequently, when we improve browneld systems, we should not only strive
to reduce their complexity and improve their reliability and stability, we should
also make them faster, safer, and easier to change. Even when new functionality
is added just to greeneld systems of engagement, they often cause reliability
problems in the browneld systems of record they rely on. By making these
downstream systems safer to change, we help the entire organization more
quickly and safely achieve its goals.
START WITH THE MOST SYMPATHETIC AND
INNOVATIVE GROUPS
Within every organization, there will be teams and individuals with a
wide range of attitudes toward the adoption of new ideas. Geoffrey A.
Moore first depicted this spectrum in the form of the technology adoption
life cycle in Crossing The Chasm, where the chasm represents the classic
difficulty of reaching groups beyond the innovators and early adopters
(see figure 9).
In other words, new ideas are often quickly embraced by innovators and early
adopters, while others with more conservative attitudes resist them (the early
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58 • Part II
majority, late majority, and laggards). Our goal is to nd those teams that already
believe in the need for DevOps principles and practices, and who possess a
desire and demonstrated ability to innovate and improve their own processes.
Ideally, these groups will be enthusiastic supporters of the DevOps journey.
Market Growth
Time
Innovators
(Techies)
“Just try it.”
Early adopters
(Visionaries)
“Get ahead
of the herd.”
Early majority
(Pragmatists)
“Stick with
the herd.”
Late majority
(Conservatives)
“Stick with
what’s proven.”
Laggards
(Skeptics)
“Just say no.”
The Chasm
2.5% 13.5% 34% 34% 16%
Figure 9: The Technology Adoption Curve (Source: Moore and McKenna,
Crossing The Chasm, 15.)
Especially in the early stages, we will not spend much time trying to convert
the more conservative groups. Instead, we will focus our energy on creating
successes with less risk-averse groups and build out our base from there (a
process that is discussed further in the next section). Even if we have the
highest levels of executive sponsorship, we will avoid the big bang approach
(i.e., starting everywhere all at once), choosing instead to focus our eorts in
a few areas of the organization, ensuring that those initiatives are successful,
and expanding from there.†
EXPANDING DEVOPS ACROSS OUR ORGANIZATION
Regardless of how we scope our initial eort, we must demonstrate early wins
and broadcast our successes. We do this by breaking up our larger improvement
† Big bang, top-down transformations are possible, such as the Agile transformation at PayPal
in 2012 that was led by their vice president of technology, Kirsten Wolberg. However, as with
any sustainable and successful transformation, this required the highest level of management
support and a relentless, sustained focus on driving the necessary outcomes.
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Chapter 5 • 59
goals into small, incremental steps. This not only creates our improvements
faster, it also enables us to discover when we have made the wrong choice of
value stream—by detecting our errors early, we can quickly back up and try
again, making dierent decisions armed with our new learnings.
As we generate successes, we earn the right to expand the scope of our DevOps
initiative. We want to follow a safe sequence that methodically grows our
levels of credibility, inuence, and support. The following list, adapted from
a course taught by Dr. Roberto Fernandez, a William F. Pounds Professor in
Management at MIT, describes the ideal phases used by change agents to build
and expand their coalition and base of support:
1.
Find Innovators and Early Adopters: In the beginning, we focus
our eorts on teams who actually want to help—these are our
kindred spirits and fellow travelers who are the rst to volunteer
to start the DevOps journey. In the ideal, these are also people
who are respected and have a high degree of inuence over the
rest of the organization, giving our initiative more credibility.
2.
Build Critical Mass and Silent Majority: In the next phase, we
seek to expand DevOps practices to more teams and value streams
with the goal of creating a stable base of support. By working
with teams who are receptive to our ideas, even if they are not
the most visible or inuential groups, we expand our coalition
who are generating more successes, creating a “bandwagon eect”
that further increases our inuence. We specically bypass dan-
gerous political battles that could jeopardize our initiative.
3.
Identify the Holdouts: The “holdouts” are the high prole,
inuential detractors who are most likely to resist (and maybe
even sabotage) our eorts. In general, we tackle this group only
after we have achieved a silent majority, when we have established
enough successes to successfully protect our initiative.
Expanding DevOps across an organization is no small task. It can create risk
to individuals, departments, and the organization as a whole. But as Ron van
Kemenade, CIO of ING, who helped transform the organization into one of
the most admired technology organizations, said, “Leading change requires
courage, especially in corporate environments where people are scared and
ght you. But if you start small, you really have nothing to fear. Any leader
needs to be brave enough to allocate teams to do some calculated
risk-taking.”
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60 • Part II
CONCLUSION
Peter Drucker, a leader in the development of management education, observed
that “little sh learn to be big sh in little ponds.” By choosing carefully where
and how to start, we are able to experiment and learn in areas of our organi-
zation that create value without jeopardizing the rest of the organization. By
doing this, we build our base of support, earn the right to expand the use of
DevOps in our organization, and gain the recognition and gratitude of an
ever-larger constituency.
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Understanding the Work in
Our Value Stream, Making it
Visible, and Expanding it
Across the Organization
Once we have identied a value stream to which we want to apply DevOps
principles and patterns, our next step is to gain a sucient understanding of
how value is delivered to the customer: what work is performed and by whom,
and what steps can we take to improve ow.
In the previous chapter, we learned about the DevOps transformation led by
Courtney Kissler and the team at Nordstrom. Over the years, they have learned
that one of the most ecient ways to start improving any value stream is to
conduct a workshop with all the major stakeholders and perform a value
stream mapping exercise—a process (described later in this chapter) designed
to help capture all the steps required to create value.
Kissler’s favorite example of the valuable and unexpected insights that can
come from value stream mapping is when they tried to improve the long lead
times associated with requests going through the Cosmetics Business Oce
application, a COBOL mainframe application that supported all the oor and
department managers of their in-store beauty and cosmetic departments.
This application allowed department managers to register new salespeople
for various product lines carried in their stores, so that they could track sales
commissions, enable vendor rebates, and so forth.
Kissler explained:
I knew this particular mainframe application well—earlier in my
career, I supported this technology team, so I know rsthand that for
nearly a decade, during each annual planning cycle, we would debate
about how we needed to get this application o the mainframe. Of
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62 • Part II
course, like in most organizations, even when there was full manage-
ment support, we never seemed to get around to migrating it.
My team wanted to conduct a value stream mapping exercise to de-
termine whether the COBOL application really was the problem, or
maybe there was a larger problem that we needed to address. They
conducted a workshop that assembled everyone with any account-
ability for delivering value to our internal customers, including our
business partners, the mainframe team, the shared service teams,
and so forth.
What they discovered was that when department managers were
submitting the ‘product line assignment’ request form, we were asking
them for an employee number, which they didn’t have—so they would
either leave it blank or put in something like ‘I don’t know.’ Worse,
in order to ll out the form, department managers would have to
inconveniently leave the store oor in order to use a PC in the back
oce. The end result was all this wasted time, with work bouncing
back and forth in the process.
During the workshop, the participants conducted several experiments, in-
cluding deleting the employee number eld in the form and letting another
department get that information in a downstream step. These experiments,
conducted with the help of department managers, showed a four-day reduction
in processing time. The team later replaced the PC application with an iPad
application, which allowed managers to submit the necessary information
without leaving the store oor, and the processing time was further reduced
to seconds.
She said proudly, “With those amazing improvements, all the demands to get
this application o the mainframe disappeared. Furthermore, other business
leaders took notice and started coming to us with a whole list of further ex-
periments they wanted to conduct with us in their own organizations. Everyone
in the business and technology teams were excited by the outcome because
they solved a real business problem, and, most importantly, they learned
something in the process.”
In the remainder of this chapter, we will go through the following steps:
identifying all the teams required to create customer value, creating a value
stream map to make visible all the required work, and using it to guide the
teams in how to better and more quickly create value. By doing this, we can
replicate the amazing outcomes described in this Nordstrom example.
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Chapter 6 • 63
IDENTIFYING THE TEAMS SUPPORTING OUR
VALUE STREAM
As this Nordstrom example demonstrates, in value streams of any complexity,
no one person knows all the work that must be performed in order to create
value for the customer—especially since the required work must be performed
by many dierent teams, often far removed from each other on the organization
charts, geographically, or by incentives.
As a result, after we select a candidate application or service for our DevOps
initiative, we must identify all the members of the value stream who are re-
sponsible for working together to create value for the customers being served.
In general, this includes:
• Product owner: the internal voice of the business that denes
the next set of functionality in the service
• Development: the team responsible for developing application
functionality in the service
• QA: the team responsible for ensuring that feedback loops exist
to ensure the service functions as desired
•
Operations: the team often responsible for maintaining the
production environment and helping ensure that required service
levels are met
• Infosec: the team responsible for securing systems and data
•
Release managers: the people responsible for managing and
coordinating the production deployment and release processes
•
Technology executives or value stream manager: in Lean
literature, someone who is responsible for “ensuring that the
value stream meets or exceeds the customer [and organizational]
requirements for the overall value stream, from start to nish”
CREATE A VALUE STREAM MAP TO SEE THE WORK
After we identify our value stream members, our next step is to gain a
concrete understanding of how work is performed, documented in the form
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64 • Part II
of a value stream map. In our value stream, work likely begins with the
product owner, in the form of a customer request or the formulation of a
business hypothesis. Some time later, this work is accepted by Development,
where features are implemented in code and checked in to our version
control repository. Builds are then integrated, tested in a production-like
environment, and nally deployed into production, where they (ideally)
create value for our customer.
In many traditional organizations, this value stream will consist of hundreds,
if not thousands, of steps, requiring work from hundreds of people. Because
documenting any value stream map this complex likely requires multiple
days, we may conduct a multi-day workshop, where we assemble all the key
constituents and remove them from the distractions of their daily work.
