Microservices for Java Developers

Christian Posta
A Hands-On Introduction
to Frameworks & Containers
Microservices
forJavaDevelopers
Com
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Christian Posta
Microservices for
Java Developers
A Hands-on Introduction
to Frameworks and Containers
Boston Farnham Sebastopol TokyoBeijing Boston Farnham Sebastopol TokyoBeijing

978-1-491-96308-1
[LSI]
by Christian Posta
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Table of Contents
1. Microservices for Java Developers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Can You Expect from This Book? 1
You Work for a Software Company 2
What Is a Microservice Architecture? 6
Challenges 8
Technology Solutions 15
Preparing Your Environment 16
2. Spring Boot for Microservices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Getting Started 21
Hello World 23
Calling Another Service 29
Where to Look Next 35
3. Dropwizard for Microservices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Getting Started 40
Hello World 45
4. WildFly Swarm for Microservices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Getting Started 63
Hello World 68
iii

5. Deploy Microservices at Scale with Docker and Kubernetes. . . . . . . 79
Immutable Delivery 80
Docker, Docker, Docker 81
Kubernetes 83
Getting Started with Kubernetes 86
Microservices and Linux Containers 86
6. Hands-on Cluster Management, Failover, and Load Balancing. . . . 91
Fault Tolerance 102
Load Balancing 110
7. Where Do We Go from Here?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Configuration 117
Logging, Metrics, and Tracing 118
Continuous Delivery 119
Summary 119
iv | Table of Contents

CHAPTER 1
What Can You Expect from This Book?
This book is for Java developers and architects interested in develop‐
ing microservices. We start the book with the high-level under‐
standing and fundamental prerequisites that should be in place to be
successful with a microservice architecture. Unfortunately, just
using new technology doesn’t magically solve distributed systems
problems. We take a look at some of the forces involved and what
successful companies have done to make microservices work for
them, including culture, organizational structure, and market pres‐
sures. Then we take a deep dive into a few Java frameworks for
implementing microservices. The accompanying source-code repos‐
itory can be found on GitHub. Once we have our hands dirty, we’ll
come back up for air and discuss issues around deployment, cluster‐
ing, failover, and how Docker and Kubernetes deliver solutions in
these areas. Then we’ll go back into the details with some hands-on
examples with Docker, Kubernetes, and NetflixOSS to demonstrate
the power they bring for cloud-native, microservice architectures.
We finish with thoughts on topics we cannot cover in this small
book but are no less important, like configuration, logging, and con‐
tinuous delivery.
Microservices are not a technology-only discussion. Implementa‐
tions of microservices have roots in complex-adaptive theory, ser‐
vice design, technology evolution, domain-driven design,
dependency thinking, promise theory, and other backgrounds. They
all come together to allow the people of an organization to truly
1

exhibit agile, responsive, learning behaviors to stay competitive in a
fast-evolving business world. Let’s take a closer look.
You Work for a Software Company
Software really is eating the world. Businesses are slowly starting to
realize this, and there are two main drivers for this phenomenon:
delivering value through high-quality services and the rapid com‐
moditization of technology. This book is primarily a hands-on, by-
example format. But before we dive into the technology, we need to
properly set the stage and understand the forces at play. We have
been talking ad nauseam in recent years about making businesses
agile, but we need to fully understand what that means. Otherwise
it’s just a nice platitude that everyone glosses over.
The Value of Service
For more than 100 years, our business markets have been about cre‐
ating products and driving consumers to wanting those products:
desks, microwaves, cars, shoes, whatever. The idea behind this
“producer-led” economy comes from Henry Ford’s idea that “if you
could produce great volumes of a product at low cost, the market
would be virtually unlimited.” For that to work, you also need a few
one-way channels to directly market toward the masses to convince
them they needed these products and their lives would be made sub‐
stantially better with them. For most of the 20th century, these one-
way channels existed in the form of advertisements on TV, in
newspapers and magazines, and on highway billboards. However,
this producer-led economy has been flipped on its head because
markets are fully saturated with product (how many phones/
cars/TVs do you need?). Further, the Internet, along with social net‐
works, is changing the dynamics of how companies interact with
consumers (or more importantly, how consumers interact with
them).
Social networks allow us, as consumers, to more freely share infor‐
mation with one another and the companies with which we do busi‐
ness. We trust our friends, family, and others more than we trust
marketing departments. That’s why we go to social media outlets to
choose restaurants, hotels, and airlines. Our positive feedback in the
form of reviews, tweets, shares, etc., can positively favor the brand of
a company, and our negative feedback can just as easily and very
2 | Chapter 1: Microservices for Java Developers

swiftly destroy a brand. There is now a powerful bi-directional flow
of information with companies and their consumers that previously
never existed, and businesses are struggling to keep up with the
impact of not owning their brand.
Post-industrial companies are learning they must nurture their rela‐
tionship (using bi-directional communication) with customers to
understand how to bring value to them. Companies do this by pro‐
viding ongoing conversation through service, customer experience,
and feedback. Customers choose which services to consume and for
which to pay depending on which ones bring them value and good
experience. Take Uber, for example, which doesn’t own any inven‐
tory or sell products per se. I don’t get any value out of sitting in
someone else’s car, but usually I’m trying to get somewhere (a busi‐
ness meeting, for example) which does bring value. In this way,
Uber and I create value by my using its service. Going forward,
companies will need to focus on bringing valuable services to cus‐
tomers, and technology will drive these through digital services.
Commoditization of Technology
Technology follows a similar boom-to-bust cycle as economics, biol‐
ogy, and law. It has led to great innovations, like the steam engine,
the telephone, and the computer. In our competitive markets, how‐
ever, game-changing innovations require a lot of investment and
build-out to quickly capitalize on a respective market. This brings
more competition, greater capacity, and falling prices, eventually
making the once-innovative technology a commodity. Upon these
commodities, we continue to innovate and differentiate, and the
cycle continues. This commoditization has brought us from the
You Work for a Software Company | 3

mainframe to the personal computer to what we now call “cloud
computing,” which is a service bringing us commodity computing
with almost no upfront capital expenditure. On top of cloud com‐
puting, we’re now bringing new innovation in the form of digital
services.
Open source is also leading the charge in the technology space. Fol‐
lowing the commoditization curves, open source is a place develop‐
ers can go to challenge proprietary vendors by building and
innovating on software that was once only available (without source
no less) with high license costs. This drives communities to build
things like operating systems (Linux), programming languages (Go),
message queues (Apache ActiveMQ), and web servers (httpd). Even
companies that originally rejected open source are starting to come
around by open sourcing their technologies and contributing to
existing communities. As open source and open ecosystems have
become the norm, we’re starting to see a lot of the innovation in
software technology coming directly from open source communities
(e.g., Apache Spark, Docker, and Kubernetes).
Disruption
The confluence of these two factors, service design and technology
evolution, is lowering the barrier for entry to anyone with a good
idea to start experimenting and trying to build new services. You
can learn to program, use advanced frameworks, and leverage on-
demand computing for next to nothing. You can post to social net‐
works, blog, and carry out bi-directional conversations with
potential users of your service for free. With the fluidity of our busi‐
ness markets, any one of the over-the-weekend startups can put a
legacy company out of business.

And this fact scares most CIOs and CEOs. As software quickly
becomes the mechanism by which companies build digital services,
experiences, and differentiation, many are realizing that they must
become software companies in their respective verticals. Gone are
the days of massive outsourcing and treating IT as a commodity or
cost center. For companies to stay truly competitive, they must
embrace software as a differentiator and to do that, they must
embrace organization agility.
Embrace Organization Agility
Companies in the industrial-era thinking of the 20th century are not
built for agility. They are built to maximize efficiencies, reduce vari‐
ability in processes, eliminate creative thinking in workers, and
place workers into boxes the way you would organize an assembly
line. They are built like a machine to take inputs, apply a highly
tuned process, and create outputs. They are structured with top-
down hierarchical management to facilitate this machine-like think‐
ing. Changing the machine requires 18-month planning cycles.
Information from the edge goes through many layers of manage‐
ment and translation to get to the top, where decisions are made and
handed back down. This organizational approach works great when
creating products and trying to squeeze every bit of efficiency out of
a process, but does not work for delivering services.
Customers don’t fit in neat boxes or processes. They show up when‐
ever they want. They want to talk to a customer service representa‐
tive, not an automated phone system. They ask for things that aren’t
on the menu. They need to input something that isn’t on the form.
Customers want convenience. They want a conversation. And they
get mad if they have to wait.
This means our customer-facing services need to account for varia‐
bility. They need to be able to react to the unexpected. This is at
odds with efficiency. Customers want to have a conversation
through a service you provide them, and if that service isn’t suffi‐
cient for solving their needs, you need loud, fast feedback about
what’s helping solve their needs or getting in their way. This feed‐
You Work for a Software Company | 5

back can be used by the maintainers of the service to quickly adjust
the service and interaction models to better suit users. You cannot
wait for decisions to bubble up to the top and through 18-month
planning cycles; you need to make decisions quickly with the infor‐
mation you have at the edges of your business. You need autono‐
mous, purpose-driven, self-organizing teams who are responsible
for delivering a compelling experience to their customers (paying
customers, business partners, peer teams, etc.). Rapid feedback
cycles, autonomous teams, shared purpose, and conversation are the
prerequisites that organizations must embrace to be able to navigate
and live in a post-industrial, unknown, uncharted body of business
disruption.
No book on microservices would be complete without quoting Con‐
way’s law: “organizations which design systems…are constrained to
produce designs which are copies of the communication structures
of these organizations.”
To build agile software systems, we must start with building agile
organizational structures. This structure will facilitate the prerequi‐
sites we need for microservices, but what technology do we use?
Building distributed systems is hard, and in the subsequent sections,
we’ll take a look at the problems you must keep in mind when build‐
ing and designing these services.
What Is a Microservice Architecture?
Microservice architecture (MSA) is an approach to building soft‐
ware systems that decomposes business domain models into smaller,
consistent, bounded-contexts implemented by services. These serv‐
ices are isolated and autonomous yet communicate to provide some
piece of business functionality. Microservices are typically imple‐
mented and operated by small teams with enough autonomy that
each team and service can change its internal implementation
details (including replacing it outright!) with minimal impact across
the rest of the system.

Teams communicate through promises, which are a way a service
can publish intentions to other components or systems that may
wish to use the service. They specify these promises with interfaces
of their services and via wikis that document their services. If there
isn’t enough documentation, or the API isn’t clear enough, the ser‐
vice provider hasn’t done his job. A little more on promises and
promise theory in the next section.
Each team would be responsible for designing the service, picking
the right technology for the problem set, and deploying, managing
and waking up at 2 a.m. for any issues. For example, at Amazon,
there is a single team that owns the tax-calculation functionality that
gets called during checkout. The models within this service (Item,
Address, Tax, etc.) are all understood to mean “within the context of
calculating taxes” for a checkout; there is no confusion about these
objects (e.g., is the item a return item or a checkout item?). The
team that owns the tax-calculation service designs, develops, and
operates this service. Amazon has the luxury of a mature set of self-
service tools to automate a lot of the build/deploy/operate steps, but
we’ll come back to that.
With microservices, we can scope the boundaries of a service, which
helps us:
• Understand what the service is doing without being tangled into
other concerns in a larger application
• Quickly build the service locally
• Pick the right technology for the problem (lots of writes? lots of
queries? low latency? bursty?)
What Is a Microservice Architecture? | 7

• Test the service
• Build/deploy/release at a cadence necessary for the business,
which may be independent of other services
• Identify and horizontally scale parts of the architecture where
needed
• Improve resiliency of the system as a whole
Microservices help solve the “how do we decouple our services and
teams to move quickly at scale?” problem. It allows teams to focus
on providing the service and making changes when necessary and
to do so without costly synchronization points. Here are things you
won’t hear once you’ve adopted microservices:
• Jira tickets
• Unnecessary meetings
• Shared libraries
• Enterprise-wide canonical models
Is microservice architecture right for you? Microservices have a lot
of benefits, but they come with their own set of drawbacks. You can
think of microservices as an optimization for problems that require
the ability to change things quickly at scale but with a price. It’s not
efficient. It can be more resource intensive. You may end up with
what looks like duplication. Operational complexity is a lot higher. It
becomes very difficult to understand the system holistically. It
becomes significantly harder to debug problems. In some areas you
may have to relax the notion of transaction. Teams may not have
been designed to work like this.
Not every part of the business has to be able to change on a dime. A
lot of customer-facing applications do. Backend systems may not.
But as those two worlds start to blend together we may see the forces
that justify microservice architectures push to other parts of the sys‐
tem.
Challenges
Designing cloud-native applications following a microservices
approach requires thinking differently about how to build, deploy,
and operate them. We can’t just build our application thinking we

know all the ways it will fail and then just prevent those. In complex
systems like those built with microservices, we must be able to deal
with uncertainty. This section will identify five main things to keep
in mind when developing microservices.
Design for Faults
In complex systems, things fail. Hard drives crash, network cables
get unplugged, we do maintenance on the live database instead of
the backups, and VMs disappear. Single faults can be propagated to
other parts of the system and result in cascading failures that take an
entire system down.
Traditionally, when building applications, we’ve tried to predict what
pieces of our app (e.g., n-tier) might fail and build up a wall big
enough to keep things from failing. This mindset is problematic at
scale because we cannot always predict what things can go wrong in
complex systems. Things will fail, so we must develop our applica‐
tions to be resilient and handle failure, not just prevent it. We should
be able to deal with faults gracefully and not let faults propagate to
total failure of the system.
Building distributed systems is different from building shared-
memory, single process, monolithic applications. One glaring differ‐
ence is that communication over a network is not the same as a local
call with shared memory. Networks are inherently unreliable. Calls
over the network can fail for any number of reasons (e.g., signal
strength, bad cables/routers/switches, and firewalls), and this can be
a major source of bottlenecks. Not only does network unreliability
have performance implications on response times to clients of your
service, but it can also contribute to upstream systems failure.
Latent network calls can be very difficult to debug; ideally, if your
network calls cannot complete successfully, they fail immediately,
and your application notices quickly (e.g., through IOException). In
this case we can quickly take corrective action, provide degraded
functionality, or just respond with a message stating the request
could not be completed properly and that users should try again
later. But errors in network requests or distributed applications
aren’t always that easy. What if the downstream application you
must call takes longer than normal to respond? This is killer because
now your application must take into account this slowness by throt‐
tling requests, timing out downstream requests, and potentially
Challenges | 9

stalling all calls through your service. This backup can cause
upstream services to experience slowdown and grind to a halt. And
it can cause cascading failures.
Design with Dependencies in Mind
To be able to move fast and be agile from an organization or
distributed-systems standpoint, we have to design systems with
dependency thinking in mind; we need loose coupling in our teams,
in our technology, and our governance. One of the goals with
microservices is to take advantage of autonomous teams and auton‐
omous services. This means being able to change things as quickly
as the business needs without impacting those services around you
or the system at large. This also means we should be able to depend
on services, but if they’re not available or are degraded, we need to
be able to handle this gracefully.
In his book Dependency Oriented Thinking (InfoQ Enterprise Soft‐
ware Development Series), Ganesh Prasad hits it on the head when
he says, “One of the principles of creativity is to drop a constraint. In
other words, you can come up with creative solutions to problems if
you mentally eliminate one or more dependencies.” The problem is
our organizations were built with efficiency in mind, and that brings
a lot of tangled dependencies along.
For example, when you need to consult with three other teams to
make a change to your service (DBA, QA, and Security), this is not
very agile; each one of these synchronization points can cause
delays. It’s a brittle process. If you can shed those dependencies or
build them into your team (we definitely can’t sacrifice safety or
security, so build those components into your team), you’re free to
be creative and more quickly solve problems that customers face or
the business foresees without costly people bottlenecks.
Another angle to the dependency management story is what to do
with legacy systems. Exposing details of backend legacy systems
(COBOL copybook structures, XML serialization formats used by a
specific system, etc.) to downstream systems is a recipe for disaster.
Making one small change (customer ID is now 20 numeric charac‐
ters instead of 16) now ripples across the system and invalidates
assumptions made by those downstream systems, potentially break‐
ing them. We need to think carefully about how to insulate the rest
of the system from these types of dependencies.