Our goal is not to document every step and associated minutiae, but to su-
ciently understand the areas in our value stream that are jeopardizing our
goals of fast ow, short lead times, and reliable customer outcomes. Ideally,
we have assembled those people with the authority to change their portion
of the value stream.†
Damon Edwards, co-host of DevOps Café podcast, observed, “In my experience,
these types of value stream mapping exercises are always an eye-opener.
Often, it is the rst time when people see how much work and heroics are
required to deliver value to the customer. For Operations, it may be the rst
time that they see the consequences that result when developers don’t have
access to correctly congured environments, which contributes to even more
crazy work during code deployments. For Development, it may be the rst
time they see all the heroics that are required by Test and Operations in order to
deploy their code into production, long after they ag a feature as ‘completed.’”
Using the full breadth of knowledge brought by the teams engaged in the
value stream, we should focus our investigation and scrutiny on the
following areas:
•
Places where work must wait weeks or even months, such as
getting production-like environments, change approval processes,
or security review processes
• Places where signicant rework is generated or received
† Which makes it all the more important that we limit the level of detail being collected—
everyone’s time is valuable and scarce.
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Chapter 6 • 65
Our rst pass of documenting our value stream should only consist of high-level
process blocks. Typically, even for complex value streams, groups can create
a diagram with ve to fteen process blocks within a few hours. Each process
block should include the lead time and process time for a work item to be
processed, as well as the %C/A as measured by the downstream consumers
of the output.‡
Customer
Request
Aggregate values:
Total lead time: 10 weeks
Value added time: 7.5 days
Percent complete and accurate: 8.6%
Showcase &
UAT
Exploratory &
performance
testing
Verify customer
receives
expected value
Backlog
LT: 2 weeks
Wait for
deploy
LT: 1 week
Change
approval
Production
deployment
Design &
analysis
LT: 2 weeks
VA: 2 days
Design
approval
%C/A: 60%
LT: 1 week
VA: 2 hours
%C/A: 75%
LT: 2 days
VA: 1 day
%C/A: 90%
LT: 1 week
VA: 1 hour
%C/A: 50%
LT: 1 week
VA: 1 hour
%C/A: 80%
LT: 1 hour
VA: 0 hours
%C/A: 30%
LT: 3 days
VA: 0 hours
Estimation
%C/A: 60%
LT: 1 week
VA: 1 hour
Development
(including test
automation)
%C/A: 80%
LT: 1 week
VA: 4 days
Figure 10: An example of a value stream map
(Source: Humble, Molesky, and O’Reilly, Lean Enterprise, 139.)
We use the metrics from our value stream map to guide our improvement
eorts. In the Nordstrom example, they focused on the low %C/A rates on
the request form submitted by department managers due to the absence of
employee numbers. In other cases, it may be long lead times or low %C/A
rates when delivering correctly congured test environments to Development
teams, or it might be the long lead times required to execute and pass regression
testing before each software release.
Once we identify the metric we want to improve, we should perform the next
level of observations and measurements to better understand the problem
‡ Conversely, there are many examples of using tools in a way that guarantees no behavior
changes occur. For instance, an organization commits to an agile planning tool but then
congures it for a waterfall process, which merely maintains status quo.
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66 • Part II
and then construct an idealized, future value stream map, which serves as a
target condition to achieve by some date (e.g., usually three to twelve months).
Leadership helps dene this future state and then guides and enables the
team to brainstorm hypotheses and countermeasures to achieve the desired
improvement to that state, perform experiments to test those hypotheses,
and interpret the results to determine whether the hypotheses were correct.
The teams keep repeating and iterating, using any new learnings to inform
the next experiments.
CREATING A DEDICATED TRANSFORMATION TEAM
One of the inherent challenges with initiatives such as DevOps transformations
is that they are inevitably in conict with ongoing business operations. Part
of this is a natural outcome of how successful businesses evolve. An organi-
zation that has been successful for any extended period of time (years, decades,
or even centuries) has created mechanisms to perpetuate the practices that
made them successful, such as product development, order administration,
and supply chain operations.
Many techniques are used to perpetuate and protect how current processes
operate, such as specialization, focus on eciency and repeatability, bureau-
cracies that enforce approval processes, and controls to protect against
variance. In particular, bureaucracies are incredibly resilient and are designed
to survive adverse conditions—one can remove half the bureaucrats, and the
process will still survive.
While this is good for preserving status quo, we often need to change how we
work to adapt to changing conditions in the marketplace. Doing this requires
disruption and innovation, which puts us at odds with groups who are currently
responsible for daily operations and the internal bureaucracies, and who will
almost always win.
In their book The Other Side of Innovation: Solving the Execution Challenge, Dr.
Vijay Govindarajan and Dr. Chris Trimble, both faculty members of Dartmouth
College’s Tuck School of Business, described their studies of how disruptive
innovation is achieved despite these powerful forces of daily operations. They
documented how customer-driven auto insurance products were successfully
developed and marketed at Allstate, how the protable digital publishing
business was created at the Wall Street Journal, the development of the break-
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Chapter 6 • 67
through trail-running shoe at Timberland, and the development of the rst
electric car at BMW.
Based on their research, Dr. Govindarajan and Dr. Trimble assert that orga-
nizations need to create a dedicated transformation team that is able to operate
outside of the rest of the organization that is responsible for daily operations
(which they call the “dedicated team” and “performance engine”
respectively).
First and foremost, we will hold this dedicated team accountable for achieving
a clearly dened, measurable, system-level result (e.g., reduce the deployment
lead time from “code committed into version control to successfully running
in production” by 50%). In order to execute such an initiative, we do
the following:
• Assign members of the dedicated team to be solely allocated to
the DevOps transformation eorts (as opposed to “maintain all
your current responsibilities, but spend 20% of your time on this
new DevOps thing.”).
•
Select team members who are generalists, who have skills across
a wide variety of domains.
• Select team members who have longstanding and mutually re-
spectful relationships with the rest of the organization.
•
Create a separate physical space for the dedicated team, if possible,
to maximize communication ow within the team, and creating
some isolation from the rest of the organization.
If possible, we will free the transformation team from many of the rules and
policies that restrict the rest of the organization, as National Instruments did,
described in the previous chapter. After all, established processes are a form
of institutional memory—we need the dedicated team to create the new
processes and learnings required to generate our desired outcomes, creating
new institutional memory.
Creating a dedicated team is not only good for the team, but also good for the
performance engine. By creating a separate team, we create the space for them
to experiment with new practices, protecting the rest of the organization from
the potential disruptions and distractions associated with it.
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68 • Part II
AGREE ON A SHARED GOAL
One of the most important parts of any improvement initiative is to dene a
measurable goal with a clearly dened deadline, between six months and two
years in the future. It should require considerable eort but still be achievable.
And achievement of the goal should create obvious value for the organization
as a whole and to our customers.
These goals and the time frame should be agreed upon by the executives and
known to everyone in the organization. We also want to limit the number of
these types of initiatives going on simultaneously to prevent us from overly
taxing the organizational change management capacity of leaders and the
organization. Examples of improvement goals might include:
• Reduce the percentage of the budget spent on product support
and unplanned work by 50%.
•
Ensure lead time from code check-in to production release is one
week or less for 95% of changes.
•
Ensure releases can always be performed during normal business
hours with zero downtime.
•
Integrate all the required information security controls
into the deployment pipeline to pass all required compliance
requirements.
Once the high-level goal is made clear, teams should decide on a regular
cadence to drive the improvement work. Like product development work, we
want transformation work to be done in an iterative, incremental manner. A
typical iteration will be in the range of two to four weeks. For each iteration,
the teams should agree on a small set of goals that generate value and makes
some progress toward the long-term goal. At the end of each iteration, teams
should review their progress and set new goals for the next iteration.
KEEP OUR IMPROVEMENT PLANNING HORIZONS SHORT
In any DevOps transformation project, we need to keep our planning horizons
short, just as if we were in a startup doing product or customer development.
Our initiative should strive to generate measurable improvements or actionable
data within weeks (or, in the worst case, months).
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Chapter 6 • 69
By keeping our planning horizons and iteration intervals short, we achieve
the following:
• Flexibility and the ability to reprioritize and replan quickly
•
Decrease the delay between work expended and improvement
realized, which strengthens our feedback loop, making it more
likely to reinforce desired behaviors—when improvement initia-
tives are successful, it encourages more investment
•
Faster learning generated from the rst iteration, meaning faster
integration of our learnings into the next iteration
• Reduction in activation energy to get improvements
•
Quicker realization of improvements that make meaningful
dierences in our daily work
•
Less risk that our project is killed before we can generate any
demonstrable outcomes
RESERVE 20% OF CYCLES FOR NONFUNCTIONAL REQUIREMENTS
AND REDUCING TECHNICAL DEBT
A problem common to any process improvement eort is how to properly
prioritize it—after all, organizations that need it most are those that have the
least amount of time to spend on improvement. This is especially true in
technology organizations because of technical debt.
Organizations that struggle with nancial debt only make interest payments
and never reduce the loan principal, and may eventually nd themselves in
situations where they can no longer service the interest payments. Similarly,
organizations that don’t pay down technical debt can nd themselves so
burdened with daily workarounds for problems left unxed that they can no
longer complete any new work. In other words, they are now only making the
interest payment on their technical debt.
We will actively manage this technical debt by ensuring that we invest at least
20% of all Development and Operations cycles on refactoring, investing in
automation work and architecture and non-functional requirements (NFRs,
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70 • Part II
sometimes referred to as the “ilities”), such as maintainability, manageability,
scalability, reliability, testability, deployability, and security.
User-visible
Positive
Value Feature
Defect Technical
debt
Architecture,
non-functional
requirements,
process improvement
Negative
Value
User-invisible
Figure 11: Invest 20% of cycles on those that create positive, user-invisible value
(Source: “Machine Learning and Technical Debt with D. Sculley,” Software Engineering Daily podcast,
November 17, 2015, http://softwareengineeringdaily.com/2015/11/17/
machine-learning-and-technical-debt-with-d-sculley/.)
After the near-death experience of eBay in the late 1990s, Marty Cagan, author
of Inspired: How To Create Products Customers Love, the seminal book on product
design and management, codied the following lesson:
The deal [between product owners and] engineering goes like this:
Product management takes 20% of the team’s capacity right o the
top and gives this to engineering to spend as they see t. They might
use it to rewrite, re-architect, or re-factor problematic parts of the
code base...whatever they believe is necessary to avoid ever having
to come to the team and say, ‘we need to stop and rewrite [all our
code].’ If you’re in really bad shape today, you might need to make
this 30% or even more of the resources. However, I get nervous when
I nd teams that think they can get away with much less than 20%.
Cagan notes that when organizations do not pay their “20% tax,” technical
debt will increase to the point where an organization inevitably spends all of
its cycles paying down technical debt. At some point, the services become so
fragile that feature delivery grinds to a halt because all the engineers are
working on reliability issues or working around problems.
By dedicating 20% of our cycles so that Dev and Ops can create lasting counter
-
measures to the problems we encounter in our daily work, we ensure that
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Chapter 6 • 71
technical debt doesn’t impede our ability to quickly and safely develop and
operate our services in production. Elevating added pressure of technical debt
from workers can also reduce levels of burnout.
Case Study
Operation InVersion at LinkedIn (2011)
LinkedIn’s Operation InVersion presents an interesting case study that illus-
trates the need to pay down technical debt as a part of daily work. Six months
after their successful IPO in 2011, LinkedIn continued to struggle with
problematic deployments that became so painful that they launched Operation
InVersion, where they stopped all feature development for two months in
order to overhaul their computing environments, deployments, and
architecture.
LinkedIn was created in 2003 to help users “connect to your network for
better job opportunities.” By the end of their first week of operation, they
had 2,700 members. One year later, they had over one million members,
and have grown exponentially since then. By November 2015, LinkedIn had
over 350 million members, who generate tens of thousands of requests per
second, resulting in millions of queries per second on the LinkedIn
backend systems.
From the beginning, LinkedIn primarily ran on their homegrown Leo appli-
cation, a monolithic Java application that served every page through servlets
and managed JDBC connections to various backend Oracle databases.
However, to keep up with growing trac in their early years, two critical
services were decoupled from Leo: the first handled queries around the
member connection graph entirely in-memory, and the second was member
search, which layered over the first.
By 2010, most new development was occurring in new services, with nearly
one hundred services running outside of Leo. The problem was that Leo was
only being deployed once every two weeks.
Josh Clemm, a senior engineering manager at LinkedIn, explained that by
2010, the company was having significant problems with Leo. Despite vertically
scaling Leo by adding memory and CPUs, “Leo was often going down in
production, it was dicult to troubleshoot and recover, and dicult to release
new code….It was clear we needed to ‘Kill Leo’ and break it up into many small
functional and stateless services.”
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72 • Part II
In 2013, journalist Ashlee Vance of Bloomberg described how “when LinkedIn
would try to add a bunch of new things at once, the site would crumble into
a broken mess, requiring engineers to work long into the night and fix the
problems.” By Fall 2011, late nights were no longer a rite of passage or a
bonding activity, because the problems had become intolerable. Some of
LinkedIn’s top engineers, including Kevin Scott, who had joined as the LinkedIn
VP of Engineering three months before their initial public oering, decided
to completely stop engineering work on new features and dedicate the whole
department to fixing the site’s core infrastructure. They called the eort
Operation InVersion.
Scott launched Operation InVersion as a way to “inject the beginnings of a
cultural manifesto into his team’s engineering culture. There would be no
new feature development until LinkedIn’s computing architecture was re-
vamped—it’s what the business and his team needed.”
Scott described one downside, “You go public, have all the world looking at
you, and then we tell management that we’re not going to deliver anything
new while all of engineering works on this [InVersion] project for the next
two months. It was a scary thing.”
However, Vance described the massively positive results of Operation In-
Version. “LinkedIn created a whole suite of software and tools to help it
develop code for the site. Instead of waiting weeks for their new features to
make their way onto LinkedIn’s main site, engineers could develop a new
service, have a series of automated systems examine the code for any bugs
and issues the service might have interacting with existing features, and
launch it right to the live LinkedIn site...LinkedIn’s engineering corps [now]
performs major upgrades to the site three times a day.” By creating a safer
system of work, the value they created included fewer late night cram sessions,
with more time to develop new, innovative features.
As Josh Clemm described in his article on scaling at LinkedIn, “Scaling can
be measured across many dimensions, including organizational…. [Operation
InVersion] allowed the entire engineering organization to focus on improving
tooling and deployment, infrastructure, and developer productivity. It was
successful in enabling the engineering agility we need to build the scalable
new products we have today….[In] 2010, we already had over 150 separate
services. Today, we have over 750 services.”
Kevin Scott stated, “Your job as an engineer and your purpose as a technology
team is to help your company win. If you lead a team of engineers, it’s better
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Chapter 6 • 73
to take a CEO’s perspective. Your job is to figure out what it is that your
company, your business, your marketplace, your competitive environment
needs. Apply that to your engineering team in order for your company to win.”
By allowing LinkedIn to pay down nearly a decade of technical debt, Project
InVersion enabled stability and safety, while setting the next stage of growth
for the company. However, it required two months of total focus on non-
functional requirements, at the expense of all the promised features made
to the public markets during an IPO. By finding and fixing problems as
part of our daily work, we manage our technical debt so that we avoid these
“near death” experiences.
INCREASE THE VISIBILITY OF WORK
In order to be able to know if we are making progress toward our goal, it’s
essential that everyone in the organization knows the current state of work.
There are many ways to make the current state visible, but what’s most
important is that the information we display is up to date, and that we
constantly revise what we measure to make sure it’s helping us understand
progress toward our current target conditions.
The following section discusses patterns that can help create visibility and
alignment across teams and functions.
USE TOOLS TO REINFORCE DESIRED BEHAVIOR
As Christopher Little, a software executive and one of the earliest chroniclers
of DevOps, observed, “Anthropologists describe tools as a cultural artifact.
Any discussion of culture after the invention of re must also be about tools.”
Similarly, in the DevOps value stream, we use tools to reinforce our culture
and accelerate desired behavior changes.
One goal is that our tooling reinforces that Development and Operations not
only have shared goals, but have a common backlog of work, ideally stored
in a common work system and using a shared vocabulary, so that work can
be prioritized globally.
By doing this, Development and Operations may end up creating a shared
work queue, instead of each silo using a dierent one (e.g., Development uses
JIRA while Operations uses ServiceNow). A signicant benet of this is that
when production incidents are shown in the same work systems as develop-
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74 • Part II
ment work, it will be obvious when ongoing incidents should halt other work,
especially when we have a kanban board.
Another benet of having Development and Operations using a shared tool
is a unied backlog, where everyone prioritizes improvement projects from
a global perspective, selecting work that has the highest value to the organi-
zation or most reduces technical debt. As we identify technical debt, we add
it to our prioritized backlog if we can’t address it immediately. For issues that
remain unaddressed, we can use our “20% time for non-functional require-
ments” to x the top items from our backlog.
Other technologies that reinforce shared goals are chat rooms, such as IRC
channels, HipChat, Campre, Slack, Flowdock, and OpenFire. Chat rooms
allow the fast sharing of information (as opposed to lling out forms that are
processed through predened workows), the ability to invite other people
as needed, and history logs that are automatically recorded for posterity and
can be analyzed during post-mortem sessions.
An amazing dynamic is created when we have a mechanism that allows any
team member to quickly help other team members, or even people outside
their team—the time required to get information or needed work can go from
days to minutes. In addition, because everything is being recorded, we may
not need to ask someone else for help in the future—we simply search for it.
However, the rapid communication environment facilitated by chat rooms
can also be a drawback. As Ryan Martens, the founder and CTO of Rally Soft-
ware, observes, “In a chat room, if someone doesn’t get an answer in a couple
of minutes, it’s totally accepted and expected that you can bug them again
until they get what they need.”
The expectations of immediate response can, of course, lead to undesired
outcomes. A constant barrage of interruptions and questions can prevent
people from getting necessary work done. As a result, teams may decide
that certain types of requests should go through more structured and
asynchronous tools.
CONCLUSION
In this chapter, we identied all the teams supporting our value stream and
captured in a value stream map what work is required in order to deliver value
to the customer. The value stream map provides the basis for understanding
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Chapter 6 • 75
our current state, including our lead time and %C/A metrics for problematic
areas, and informs how we set a future state.
This enables dedicated transformation teams to rapidly iterate and experiment
to improve performance. We also make sure that we allocate a sucient
amount of time for improvement, xing known problems and architectural
issues, including our non-functional requirements. The case studies from
Nordstrom and LinkedIn demonstrate how dramatic improvements can be
made in lead times and quality when we nd problems in our value stream
and pay down technical debt.
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How to Design Our
Organization and
Architecture with
Conway’s Law in Mind
In the previous chapters, we identied a value stream to start our DevOps
transformation and established shared goals and practices to enable a dedicated
transformation team to improve how we deliver value to the customer.