Design with the Domain in Mind
Models have been used for centuries to simplify and understand a
problem through a certain lens. For example, the GPS maps on our
phones are great models for navigating a city while walking or driv‐
ing. This model would be completely useless to someone flying a
commercial airplane. The models they use are more appropriate to
describe way points, landmarks, and jet streams. Different models
make more or less sense depending on the context from which
they’re viewed. Eric Evans’s seminal book Domain-Driven Design
(Addison-Wesley, 2004) helps us build models for complex business
processes that can also be implemented in software. Ultimately the
real complexity in software is not the technology but rather the
ambiguous, circular, contradicting models that business folks sort
out in their heads on the fly. Humans can understand models given
some context, but computers need a little more help; these models
and the context must be baked into the software. If we can achieve
this level of modeling that is bound to the implementation (and vice
versa), anytime the business changes, we can more clearly under‐
stand how that changes in the software. The process we embark
upon to build these models and the language surrounding it take
time and require fast feedback loops.
One of the tools Evans presents is identifying and explicitly separat‐
ing the different models and ensuring they’re cohesive and unam‐
biguous within their own bounded context.
A bounded context is a set of domain objects that implement a
model that tries to simplify and communicate a part of the business,
code, and organization. For example, we strive for efficiency when
designing our systems when we really need flexibility (sound famil‐
Challenges | 11

iar?). In a simple auto-part application, we try to come up with a
unified “canonical model” of the entire domain, and we end up with
objects like Part, Price, and Address. If the inventory application
used the “Part” object it would be referring to a type of part like a
type of “brake” or “wheel.” In an automotive quality assurance sys‐
tem, Part might refer to a very specific part with a serial number
and unique identifier to track certain quality tests results and so
forth. We tried diligently to efficiently reuse the same canonical
model, but the issues of inventory tracking and quality assurance are
different business concerns that use the Part object, semantically
differently. With a bounded context, a Part would explicitly be
modeled as PartType and be understood within that context to rep‐
resent a “type of part,” not a specific instance of a part. With two
separate bounded contexts, these Part objects can evolve consis‐
tently within their own models without depending on one another
in weird ways, and thus we’ve achieved a level of agility or flexibility.
This deep understanding of the domain takes time. It may take a few
iterations to fully understand the ambiguities that exist in business
models and properly separate them out and allow them to change
independently. This is at least one reason starting off building
microservices is difficult. Carving up a monolith is no easy task, but
a lot of the concepts are already baked into the monolith; your job is
to identify and carve it up. With a greenfield project, you cannot
carve up anything until you deeply understand it. In fact, all of the
microservice success stories we hear about (like Amazon and Net‐
flix) all started out going down the path of the monolith before they
successfully made the transition to microservices.
Design with Promises in Mind
In a microservice environment with autonomous teams and serv‐
ices, it’s very important to keep in mind the relationship between
service provider and service consumer. As an autonomous service
team, you cannot place obligations on other teams and services
because you do not own them; they’re autonomous by definition. All
you can do is choose whether or not to accept their promises of
functionality or behavior. As a provider of a service to others, all you
can do is promise them a certain behavior. They are free to trust you
or not. Promise theory, a model first proposed by Mark Burgess in
2004 and covered in his book In Search of Certainty (O’Reilly, 2015),

is a study of autonomous systems including people, computers, and
organizations providing service to each other.
In terms of distributed systems, promises help articulate what a ser‐
vice may provide and make clear what assumptions can and cannot
be made. For example, our team owns the book-recommendation
service, and we promise a personalized set of book recommenda‐
tions for a specific user you may ask about. What happens when you
call our service, and one of our backends (the database that stores
that user’s current view of recommendations) is unavailable? We
could throw exceptions and stack traces back to you, but that would
not be a very good experience and could potentially blow up other
parts of the system. Because we made a promise, we can try to do
everything we can to keep it, including returning a default list of
books, or a subset of every book. There are times when promises
cannot be kept and identifying the best course of action should be
driven by the desired experience or outcome for our users we wish
to keep. The key here is the onus on our service to try to keep its
promise (return some recommendations), even if our dependent
services cannot keep theirs (the database was down). In the course
of trying to keep a promise, it helps to have empathy for the rest of
the system and the service quality we’re trying to uphold.
Another way to look at a promise is as an agreed-upon exchange
that provides value for both parties (like a producer and a con‐
sumer). But how do we go about deciding between two parties what
is valuable and what promises we’d like to agree upon? If nobody
calls our service or gets value from our promises, how useful is the
service? One way of articulating the promise between consumers
Challenges | 13

and providers is driving promises with consumer-driven contracts.
With consumer-driven contracts, we are able to capture the value of
our promises with code or assertions and as a provider, we can use
this knowledge to test whether we’re upholding our promises.
Distributed Systems Management
At the end of the day, managing a single system is easier than a dis‐
tributed one. If there’s just one machine, and one application server,
and there are problems with the system, we know where to look. If
you need to make a configuration change, upgrade to a specific ver‐
sion, or secure it, it’s still all in one physical and logical location.
Managing, debugging, and changing it is easier. A single system may
work for some use cases; but for ones where scale is required, we
may look to leverage microservices. As we discussed earlier, how‐
ever, microservices are not free; the trade-off for having flexibility
and scalability is having to manage a complicated system.
Some quick questions about the manageability of a microservices
deployment:
• How do we start and stop a fleet of services?
• How do we aggregate logs/metrics/SLAs across microservices?
• How do we discover services in an elastic environment where
they can be coming, going, moving, etc.?
• How do we do load balancing?
• How do we learn about the health of our cluster or individual
services?
• How do we restart services that have fallen over?
• How do we do fine-grained API routing?
• How do we secure our services?
• How do we throttle or disconnect parts of a cluster if it starts to
crash or act unexpectedly?
• How do we deploy multiple versions of a service and route to
them appropriately?
• How do we make configuration changes across a large fleet of
services?

• How do we make changes to our application code and configu‐
ration in a safe, auditable, repeatable manner?
These are not easy problems to solve. The rest of the book will be
devoted to getting Java developers up and running with microservi‐
ces and able to solve some of the problems listed. The full, complete
list of how-to for the preceding questions (and many others) should
be addressed in a second edition of this book.
Technology Solutions
Throughout the rest of the book, we’ll introduce you to some popu‐
lar technology components and how they help solve some of the
problems of developing and delivering software using a microservi‐
ces architecture. As touched upon earlier, microservices is not just a
technological problem, and getting the right organizational struc‐
ture and teams in place to facilitate microservices is paramount.
Switching from SOAP to REST doesn’t make a microservices archi‐
tecture.
The first step for a Java development team creating microservices is
to get something working locally on their machine! This book will
introduce you to three opinionated Java frameworks for working
with microservices: Spring Boot, Dropwizard, and WildFly Swarm.
Each framework has upsides for different teams, organizations, and
approaches to microservices. Just as is the norm with technology,
some tools are a better fit for the job or the team using them. These
are not the only frameworks to use. There are a couple that take a
reactive approach to microservices like Vert.x and Lagom. The
mindshift for developing with an event-based model is a bit differ‐
ent and requires a different learning curve so for this book we’ll stick
with a model that most enterprise Java developers will find comfort‐
able.
The goal of this book is to get you up and running with the basics
for each framework. We’ll dive into a couple advanced concepts in
the last chapter, but for the first steps with each framework, we’ll
assume a hello-world microservice application. This book is not an
all-encompassing reference for developing microservices; each sec‐
tion will leave you with links to reference material to explore more
as needed. We will iterate on the hello-world application by creating
multiple services and show some simple interaction patterns.
Technology Solutions | 15

The final iteration for each framework will look at concepts like bul‐
kheading and promise theory to make our services resilient in the
face of faults. We will dig into parts of the NetflixOSS stack like Hys‐
trix that can make our lives easier for implementing this functional‐
ity. We will discuss the pros and cons of this approach and explore
what other options exist.
As we go through the examples, we’ll also discuss the value that
Linux containers bring to the microservices story for deployment,
management, and isolation as well as local development. Docker
and Kubernetes bring a wealth of simplifications for dealing with
distributed systems at scale, so we’ll discuss some good practices
around containers and microservices.
In the last section of the book, we’ll leave you with a few thoughts on
distributed configuration, logging, metrics, and continuous delivery.
Preparing Your Environment
We will be using Java 1.8 for these examples and building them with
Maven. Please make sure for your environment you have the follow‐
ing prerequisites installed:
• JDK 1.8
• Maven 3.2+
• Access to a command-line shell (bash, PowerShell, cmd, Cyg‐
win, etc.)
The Spring ecosystem has some great tools you may wish to use
either at the command line or in an IDE. Most of the examples will
stick to the command line to stay IDE neutral and because each IDE
has its own way of working with projects. For Spring Boot, we’ll use
the Spring Boot CLI 1.3.3.
Alternative IDEs and tooling for Spring:
• Eclipse based IDE: Spring Tool Suite
• Spring Initializr web interface
For both Dropwizard and WildFly Swarm, we’ll use JBoss Forge CLI
and some addons to create and interact with our projects:

• JBoss Forge 3.0+
Alternative IDEs and tooling for Spring, Dropwizard, or WildFly
Swarm projects (and works great with JBoss Forge):
• Eclipse based IDE: JBoss Developer Studio
• Netbeans
• IntelliJ IDEA
Finally, when we build and deploy our microservices as Docker con‐
tainers running inside of Kubernetes, we’ll want the following tools
to bootstrap a container environment on our machines:
• Vagrant 1.8.1
• VirtualBox 5.0.x
• Container Development Kit 2.x
• Kubernetes/Openshift CLI
• Docker CLI (optional)
Preparing Your Environment | 17

CHAPTER 2
Spring Boot for Microservices
Spring Boot is an opinionated Java framework for building micro‐
services based on the Spring dependency injection framework.
Spring Boot allows developers to create microservices through
reduced boilerplate, configuration, and developer friction. This is a
similar approach to the two other frameworks we’ll look at. Spring
Boot does this by:
• Favoring automatic, conventional configuration by default
• Curating sets of popular starter dependencies for easier con‐
sumption
• Simplifying application packaging
• Baking in application insight (e.g., metrics and environment
info)
Simplified Configuration
Spring historically was a nightmare to configure. Although the
framework improved upon other high-ceremony component mod‐
els (EJB 1.x, 2.x, etc.), it did come along with its own set of heavy‐
weight usage patterns. Namely, Spring required a lot of XML
configuration and a deep understanding of the individual beans
needed to construct JdbcTemplates, JmsTemplates, BeanFactory life‐
cycle hooks, servlet listeners, and many other components. In fact,
writing a simple “hello world” with Spring MVC required under‐
standing of DispatcherServlet and a whole host of Model-View-
19

Controller classes. Spring Boot aims to eliminate all of this
boilerplate configuration with some implied conventions and sim‐
plified annotations—although, you can still finely tune the underly‐
ing beans if you need to.
Starter Dependencies
Spring was used in large enterprise applications that typically lever‐
aged lots of different technology to do the heavy lifting: JDBC data‐
bases, message queues, file systems, application-level caching, etc. A
developer would have to stop what she’s doing, switch cognitive con‐
texts, figure out what dependencies belonged to which piece of func‐
tionality (“Oh, I need the JPA dependencies!”) and spend lots of
time sorting out versioning mismatches or issues that would arise
when trying to use these various pieces together. Spring Boot offers
a large collection of curated sets of libraries for adding these pieces
of functionality. These starter modules allow you to add things like:
• JPA persistence
• NoSQL databases like MongoDB, Cassandra, and Couchbase
• Redis caching
• Tomcat/Jetty/Undertow servlet engine
• JTA transactions
Adding a submodule to your application brings in the curated set of
transitive dependencies and versions that are known to work
together saving developers from having to sort out dependencies
themselves.
Application Packaging
Spring Boot really is a set of bootstrap libraries with some conven‐
tion for configurations, but there’s no reason why you couldn’t run a
Spring Boot application inside your existing application servers (as a
WAR). The idiom that most developers who use Spring Boot prefer
is the self-contained JAR packaging for their application. This
means Spring Boot packages all dependencies and application code
into a self-contained JAR with a flat class loader. This makes it easier
to understand application startup, dependency ordering, and log
statements; but more importantly, it helps reduce the number of
moving pieces required to take an app safely to production. This
20 | Chapter 2: Spring Boot for Microservices

means you don’t take an app and chuck it into an app server; the
app, once it’s built, is ready to run as is—standalone—including
embedding its own servlet container if it uses servlets. That’s right, a
simple java -jar <name.jar> is enough to start your application
now! Spring Boot, Dropwizard, and WildFly Swarm all follow this
pattern of packaging everything into an executable uber JAR.
But what about management things we typically expect out of an
application server?
Production Ready
Spring Boot ships with a module called actuator which enables
things like metrics and statistics about your application. For exam‐
ple, we can collect logs, view metrics, perform thread dumps, show
environment variables, understand garbage collection, and show
what beans are configured in the BeanFactory. You can expose this
information via HTTP, JMX, or you can even log in directly to the
process via SSH.
With Spring Boot, we can leverage the power of the Spring Frame‐
work and reduce boilerplate configuration and code to more quickly
build powerful, production-ready microservices. Let’s see how.
Getting Started
We’re going to use the Spring Boot command-line interface (CLI) to
bootstrap our first Spring Boot application (the CLI uses Spring Ini‐
tializr under the covers). You are free to explore the different ways to
do this if you’re not comfortable with the CLI. Alternatives include
using Spring Initializr plug-ins for your favorite IDE or visiting the
web version of Spring Initializr. The Spring Boot CLI can be
installed a few different ways, including through package managers
and by downloading it straight from the website. Check for instruc‐
tions on installing the CLI most appropriate for your development
environment.
Once you’ve installed the CLI tools, you should be able to check the
version of Spring you have:
$ spring --version
Spring CLI v1.3.3.RELEASE
Getting Started | 21