In this chapter, we will start thinking about how to organize ourselves to best
achieve our value stream goals. After all, how we organize our teams aects
how we perform our work. Dr. Melvin Conway performed a famous experiment
in 1968 with a contract research organization that had eight people who were
commissioned to produce a COBOL and an ALGOL compiler. He observed,
“After some initial estimates of diculty and time, ve people were assigned
to the COBOL job and three to the ALGOL job. The resulting COBOL compiler
ran in ve phases, the ALGOL compiler ran in three.”
These observations led to what is now known as Conway’s Law, which states
that “organizations which design systems...are constrained to produce designs
which are copies of the communication structures of these organizations….
The larger an organization is, the less exibility it has and the more pronounced
the phenomenon.” Eric S. Raymond, author of the book The Cathedral and the
Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary, crafted
a simplied (and now, more famous) version of Conway’s Law in his Jargon
File: “The organization of the software and the organization of the software
team will be congruent; commonly stated as ‘if you have four groups working
on a compiler, you’ll get a 4-pass compiler.’”
In other words, how we organize our teams has a powerful eect on the
software we produce, as well as our resulting architectural and production
outcomes. In order to get fast ow of work from Development into Operations,
with high quality and great customer outcomes, we must organize our teams
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78 • Part II
and our work so that Conway’s Law works to our advantage. Done poorly,
Conway’s Law will prevent teams from working safely and independently;
instead, they will be tightly coupled together, all waiting on each other for
work to be done, with even small changes creating potentially global, cata-
strophic consequences.
An example of how Conway’s Law can either impede or reinforce our goals
can be seen in a technology that was developed at Etsy called Sprouter. Etsy’s
DevOps journey began in 2009, and is one of the most admired DevOps orga-
nizations, with 2014 revenue of nearly $200 million and a successful IPO
in 2015.
Originally developed in 2007, Sprouter connected people, processes, and
technology in ways that created many undesired outcomes. Sprouter, shorthand
for “stored procedure router,” was originally designed to help make life easier
for the developers and database teams. As Ross Snyder, a senior engineer at
Etsy, said during his presentation at Surge 2011, “Sprouter was designed to
allow the Dev teams to write PHP code in the application, the DBAs to write
SQL inside Postgres, with Sprouter helping them meet in the middle.”
Sprouter resided between their front-end PHP application and the Postgres
database, centralizing access to the database and hiding the database imple-
mentation from the application layer. The problem was that adding any
changes to business logic resulted in signicant friction between developers
and the database teams. As Snyder observed, “For nearly any new site func-
tionality, Sprouter required that the DBAs write a new stored procedure. As
a result, every time developers wanted to add new functionality, they would
need something from the DBAs, which often required them to wade through
a ton of bureaucracy.” In other words, developers creating new functionality
had a dependency on the DBA team, which needed to be prioritized, commu-
nicated, and coordinated, resulting in work sitting in queues, meetings, longer
lead times, and so forth. This is because Sprouter created a tight coupling
between the development and database teams, preventing developers
from being able to independently develop, test, and deploy their code
into production.
Also, the database stored procedures were tightly coupled to Sprouter—any
time a stored procedure was changed, it required changes to Sprouter too.
The result was that Sprouter became an ever-larger single point of failure.
Snyder explained that everything was so tightly coupled and required such a
high level of synchronization as a result, that almost every deployment caused
a mini-outage.
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Chapter 7 • 79
Both the problems associated with Sprouter and their eventual solution can
be explained by Conway’s Law. Etsy initially had two teams, the developers
and the DBAs, who were each responsible for two layers of the service, the
application logic layer and stored procedure layer. Two teams working on two
layers, as Conway’s Law predicts. Sprouter was intended to make life easier
for both teams, but it didn’t work as expected—when business rules changed,
instead of changing only two layers, they now needed to make changes to
three layers (in the application, in the stored procedures, and now in Sprouter).
The resulting challenges of coordinating and prioritizing work across three
teams signicantly increased lead times and caused reliability problems.
In the spring of 2009, as part of what Snyder called “the great Etsy cultural
transformation,” Chad Dickerson joined as their new CTO. Dickerson put into
motion many things, including a massive investment into site stability, having
developers perform their own deployments into production, as well as begin-
ning a two-year journey to eliminate Sprouter.
To do this, the team decided to move all the business logic from the database
layer into the application layer, removing the need for Sprouter. They created
a small team that wrote a PHP Object Relational Mapping (ORM) layer,† enabling
the front-end developers to make calls directly to the database and reducing
the number of teams required to change business logic from three teams down
to one team.
As Snyder described, “We started using the ORM for any new areas of the site
and migrated small parts of our site from Sprouter to the ORM over time.
It took us two years to migrate the entire site o of Sprouter. And even
though we all grumbled about Sprouter the entire time, it remained in pro-
duction throughout.”
By eliminating Sprouter, they also eliminated the problems associated with
multiple teams needing to coordinate for business logic changes, decreased
the number of handos, and signicantly increased the speed and success of
production deployments, improving site stability. Furthermore, because small
teams could independently develop and deploy their code without requiring
another team to make changes in other areas of the system, developer pro-
ductivity increased.
† Among many things, an ORM abstracts a database, enabling developers to do queries and data
manipulation as if they were merely another object in the programming language. Popular
ORMs include Hibernate for Java, SQLAlchemy for Python, and ActiveRecord for Ruby on
Rails.
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80 • Part II
Sprouter was nally removed from production and Etsy’s version control
repositories in early 2001. As Snyder said, “Wow, it felt good.”†
As Snyder and Etsy experienced, how we design our organization dictates
how work is performed, and, therefore, the outcomes we achieve. Throughout
the rest of this chapter we will explore how Conway’s Law can negatively
impact the performance of our value stream, and, more importantly, how we
organize our teams to use Conway’s Law to our advantage.
ORGANIZATIONAL ARCHETYPES
In the eld of decision sciences, there are three primary types of organizational
structures that inform how we design our DevOps value streams with Conway’s
Law in mind: functional, matrix, and market. They are dened by Dr. Roberto
Fernandez as follows:
•
Functional-oriented organizations optimize for expertise, division
of labor, or reducing cost. These organizations centralize expertise,
which helps enable career growth and skill development, and
often have tall hierarchical organizational structures. This has
been the prevailing method of organization for Operations, (i.e.,
server admins, network admins, database admins, and so forth
are all organized into separate groups).
•
Matrix-oriented organizations attempt to combine functional
and market orientation. However, as many who work in or manage
matrix organizations observe, matrix organizations often result
in complicated organizational structures, such as individual
contributors reporting to two managers or more, and sometimes
achieving neither of the goals of functional or market orientation.
• Market-oriented organizations optimize for responding quickly
to customer needs. These organizations tend to be at, composed
of multiple, cross-functional disciplines (e.g., marketing, engi-
neering, etc.), which often lead to potential redundancies across
the organization. This is how many prominent organizations
adopting DevOps operate—in extreme examples, such as at
† Sprouter was one of many technologies used in development and production that Etsy elimi-
nated as part of their transformation.
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Chapter 7 • 81
Amazon or Netix, each service team is simultaneously respon-
sible for feature delivery and service support.‡
With these three categories of organizations in mind, let’s explore further
how an overly functional orientation, especially in Operations, can cause
undesired outcomes in the technology value stream, as Conway’s Law
would predict.
PROBLEMS OFTEN CAUSED BY OVERLY FUNCTIONAL
ORIENTATION “OPTIMIZING FOR COST”
In traditional IT Operations organizations, we often use functional orientation
to organize our teams by their specialties. We put the database administrators in
one group, the network administrators in another, the server administrators
in a third, and so forth. One of the most visible consequences of this is long
lead times, especially for complex activities like large deployments where we
must open up tickets with multiple groups and coordinate work handos,
resulting in our work waiting in long queues at every step.
Compounding the issue, the person performing the work often has little
visibility or understanding of how their work relates to any value stream goals
(e.g., “I’m just conguring servers because someone told me to.”). This places
workers in a creativity and motivation vacuum.
The problem is exacerbated when each Operations functional area has to serve
multiple value streams (i.e., multiple Development teams) who all compete
for their scarce cycles. In order for Development teams to get their work done
in a timely manner, we often have to escalate issues to a manager or director,
and eventually to someone (usually an executive) who can nally prioritize
the work against the global organizational goals instead of the functional silo
goals. This decision must then get cascaded down into each of the functional
areas to change the local priorities, and this, in turn, slows down other teams.
When every team expedites their work, the net result is that every project
ends up moving at the same slow crawl.
In addition to long queues and long lead times, this situation results in poor
handos, large amounts of re-work, quality issues, bottlenecks, and delays.
‡ However, as will be explained later, equally prominent organizations such as Etsy and GitHub
have functional orientation.
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82 • Part II
This gridlock impedes the achievement of important organizational goals,
which often far outweigh the desire to reduce costs.†
Similarly, functional orientation can also be found with centralized QA and
Infosec functions, which may have worked ne (or at least, well enough) when
performing less frequent software releases. However, as we increase the
number of Development teams and their deployment and release frequencies,
most functionally-oriented organizations will have diculty keeping up and
delivering satisfactory outcomes, especially when their work is being performed
manually. Now we’ll study how market oriented organizations work.
ENABLE MARKETORIENTED TEAMS “OPTIMIZING
FOR SPEED”
Broadly speaking, to achieve DevOps outcomes, we need to reduce the eects
of functional orientation (“optimizing for cost”) and enable market orientation
(“optimizing for speed”) so we can have many small teams working safely and
independently, quickly delivering value to the customer.
Taken to the extreme, market-oriented teams are responsible not only for
feature development, but also for testing, securing, deploying, and supporting
their service in production, from idea conception to retirement. These teams
are designed to be cross-functional and independent—able to design and run
user experiments, build and deliver new features, deploy and run their service
in production, and x any defects without manual dependencies on other
teams, thus enabling them to move faster. This model has been adopted by
Amazon and Netix and is touted by Amazon as one of the primary reasons
behind their ability to move fast even as they grow.