If you can see a version for your installation of the CLI, congrats!
Now navigate to a directory where you want to host your examples
from the book and run the following command:
spring init --build maven --groupId com.redhat.examples
--version 1.0 --java-version 1.8 --dependencies web
--name hola-springboot hola-springboot
After running this command, you should have a directory named
hola-springboot with a complete Spring Boot application. If you run
the command and end up with a demo.zip, then just unzip it and
continue. Let’s take a quick look at what those command-line
options are.
--build
The build-management tool we want to use. maven or gradle
are the two valid options at this time.
--groupId
The groupId to use in our maven coordinates for our pom.xml;
unfortunately this does not properly extend to the Java package
names that get created. These need to be modified by hand.
--version
The version of our application; will be used in later iterations, so
set to 1.0.
--java-version
Allows us to specify the build compiler version for the JDK.
--dependencies
This is an interesting parameter; we can specify fully baked sets
of dependencies for doing common types of development. For
example, web will set up Spring MVC and embed an internal
servlet engine (Tomcat by default; Jetty and Undertow as
options). Other convenient dependency bundles/starters
include jpa, security, and cassandra).
Now if you navigate to the hola-springboot directory, try running the
following command:
$ mvn spring-boot:run
If everything boots up without any errors, you should see some log‐
ging similar to this:

2016-03-25 10:57:08.920 [main] AnnotationMBeanExporter
: Registering beans for JMX exposure on startup
2016-03-25 10:57:08.982 [main] TomcatEmbeddedServletContainer
: Tomcat started on port(s): 8080 (http)
2016-03-25 10:57:08.987 [main] HolaSpringbootApplication
: Started HolaSpringbootApplication in 1.0 seconds
(JVM running for 4.7)
Congrats! You have quickly gotten a Spring Boot application up and
running! You can even navigate to http://localhost:8080 in your
browser and should see the following output:
This default error page is expected since our application doesn’t do
anything yet! Let’s move on to the next section to add a REST end‐
point to put together a hello-world use case!
Hello World
Now that we have a Spring Boot application that can run, let’s add
some simple functionality. We want to expose an HTTP/REST end‐
point at /api/hola that will return “Hola Spring Boot from X” where
X is the IP address where the service is running. To do this, navigate
to src/main/java/com/example. This location should have been cre‐
ated for you if you followed the preceding steps; remember, the
groupId we passed to the spring init program did not apply
groupId to the Java package hierarchy, and we’ve left it as it is which
should be “com.example”. Then create a new Java class called HolaR
estController, as shown in Example 2-1. We’ll add a method
named hola() that returns a string along with the IP address of
where the service is running. You’ll see in Chapter 5, in our load bal‐
ancing and service discovery sections, how the host IPs can be used
to demonstrate proper failover, loadbalancing, etc.
Hello World | 23

Example 2-1. src/main/java/com/example/HolaRestController.java
public class HolaRestController {
public String hola() throws UnknownHostException {
String hostname = null;
try {
hostname = InetAddress.getLocalHost()
.getHostAddress();
} catch (UnknownHostException e) {
hostname = "unknown";
}
return "Hola Spring Boot de " + hostname;
}
}
Add the HTTP Endpoints
At this point, this piece of code is just a POJO (plain old Java object)
and you could (and should) write a unit test that verifies its behav‐
ior. To expose this as a REST endpoint, we’re going to make use of
the following annotations in Example 2-2:
@RestController
Tell Spring this is an HTTP controller capable of exposing
HTTP endpoints (GET, PUT, POST, etc.).
@RequestMapping
Map specific parts of the HTTP URI path to classes, methods,
and parameters in the Java code.
Note, import statements are omitted.
@RestController
@RequestMapping("/api")
@RequestMapping(method = RequestMethod.GET, value = "/hola",
produces = "text/plain")
try {
.getHostAddress();

}
return "Hola Spring Boot de " + hostname;
}
}
In this code, all we’ve done is add the aforementioned annotations.
For example, @RequestMapping("/api") at the Class level says “map
any method-level HTTP endpoints under this root URI path.” When
we add @RequestMapping(method = RequestMethod.GET, value =
"/hola", produces = "text/plain"), we are telling Spring to
expose an HTTP GET endpoint at /hola (which will really be /api/
hola) and map requests with media type of Accept: text/plain to
this method. Spring Boot defaults to using an embedded Tomcat
servlet container, but this can be switched to other options like
Undertow or Jetty.
If we build our application and run spring-boot:run again, we
should be able to reach our HTTP endpoint:
$ mvn clean package spring-boot:run
Now if we point our browser to http://localhost:8080/api/hola, we
should see a response similar to:
What if we want to add some environment-aware configuration to
our application? For example, instead of saying “Hola,” maybe we
want to say “Guten Tag” if we deploy our app in production for Ger‐
man users? We need a way to inject properties to our app.
Externalize Configuration
Spring Boot makes it easy to use external property sources like
properties files, command-line arguments, the OS environment, or
Java System properties. We can even bind entire “classes” of proper‐
ties to objects in our Spring context. For example, if I want to bind
all helloapp.* properties to my HolaRestController, I can add
@ConfigurationProperties(prefix="helloapp"), and Spring
Boot will automatically try to bind helloapp.foo and helloapp.bar
Hello World | 25

to Java Bean properties in the HolaRestController class. Let’s
define a new property in src/main/resources/application.properties
called helloapp.saying. The application.properties file was auto‐
matically created for us when we created our project. Note we could
change the file name to application.yml and Spring would still recog‐
nize it as a YAML file as the source of properties.
Let’s add a new property to our src/main/resources/application.prop‐
erties file:
helloapp.saying=Guten Tag aus
In the HolaRestController in Example 2-3, let’s add the @Configu
rationProperties annotation and our new saying field. Note we
also need setters and getters.
@RestController
@ConfigurationProperties(prefix="helloapp")
private String saying;
@RequestMapping(method = RequestMethod.GET, value = "/hola",
produces = "text/plain")
try {
.getHostAddress();
}
return saying + " " + hostname;
}
public String getSaying() {
return saying;
}
public void setSaying(String saying) {
this.saying = saying;
}
}

Let’s stop our application from running before (if we haven’t) and
restart it:
Now if we navigate to http://localhost:8080/api/hola, we should see
the German version of the saying:
We can now externalize properties that would change depending on
the environment in which we are running. Things like service URIs,
database URIs and passwords, and message queue configurations
would all be great candidates for external configuration. Don’t
overdo it though; not everything needs to change depending on the
environment in which it runs! Ideally an application would be con‐
figured exactly the same in all environments including timeouts,
thread pools, retry thresholds, etc.
Expose Application Metrics and Information
If we want to put this microservice into production, how will we
monitor it? How can we get any insight about how things are run‐
ning? Often our microservices are black boxes unless we explicitly
think through how we want to expose metrics to the outside world.
Spring Boot comes with a prepackaged starter called actuator that
makes doing this a breeze.
Let’s see what it takes to enable the actuator. Open up the pom.xml
file for your hola-springboot microservice and add the following
Maven dependency within the <dependencies>...</dependen
cies> section:
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
Now restart your microservice by stopping it and running:
Just by adding the actuator dependency, our application now has a
lot of information exposed that would be very handy for debugging
Hello World | 27

or general microservice insight. Try hitting the following URLs and
examine what gets returned:
• http://localhost:8080/beans
• http://localhost:8080/env
• http://localhost:8080/health
• http://localhost:8080/metrics
• http://localhost:8080/trace
• http://localhost:8080/mappings
Here’s an example of what the http://localhost:8080/env endpoint
looks like:
Exposing runtime insight like this relieves the developer to just
focus on writing code for the microservice that delivers business
value. Delegating to frameworks to do heavy lifting and boilerplate
is definitely a good idea.
How to Run This Outside of Maven?
Up to this point we’ve been thinking through development and
building our hello-world microservice from the perspective of a
developer’s laptop using Maven. But what if you want to distribute

your microservice to others or run it in a live environment (devel‐
opment, QA, production)?
Luckily, with Spring Boot it only takes a few steps to get us ready for
shipment and production. Spring Boot prefers atomic, executable
JARs with all dependencies packed into a flat classpath. This means
the JAR that we create as part of a call to mvn clean package is exe‐
cutable and contains all we need to run our microservice in a Java
environment! To test this out, go to the root of our hola-
springboot microservice project and run the following commands:
$ mvn clean package
$ java -jar target/hola-springboot-1.0.jar
If your project was named demo instead of hola-springboot, then
substitute the properly named JAR file (demo-1.0.jar).
That’s it!
We’ll notice this sort of idiom when we explore Dropwizard and
WildFly Swarm.
Calling Another Service
In a microservice environment, each service is responsible for pro‐
viding the functionality or service to other collaborators. As we’ve
discussed in the first chapter, building distributed systems is hard,
and we cannot abstract away the network or the potential for fail‐
ures. We will cover how to build resilient interactions with our
dependencies in Chapter 5. In this section, however, we will just
focus on getting a service to talk to a dependent service.
If we wish to extend the hello-world microservice, we will need to
create a service to which we can call using Spring’s REST client func‐
tionality. For this example and the rest of the examples, we’ll use a
backend service and modify our service to reach out to the backend
to generate the greetings we want to be able to use.
If you look in the source code for this book, we’ll see a Maven mod‐
ule called backend which contains a very simple HTTP servlet that
Calling Another Service | 29

can be invoked with a GET request and query parameters. The code
for this backend is very simple, and does not use any of the micro‐
service frameworks (Spring Boot, Dropwizard, or WildFly Swarm).
We have created a ResponseDTO object that encapsulates time, ip,
and greeting fields. We also leverage the awesome Jackson library
for JSON data binding, as seen here:
public class BackendHttpServlet extends HttpServlet {
@Override
protected void doGet(HttpServletRequest req,
HttpServletResponse resp)
throws ServletException, IOException {
resp.setContentType("application/json");
ObjectMapper mapper = new ObjectMapper();
String greeting = req.getParameter("greeting");
ResponseDTO response = new ResponseDTO();
response.setGreeting(greeting +
" from cluster Backend");
response.setTime(System.currentTimeMillis());
response.setIp(getIp());
PrintWriter out = resp.getWriter();
mapper.writerWithDefaultPrettyPrinter()
.writeValue(out, response);
}
private String getIp() {
try {
.getHostAddress();
}
return hostname;
}
}
To start up the backend service on port 8080, navigate to the back
end directory and run the following:
$ mvn clean install jetty:run
The backend project uses the Maven Jetty plug-in, which allows us
to quickly boot up our app using mvn jetty:run.

This service is exposed at /api/backend and takes a query parameter
greeting. For example, when we call this service with this path /api/
backend?greeting=Hello, then the backend service will respond with
a JSON object like this (can also visit this URL with your browser):
$ curl -X GET http://localhost:8080/api/backend?greeting=Hello
We get something like this:
{
"greeting" : "Hello from cluster Backend",
"time" : 1459189860895,
"ip" : "172.20.10.3"
}
We will create a new HTTP endpoint, /api/greeting, in our Spring
Boot hola-springboot example and use Spring to call this backend!
Create a new class in src/main/java/com/example called GreeterRest
Controller and fill it in similarly to how we filled it in for the HolaR
estController (see Example 2-4).
Example 2-4. src/main/java/com/example/GreeterRestController.java
@RestController
@ConfigurationProperties(prefix="greeting")
public class GreeterRestController {
private String backendServiceHost;
private int backendServicePort;
@RequestMapping(value = "/greeting",
method = RequestMethod.GET, produces = "text/plain")
public String greeting(){
String backendServiceUrl =
String.format(
"http://%s:%d/hello?greeting={greeting}",
backendServiceHost, backendServicePort);
System.out.println("Sending to: " + backendServiceUrl);
return backendServiceUrl;
}
}
I’ve left out the getters/setters for the properties in this class, but
make sure to have them in your source code! Note we are using the

@ConfigureProperties annotation again to configure a block of
configuration for our REST controller here, although this time we
are using the greeting prefix. We also create a GET endpoint like
we did with the hola service, and all it returns at the moment is a
string with the values of the backend service host and port con‐
catenated (and these values are injected in via the @ConfigurePro
perties annotation). Let’s add the backendServiceHost and
backendServicePort to our application.properties file:
greeting.saying=Hola Spring Boot
greeting.backendServiceHost=localhost
greeting.backendServicePort=8080
Next, we’re going to use Spring’s RestTemplate to do the invocation
of the remote service. Following a long-lived Spring convention with
its template patterns, the RestTemplate wraps common HTTP/
REST idioms inside of this convenient wrapper abstraction which
then handles all the connections and marshalling/unmarshalling the
results of an invocation. RestTemplate uses the native JDK for
HTTP/network access, but you can swap that out for Apache
HttpComponents, OkHttp, Netty, or others.
Here’s what the source looks like when using the RestTemplate
(again, the getters/setters omitted, but required). We are communi‐
cating with the backend service by constructing a URL based on the
host and port that have been injected and we add a GET query
parameter called greeting. The value we send to the backend ser‐
vice for the greeting parameter is from the saying field of the
GreeterRestController object, which gets injected as part of the
configuration when we added the @ConfigurationProperties
annotation (Example 2-5).
Example 2-5. src/main/java/com/example/GreeterRestController.java
@RestController
@ConfigurationProperties(prefix="greeting")
public class GreeterRestController {
private RestTemplate template = new RestTemplate();

@RequestMapping(value = "/greeting",
method = RequestMethod.GET, produces = "text/plain")
public String greeting(){
String.format(
"http://%s:%d/api/backend?greeting={greeting}",
BackendDTO response = template.getForObject(
backendServiceUrl, BackendDTO.class, saying);
return response.getGreeting() + " at host: " +
response.getIp();
}
}
Let’s add the BackendDTO class, which is used to encapsulate respon‐
ses from the backend (Example 2-6).
Example 2-6. src/main/java/com/example/BackendDTO.java
public class BackendDTO {
private String greeting;
private long time;
private String ip;
public String getGreeting() {
return greeting;
}
public void setGreeting(String greeting) {
this.greeting = greeting;
}
public long getTime() {
return time;
}
public void setTime(long time) {
this.time = time;
}
public String getIp() {
return ip;
}

public void setIp(String ip) {
this.ip = ip;
}
}
Now let’s build the microservice and verify that we can call this new
Greeting endpoint and that it properly calls the backend. First, let’s
start the backend if it’s not already running. Navigate to the backend
directory of the source code that comes with this application and
run it:
Next let’s build and run our Spring Boot microservice. Let’s also
configure this service to run on a different port than it’s default port
(8080) so that it doesn’t collide with the backend service which is
already running on port 8080.
$ mvn clean install spring-boot:run -Dserver.port=9090
Later on in the book we can see how running these microservices in
their own Linux container removes the restriction of port swizzling
at runtime. Now, let’s navigate our browser to http://localhost:
9090/api/greeting to see if our microservice properly calls the back‐
end and displays what we’re expecting:

Where to Look Next
In this chapter, we learned what Spring Boot was, how it’s different
from traditional WAR/EAR deployments, and some simple use
cases, including exposing an HTTP/REST endpoint, externalizing
configuration, metrics, and how to call another service. This is just
scratching the surface, and if you’re interested in learning more
about Spring Boot, please take a look at the following links and
book:
• Spring Boot
• Spring Boot Reference Guide
• Spring Boot in Action
• Spring Boot on GitHub
• Spring Boot Samples on GitHub
Where to Look Next | 35