To achieve market orientation, we won’t do a large, top-down reorganization,
which often creates large amounts of disruption, fear, and paralysis. Instead,
we will embed the functional engineers and skills (e.g., Ops, QA, Infosec) into
each service team, or provide their capabilities to teams through automated
† Adrian Cockcroft remarked, “For companies who are now coming o of ve-year IT outsourcing
contracts, it’s like they’ve been frozen in time, during one of the most disruptive times in
technology.” In other words, IT outsourcing is a tactic used to control costs through contrac-
tually-enforced stasis, with rm xed prices that schedule annual cost reductions. However,
it often results in organizations being unable to respond to changing business and
technology needs.
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Chapter 7 • 83
self-service platforms that provide production-like environments, initiate
automated tests, or perform deployments.
This enables each service team to independently deliver value to the customer
without having to open tickets with other groups, such as IT Operations, QA,
or Infosec.‡
MAKING FUNCTIONAL ORIENTATION WORK
Having just recommended market-orientated teams, it is worth pointing
out that it is possible to create eective, high-velocity organizations with
functional orientation. Cross-functional and market-oriented teams are one
way to achieve fast ow and reliability, but they are not the only path. We
can also achieve our desired DevOps outcomes through functional orienta-
tion, as long as everyone in the value stream views customer and organiza-
tional outcomes as a shared goal, regardless of where they reside in
the organization.
Business
Units Feature
Teams Centralized
Operations Users Product Teams Operations Users
Platform as
Service
(optimized for speed) (optimized for cost)
Each team opens ticket
to have code deployed
Server
team Network
team Database
team VM
team
(optimized for speed) (optimized for speed
and expertise)
Each team can
independently develop, test
and code into production Ops management
Service desk
Platform team
?
Figure 12: Functional vs. market orientation
Left: Functional orientation: all work ows through centralized IT Operations; Right: Market orientation: all
product teams can deploy their loosely coupled components self-service into production. (Source: Humble,
Molesky, and O’Reilly, Lean Enterprise, Kindle edition, 4523 & 4592.)
‡ For the remainder of this books, we will use service teams interchangeably with feature teams,
product teams, development teams, and delivery teams. The intent is to specify the team primarily
developing, testing, and securing the code so that value is delivered to the customer.
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84 • Part II
For example, high performance with a functional-oriented and centralized
Operations group is possible, as long as service teams get what they need
from Operationsreliably and quickly (ideally on demand) and vice-versa.
Many of the most admired DevOps organizations retain functional orientation
of Operations, including Etsy, Google, and GitHub.
What these organizations have in common is a high-trust culture that enables
all departments to work together eectively, where all work is transparently
prioritized and there is sucient slack in the system to allow high-priority
work to be completed quickly. This is, in part, enabled by automated self-service
platforms that build quality into the products everyone is building.
In the Lean manufacturing movement of the 1980s, many researchers were
puzzled by Toyota’s functional orientation, which was at odds with the best
practice of having cross-functional, market-oriented teams. They were so
puzzled it was called “the second Toyota paradox.”
As Mike Rother wrote in Toyota Kata, “As tempting as it seems, one cannot
reorganize your way to continuous improvement and adaptiveness. What is
decisive is not the form of the organization, but how people act and react. The
roots of Toyota’s success lie not in its organizational structures, but in devel-
oping capability and habits in its people. It surprises many people, in fact, to
nd that Toyota is largely organized in a traditional, functional-department
style.” It is this development of habits and capabilities in people and the
workforce that are the focus of our next sections.
TESTING, OPERATIONS, AND SECURITY AS EVERYONE’S
JOB, EVERY DAY
In high-performing organizations, everyone within the team shares a common
goal—quality, availability, and security aren’t the responsibility of individual
departments, but are a part of everyone’s job, every day.
This means that the most urgent problem of the day may be working on or
deploying a customer feature or xing a Severity 1 production incident. Al-
ternatively, the day may require reviewing a fellow engineer’s change, applying
emergency security patches to production servers, or making improvements
so that fellow engineers are more productive.
Reecting on shared goals between Development and Operations, Jody Mulkey,
CTO at Ticketmaster, said, “For almost 25 years, I used an American football
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Chapter 7 • 85
metaphor to describe Dev and Ops. You know, Ops is defense, who keeps the
other team from scoring, and Dev is oense, trying to score goals. And one
day, I realized how awed this metaphor was, because they never all play on
the eld at the same time. They’re not actually on the same team!”
He continued, “The analogy I use now is that Ops are the oensive linemen,
and Dev are the ‘skill’ positions (like the quarterback and wide receivers) whose
job it is to move the ball down the eld—the job of Ops is to help make sure
Dev has enough time to properly execute the plays.”
A striking example of how shared pain can reinforce shared goals is when
Facebook was undergoing enormous growth in 2009. They were experiencing
signicant problems related to code deployments—while not all issues caused
customer-impacting issues, there was chronic reghting and long hours.
Pedro Canahuati, their director of production engineering, described a meeting
full of Ops engineers where someone asked that all people not working on an
incident close their laptops, and no one could.
One of the most signicant things they did to help change the outcomes of
deployments was to have all Facebook engineers, engineering managers, and
architects rotate through on-call duty for the services they built. By doing
this, everyone who worked on the service experienced visceral feedback on
the upstream architectural and coding decisions they made, which made an
enormous positive impact on the downstream outcomes.
ENABLE EVERY TEAM MEMBER TO BE A GENERALIST
In extreme cases of a functionally-oriented Operations organization, we have
departments of specialists, such as network administrators, storage admin-
istrators, and so forth. When departments over-specialize, it causes siloization,
which Dr. Spear describes as when departments “operate more like sovereign
states.” Any complex operational activity then requires multiple handos
and queues between the dierent areas of the infrastructure, leading to longer
lead times (e.g., because every network change must be made by someone in
the networking department).
Because we rely upon an ever increasing number of technologies, we must
have engineers who have specialized and achieved mastery in the technology
areas we need. However, we don’t want to create specialists who are “frozen
in time,” only understanding and able to contribute to that one area of the
value stream.
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86 • Part II
One countermeasure is to enable and encourage every team member to be a
generalist. We do this by providing opportunities for engineers to learn all
the skills necessary to build and run the systems they are responsible for, and
regularly rotating people through dierent roles. The term full stack engineer
is now commonly used (sometimes as a rich source of parody) to describe
generalists who are familiar—at least have a general level of understanding—
with the entire application stack (e.g., application code, databases, operating
systems, networking, cloud).
Table 2: Specialists vs. Generalists vs. “E-shaped” Sta (experience, expertise, exploration, and execution)
“I-shaped”
(Specialists)
“T-shaped”
(Generalists)
“E-shaped”
Deep expertise in one area Deep expertise in one area Deep expertise in a few
areas
Very few skills or
experience in other areas
Broad skills across many
areas
Experience across
many areas
Proven execution skills
Always innovating
Creates bottlenecks
quickly
Can step up to remove
bottlenecks
Almost limitless
potential
Insensitive to downstream
waste and impact
Sensitive to downstream
waste and impact
Prevents planning
exibility or absorption of
variability
Helps make planning
exible and absorbs
variability
(Source: Scott Prugh, “Continuous Delivery,” ScaledAgileFramework.com, February 14, 2013, http://
scaledagileframework.com/continuous-delivery/.)
Scott Prugh writes that CSG International has undergone a transformation
that brings most resources required to build and run the product onto one
team, including analysis, architecture, development, test, and operations.
“By cross-training and growing engineering skills, generalists can do orders
of magnitude more work than their specialist counterparts, and it also
improves our overall flow of work by removing queues and wait time.”
This approach is at odds with traditional hiring practices, but, as Prugh
explains, it is well worth it. “Traditional managers will often object to
hiring engineers with generalist skill sets, arguing that they are more
expensive and that ‘I can hire two server administrators for every multi-
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Chapter 7 • 87
skilled operations engineer.’” However, the business benefits of enabling
faster flow are overwhelming. Furthermore, as Prugh notes, “[I]nvesting
in cross training is the right thing for [employees’] career growth, and
makes everyone’s work more fun.”
When we value people merely for their existing skills or performance in their
current role rather than for their ability to acquire and deploy new skills, we
(often inadvertently) reinforce what Dr. Carol Dweck describes as the xed
mindset, where people view their intelligence and abilities as static “givens”
that can’t be changed in meaningful ways.
Instead, we want to encourage learning, help people overcome learning
anxiety, help ensure that people have relevant skills and a dened career
road map, and so forth. By doing this, we help foster a growth mindset in our
engineers—after all, a learning organization requires people who are willing
to learn. By encouraging everyone to learn, as well as providing training
and support, we create the most sustainable and least expensive way to
create greatness in our teams—by investing in the development of the people
we already have.
As Jason Cox, Director of Systems Engineering at Disney, described, “Inside
of Operations, we had to change our hiring practices. We looked for people
who had ‘curiosity, courage, and candor,’ who were not only capable of being
generalists but also renegades...We want to promote positive disruption so
our business doesn’t get stuck and can move into the future.” As we’ll see in
the next section, how we fund our teams also aects our outcomes.
FUND NOT PROJECTS, BUT SERVICES AND PRODUCTS
Another way to enable high-performing outcomes is to create stable service
teams with ongoing funding to execute their own strategy and road map of
initiatives. These teams have the dedicated engineers needed to deliver on
concrete commitments made to internal and external customers, such as
features, stories, and tasks.