CHAPTER 3
Dropwizard for Microservices
Dropwizard was created well before either Spring Boot or WildFly
Swarm (the other two microservices frameworks we’re looking at in
this book). Its first release, v0.1.0, came out December 2011. At the
time of this writing, v0.9.2 is the latest, and 1.0 is just around the
corner. Dropwizard was created by Coda Hale at Yammer to power
the company’s distributed-systems architectures (now called micro‐
services!), heavily leveraging the JVM. It started out as a set of glue
code to combine some powerful libraries for writing REST web
services and has evolved since then, although it still maintains its
identity as a minimalist, production-ready, easy-to-use web frame‐
work.
Dropwizard is an opinionated framework like Spring Boot; however,
it’s a little more prescriptive than Spring Boot. There are some com‐
ponents that are just part of the framework and cannot be easily
changed. The sweet-spot use case is writing REST-based web appli‐
cations/microservices without too many fancy frills. For example,
Dropwizard has chosen the Servlet container (Jetty), REST library
(Jersey), and serialization and deserialization (Jackson) formats for
you. Changing them out if you want to switch (i.e., changing the
servlet container to Undertow) isn’t very straightforward.
Dropwizard also doesn’t come with a dependency-injection con‐
tainer (like Spring or CDI). You can add one, but Dropwizard favors
keeping development of microservices simple, with no magic.
Spring Boot hides a lot of the underlying complexity from you, since
Spring under the covers is pretty complex (i.e., spinning up all the
37

beans actually needed to make Spring run is not trivial) and hides a
lot of bean wiring with Java Annotations. Although annotations can
be handy and save a lot of boilerplate in some areas, when debug‐
ging production applications, the more magic there is, the more dif‐
ficult it is. Dropwizard prefers to keep everything out in the open
and very explicit about what’s wired up and how things fit together.
If you need to drop into a debugger, line numbers and stack traces
should match up very nicely with the source code.
Just like Spring Boot, Dropwizard prefers to bundle your entire
project into one, executable uber JAR. This way, developers don’t
worry about which application server it needs to run in and how to
deploy and configure the app server. Applications are not built as
WARs and subject to complicated class loaders. The class loader in a
Dropwizard application is flat, which is a stark difference from try‐
ing to run your application in an application server where there may
be many hierarchies or graphs of class loaders. Figuring out class
load ordering, which can vary between servers, often leads to a com‐
plex deployment environment with dependency collisions and run‐
time issues (e.g., NoSuchMethodError). Running your
microservices in their own process gives isolation between applica‐
tions so you can tune each JVM individually as needed and monitor
them using operating system tools very familiar to operations folks.
Gone are the GC or OutOfMemoryExceptions which allow one
application to take down an entire set of applications just because
they share the same process space.
The Dropwizard Stack
Dropwizard provides some very intuitive abstractions on top of
these powerful libraries to make it very easy to write production-
ready microservices:
• Jetty for the servlet container
• Jersey for the REST/JAX-RS implementation
• Jackson for JSON serialization/deserialization
• Hibernate Validator
• Guava
• Metrics
• Logback + SLF4J
38 | Chapter 3: Dropwizard for Microservices

• JDBI for dealing with databases
Dropwizard is very opinionated in favor of “just get to writing code.”
The trade-off is if you want to tinker with the underlying stack, it’s
not very easy. On the other hand, getting up and running quickly so
you can delivery business value is much easier than configuring the
pieces yourself. Jetty, Jersey, and Jackson are well-known,
production-grade libraries for writing REST-based services. Google’s
Guava library is around to provide utilities and immutable pro‐
gramming. The Dropwizard Metrics library is a very powerful met‐
rics library that exposes more than enough insight to manage your
services in production. In fact, the Metrics library is so powerful and
popular it was spun out into its own project and can be used with
Spring Boot or WildFly Swarm.
Dropwizard exposes the following abstractions with which a devel‐
oper should be familiar. If you can understand these simple abstrac‐
tions, you can be very productive with Dropwizard:
Application
Contains our public void main()
Environment
Where we put our servlets, resources, filters, health checks,
tasks, commands, etc.
Configuration
How we inject environment- and application-specific configura‐
tion
Commands
Tells Dropwizard what action to take when starting our micro‐
service (e.g., start a server)
Resources
REST/JAX-RS resources
Tasks
Admin tasks to be performed when managing the application
(like change the log level or pause database connections)
When you run your Dropwizard application out of the box, one
Jetty server gets created with two handlers: one for your application
(8080 by default) and one for the administration interface (8081 by
default). Dropwizard does this so you can expose your microservice
Dropwizard for Microservices | 39

without exposing administration details over the same port (i.e., can
keep 8081 behind a firewall so it’s inaccessible). Things like metrics
and health checks get exposed over the admin port, so take care to
secure it properly.
Getting Started
Dropwizard doesn’t have any fancy project-initialization helpers or
Maven plug-ins. Getting started with Dropwizard follows a similar
pattern to any plain-old Java project: use a Maven archetype, or add
it to an existing application with whatever tools you currently use.
You could also use JBoss Forge, which is a technology-agnostic Java
project coordination and management tool that allows you to
quickly create projects, add dependencies, add classes, etc. For this
section, we’ll just use Maven archetypes.
Choose a directory where you want to create your new Dropwizard
project. Also verify you have Maven installed. You can run the fol‐
lowing command from your operating system’s command prompt,
or you can use the following information in the following command
to populate a dialog box or wizard for your favorite IDE:
$ mvn -B archetype:generate
-DarchetypeGroupId=io.dropwizard.archetypes
-DarchetypeArtifactId=java-simple -DarchetypeVersion=0.9.2
-DgroupId=com.redhat.examples.dropwizard
-DartifactId=hola-dropwizard -Dversion=1.0 -Dname=HolaDropwizard
Navigate to the directory that the Maven archetype generator cre‐
ated for us in hola-dropwizard and run the following command to
build our project:
$ mvn clean install
You should have a successful build!
This uses the Dropwizard archetype java-simple to create our micro‐
service. If you go into the hola-dropwizard directory, you should see
this structure:
./src/main/java/com/redhat/examples/dropwizard/api
./src/main/java/com/redhat/examples/dropwizard/cli
./src/main/java/com/redhat/examples/dropwizard/client
./src/main/java/com/redhat/examples/dropwizard/core
./src/main/java/com/redhat/examples/dropwizard/db
./src/main/java/com/redhat/examples/dropwizard/health
./src/main/java/com/redhat/examples/dropwizard

/HolaDropwizardApplication.java
./src/main/java/com/redhat/examples/dropwizard
/HolaDropwizardConfiguration.java
./src/main/java/com/redhat/examples/dropwizard/resources
./src/main/resources
./src/main/resources/assets
./src/main/resources/banner.txt
./pom.xml
Note that Dropwizard creates a package structure for you that fol‐
lows their convention:
api
POJOs that define the objects used in your REST resources
(some people call these objects domain objects or DTOs).
cli
This is where your Dropwizard commands go (additional com‐
mands you wish to add to startup of your application).
client
Client helper classes go here.
db
Any DB related code or configuration goes here.
health
Microservice-specific health checking that can be exposed at
runtime in the admin interface goes here.
resources
Our REST resource classes go here.
We also have the files HolaDropwizardApplication.java and Hola‐
DropwizardConfiguration.java, which is where our configuration
and bootstrapping code goes. Take a look at the HolaDropwizardAp
plication class in Example 3-1, for example.
Example 3-1. src/main/java/com/redhat/examples/dropwizard/
HolaDropwizardApplication.java
public class HolaDropwizardApplication extends
Application<HolaDropwizardConfiguration> {
public static void main(final String[] args)
throws Exception {
new HolaDropwizardApplication().run(args);
}

@Override
public String getName() {
return "HolaDropwizard";
}
@Override
public void initialize(
Bootstrap<HolaDropwizardConfiguration> bootstrap) {
// TODO: application initialization
}
@Override
public void run(HolaDropwizardConfiguration configuration,
final Environment environment) {
// TODO: implement application
}
}
This class contains our public static void main() method,
which doesn’t do too much except call our microservice’s run()
method. It also has a getName() method, which is shown at startup.
The initialize() and run() methods are the key places where we
can bootstrap our application as we’ll show in the next section.
The configuration class that was generated, HolaDropwizardConfigu
ration, is empty for now (Example 3-2).
HolaDropwizardConfiguration.java
public class HolaDropwizardConfiguration
extends Configuration {
// TODO: implement service configuration
}
Although Dropwizard doesn’t have any special Maven plug-ins on
its own, take a look at the pom.xml that was generated. We see that
the Dropwizard dependencies are on the classpath and that we’ll be
using the maven-shade-plugin to package up our JAR as an uber
JAR. This means all of our project’s dependencies will be unpacked
(i.e., all dependency JARs unpacked) and combined into a single
JAR that our build will create. For that JAR, we use the maven-jar-
plugin to make it executable.

One plug-in we do want to add is the exec-maven-plugin. With
Spring Boot we were able to just run our microservice with mvn
spring-boot:run. We want to be able to do the same thing with our
Dropwizard application, so let’s add the following plug-in within the
<build> section of the pom.xml, shown in Example 3-3.
Example 3-3. pom.xml
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>exec-maven-plugin</artifactId>
<configuration>
<mainClass>
com.redhat.examples.dropwizard.HolaDropwizardApplication
</mainClass>
<arguments>
<argument>server</argument>
</arguments>
</configuration>
</plugin>
Now we can execute our application from the command line like
this:
$ mvn exec:java
We should see something like what’s in Example 3-4.
Example 3-4. HolaDropwizard
====================================================================
HolaDropwizard
====================================================================
INFO [2016-03-27 21:54:22,279] io.dropwizard.server.DefaultServer...
: Registering jersey handler with root path prefix: /
INFO [2016-03-27 21:54:22,291] io.dropwizard.server.DefaultServer...
: Registering admin handler with root path prefix: /
INFO [2016-03-27 21:54:22,326] org.eclipse.jetty.setuid.SetUIDL...
: Opened application@5dee571c{HTTP/1.1}{0.0.0.0:8080}
INFO [2016-03-27 21:54:22,327] org.eclipse.jetty.setuid.SetUIDL...
: Opened admin@f8bd099{HTTP/1.1}{0.0.0.0:8081}
INFO [2016-03-27 21:54:22,329] org.eclipse.jetty.server.Server
: jetty-9.2.13.v20150730
INFO [2016-03-27 21:54:22] io.dropwizard.jersey.DropwizardResou...
: The following paths were found for the configured resources:

NONE
INFO [2016-03-27 21:54] org.eclipse.jetty.server.handler.Context...
: Started i.d.j.MutableServletContextHandler@1dfb9685{/,null,AVAI...
INFO [2016-03-27 21:54:22] io.dropwizard.setup.AdminEnvironment:...
POST /tasks/log-level (dropwizard.servlets.tasks.LogConfigurat...
POST /tasks/gc (io.dropwizard.servlets.tasks.GarbageCollection...
WARN [2016-03-27 21:54:22,695] io.dropwizard.setup.AdminEnvironm...
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!THIS APPLICATION HAS NO HEALTHCHECKS. THIS MEANS YOU WILL NEVER...
! IF IT DIES IN PRODUCTION, WHICH MEANS YOU WILL NEVER KNOW IF...
!LETTING YOUR USERS DOWN. YOU SHOULD ADD A HEALTHCHECK FOR EACH...
! APPLICATION'S DEPENDENCIES WHICH FULLY (BUT LIGHTLY) TESTS...
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
INFO [2016-03-27 21:54] org.eclipse.jetty.server.handler.ContextH...
: Started i.d.j.MutableServletContextHandler@4969dc6{/,null,AVA...
INFO [2016-03-27 21:54:22,704] org.eclipse.jetty.server.ServerCo...
: Started application@5dee571c{HTTP/1.1}{0.0.0.0:8080}
INFO [2016-03-27 21:54:22,705] org.eclipse.jetty.server.ServerCo...
: Started admin@f8bd099{HTTP/1.1}{0.0.0.0:8081}
INFO [2016-03-27 21:54:22,705] org.eclipse.jetty.server.Server
: Started @2914ms
If you see the application start up, you can try to navigate in your
browser to the default location for RESTful endpoints: http://local
host:8080. You probably won’t see too much:
{"code":404,"message":"HTTP 404 Not Found"}
If you try going to the admin endpoint http://localhost:8081, you
should see a simple page with a few links. Try clicking around to see
what kind of value is already provided out of the box for managing
your microservice!

Hello World
Now that we have our Dropwizard microservice template ready to
go, let’s add a REST endpoint. We want to expose an HTTP/REST
endpoint at /api/hola that will return “Hola Dropwizard from X”
where X is the IP address where the service is running. To do this,
navigate to src/main/java/com/redhat/examples/dropwizard/resources
(remember, this is the convention that Dropwizard follows for
where to put REST resources) and create a new Java class called
HolaRestResource. We’ll add a method named hola() that returns
a string along with the IP address of where the service is running, as
shown in Example 3-5.
resources/HolaRestResource.java
public class HolaRestResource {
try {
.getHostAddress();
}
return "Hola Dropwizard de " + hostname;
}
}
If this is familiar to what we did with Spring Boot, it’s for a reason.
We want to be able to create REST endpoints and services with
POJO code where possible, and a Hello World application is the per‐
fect place to do that. To expose this as a REST service, we’re going to
leverage good old JAX-RS annotations (see Example 3-6):
@Path
Tell JAX-RS what the context path for this service should be.
@GET
Add a GET HTTP service.
Hello World | 45

HolaRestResource.java
@Path("/api")
@Path("/hola")
@GET
try {
.getHostAddress();
}
return "Hola Dropwizard de " + hostname;
}
}
Now, in our HolaDropwizardApplication class, let’s give the run()
method an implementation to add our new REST resource to our
microservice (Example 3-7).
@Override
Environment environment) {
environment.jersey().register(new HolaRestResource());
}
Now we should be able to build and run our Dropwizard microser‐
vice:
$ mvn clean package exec:java
When we go to the endpoint at http://localhost:8080/api/hola we
should see the following:

Dropwizard has many options for configuring the built-in compo‐
nents (like the servlet engine or data sources for databases) as well as
creating entirely new commands that you can run and configure
with a configurations file. We can also inject environment variables
and system properties for those types of configurations that expect
to change based on the environment in which they’re running. Just
like with Spring Boot, we can bind entire classes of properties to
specific objects. In this example, let’s bind all of the helloapp.*
properties to our HolaRestResource class. With Spring Boot we had
the options to write our configs in property files with key-value
tuples. We could have also used YAML. With Dropwizard, we only
have the YAML option.
So let’s create a file in the root of our project called conf/applica‐
tion.yml (note, you’ll need to create the conf directory if it doesn’t
exist). We put our configuration files in the conf folder to help us
organize our project’s different configuration files (the naming of
the directory is not significant (i.e., it does not have any conven‐
tional meaning to Dropwizard). Let’s add some configuration to our
conf/application.yml file:
# configurations for our sayingFactory
helloapp:
saying: Hola Dropwizard de
In this case, we’re setting the property to a specific value. What if we
wanted to be able to override it based on some environment condi‐
tions? We could override it by passing in a Java system variable like
this -Ddw.helloapp.saying=Guten Tag. Note that the dw.* part of
the system property name is significant; it tells Dropwizard to apply
the value to one of the configuration settings of our application.
What if we wanted to override the property based on the value of an
OS environment variable? That would look like Example 3-8.
Example 3-8. conf/application.yml
# configurations for our sayingFactory
helloapp:
saying:${HELLOAPP_SAYING:-Guten Tag aus}
Hello World | 47