Contrast this to the more traditional model where Development and Test
teams are assigned to a “project” and then reassigned to another project as
soon as the project is completed and funding runs out. This leads to all sorts
of undesired outcomes, including developers being unable to see the long-
term consequences of decisions they make (a form of feedback) and a funding
model that only values and pays for the earliest stages of the software life
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88 • Part II
cycle—which, tragically, is also the least expensive part for successful products
or services.†
Our goal with a product-based funding model is to value the achievement of
organizational and customer outcomes, such as revenue, customer lifetime
value, or customer adoption rate, ideally with the minimum of output (e.g.,
amount of eort or time, lines of code). Contrast this to how projects are
typically measured, such as whether it was completed within the promised
budget, time, and scope.
DESIGN TEAM BOUNDARIES IN ACCORDANCE WITH
CONWAY’S LAW
As organizations grow, one of the largest challenges is maintaining eective
communication and coordination between people and teams. All too often,
when people and teams reside on a dierent oor, in a dierent building, or
in a dierent time zone, creating and maintaining a shared understanding
and mutual trust becomes more dicult, impeding eective collaboration.
Collaboration is also impeded when the primary communication mechanisms
are work tickets and change requests, or worse, when teams are separated by
contractual boundaries, such as when work is performed by an outsourced
team.
As we saw in the Etsy Sprouter example at the beginning of this chapter, the
way we organize teams can create poor outcomes, a side eect of Conway’s
Law. These include splitting teams by function (e.g., by putting developers
and testers in dierent locations or by outsourcing testers entirely) or by ar-
chitectural layer (e.g., application, database).
These congurations require signicant communication and coordination
between teams, but still results in a high amount of rework, disagreements
over specications, poor handos, and people sitting idle waiting for somebody
else.
Ideally, our software architecture should enable small teams to be independent-
ly productive, suciently decoupled from each other so that work can be done
without excessive or unnecessary communication and coordination.
† As John Lauderbach, currently VP of Information Technology at Roche Bros. Supermarkets,
quipped, “Every new application is like a free puppy. It’s not the upfront capital cost that kills
you…It’s the ongoing maintenance and support.”
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Chapter 7 • 89
CREATE LOOSELYCOUPLED ARCHITECTURES TO ENABLE
DEVELOPER PRODUCTIVITY AND SAFETY
When we have a tightly coupled architecture, small changes can result in
large scale failures. As a result, anyone working in one part of the system must
constantly coordinate with anyone else working in another part of the system
they may aect, including navigating complex and bureaucratic change
management processes.
Furthermore, to test that the entire system works together requires integrating
changes with the changes from hundreds, or even thousands, of other devel-
opers, which may, in turn, have dependencies on tens, hundreds, or thousands
of interconnected systems. Testing is done in scarce integration test environ
-
ments, which often require weeks to obtain and congure. The result is not
only long lead times for changes (typically measured in weeks or months) but
also low developer productivity and poor deployment outcomes.
In contrast, when we have an architecture that enables small teams of devel-
opers to independently implement, test, and deploy code into production
safely and quickly, we can increase and maintain developer productivity and
improve deployment outcomes. These characteristics can be found in
service-oriented architectures (SOAs) rst described in the 1990s, in which
services are independently testable and deployable. A key feature of SOAs is
that they’re composed of loosely coupled services with bounded contexts.‡
Having architecture that is loosely coupled means that services can update
in production independently, without having to update other services.
Services must be decoupled from other services and, just as important,
from shared databases (although they can share a database service, provided
they don’t have any common schemas).
Bounded contexts are described in the book Domain Driven Design by Eric J.
Evans. The idea is that developers should be able to understand and update
the code of a service without knowing anything about the internals of its peer
services. Services interact with their peers strictly through APIs and thus don’t
share data structures, database schemata, or other internal representations
of objects. Bounded contexts ensure that services are compartmentalized and
have well-dened interfaces, which also enables easier testing.
‡ These properties are also found in “microservices,” which build upon the principles of SOA.
One popular set of patterns for modern web architecture based on these principles is the “12-
factor app.”
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90 • Part II
Randy Shoup, former Engineering Director for Google App Engine, observed
that “organizations with these types of service-oriented architectures, such
as Google and Amazon, have incredible exibility and scalability. These or-
ganizations have tens of thousands of developers where small teams can still
be incredibly productive.”
KEEP TEAM SIZES SMALL THE “TWOPIZZA TEAM” RULE
Conway’s Law helps us design our team boundaries in the context of desired
communication patterns, but it also encourages us to keep our team sizes
small, reducing the amount of inter-team communication and encouraging
us to keep the scope of each team’s domain small and bounded.
As part of its transformation initiative away from a monolithic code base in
2002, Amazon used the two-pizza rule to keep team sizes small—a team only
as large as can be fed with two pizzas—usually about ve to ten people.
This limit on size has four important eects:
1.
It ensures the team has a clear, shared understanding of the system
they are working on. As teams get larger, the amount of commu-
nication required for everybody to know what’s going on scales
in a combinatorial fashion.
2. It limits the growth rate of the product or service being worked
on. By limiting the size of the team, we limit the rate at which
their system can evolve. This also helps to ensure the team main-
tains a shared understanding of the system.
3.
It decentralizes power and enables autonomy. Each two-pizza
team (2PT) is as autonomous as possible. The team’s lead, working
with the executive team, decides on the key business metric that
the team is responsible for, known as the tness function, which
becomes the overall evaluation criteria for the team’s experiments.
The team is then able to act autonomously to maximize that
metric.†
4.
Leading a 2PT is a way for employees to gain some leadership
experience in an environment where failure does not have cata-
strophic consequences. An essential element of Amazon’s strategy
† In the Netix culture, one of the seven key values is “highly aligned, loosely coupled.”
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Chapter 7 • 91
was the link between the organizational structure of a 2PT and
the architectural approach of a service-oriented architecture.
Amazon CTO Werner Vogels explained the advantages of this structure to
Larry Dignan of Baseline in 2005. Dignan writes:
“Small teams are fast...and don’t get bogged down in so-called
administrivia….Each group assigned to a particular business is com-
pletely responsible for it….The team scopes the x, designs it, builds
it, implements it and monitors its ongoing use. This way, technology
programmers and architects get direct feedback from the business
people who use their code or applications—in regular meetings and
informal conversations.”
Another example of how architecture can profoundly improve productivity
is the API Enablement program at Target, Inc.
Case Study
API Enablement at Target (2015)
Target is the sixth largest retailer in the US and spends over $1 billion on
technology annually. Heather Mickman, a director of development for Target,
described the beginnings of their DevOps journey: “In the bad old days, it
used to take ten dierent teams to provision a server at Target, and when
things broke, we tended to stop making changes to prevent further issues,
which of course makes everything worse.”
The hardships associated with getting environments and performing deploy-
ments created significant diculties for development teams, as did getting
access to data they needed. As Mickman described:
The problem was that much of our core data, such as infor-
mation on inventory, pricing, and stores, was locked up in
legacy systems and mainframes. We often had multiple
sources of truths of data, especially between e-commerce
and our physical stores, which were owned by dierent teams,
with dierent data structures and dierent priorities....The
result was that if a new development team wanted to build
something for our guests, it would take three to six months
to build the integrations to get the data they needed. Worse,
it would take another three to six months to do the manual
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92 • Part II
testing to make sure they didn’t break anything critical,
because of how many custom point-to-point integrations
we had in a very tightly coupled system. Having to manage
the interactions with the twenty to thirty dierent teams,
along with all their dependencies, required lots of project
managers, because of all the coordination and handos. It
meant that development was spending all their time waiting
in queues, instead of delivering results and getting stu done.
This long lead time for retrieving and creating data in their systems of record
was jeopardizing important business goals, such as integrating the supply
chain operations of Target’s physical stores and their e-commerce site, which
now required getting inventory to stores and customer homes. This pushed
the Target supply chain well beyond what it was designed for, which was
merely to facilitate the movement of goods from vendors to distribution
centers and stores.
In an attempt to solve the data problem, in 2012 Mickman led the API
Enablement team to enable development teams to “deliver new capabilities
in days instead of months.” They wanted any engineering team inside of
Target to be able to get and store the data they needed, such as information
on their products or their stores, including operating hours, location, whether
there was as Starbucks on-site, and so forth.
Time constraints played a large role in team selection. Mickman explained that:
Because our team also needed to deliver capabilities in days,
not months, I needed a team who could do the work, not
give it to contractors—we wanted people with kickass engi-
neering skills, not people who knew how to manage contracts.
And to make sure our work wasn’t sitting in queue, we needed
to own the entire stack, which meant that we took over the
Ops requirements as well....We brought in many new tools
to support continuous integration and continuous delivery.
And because we knew that if we succeeded, we would have
to scale with extremely high growth, we brought in new tools
such as the Cassandra database and Kaa message broker.
When we asked for permission, we were told no, but we did
it anyway, because we knew we needed it.
In the following two years, the API Enablement team enabled fifty-three
new business capabilities, including Ship to Store and Gift Registry, as well
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Chapter 7 • 93
as their integrations with Instacart and Pinterest. As Mickman described,
“Working with Pinterest suddenly became very easy, because we just provided
them our APIs.”
In 2014, the API Enablement team served over 1.5 billion API calls per month.
By 2015, this had grown to seventeen billion calls per month spanning ninety
dierent APIs. To support this capability, they routinely performed eighty
deployments per week.
These changes have created major business benefits for Target—digital sales
increased 42% during the 2014 holiday season and increased another 32%
in Q2. During the Black Friday weekend of 2015, over 280k in-store pickup
orders were created. By 2015, their goal is to enable 450 of their 1,800
stores to be able to fulfill e-commerce orders, up from one hundred.
“The API Enablement team shows what a team of passionate change agents
can do,” Mickman says. “And it help set us up for the next stage, which is to
expand DevOps across the entire technology organization.”