The pattern for the value of the property is to first look at an envi‐
ronment variable if it exists. If the environment variable is unset,
then use the default value provided. We also need to tell our applica‐
tion specifically that we want to use environment-variable substitu‐
tion. In the HolaDropwizardApplication class, edit the
initialize() method to look like Example 3-9.
@Override
public void initialize(
Bootstrap<HolaDropwizardConfiguration> bootstrap) {
// Enable variable substitution with environment variables
bootstrap.setConfigurationSourceProvider(
new SubstitutingSourceProvider(
bootstrap.getConfigurationSourceProvider(),
new EnvironmentVariableSubstitutor(false)
)
);
}
Now we’ve set up our configuration, let’s build the backing object for
it. We purposefully created a subconfiguration named helloapp
which allows us to namespace our configs to organize them. We
could have left it as a top-level configuration item, but since we
didn’t, let’s see how we bind our application.yml file to our Dropwi‐
zard configuration objects.
Let’s create a new class called HelloSayingFactory in src/main/
java/com/redhat/examples/dropwizard/resources directory. Fill it out
like this:
public class HelloSayingFactory {
@NotEmpty
@JsonProperty
return saying;
}
@JsonProperty

}
}
This is a simple Java Bean with some validation (@NotEmpty, from
the hibernate validators library) and Jackson (@JsonProperty)
annotations. This class wraps whatever configurations are under our
helloapp configuration in our YAML file; at the moment, we only
have “saying.” When we first created our application, a HolaDropwi
zardConfiguration class was created. Let’s open that class and add
our new HelloSayingFactory, as shown in Example 3-10.
public class HolaDropwizardConfiguration
extends Configuration {
private HelloSayingFactory sayingFactory;
@JsonProperty("helloapp")
public HelloSayingFactory getSayingFactory() {
return sayingFactory;
}
public void setSayingFactory(
HelloSayingFactory sayingFactory) {
this.sayingFactory = sayingFactory;
}
}
Lastly, we need to inject the configuration into our HolaRestRe
source (Example 3-11).
@Path("/api")
public HolaRestResource(final String saying) {
}
@Path("/hola")
@GET
Hello World | 49

try {
.getHostAddress();
}
}
}
Since there is no magic dependency injection framework that you’re
forced to use, you’ll need to update our HolaDropwizardApplica
tion to inject the configurations to our REST Resource
(Example 3-12).
@Override
environment.jersey().register(
new HolaRestResource(configuration
.getSayingFactory()
.getSaying()));
}
Now we should have a fairly sophisticated configuration injection-
capability available to us. In this example, we purposefully made it
slightly more complicated to cover what you’d probably do in a real-
world use case. Also note, although the mechanics of hooking all of
this up is more boilerplate, there’s a very clear pattern here: bind our
Java objects to a YAML configuration file and keep everything very
simple and intuitive without leveraging complex frameworks.
Let’s run our application. To do this, let’s update our pom.xml to pass
our new conf/application.yml file, as shown in Example 3-13.
Example 3-13. pom.xml
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>exec-maven-plugin</artifactId>
<configuration>

<mainClass>
com.redhat.examples.dropwizard.HolaDropwizardApplication
</mainClass>
<arguments>
<argument>server</argument>
<argument>conf/application.yml</argument>
</arguments>
</configuration>
</plugin>
From the command line, we can now run:
When you navigate to http://localhost:8080/api/hola, we should see
one of the sayings:
If we stop the microservice, and export an environment variable, we
should see a new saying:
$ export HELLOAPP_SAYING='Hello Dropwizard from'
The great thing about Dropwizard is metrics are first-class citizens,
not an afterthought, and are already there! Metrics are enabled by
default on the admin port (8081, by default) but how does Dropwi‐
zard know anything specific about our application? Let’s sprinkle a
couple declarative annotations on our HolaRestResource
(Example 3-14).
@Path("/hola")
@GET
Hello World | 51

@Timed
try {
hostname = InetAddress.getLocalHost().getHostAddress();
}
}
We’ve added the @Timed annotation which tracks how long invoca‐
tions to our service take. Other annotations for gathering metrics
include:
@Metered
The rate at which the service is called
@ExceptionMetered
The rate at which exceptions are thrown
Build and restart your microservice and try hitting your service at
http://localhost:8080/api/hola a couple times. Then if you navigate to
http://localhost:8081/metrics?pretty=true and scroll toward the bot‐
tom (may be different for yours), you should see the metrics for our
service:
com.redhat.examples.dropwizard.resources.HolaRestResource.hola{
count: 3,
max: 0.0060773830000000004,
mean: 0.002282724632345539,
min: 0.000085167,
p50: 0.0007421190000000001,
p75: 0.0060773830000000004,
p95: 0.0060773830000000004,
p98: 0.0060773830000000004,
p99: 0.0060773830000000004,
p999: 0.0060773830000000004,
stddev: 0.002676717391830948,
m15_rate: 0,
m1_rate: 0,
m5_rate: 0,
mean_rate: 0.12945390398989548,
duration_units: "seconds",
rate_units: "calls/second"
}

How to Run This Outside of Maven?
Dropwizard packages our microservice as a single executable uber
JAR. All we have to do is build our application and run it like this:
$ mvn clean package
$ java -jar target/hola-dropwizard-1.0.jar
server conf/application.yml
viding the functionality or service to other collaborators. If we wish
to extend the “hello world” microservice, we will need to create a
service to which we can call using Dropwizard’s REST client func‐
tionality. Just like we did for the Spring Boot microservice, we’ll lev‐
erage the backend service from the source code that accompanies
the book. The interaction will look similar to this:
If you look in this book’s source code, we’ll see a Maven module
called backend which contains a very simple HTTP servlet that can
be invoked with a GET request and query parameters. The code for
this backend is very simple, and does not use any of the microser‐
vice frameworks (Spring Boot, Dropwizard, or WildFly Swarm).
To start up the backend service on port 8080, navigate to the back
greeting. For example, when we call this service with this
path /api/backend?greeting=Hello, then the backend service will
respond with a JSON object like this:
Before we get started, let’s add the dropwizard-client dependency
to our pom.xml:
<dependency>
<groupId>io.dropwizard</groupId>

<artifactId>dropwizard-client</artifactId>
</dependency>
We will create a new HTTP endpoint, /api/greeting in our Dropwi‐
zard hola-dropwizard example and use Jersey to call this backend!
Let’s start off by creating two new classes in the src/main/java/com/
redhat/examples/dropwizard/resources folder of our hola-
dropwizard project named GreeterRestResource and Greeter
SayingFactory. The GreeterRestResource will implement our
JAX-RS REST endpoints, and the GreeterSayingFactory class will
encapsulate the configuration options we wish to use for our Greeter
service.
The GreeterRestResource class is shown in Example 3-15.
resources/GreeterRestResource.java
@Path("/api")
public class GreeterRestResource {
private Client client;
public GreeterRestResource(final String saying, String host,
int port, Client client) {
this.backendServiceHost = host;
this.backendServicePort = port;
this.client = client;
}
@Path("/greeting")
@GET
@Timed
public String greeting() {
String.format("http://%s:%d",
}
}

The GreeterSayingFactory class encapsulates configuration
options we’ll need, like the service host and service port, which
could change in different environments (see Example 3-16). It also
declares a JerseyClientConfiguration, which provides us a nice
DSL for building up our Jersey client. Let’s add this as a section to
our HolaDropwizardConfiguration class, as shown in
Example 3-17.
resources/GreeterSayingFactory.java
public class GreeterSayingFactory {
@NotEmpty
@NotEmpty
private String host;
@NotEmpty
private int port;
private JerseyClientConfiguration jerseyClientConfig =
new JerseyClientConfiguration();
@JsonProperty("jerseyClient")
public JerseyClientConfiguration getJerseyClientConfig() {
return jerseyClientConfig;
}
return saying;
}
}
public String getHost() {
return host;
}
public void setHost(String host) {
this.host = host;
}
public int getPort() {
return port;

}
public void setPort(int port) {
this.port = port;
}
}
public class HolaDropwizardConfiguration extends Configuration {
private HelloSayingFactory sayingFactory;
private GreeterSayingFactory greeterSayingFactory;
public HelloSayingFactory getSayingFactory() {
return sayingFactory;
}
public void setSayingFactory(
HelloSayingFactory sayingFactory) {
this.sayingFactory = sayingFactory;
}
@JsonProperty("greeter")
public GreeterSayingFactory getGreeterSayingFactory() {
return greeterSayingFactory;
}
@JsonProperty("greeter")
public void setGreeterSayingFactory(
GreeterSayingFactory greeterSayingFactory) {
this.greeterSayingFactory = greeterSayingFactory;
}
}
We can also update our conf/application.yml file to add these values
in:
greeter:
saying:${GREETER_SAYING:-Guten Tag Dropwizard}
host:${GREETER_BACKEND_HOST:-localhost}
port:${GREETER_BACKEND_PORT:-8080}
Note we specify defaults for the various configuration options if
they’re not provided by OS environment variables.

Now let’s wire up the client and the greeter resource inside our Hola
DropwizardApplication class, which is where all of our services
and resources are wired into the Environment object
(Example 3-18).
@Override
new HolaRestResource(configuration
.getSayingFactory()
.getSaying()));
// greeter service
GreeterSayingFactory greeterSayingFactory =
configuration.getGreeterSayingFactory();
Client greeterClient =
new JerseyClientBuilder(environment)
.using(
greeterSayingFactory.getJerseyClientConfig())
.build("greeterClient");
new GreeterRestResource(
greeterSayingFactory.getSaying(),
greeterSayingFactory.getHost(),
greeterSayingFactory.getPort(), greeterClient));
}
Lastly, let’s implement the client that will make the call to the back‐
end service with all of the appropriate host and port information
injected, see Example 3-19.
resources/GreeterRestResource.java
@Path("/greeting")
@GET
@Timed
String backendServiceUrl = String.format(
"http://%s:%d", backendServiceHost,

backendServicePort);
BackendDTO backendDTO = client
.target(backendServiceUrl)
.path("api")
.path("backend")
.queryParam("greeting", saying)
.request().accept("application/json")
.get(BackendDTO.class);
return backendDTO.getGreeting() +
" at host: " + backendDTO.getIp();
}
Dropwizard offers two convenience wrappers for making REST
calls: the HttpComponents library directly (if you need low-level
HTTP access) or the Jersey/JAX-RS REST client libraries, which have
a nice higher-level DSL for making HTTP calls. In the previous
example, we’re using the Jersey client to make the call.
Now let’s build the microservice and verify that we can call this new
greeting endpoint and that it properly calls the backend. First, let’s
start the backend if it’s not already running. Navigate to the backend
directory of the source code that comes with this application and
run it:
Next let’s build and run our Dropwizard microservice. Let’s also
configure this service to run on a different port than its default port
(8080) so that it doesn’t collide with the backend service which is
already running on port 8080. Later on in the book we can see how
running these microservices in their own container removes the
restriction of port swizzling at runtime.
First let’s make explicit which port we want to use as default. Open
the conf/application.yml file and add the server port information:
server:
applicationConnectors:
- type: http
port: 8080
Now start the microservice by overriding the default port:

$ mvn clean install exec:java
-Ddw.server.applicationConnectors[0].port=9090
Now, let’s navigate our browser to http://localhost:9090/api/greeting
to see if our microservice properly calls the backend and displays
what we’re expecting:
Where to Look Next
In this chapter we learned about Dropwizard, saw some differences
and similarities with Spring Boot and how to expose REST end‐
points, configuration, and metrics, and make calls to external serv‐
ices. This was meant as a quick introduction to Dropwizard and is
by no means a comprehensive guide. Check out the following links
for more information:
• Dropwizard Core
• Dropwizard Getting Started
• Client API
• Dropwizard on GitHub
• Dropwizard examples on GitHub

CHAPTER 4
WildFly Swarm for Microservices
The last Java microservice framework we’ll look at is a relative new‐
comer to the scene yet leverages tried-and-trusted Java EE function‐
ality found in the JBoss WildFly application server. WildFly Swarm
is a complete teardown of the WildFly application server into bite-
sized, reusable components called fractions that can be assembled
and formed into a microservice application that leverages Java EE
APIs. Assembling these fractions is as simple as including a depend‐
ency in your Java Maven (or Gradle) build file, and WildFly Swarm
takes care of the rest.
Application servers and Java EE have been the workhorse of enter‐
prise Java applications for more than 15 years. WildFly (formerly
JBoss Application Server) emerged as an enterprise-capable, open
source application server. Many enterprises heavily invested in the
Java EE technology (whether open source or proprietary vendors)
from how they hire software talent as well as overall training, tool‐
ing, and management. Java EE has always been very capable at help‐
ing developers build tiered applications by offering functionality like
servlets/JSPs, transactions, component models, messaging, and per‐
sistence. Deployments of Java EE applications were packaged as
EARs, which typically contained many WARs, JARs, and associated
configuration. Once you had your Java archive file (EAR/WAR), you
would need to find a server, verify it was configured the way you
expect, and then install your archive. You could even take advantage
of dynamic deployment and redeployment (although doing this in
production is not recommended, it can be useful in development).
This meant your archives could be fairly lean and only include the
61

business code you needed. Unfortunately, this lead to bloated imple‐
mentations of Java EE servers that had to account for any function‐
ality that an application might need. It also led to over-optimization
in terms of which dependencies to share (just put everything in the
app server!) and which dependencies needed isolation because they
would change at a different rate from other applications.
The application server provided a single point of surface area for
managing, deploying, and configuring multiple applications within
a single instance of the app server. Typically you’d cluster these for
high availability by creating exact instances of the app server on dif‐
ferent nodes. The problems start to arise when too many applica‐
tions share a single deployment model, a single process, and a single
JVM. The impedance arises when multiple teams who develop the
applications running inside the app server have different types of
applications, velocities of change, performance or SLA needs, and so
on. Insofar as microservices architecture enables rapid change,
innovation, and autonomy, Java EE application servers and manag‐
ing a collection of applications as a single, all-in-one server don’t
enable rapid change. Additionally, from the operations side of the
house, it becomes very complex to accurately manage and monitor
the services and applications running within a single application
server. In theory a single JVM is easier to manage, since it’s just one
thing, but the applications within the JVM are all independent
deployments and should be treated as such. We can feel this pain
when we try to treat the individual applications and services within
a single process as “one thing,” which is why we have very expensive
and complicated tooling to try and accomplish that introspection.
One way teams get around some of these issues is by deploying a
single application to an application server.
Even though the deployment and management of applications
within a Java EE environment may not suit a microservices environ‐
62 | Chapter 4: WildFly Swarm for Microservices

ment, the component models, APIs, and libraries that Java EE pro‐
vides to application developers still provide a lot of value. We still
want to be able to use persistence, transactions, security, depend‐
ency injection, etc., but we want an à la carte usage of those libraries
where needed. So how do we leverage our knowledge of Java EE, the
power it brings within the context of microservices? That’s where
WildFly Swarm fits in.
WildFly Swarm evaluates your pom.xml (or Gradle file) and deter‐
mines what Java EE dependencies your microservice actually uses
(e.g., CDI, messaging, and servlet) and then builds an uber JAR (just
like Spring Boot and Dropwizard) that includes the minimal Java EE
APIs and implementations necessary to run your service. This pro‐
cess is known as “just enough application server,” which allows you
to continue to use the Java EE APIs you know and love and to
deploy them both in a microservices and traditional-application
style. You can even just start using your existing WAR projects and
WildFly Swarm can introspect them automatically and properly
include the requisite Java EE APIs/fractions without having to
explicitly specify them. This is a very powerful way to move your
existing applications to a microservice-style deployment.
Getting Started
There are three ways to get started with WildFly Swarm. You can
start out with a blank Java Maven or Gradle project and manually
add dependencies and Maven plug-ins. Another option is to use the
WildFly Swarm Generator web console to bootstrap your project
(similar to Spring Initializr for Spring Boot). Lastly, you can use the
JBoss Forge tool, which is a generic Java project creation and alter‐
ing tool which makes it easy to add Java classes, dependencies, and
entire classes of functionality (e.g., JPA and transactions) to a Java
Maven project. We highly recommend JBoss Forge in general, and
we will use it in the guide here. For completeness, we’ll also include
the minimal plug-ins and dependencies you might need for a vanilla
Java project. JBoss Forge also has plug-ins for the three most popu‐
lar Java IDEs (Eclipse, Netbeans, or IntelliJ).
Vanilla Java Project
If you have an existing Java project or you create one from scratch
using a Maven archetype or your favorite IDE, then you can add the

following pieces to your pom.xml to get up and running with Wild‐
Fly Swarm. First we want to be able to create uber JARs that know
what pieces of the Java EE API functionality should be included. To
do this, we’ll use the WildFly Swarm Maven plug-in. Let’s add the
WildFly Swarm Maven plug-in to our pom.xml:
<plugins>
<plugin>
<groupId>org.wildfly.swarm</groupId>
<artifactId>wildfly-swarm-plugin</artifactId>
<version>${version.wildfly.swarm}</version>
<executions>
<execution>
<goals>
<goal>package</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
We also want to include the WildFly Swarm BOM (bill of materials)
as a dependency in our <dependencyManagement> section to help
sort out the proper versions of all of the APIs and WildFly Swarm
fractions that we may depend on:
<dependencyManagement>
<dependencies>

<dependency>
<artifactId>bom</artifactId>
<version>${version.wildfly.swarm}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
Now you can add the fractions of the Java EE API you need (or leave
the fractions out and let WildFly Swarm autodetect them; useful if
migrating an existing WAR application)! Let’s take a look at some of
the convenience that JBoss Forge brings.

Using JBoss Forge
JBoss Forge is a set of IDE plug-ins and CLI for quickly creating and
working on Java projects. It has plug-ins for Netbeans, Eclipse, and
IntelliJ to help you create Java projects, add CDI beans, add JPA
entities, add and configure servlets, etc. Let’s look at a quick exam‐
ple. First verify you have JDK/Java 1.8 installed then install JBoss
Forge.
Once you have Forge installed, you should be able to start up the
CLI (all of these commands available in the IDE plug-in as well):
$ forge
Feel free to explore what commands are available by pressing Tab,
which also gives auto-completion for any command. JBoss Forge is
built on a modular, plug-in–based architecture which allows others
to write plug-ins to take advantage of the built-in tooling for the CLI
and your favorite IDE. Take a look at some of the addons contrib‐
uted by the community, including AsciiDoctor, Twitter, Arquillian,
and AssertJ. Let’s also install the WildFly Swarm addon for JBoss
Forge:
[temp]$ addon-install
--coordinate org.jboss.forge.addon:wildfly-swarm,1.0.0.Beta2
***SUCCESS*** Addon org.jboss.forge.addon:wildfly-swarm,
1.0.0.Beta2 was installed successfully.
Let’s try a project-new command to build a new Java EE project
that will be built and packaged with WildFly Swarm. Follow the
interactive command prompt with the following inputs:
[swarm]$ project-new
***INFO*** Required inputs not satisfied, interactive mode
* Project name: hola-wildflyswarm
? Package [org.hola.wildflyswarm]: com.redhat.examples.wfswarm
? Version [1.0.0-SNAPSHOT]: 1.0
? Final name: hola-wildflyswarm
? Project location [/Users/ceposta/temp/swarm]:
[0] (x) war
[1] ( ) jar
[2] ( ) parent
[3] ( ) forge-addon
[4] ( ) resource-jar
[5] ( ) ear
[6] ( ) from-archetype
[7] ( ) generic

Press <ENTER> to confirm, or <CTRL>+C to cancel.
* Project type: [0-7]
[0] (x) Maven
* Build system: [0]
[0] ( ) JAVA_EE_7
[1] ( ) JAVA_EE_6
[2] ( ) NONE
? Stack (The technology stack to be used in project): [0-2] 2
***SUCCESS*** Project named 'hola-wildflyswarm'
has been created.
So what we have right now is an empty Java project that doesn’t do
too much. That’s OK, though; we’re just getting started. Let’s set it up
for a JAX-RS application:
[hola-wildflyswarm]$ rest-setup --application-path=/
***SUCCESS*** JAX-RS has been installed.
Now, let’s add in the WildFly Swarm configurations like the Maven
plug-in and the BOM dependency management section:
[hola-wildflyswarm]$ wildfly-swarm-setup --context-path=/
***SUCCESS*** Wildfly Swarm is now set up! Enjoy!
That’s it! Now let’s build and try to run our new WildFly Swarm
microservice:
[HelloResource.java]$ cd ~~
[hola-wildflyswarm]$ wildfly-swarm-run
You should see it successfully start, but it doesn’t do anything or
expose any REST services. But what did JBoss Forge create for us
here? If you look at the directory structure, you should see some‐
thing similar:
./pom.xml
./src
./src/main
./src/main/java
./src/main/java/com/redhat/examples/wfswarm/rest
/RestApplication.java
./src/main/resources
./src/main/webapp
./src/test

./src/test/java
./src/test/resources
Pretty bare bones! If we look at the pom.xml, we see some relevant
Java EE APIs and the WildFly Swarm plug-in/BOM:
<dependencyManagement>
<dependencies>
<dependency>
<artifactId>bom</artifactId>
<version>${version.wildfly-swarm}</version>
<type>pom</type>
</dependency>
<dependency>
<groupId>com.redhat.spec</groupId>
<artifactId>jboss-javaee-6.0</artifactId>
<version>3.0.3.Final</version>
<type>pom</type>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
<groupId>com.redhat.spec.javax.servlet</groupId>
<artifactId>jboss-servlet-api_3.0_spec</artifactId>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>com.redhat.spec.javax.ws.rs</groupId>
<artifactId>jboss-jaxrs-api_1.1_spec</artifactId>
<scope>provided</scope>
</dependency>
</dependencies>
<build>
<finalName>hola-wildflyswarm</finalName>
<plugins>
<plugin>
<artifactId>wildfly-swarm-plugin</artifactId>
<version>${version.wildfly-swarm}</version>
<executions>
<execution>
<goals>
<goal>package</goal>
</goals>
</execution>
</executions>
<configuration>

<properties>
<swarm.context.path>/</swarm.context.path>
</properties>
</configuration>
</plugin>
</plugins>
</build>
Remember, however, WildFly Swarm will only package the pieces of
the Java EE framework that you need to run your application. In this
case, we’ve already set up JAX-RS APIs, so WildFly Swarm will auto‐
matically include the JAX-RS and servlet fractions of an application
server and embed them in your application.
Let’s see how we can add some more functionality.
Hello World
Just like with the other frameworks in the preceding chapters, we
want to add some basic hello-world functionality and then incre‐
mentally add more functionality on top of it. Let’s start by creating a
HolaResource in our project. You can do this with your IDE, or
however you’d like; but again we can leverage JBoss Forge to do any
of the heavy lifting for us here.
Navigate to the directory where you have your project, and fire up
forge if it’s not already running:
$ forge
Now let’s create a new JAX-RS endpoint with the rest-new-
endpoint command and the interactive wizard, filling in the
prompts using the following example as guidance:
[hola-wildflyswarm]$ rest-new-endpoint
***INFO*** Required inputs not satisfied, interactive mode
? Package Name (The package name where type will be created)
[com.redhat.examples.wfswarm.rest]:
* Type Name (The type name): HolaResource
[0] (x) GET
[1] ( ) POST
[2] ( ) PUT
[3] ( ) DELETE

? Methods (REST methods to be defined): [0-3]
? Path (The root path of the endpoint): api/hola
***SUCCESS***
REST com.redhat.examples.wfswarm.rest.HolaResource created
That’s it! Forge has created the ./src/main/java/com/redhat/examples/
wfswarm/rest/HolaResource.java JAX-RS resource for us, and it
looks similar to this:
package com.redhat.examples.wfswarm.rest;
import javax.ws.rs.Path;
import javax.ws.rs.core.Response;
import javax.ws.rs.GET;
import javax.ws.rs.Produces;
@Path("/api/hola")
public class HolaResource {
@GET
@Produces("text/plain")
public Response doGet() {
return Response.ok("method doGet invoked")
.build();
}
}
Let’s go to the root of the project, build it, and try to fire it up again:
[HelloResource.java]$ cd ~~
[hola-wildflyswarm]$ wildfly-swarm-run
And navigate in a web browser to http://localhost:8080/api/hola (if
an endpoint is not correctly displayed at this endpoint, please go
back and check the preceding steps):
What did we just do? We built a JAX-RS web application using
native Java EE with the JBoss Forge tooling and then ran it as a
microservice inside WildFly Swarm!
Hello World | 69

At the time of this writing, WildFly Swarm does not have an opin‐
ionated way of doing configuration and folks can choose to use well-
established configuration, frameworks like Apache Commons
Configuration or Apache DeltaSpike Configuration. Feel free to pay
attention to this JIRA thread for more. In this section, we’ll take a
look at quickly adding Apache DeltaSpike Configuration for our
configuration needs.
Apache DeltaSpike is a collection of CDI extensions that can be used
to simplify things like configuration, data access, and security. Take
a look at the DeltaSpike documentation. We’re going to use a CDI
extension that lets us easily inject configuration properties that can
be sourced from properties files, the command line, JNDI, and envi‐
ronment variables. To take advantage of CDI, JAX-RS, and DeltaS‐
pike, let’s add a dependency on the jaxrs-cdi WildFly Swarm
Fraction for integrating CDI and JAX-RS:
<dependency>
<artifactId>jaxrs-cdi</artifactId>
</dependency>
We’ll also want to add a dependency on the DeltaSpike libraries:
<dependency>
<groupId>org.apache.deltaspike.core</groupId>
<artifactId>deltaspike-core-api</artifactId>
<version>1.5.3</version>
</dependency>
<dependency>
<groupId>org.apache.deltaspike.core</groupId>
<artifactId>deltaspike-core-impl</artifactId>
</dependency>
We can create a new file called META-INF/apache-
deltaspike.properties as well to store our application-specific proper‐
ties. In this example, we’ll try to grab our environment-specific
properties from OS environment variables like we’ve been doing
with the other frameworks and then default to values that may not
exist. Edit your HolaResource class to add the @ConfigProperty
annotation:
@Path("/api/hola")
public class HolaResource {

@Inject
@ConfigProperty(name = "WF_SWARM_SAYING",
defaultValue = "Hola")
@GET
@Produces("text/plain")
public Response doGet() {
return Response.ok(saying + " from WF Swarm").build();
}
}
With this simple annotation, we’re able to quickly inject our proper‐
ties from either the META-INF/apache-deltaspike.properties file,
from the command line, from environment variables, or from JNDI.
We will default to “Hola” if there is no environmental variable set.
Take a look at the Apache DeltaSpike documentation for more ways
to group or customize the functionality.
Now we can run our service with either java -jar target/hola-
wildflyswarm-swarm.jar or with mvn clean install wildfly-
swarm:run. We should see the default response “Hola from WF
Swarm” and if we set the environment variable WF_SWARM_SAYING,
then we should be able to alter the saying:
$ mvn clean install wildfly-swarm:run
$ export WF_SWARM_SAYING=Yo
To expose useful information about our microserivce, all we need to
do is add the monitor fraction to our pom.xml:
Hello World | 71

<dependency>
<artifactId>monitor</artifactId>
</dependency>
This will enable the WildFly management and monitoring function‐
ality. From the monitoring perspective, WildFly Swarm exposes
some basic metrics:
• Information about the node on which WildFly Swarm is run‐
ning at /node
• JVM heap usage at /heap
• JVM/process thread information at /threads
We can also add our own health endpoints that can invoke some
actions or query parts of our microservice to easily expose how our
service is doing. You can leverage the built-in health checking
probes of most modern clusters to call your microservice health
endpoints to determine whether or not the microservice is healthy,
and in some cases, just kill it and restart. See the WildFly Swarm
documentation for more on adding health probes.
How to Run This Outside of Maven
We’ve seen a couple ways to run our WildFly Swarm microservice.
For development, you’ll probably run with the Maven plug-in like
this:
When you build the binary, you can run it like this:
$ mvn clean package
This will take our project, whether it’s packaged as a JAR or a WAR
(as specified by the <packaging> definition in your pom.xml) and
turn it into an executable uber JAR. Then you can run it like this:
$ java -jar target/hola-wildfly-swarm.jar
Note, whatever your final build-artifact is named, the WildFly
Swarm Maven plug-in will add the -swarm.jar extension to the
name.

viding the functionality or service to other collaborators. If we wish
to extend the “hello world” microservice, we will need to create a
service to which we can call using JAX-RS client functionality. Just
like we did for the Spring Boot microservice, we’ll leverage the back‐
end service from the source code that accompanies the book. The
interaction will look similar to this:
If you look in this book’s source code, we’ll see a Maven module
called backend which contains a very simple HTTP servlet that can
be invoked with a GET request and query parameters. The code for
this backend is very simple, and does not use any of the microser‐
vice frameworks (Spring Boot, Dropwizard, or WildFly Swarm).
To start up the backend service on port 8080, navigate to the back‐
greeting. For example, when we call this service with this
path /api/backend?greeting=Hello, then the backend service will
respond with a JSON object like this:
We get something like this:
{
"greeting" : "Hello from cluster Backend",
"time" : 1459189860895,
"ip" : "172.20.10.3"
}
We will create a new HTTP endpoint, /api/greeting in our WildFly
Swarm hola-wildflyswarm example and use JAX-RS client to call
this backend!

Create a new class in src/main/java/com/redhat/examples/wfswarm/
rest called GreeterResource, and fill it in similar to what we did for
the HolaResource like in Example 4-1.
Example 4-1. src/main/java/com/redhat/examples/wfswarm/rest/
GreeterResource.java
@Path("/api")
public class GreeterResource {
@Inject
@Inject
@ConfigProperty(name = "GREETING_BACKEND_SERVICE_HOST",
defaultValue = "localhost")
@Inject
@ConfigProperty(name = "GREETING_BACKEND_SERVICE_PORT",
defaultValue = "8080")
@Path("/greeting")
@GET
"http://%s:%d",
backendServiceHost,backendServicePort);
}
}
We’ve created a simple JAX-RS resource here that exposes an /api/
greeting endpoint that just returns the value of the backendServi
ceUrl field. Also note, we’re injecting the backend host and port as
environment variables that have default values if no environment
variables are set. Again, we’re just using DeltaSpike @ConfigProp
erty to accomplish this.
Let’s also add the BackendDTO class as shown in Example 4-2, which
is used to encapsulate responses from the backend.