CONCLUSION
Through the Etsy and Target case studies, we can see how architecture and
organizational design can dramatically improve our outcomes. Done incor-
rectly, Conway’s Law will ensure that the organization creates poor outcomes,
preventing safety and agility. Done well, the organization enables developers
to safely and independently develop, test, and deploy value to the customer.
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How to Get Great
Outcomes by Integrating
Operations into the Daily
Work of Development
Our goal is to enable market-oriented outcomes where many small teams can
quickly and independently deliver value to the customer. This can be a chal-
lenge to achieve when Operations is centralized and functionally-oriented,
having to serve the needs of many dierent development teams with potentially
wildly dierent needs. The result can often be long lead times for needed Ops
work, constant reprioritization and escalation, and poor deployment
outcomes.
We can create more market-oriented outcomes by better integrating Ops
capabilities into Dev teams, making both more ecient and productive. In
this chapter, we’ll explore many ways to achieve this, both at the organizational
level and through daily rituals. By doing this, Ops can signicantly improve
the productivity of Dev teams throughout the entire organization, as well as
enable better collaboration and organizational outcomes.
At Big Fish Games, which develops and supports hundreds of mobile and
thousands of PC games and had more than $266 million in revenue in 2013,
VP of IT Operations Paul Farrall was in charge of the centralized Operations
organization. He was responsible for supporting many dierent business
units that had a great deal of autonomy.
Each of these business units had dedicated development teams who often
chose wildly dierent technologies. When these groups wanted to deploy
new functionality, they would have to compete for a common pool of scarce
Ops resources. Furthermore, everyone was struggling with unreliable Test
and Integration environments, as well as extremely cumbersome release
processes.
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96 • Part II
Farrall thought the best way to solve this problem was by embedding Ops
expertise into Development teams. He observed, “When Dev teams had
problems with testing or deployment, they needed more than just technology
or environments. What they also needed was help and coaching. At rst, we
embedded Ops engineers and architects into each of the Dev teams, but there
simply weren’t enough Ops engineers to cover that many teams. We were able
to help more teams with what we called an Ops liaison model and with fewer
people.”
Farrall dened two types of Ops liaisons: the business relationship manager
and the dedicated release engineer. The business relationship managers
worked with product management, line-of-business owners, project manage-
ment, Dev management, and developers. They became intimately familiar
with product group business drivers and product road maps, acted as advocates
for product owners inside of Operations, and helped their product teams
navigate the Operations landscape to prioritize and streamline work
requests.
Similarly, the dedicated release engineer became intimately familiar with the
product’s Development and QA issues, and helped them get what they needed
from the Ops organization to achieve their goals. They were familiar with the
typical Dev and QA requests for Ops, and would often execute the needed
work themselves. As needed, they would also pull in dedicated technical Ops
engineers (e.g., DBAs, Infosec, storage engineers, network engineers), and
help determine which self-service tools the entire Operations group should
prioritize building.
By doing this, Farrall was able to help Dev teams across the organization
become more productive and achieve their team goals. Furthermore, he helped
the teams prioritize around his global Ops constraints, reducing the number
of surprises discovered mid-project and ultimately increasing the overall
project throughput.
Farrall notes that both working relationships with Operations and code release
velocity were noticeably improved as a result of the changes. He concludes,
“The Ops liaison model allowed us to embed IT Operations expertise into the
Dev and Product teams without adding new headcount.”
The DevOps transformation at Big Fish Games shows how a centralized Op-
erations team was able to achieve the outcomes typically associated with
market-oriented teams. We can employ the three following broad
strategies:
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Chapter 8 • 97
•
Create self-service capabilities to enable developers in the service
teams to be productive.
• Embed Ops engineers into the service teams.
•
Assign Ops liaisons to the service teams when embedding Ops is
not possible.
Lastly, we describe how Ops engineers can integrate into the Dev team
rituals used in their daily work, including daily standups, planning, and
retrospectives.
CREATE SHARED SERVICES TO INCREASE DEVELOPER
PRODUCTIVITY
One way to enable market-oriented outcomes is for Operations to create a set
of centralized platforms and tooling services that any Dev team can use to
become more productive, such as getting production-like environments,
deployment pipelines, automated testing tools, production telemetry dash-
boards, and so forth.† By doing this, we enable Dev teams to spend more time
building functionality for their customer, as opposed to obtaining all the
infrastructure required to deliver and support that feature in production.
All the platforms and services we provide should (ideally) be automated and
available on demand, without requiring a developer to open up a ticket and
wait for someone to manually perform work. This ensures that Operations
doesn’t become a bottleneck for their customers (e.g., “We received your work
request, and it will take six weeks to manually configure those test
environments.”).‡
By doing this, we enable the product teams to get what they need, when they
need it, as well as reduce the need for communications and coordination. As
Damon Edwards observed, “Without these self-service Operations platforms,
the cloud is just Expensive Hosting 2.0.”
In almost all cases, we will not mandate that internal teams use these platforms
and services—these platform teams will have to win over and satisfy their
† The terms platform, shared service, and toolchain will be used interchangeably in this book.
‡ Ernest Mueller observed, “At Bazaarvoice, the agreement was that these platform teams that
make tools accept requirements, but not work from other teams.”
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98 • Part II
internal customers, sometimes even competing with external vendors. By
creating this eective internal marketplace of capabilities, we help ensure
that the platforms and services we create are the easiest and most appealing
choice available (the path of least resistance).
For instance, we may create a platform that provides a shared version control
repository with pre-blessed security libraries, a deployment pipeline that
automatically runs code quality and security scanning tools, which deploys
our applications into known, good environments that already have production
monitoring tools installed on them. Ideally, we make life so much easier for
Dev teams that they will overwhelmingly decide that using our platform is
the easiest, safest, and most secure means to get their applications into
production.
We build into these platforms the cumulative and collective experience of
everyone in the organization, including QA, Operations, and Infosec, which
helps to create an ever safer system of work. This increases developer produc-
tivity and makes it easy for product teams to leverage common processes,
such as performing automated testing and satisfying security and compliance
requirements.
Creating and maintaining these platforms and tools is real product develop-
ment—the customers of our platform aren’t our external customer but our
internal Dev teams. Like creating any great product, creating great platforms
that everyone loves doesn’t happen by accident. An internal platform team
with poor customer focus will likely create tools that everyone will hate and
quickly abandon for other alternatives, whether for another internal platform
team or an external vendor.
Dianne Marsh, Director of Engineering Tools at Netix, states that her team’s
charter is to “support our engineering teams’ innovation and velocity. We
don’t build, bake, or deploy anything for these teams, nor do we manage their
congurations. Instead, we build tools to enable self-service. It’s okay for
people to be dependent on our tools, but it’s important that they don’t become
dependent on us.”
Often, these platform teams provide other services to help their customers
learn their technology, migrate o of other technologies, and even provide
coaching and consulting to help elevate the state of the practice inside the
organization. These shared services also facilitate standardization, which
enable engineers to quickly become productive, even if they switch between
teams. For instance, if every product team chooses a dierent toolchain, en-
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Chapter 8 • 99
gineers may have to learn an entirely new set of technologies to do their work,
putting the team goals ahead of the global goals.
In organizations where teams can only use approved tools, we can start by
removing this requirement for a few teams, such as the transformation team,
so that we can experiment and discover what capabilities make those teams
more productive.
Internal shared services teams should continually look for internal tool-
chains that are widely being adopted in the organization, deciding which
ones make sense to be supported centrally and made available to everyone.
In general, taking something that’s already working somewhere and
expanding its usage is far more likely to succeed than building these ca-
pabilities from scratch.†
EMBED OPS ENGINEERS INTO OUR SERVICE TEAMS
Another way we can enable more market-oriented outcomes is by enabling
product teams to become more self-sucient by embedding Operations en-
gineers within them, thus reducing their reliance on centralized Operations.
These product teams may also be completely responsible for service delivery
and service support.
By embedding Operations engineers into the Dev teams, their priorities are
driven almost entirely by the goals of the product teams they are embedded
in—as opposed to Ops focusing inwardly on solving their own problems. As
a result, Ops engineers become more closely connected to their internal and
external customers. Furthermore, the product teams often have the budget
to fund the hiring of these Ops engineers, although interviewing and hiring
decisions will likely still be done from the centralized Operations group, to
ensure consistency and quality of sta.
Jason Cox said, “In many parts of Disney we have embedded Ops (system
engineers) inside the product teams in our business units, along with inside
Development, Test, and even Information Security. It has totally changed the
dynamics of how we work. As Operations Engineers, we create the tools and
capabilities that transform the way people work, and even the way they think.
In traditional Ops, we merely drove the train that someone else built. But in
† After all, designing a system upfront for re-use is a common and expensive failure mode of
many enterprise architectures.
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100 • Part II
modern Operations Engineering, we not only help build the train, but also
the bridges that the trains roll on.”
For new large Development projects, we may initially embed Ops engineers
into those teams. Their work may include helping decide what to build and
how to build it, inuencing the product architecture, helping inuence
internal and external technology choices, helping create new capabilities
in our internal platforms, and maybe even generating new operational
capabilities. After the product is released to production, embedded Ops
engineers may help with the production responsibilities of the Dev team.
They will take part in all of the Dev team rituals, such as planning meetings,
daily standups, and demonstrations where the team shows o new features
and decides which ones to ship. As the need for Ops knowledge and capabilities
decreases, Ops engineers may transition to dierent projects or engagements,
following the general pattern that the composition within product teams
changes throughout its life cycle.