BackendDTO.java
public class BackendDTO {
private String greeting;
private long time;
private String ip;
public String getGreeting() {
return greeting;
}
public void setGreeting(String greeting) {
this.greeting = greeting;
}
public long getTime() {
return time;
}
public void setTime(long time) {
this.time = time;
}
public String getIp() {
return ip;
}
public void setIp(String ip) {
this.ip = ip;
}
}
Next, let’s add our JAX-RS client implementation to communicate
with the backend service. It should look like Example 4-3.
GreeterResource.java
@Path("/api")
public class GreeterResource {
@Inject
@Inject
@ConfigProperty(name = "GREETING_BACKEND_SERVICE_HOST",

defaultValue = "localhost")
@Inject
@ConfigProperty(name = "GREETING_BACKEND_SERVICE_PORT",
defaultValue = "8080")
@Path("/greeting")
@GET
String backendServiceUrl = String.format("http://%s:%d",
backendServiceHost,backendServicePort);
Client client = ClientBuilder.newClient();
BackendDTO backendDTO = client.target(backendServiceUrl)
.path("api")
.path("backend")
.request(MediaType.APPLICATION_JSON_TYPE)
return backendDTO.getGreeting()
+ " at host: " + backendDTO.getIp();
}
}
Now we can build our microservice either using Maven at the com‐
mand line; or if you’re still in JBoss Forge, you can run the build
command:
$ mvn clean install
or:
[hola-wildflyswarm]$ build
When we start up our WildFly Swarm microservice, we will need to
specify a new HTTP port (since the backend service is already run‐
ning on port 8080), or we can just specify a port offset. If we specify
a port offset, WildFly Swarm will try to deploy under its default port
of 8080; but if that port is already in use, then it will increment the
port by the swarm.port.offset amount and try again. If we use an
offset of 1, and there is a collision on port 8080, then port 8081 will
be what WildFly Swarm tries next. Let’s run our microservice with a
port offset:
$ mvn clean install wildfly-swarm:run -Dswarm.port.offset=1

Now, let’s navigate our browser to http://localhost:8081/api/greeting
to see if our microservice properly calls the backend and displays
what we’re expecting:
Where to Look Next
In this chapter, we learned about WildFly Swarm and saw some dif‐
ferences and similarities with Dropwizard and Spring Boot. We also
learned how to expose REST endpoints, configuration, and metrics
and make calls to external services. This was meant as a quick intro‐
duction to WildFly Swarm and is by no means a comprehensive
guide. Check out the following links for more information:
• WildFly Swarm
• WildFly Swarm documentation
• WildFly Swarm examples on GitHub
• WildFly Swarm Core examples on GitHub
• WildFly Swarm blog
• http://wildfly-swarm.io/community/

CHAPTER 5
Deploy Microservices at Scale with
Docker and Kubernetes
Up to now, we’ve talked about microservices at a higher level, cover‐
ing organizational agility, designing with dependency thinking,
domain-driven design, and promise theory. Then we took a deep
dive into the weeds with three popular Java frameworks for develop‐
ing microservices: Spring Boot, Dropwizard, and WildFly Swarm.
We can leverage powerful out-of-the-box capabilities easily by
exposing and consuming REST endpoints, utilizing environment
configuration options, packaging as all-in-one executable JAR files,
and exposing metrics. These concepts all revolve around a single
instance of a microservice. But what happens when you need to
manage dependencies, get consistent startup or shutdown, do health
checks, and load balance your microservices at scale? In this chapter,
we’re going to discuss those high-level concepts to understand more
about the challenges of deploying microservices, regardless of lan‐
guage, at scale.
When we start to break out applications and services into microser‐
vices, we end up with more moving pieces by definition: we have
more services, more binaries, more configuration, more interaction
points, etc. We’ve traditionally dealt with deploying Java applications
by building binary artifacts (JARs, WARs, and EARs), staging them
somewhere (shared disks, JIRAs, and artifact repositories), opening
a ticket, and hoping the operations team deploys them into an appli‐
cation server as we intended, with the correct permissions and envi‐
ronment variables and configurations. We also deploy our
79

application servers in clusters with redundant hardware, load bal‐
ancers, and shared disks and try to keep things from failing as much
as possible. We may have built some automation around the infra‐
structure that supports this with great tools like Chef or Ansible, but
somehow deploying applications still tends to be fraught with mis‐
takes, configuration drift, and unexpected behaviors.
With this model, we do a lot of hoping, which tends to break down
quickly in current environments, nevermind at scale. Is the applica‐
tion server configured in Dev/QA/Prod like it is on our machine? If
it’s not, have we completely captured the changes that need to be
made and expressed to the operations folks? Do any of our changes
impact other applications also running in the same application
server(s)? Are the runtime components like the operating system,
JVM, and associated dependencies exactly the same as on our devel‐
opment machine? The JVM on which you run your application is
very much a highly coupled implementation detail of our applica‐
tion in terms of how we configure, tune, and run, so these variations
across environments can wreak havoc. When you start to deliver
microservices, do you run them in separate processes on traditional
servers? Is process isolation enough? What happens if one JVM goes
berserk and takes over 100% of the CPU? Or the network IO? Or a
shared disk? What if all of the services running on that host crash?
Are your applications designed to accommodate that? As we split
our applications into smaller pieces, these issues become magnified.
Immutable Delivery
Immutable delivery concepts help us reason about these problems.
With immutable delivery, we try to reduce the number of moving
pieces into prebaked images as part of the build process. For exam‐
ple, imagine in your build process you could output a fully baked
image with the operating system, the intended version of the JVM,
any sidecar applications, and all configuration? You could then
deploy this in one environment, test it, and migrate it along a deliv‐
ery pipeline toward production without worrying about “whether
the environment or application is configured consistently.” If you
needed to make a change to your application, you rerun this pipe‐
line, which produces a new immutable image of your application,
and then do a rolling upgrade to deliver it. If it doesn’t work, you
can rollback by deploying the previous image. No more worrying
80 | Chapter 5: Deploy Microservices at Scale with Docker and Kubernetes

about configuration or environment drift or whether things were
properly restored on a rollback.
This sounds great, but how do we do this? Executable JARs is one
facet to get us part of the way there, but still falls short. The JVM is
an implementation detail of our microservice, so how do we bundle
the JVM? JVMs are written in native code and have native OS-level
dependencies that we’ll need to also package. We will also need con‐
figuration, environment variables, permissions, file directories, and
other things that must be packaged. All of these details cannot be
captured within a single executable JAR. Other binary formats like
virtual machine (VM) images can properly encapsulate these details.
However, for each microservice that may have different packaging
requirements (JVM? NodeJS? Golang? properties files? private
keys?), we could easily see an explosion of VM images and combina‐
tions of language runtimes. If you automate this with infrastructure
as code and have access to infrastructure as a service with properly
exposed APIs, you can certainly accomplish this. In fact, building up
VMs as part of an automated delivery pipeline is exactly what Net‐
flix did to achieve this level of immutable delivery. But VMs become
hard to manage, patch, and change. Each VM virtualizes an entire
machine with required device drivers, operating systems, and man‐
agement tooling.
What other lightweight packaging and image formats can we
explore?
Docker, Docker, Docker
Docker came along a few years ago with an elegant solution to
immutable delivery. Docker allows us to package our applications
with all of the dependencies it needs (OS, JVM, other application
dependencies, etc.) in a lightweight, layered, image format. Addi‐
tionally, Docker uses these images to run instances which run our
applications inside Linux containers with isolated CPU, memory,
network, and disk usage. In a way, these containers are a form of
application virtualization or process virtualization. They allow a pro‐
cess to execute thinking it’s the only thing running (e.g., list pro‐
cesses with ps and you see only your application’s process there),
and that it has full access to the CPUs, memory, disk, network, and
other resources, when in reality, it doesn’t. It can only use resources
it’s allocated. For example, I can start a Docker container with a slice
Docker, Docker, Docker | 81

of CPU, a segment of memory, and limits on how much network IO
can be used. From outside the Linux container, on the host, the
application just looks like another process. No virtualization of
device drivers, operating systems, or network stacks, and special
hypervisors. It’s just a process. This fact also means we can get even
more applications running on a single set of hardware for higher
density without the overhead of additional operating systems and
other pieces of a VM which would be required to achieve similar
isolation qualities.
What’s happening under the covers is nothing revolutionary either.
Features called cgroups, namespaces, and chroot, which have been
built into the Linux kernel (and have for some time), are used to cre‐
ate the appearance of this application virtualization. Linux contain‐
ers have been around for over 10 years, and process virtualization
existed in Solaris and FreeBSD even before that. Traditionally, using
these underlying Linux primitives, or even higher-level abstractions
like lxc, have been complicated at best. Docker came along and sim‐
plified the API and user experience around Linux containers.
Docker brought a single client CLI that can easily spin up these
Linux containers based on the Docker image format, which has now
been opened to the larger community in the Open Container Initia‐
tive (OCI). This ease of use and image format is changing the way
we package and deliver software.
Once you have an image, spinning up many of these Linux contain‐
ers becomes trivial. The layers are built as deltas between a base
image (e.g., RHEL, Debian, or some other Linux operating system)
and the application files. Distributing new applications just distrib‐
utes the new layers on top of existing base layers. This makes dis‐
tributing images much easier than shuttling around bloated cloud
machine images. Also, if a vulnerability (e.g., shell shock, heartbleed,
etc.) is found in the base image, the base images can be rebuilt
without having to try patch each and every VM. This makes it easy
to run a container anywhere: they can then be moved from a develo‐
per’s desktop to dev, QA, or production in a portable way without
having to manually hope that all of the correct dependencies are in
the right place (does this application use JVM 1.6, 1.7, 1.8?). If we
need to redeploy with a change (new app) or fix a base image, doing
so just changes the layers in the image that require changes.
When we have standard APIs and open formats, we can build tool‐
ing that doesn’t have to know or care what’s running in the con‐

tainer. How do we start an application? How do we stop? How do we
do health checking? How do we aggregate logs, metrics, and insight?
We can build or leverage tooling that does these things in a
technology-agnostic way. Powerful clustering mechanics like service
discovery, load balancing, fault tolerance, and configuration also can
be pushed to lower layers in the application stack so that application
developers don’t have to try and hand cobble this together and com‐
plicate their application code.
Kubernetes
Google is known for running Linux containers at scale. In fact,
“everything” running at Google runs in Linux containers and is
managed by their Borg cluster management platform. Former Goo‐
gle engineer Joe Beda said the company starts over two billion con‐
tainers per week. Google even had a hand in creating the underlying
Linux technology that makes containers possible. In 2006 they
started working on “process containers,” which eventually became
cgroups and was merged into the Linux kernel code base and
released in 2008. With its breadth and background of operating con‐
tainers at scale, it’s not a surprise Google has had such an influence
on platforms built around containers. For example, some popular
container management projects that preceded Kubernetes were
influenced by Google:
• The original Cloud Foundry creators (Derek Collison and
Vadim Spivak) worked at Google and spent several years using
Google’s Borg cluster management solution.
• Apache Mesos was created for a PhD thesis, and its creator (Ben
Hindman) interned at Google and had many conversations with
Google engineers around container clustering, scheduling, and
management.
• Kubernetes, an open source container cluster management plat‐
form and community, was originally created by the same engi‐
neers who built Borg at Google.
Back in 2013 when Docker rocked the technology industry, Google
decided it was time to open source their next-generation successor
to Borg, which they named Kubernetes. Today, Kubernetes is a large,
open, and rapidly growing community with contributions from
Google, Red Hat, CoreOS, and many others (including lots of inde‐
Kubernetes | 83

pendent individuals!). Kubernetes brings a lot of functionality for
running clusters of microservices inside Linux containers at scale.
Google has packaged over a decade of experience into Kubernetes,
so being able to leverage this knowledge and functionality for our
own microservices deployments is game changing. The web-scale
companies have been doing this for years, and a lot of them (Netflix,
Amazon, etc.) had to hand build a lot of the primitives that Kuber‐
netes now has baked in. Kubernetes has a handful of simple primi‐
tives that you should understand before we dig into examples. In
this chapter, we’ll introduce you to these concepts; and in the follow‐
ing chapter, we’ll make use of them for managing a cluster of micro‐
services.
Pods
A pod is a grouping of one or more Docker containers (like a pod of
whales?). A typical deployment of a pod, however, will often be one-
to-one with a Docker container. If you have sidecar, ambassador, or
adapter deployments that must always be co-located with the appli‐
cation, a pod is the way to group them. This abstraction is also a way
to guarantee container affinity (i.e., Docker container A will always
be deployed alongside Docker container B on the same host).
Kubernetes orchestrates, schedules, and manages pods. When we
refer to an application running inside of Kubernetes, it’s running
within a Docker container inside of a pod. A pod is given its own IP
address, and all containers within the pod share this IP (which is
different from plain Docker, where each container gets an IP
address). When volumes are mounted to the pod, they are also
shared between the individual Docker containers running in the
pod.
One last thing to know about pods: they are fungible. This means
they can disappear at any time (either because the service crashed or
the cluster killed it). They are not like a VM, which you care for and
nurture. Pods can be destroyed at any point. This falls within our
expectation in a microservice world that things will (and do) fail, so
we are strongly encouraged to write our microservices with this
premise in mind. This is an important distinction as we talk about
some of the other concepts in the following sections.