This paradigm has another important advantage: pairing Dev and Ops engi-
neers together is an extremely ecient way to cross-train operations knowledge
and expertise into a service team. It can also have the powerful benet of
transforming operations knowledge into automated code that can be far more
reliable and widely reused.
ASSIGN AN OPS LIAISON TO EACH SERVICE TEAM
For a variety of reasons, such as cost and scarcity, we may be unable to embed
Ops engineers into every product team. However, we can get many of the same
benets by assigning a designated liaison for each product team.
At Etsy, this model is called “designated Ops.” Their centralized Operations
group continues to manage all the environments—not just production envi-
ronments but also pre-production environments—to help ensure they remain
consistent. The designated Ops engineer is responsible for understanding:
• What the new product functionality is and why we’re building it
• How it works as it pertains to operability, scalability, and observ-
ability (diagramming is strongly encouraged)
•
How to monitor and collect metrics to ensure the progress, success,
or failure of the functionality
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Chapter 8 • 101
•
Any departures from previous architectures and patterns, and
the justications for them
• Any extra needs for infrastructure and how usage will aect in-
frastructure capacity
• Feature launch plans
Furthermore, just like in the embedded Ops model, this liaison attends the
team standups, integrating their needs into the Operations road map and
performing any needed tasks. We rely on these liaisons to escalate any resource
contention or prioritization issue. By doing this, we identify any resource or
time conicts that should be evaluated and prioritized in the context of wider
organizational goals.
Assigning Ops liaisons allows us to support more product teams than
the embedded Ops model. Our goal is to ensure that Ops is not a constraint
for the product teams. If we nd that Ops liaisons are stretched too thin,
preventing the product teams from achieving their goals, then we will likely
need to either reduce the number of teams each liaison supports or tempo-
rarily embed an Ops engineer into specic teams.
INTEGRATE OPS INTO DEV RITUALS
When Ops engineers are embedded or assigned as liaisons into our product
teams, we can integrate them into our Dev team rituals. In this section, our
goal is to help Ops engineers and other non-developers better understand the
existing Development culture and proactively integrate them into all aspects
of planning and daily work. As a result, Operations is better able to plan and
radiate any needed knowledge into the product teams, inuencing work long
before it gets into production. The following sections describe some of the
standard rituals used by Development teams using agile methods and how
we would integrate Ops engineers into them. By no means are agile practices
a prerequisite for this step—as Ops engineers, our goal is to discover what
rituals the product teams follow, integrate into them, and add value to them.
†
† However, if we discover that the entire Development organization merely sits at their desks
all day without ever talking to each other, we may have to nd a dierent way to engage them,
such as buying them lunch, starting a book club, taking turns doing “lunch and learn” presen-
tations, or having conversations to discover what everyone’s biggest problems are, so that we
can gure out how we can make their lives better.
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102 • Part II
As Ernest Mueller observed, “I believe DevOps works a lot better if Operations
teams adopt the same agile rituals that Dev teams have used—we’ve had
fantastic successes solving many problems associated with Ops pain points,
as well as integrating better with Dev teams.”
INVITE OPS TO OUR DEV STANDUPS
One of the Dev rituals popularized by Scrum is the daily standup, a quick
meeting where everyone on the team gets together and presents to each other
three things: what was done yesterday, what is going to be done today, and
what is preventing you from getting your work done.†
The purpose of this ceremony is to radiate information throughout the team
and to understand the work that is being done and is going to be done. By
having team members present this information to each other, we learn about
any tasks that are experiencing roadblocks and discover ways to help each
other move our work toward completion. Furthermore, by having managers
present, we can quickly resolve prioritization and resource conicts.
A common problem is that this information is compartmentalized within the
Development team. By having Ops engineers attend, Operations can gain an
awareness of the Development team’s activities, enabling better planning and
preparation—for instance, if we discover that the product team is planning
a big feature rollout in two weeks, we can ensure that the right people and
resources are available to support the rollout. Alternatively, we may highlight
areas where closer interaction or more preparation is needed (e.g., creating
more monitoring checks or automation scripts). By doing this, we create the
conditions where Operations can help solve our current team problems (e.g.,
improving performance by tuning the database, instead of optimizing code)
or future problems before they turn into a crisis (e.g., creating more integration
test environments to enable performance testing).
INVITE OPS TO OUR DEV RETROSPECTIVES
Another widespread agile ritual is the retrospective. At the end of each de-
velopment interval, the team discusses what was successful, what could be
improved, and how to incorporate the successes and improvements in future
iterations or projects. The team comes up with ideas to make things better
† Scrum is an agile development methodology, described as “a exible, holistic product devel-
opment strategy where a development team works as a unit to reach a common goal.” It was
rst fully described by Ken Schwaber and Mike Beedle in the book Agile Software Development
with Scrum. In this book, we use the term “agile development” or “iterative development” to
encompass the various techniques used by special methodologies such as Agile and Scrum.
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Chapter 8 • 103
and reviews experiments from the previous iteration. This is one of the primary
mechanisms where organizational learning and the development of counter-
measures occurs, with resulting work implemented immediately or added to
the team’s backlog.
Having Ops engineers attend our project team retrospectives means they can
also benet from any new learnings. Furthermore, when there is a deployment
or release in that interval, Operations should present the outcomes and any
resulting learnings, creating feedback into the product team. By doing this,
we can improve how future work is planned and performed, improving our
outcomes. Examples of feedback that Operations can bring to a retrospective
include:
• “Two weeks ago, we found a monitoring blind-spot and agreed
on how to x it. It worked. We had an incident last Tuesday, and
we were able to quickly detect and correct it before any customers
were impacted.”
•
“Last week’s deployment was one of the most dicult and lengthy
we’ve had in over a year. Here are some ideas on how it can be
improved.”
•
“The promotion campaign we did last week was far more dicult
than we thought it would be, and we should probably not make
an oer like that again. Here are some ideas on other oers we
can make to achieve our goals.”
•
“During the last deployment, the biggest problem we had was
our rewall rules are now thousands of lines long, making it
extremely dicult and risky to change. We need to re-architect
how we prevent unauthorized network trac.”
Feedback from Operations helps our product teams better see and understand
the downstream impact of decisions they make. When there are negative
outcomes, we can make the changes necessary to prevent them in the future.
Operations feedback will also likely identify more problems and defects that
should be xed—it may even uncover larger architectural issues that need to
be addressed.
The additional work identied during project team retrospectives falls into
the broad category of improvement work, such as xing defects, refactoring,
and automating manual work. Product managers and project managers may
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104 • Part II
want to defer or deprioritize improvement work in favor of customer
features.
However, we must remind everyone that improvement of daily work is more
important than daily work itself, and that all teams must have dedicated ca-
pacity for this (e.g., reserving 20% of all cycles for improvement work, sched-
uling one day per week or one week per month, etc.). Without doing this, the
productivity of the team will almost certainly grind to a halt under the weight
of its own technical and process debt.
MAKE RELEVANT OPS WORK VISIBLE ON SHARED KANBAN BOARDS
Often, Development teams will make their work visible on a project board or
kanban board. It’s far less common, however, for work boards to show the
relevant Operations work that must be performed in order for the application
to run successfully in production, where customer value is actually created.
As a result, we are not aware of necessary Operations work until it becomes
an urgent crisis, jeopardizing deadlines or creating a production outage.
Because Operations is part of the product value stream, we should put the
Operations work that is relevant to product delivery on the shared kanban
board. This enables us to more clearly see all the work required to move our
code into production, as well as keep track of all Operations work required to
support the product. Furthermore, it enables us to see where Ops work is
blocked and where work needs escalation, highlighting areas where we may
need improvement.
Kanban boards are an ideal tool to create visibility, and visibility is a key
component in properly recognizing and integrating Ops work into all the
relevant value streams. When we do this well, we achieve market-oriented
outcomes, regardless of how we’ve drawn our organization charts.
CONCLUSION
Throughout this chapter, we explored ways to integrate Operations into the
daily work of Development, and looked at how to make our work more visible
to Operations. To accomplish this, we explored three broad strategies, including
creating self-service capabilities to enable developers in service teams to be
productive, embedding Ops engineers into the service teams, and assigning
Ops liaisons to the service teams when embedding Ops engineers was not
possible. Lastly, we described how Ops engineers can integrate with the Dev
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Chapter 8 • 105
team through inclusion in their daily work, including daily standups, planning,
and retrospectives.
PART II CONCLUSION
In Part II: Where to Start, we explored a variety of ways to think about DevOps
transformations, including how to choose where to start, relevant aspects of
architecture and organizational design, and how to organize our teams. We
also explored how to integrate Ops into all aspects of Dev planning and daily
work.
In Part III: The First Way, The Technical Practices of Flow, we will now start to
explore how to implement the specic technical practices to realize the
principles of ow, which enable the fast ow of work from Development to
Operations without causing chaos and disruption downstream.
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Author Biographies
Gene Kim is a multiple award-winning CTO, researcher,
and author of The Phoenix Project: A Novel About IT,
DevOps, and Helping Your Business Win and The Visible
Ops Handbook. He is founder of IT Revolution and hosts
the DevOps Enteprise Summit conferences.
Jez Humble is co-author of the Jolt Award–winning
Continuous Delivery and the groundbreaking Lean En-
terprise. His focus is on helping organizations deliver
valuable, high-quality software frequently and reliably
through implementing eective engineering practices.
Patrick Debois is an independent IT consultant who
is bridging the gap between projects and operations by
using Agile techniques, in development, project man-
agement, and system administration.
John Willis has worked in the IT management industry
for more than thirty-ve years. He has authored six IBM
Redbooks and was the founder and chief architect at
Chain Bridge Systems. Currently he is an Evangelist at
Docker, Inc.
GENE KIM
PATRICK DEBOIS
JEZ HUMBLE
JOHN WILLIS
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