Labels
Labels are simple key/value pairs that we can assign to pods like
release=stable or tier=backend. Pods (and other resources, but
we’ll focus on pods) can have multiple labels that group and catego‐
rize in a loosely coupled fashion, which becomes quite apparent the
more you use Kubernetes. It’s not a surprise that Google has identi‐
fied such simple constructs from which we can build powerful clus‐
ters at scale. After we’ve labeled our pods, we can use label selectors
to find which pods belong in which group. For example, if we had
some pods labeled tier=backend and others labeled tier=fron
tend, using a label selector expression of tier != frontend. We can
select all of the pods that are not labeled “frontend.” Label selectors
are used under the covers for the next two concepts: replication con‐
trollers and services.
Replication Controllers
When talking about running microservices at scale, we will probably
be talking about multiple instances of any given microservice.
Kubernetes has a concept called ReplicationController that man‐
ages the number of replicas for a given set of microservices. For
example, let’s say we wanted to manage the number of pods labeled
with tier=backend and release=stable. We could create a replica‐
tion controller with the appropriate label selector and then be able
to control the number of those pods in the cluster with the value of
replica on our ReplicationController. If we set the replica
count equal to 10, then Kubernetes will reconcile its current state to
reflect 10 pods running for a given ReplicationController. If there
are only five running at the moment, Kubernetes will spin up five
more. If there are 20 running, Kubernetes will kill 10 (which 10 it
kills is nondeterministic, as far as your app is concerned). Kuber‐
netes will do whatever it needs to converge with the desired state of
10 replicas. You can imagine controlling the size of a cluster very
easily with a ReplicationController. We will see examples of Rep
licationController in action in the next chapter.
Services
The last Kubernetes concept we should understand is the Kuber‐
netes Service. ReplicationControllers can control the number of rep‐
licas of a service we have. We also saw that pods can be killed (either
Kubernetes | 85

crash on their own, or be killed, maybe as part of a ReplicationCon‐
troller scale-down event). Therefore, when we try to communicate
with a group of pods, we should not rely on their direct IP addresses
(each pod will have its own IP address) as pods can come and go.
What we need is a way to group pods to discover where they are,
how to communicate with them, and possibly load balance against
them. That’s exactly what the Kubernetes Service does. It allows us
to use a label selector to group our pods and abstract them with a
single virtual (cluster) IP that we can then use to discover them and
interact with them. We’ll show concrete examples in the next chap‐
ter.
With these simple concepts, pods, labels, ReplicationControllers,
and services, we can manage and scale our microservices the way
Google has learned to (or learned not to). It takes many years and
many failures to identify simple solutions to complex problems, so
we highly encourage you to learn these concepts and experience the
power of managing containers with Kubernetes for your microservi‐
ces.
Getting Started with Kubernetes
Docker and Kubernetes are both Linux-native technologies; there‐
fore, they must run in a Linux host operating system. We assume
most Java developers work with either a Windows or Mac developer
machine, so in order for us to take advantage of the great features
Docker and Kubernetes bring, we’ll need to use a guest Linux VM
on our host operating system. You could download Docker machine
and toolbox for your environment but then you’d need to go about
manually installing Kubernetes (which can be a little tricky). You
could use the upstream Kubernetes vagrant images, but like any fast-
moving, open source project, those can change swiftly and be unsta‐
ble at times. Additionally, to take full advantage of Docker’s
portability, it’s best to use at least the same kernel of Linux between
environments but optimally the same Linux distribution and ver‐
sion. What other options do we have?
Microservices and Linux Containers
To get started developing microservices with Docker and Kuber‐
netes, we’re going to leverage a set of developer tools called the Red
Hat Container Development Kit (CDK). The CDK is free and is a

small, self-contained VM that runs on a developer’s machine that
contains Docker, Kubernetes, and a web console (actually, it’s Red
Hat OpenShift, which is basically an enterprise-ready version of
Kubernetes with other developer self-service and application lifecy‐
cle management features; but for this book we’ll just be using the
Kubernetes APIs).
OpenShift?
Red Hat OpenShift 3.x is an Apache v2, licensed, open source devel‐
oper self-service platform OpenShift Origin that has been revamped
to use Docker and Kubernetes. OpenShift at one point had its own
cluster management and orchestration engine, but with the knowl‐
edge, simplicity, and power that Kubernetes brings to the world of
container cluster management, it would have been silly to try and
re-create yet another one. The broader community is converging
around Kubernetes and Red Hat is all in with Kubernetes.
OpenShift has many features, but one of the most important is that
it’s still native Kubernetes under the covers and supports role-based
access control, out-of-the-box software-defined networking, secu‐
rity, logins, developer builds, and many other things. We mention it
here because the flavor of Kubernetes that we’ll use for the rest of
this book is based on OpenShift. We’ll also use the oc OpenShift
command-line tools, which give us a better user experience and
allow us to easily log in to our Kubernetes cluster and control which
project into which we’re deploying. The CDK that we mentioned has
both vanilla Kubernetes and OpenShift. For the rest of this book,
we’ll be referring to OpenShift and Kubernetes interchangeably but
using OpenShift.
Getting Started with the CDK
With the CDK, you can build, deploy, and run your microservices as
Docker containers right on your laptop and then opt to deliver your
microservice in a delivery pipeline through other application lifecy‐
cle management features inside of OpenShift or with your own tool‐
ing. The best part of the CDK is that it runs in a RHEL VM, which
should match your development, QA, and production environ‐
ments.
Instructions for installing the CDK can be found at http://red.ht/
1SVUp6g. There are multiple flavors of virtualization (e.g., Virtual‐
Microservices and Linux Containers | 87

Box, VMWare, KVM) that you can use with the CDK. The installa‐
tion documents for the CDK contain all of the details you need for
getting up and running. To continue with the examples and idioms
in the rest of this book, please install the CDK (or any other Docker/
Kubernetes local VM) following the instructions.
To start up the CDK, navigate to the installation directory and to
the ./components/rhel/rhel-ose subdirectory and type the following:
$ vagrant up
This should take you through the provisioning process and boot the
VM. The VM will expose a Docker daemon at tcp://10.1.2.2:2376
and the Kubernetes API at https://10.1.2.2:8443. We will next need to
install the OpenShift oc command-line tools for your environment.
This will allow us to log in to OpenShift/Kubernetes and manage
our projects/namespaces. You could use the kubectl commands
yourself, but logging in is easier with the oc login command, so for
these examples, we’ll use oc. Download and install the oc client
tools.
Once you’ve downloaded and installed both the CDK and the oc
command-line tools, the last thing we want to do is set a couple of
environment variables so our tooling will be able to find the Open‐
Shift installation and Docker daemon. To do this, navigate to the ./
components/rhel/rhel-ose directory and run the following command:
$ eval "$(vagrant service-manager env docker)"
This will set up your environment variables. You can output the
environment variables and manually configure them if you wish:
$ vagrant service-manager env docker
export DOCKER_HOST=tcp://192.168.121.195:2376
export DOCKER_CERT_PATH=/home/john/cdk/components/rhel/rhel-ose/
.vagrant/machines/default/libvirt/.docker
export DOCKER_TLS_VERIFY=1
export DOCKER_MACHINE_NAME=081d3cd
We should now be able to log in to the OpenShift running in the
CDK:
$ oc login 10.1.2.2:8443
The server uses a certificate signed by an unknown authority.
You can bypass the certificate check, but any data you send to
the server could be intercepted by others.
Use insecure connections? (y/n): y
Authentication required for https://10.1.2.2:8443 (openshift)

Username: admin
Password:
Login successful.
You have access to the following projects and can switch between
them with 'oc project <projectname>':
* default
Using project "default".
Welcome! See 'oc help' to get started.
Let’s create a new project/namespace into which we’ll deploy our
microservices:
$ oc new-project microservice-book
Now using project "microservice-book" on server
"https://10.1.2.2:8443".
You should be ready to go to the next steps!
Although not required to run these examples, installing the Docker
CLI for your native developer laptop is useful as well. This will allow
you to list Docker images and Docker containers right from your
developer laptop as opposed to having to log in to the vagrant VM.
Once you have the Docker CLI installed, you should be able to run
Docker directly from your command-line shell (note, the environ‐
ment variables previously discussed should be set up):
$ docker ps
$ docker images
Where to Look Next
In this chapter, we learned a little about the pains of deploying and
managing microservices at scale and how Linux containers can help.
We can leverage immutable delivery to reduce configuration drift,
enable repeatable deployments, and help us reason about our appli‐
cations regardless of whether they’re running. We can use Linux
containers to enable service isolation, rapid delivery, and portability.
We can leverage scalable container management systems like Kuber‐
netes and take advantage of a lot of distributed-system features like
service discovery, failover, health-checking (and more!) that are
built in. You don’t need complicated port swizzling or complex ser‐
vice discovery systems when deploying on Kubernetes because these
are problems that have been solved within the infrastructure itself.
To learn more, please review the following links:

• Kubernetes Reference Documentation
• “An Introduction to Immutable Infrastructure” by Josha Stella
• “The Decline of Java Application Servers when Using Docker
Containers” by James Strachan
• Docker docs
• OpenShift Enterprise 3.1 Documentation
• Kubernetes
• Kubernetes Reference Documentation: Pods
• Kubernetes Reference Documentation: Labels and Selectors
• Kubernetes Reference Documentation: Replication Controller
• Kubernetes Reference Documentation: Services

CHAPTER 6
Hands-on Cluster Management,
Failover, and Load Balancing
In Chapter 5, we just had a quick introduction to cluster manage‐
ment, Linux containers, and cluster management. Let’s jump into
using these things to solve issues with running microservices at
scale. For reference, we’ll be using the microservice projects we
developed in Chapters 2, 3, and 4 (Spring Boot, Dropwizard, and
WildFly Swarm, respectively). The following steps can be accom‐
plished with any of the three Java frameworks.
Getting Started
To package our microservice as a Docker image and eventually
deploy it to Kubernetes, let’s navigate to our project (Spring Boot
example in this case) and return to JBoss Forge. JBoss Forge has
some plug-ins for making it easy to quickly add the Maven plug-ins
we need to use:
$ cd hola-springboot
$ forge
Now let’s install a JBoss Forge addon:
hola-springboot]$ addon-install
--coordinate io.fabric8.forge:devops,2.2.148
***SUCCESS*** Addon io.fabric8.forge:devops,2.2.148 was
installed successfully.
Now let’s add the Maven plug-ins:
91

[hola-springboot]$ fabric8-setup
***SUCCESS*** Added Fabric8 Maven support with base Docker
image: fabric8/java-jboss-openjdk8-jdk:1.0.10. Added the
following Maven profiles [f8-build, f8-deploy,
f8-local-deploy] to make building the project easier,
e.g., mvn -Pf8-local-deploy
Let’s take a look at what the tooling did. If we open the pom.xml file,
we see it added some properties:
<docker.assemblyDescriptorRef>
artifact
</docker.assemblyDescriptorRef>
<docker.from>
docker.io/fabric8/java-jboss-openjdk8-jdk:1.0.10
</docker.from>
<docker.image>
fabric8/${project.artifactId}:${project.version}
</docker.image>
<docker.port.container.http>8080</docker.port.container.http>
<docker.port.container.jolokia>
8778
</docker.port.container.jolokia>
<fabric8.iconRef>icons/spring-boot</fabric8.iconRef>
<fabric8.service.containerPort>
8080
</fabric8.service.containerPort>
<fabric8.service.name>hola-springboot</fabric8.service.name>
<fabric8.service.port>80</fabric8.service.port>
<fabric8.service.type>LoadBalancer</fabric8.service.type>
It also added two Maven plug-ins: docker-maven-plugin and
fabric8-maven-plugin:
<plugin>
<groupId>io.fabric8</groupId>
<artifactId>docker-maven-plugin</artifactId>
<configuration>
<images>
<image>
<name>${docker.image}</name>
<build>
<from>${docker.from}</from>
<assembly>
<basedir>/app</basedir>
<descriptorRef>
${docker.assemblyDescriptorRef}
</descriptorRef >
</assembly>
<env>
<JAR>
92 | Chapter 6: Hands-on Cluster Management, Failover, and Load Balancing

${project.artifactId}-${project.version}.war
</JAR>
<JAVA_OPTIONS>
-Djava.security.egd=/dev/./urandom<
/JAVA_OPTIONS>
</env>
</build>
</image>
</images>
</configuration>
</plugin>
<plugin>
<groupId>io.fabric8</groupId>
<artifactId>fabric8-maven-plugin</artifactId>
<executions>
<execution>
<id>json</id>
<phase>generate-resources</phase>
<goals>
<goal>json</goal>
</goals>
</execution>
<execution>
<id>attach</id>
<phase>package</phase>
<goals>
<goal>attach</goal>
</goals>
</execution>
</executions>
</plugin>
Lastly, the tooling added some convenience Maven profiles:
f8-build
Build the docker image and Kubernetes manifest YML.
f8-deploy
Build the docker image and deploy to a remote docker registry;
then deploy the application to Kubernetes.
f8-local-deploy
Build the docker image, generate the Kubernetes manifest.yml,
and deploy to a locally running Kubernetes.
The JBoss Forge addon is part of the Fabric8 open source project.
Fabric8 builds developer tooling for interacting with Docker, Kuber‐
netes, and OpenShift, including Maven plug-ins, variable injection
libraries for Spring/CDI, and clients for accessing the Kubernetes/
Hands-on Cluster Management, Failover, and Load Balancing | 93

OpenShift API. Fabric8 also builds API management, CI/CD, chaos
monkey and Kubernetes-based NetflixOSS functionality on top of
Kubernetes.
Packaging Our Microservice as a Docker Image
With the Maven plug-ins added from the previous step, all we have
to do to build the docker image is run the following Maven com‐
mand. This step, and all others related to building Docker images or
deploying to Kubernetes, assume the CDK (earlier in this chapter) is
up and running:
$ mvn -Pf8-build
[INFO] DOCKER> ... d3f157b39583 Pull complete
============= 10% ============ 20% ============
30% ============ 40% ============ 50% =============
60% ============ 70% ============ 80% ============
90% ============ 100% =
[INFO] DOCKER> ... f5a6e0d26670 Pull complete
= 100% ==
[INFO] DOCKER> ... 6d1f91fc8ac8 Pull complete
= 100% ==
[INFO] DOCKER> ... 77c58da5314d Pull complete
= 100% ==
[INFO] DOCKER> ... 1416b43aef4d Pull complete
= 100% ==
[INFO] DOCKER> ... fcc736051e6e Pull complete
[INFO] DOCKER> ... Digest: sha256:e77380a4924bb599162e3382e6443e
8aa50c0
[INFO] DOCKER> ... Downloaded image for java-jboss-openjdk8-jdk:
1.0.10
[INFO] DOCKER> [fabric8/hola-springboot:1.0] : Built image 13e72
5c3c771
[INFO]
[INFO] fabric8-maven-plugin:2.2.100:json (default-cli) @ hola-
springboot
[INFO] Configured with file: /Users/ceposta/dev/sandbox/micro
services-by-example/source/spring-boot/hola-springboot/target
/classes/kubernetes.json
[INFO] Generated env mappings: {}
[INFO] Generated port mappings: {http=ContainerPort(container
Port=8080, hostIP=null, hostPort=null, name=http, protocol=
null, additionalProperties={}), jolokia=ContainerPort(
containerPort=8778, hostIP=null, hostPort=null, name=jolokia,
protocol=null, additionalProperties={})}
[INFO] Removed 'version' label from service selector for
service ``
[INFO] Generated ports: [ServicePort(name=null, nodePort=null,

port=80, protocol=TCP, targetPort=IntOrString(IntVal=8080,
Kind=null, StrVal=null, additionalProperties={}),
additionalProperties={})]
[INFO] Icon URL: img/icons/spring-boot.svg
[INFO] Looking at repo with directory /microservices-by-example
/.git
[INFO] Added environment annotations:
[INFO] Service hola-springboot selector: {project=hola-
springboot,
[INFO] provider=fabric8, group=com.redhat.examples}
ports: 80
[INFO] ReplicationController hola-springboot replicas: 1,
[INFO] image: fabric8/hola-springboot:1.0
[INFO] Template is now:
[INFO] Service hola-springboot selector: {project=hola-
springboot,
[INFO] provider=fabric8, group=com.redhat.examples}
ports: 80
[INFO] ReplicationController hola-springboot replicas: 1,
[INFO] image: fabric8/hola-springboot:1.0
[INFO] ------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------
[INFO] Total time: 04:22 min
[INFO] Finished at: 2016-03-31T15:59:58-07:00
[INFO] Final Memory: 47M/560M
[INFO] ------------------------------------------------------
Deploying to Kubernetes
If we have the Docker tooling installed, we should see that our
microservice has been packaged in a Docker container:
$ docker images
langswif01(cdk-v2 (master))$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
fabric8/hola-springboot 1.0 13e725c3c771 3d ago 439.7 MB
We could start up the Docker container using docker run, but we
want to deploy this into a cluster and leave the management of the
microservice to Kubernetes. Let’s deploy it with the following Maven
command:
$ mvn -Pf8-local-deploy
If your environment is configured correctly (i.e., you’ve started the
CDK, installed the oc tooling, logged in with the oc login, and cre‐
ated a new project with oc new-project microservices-book),
you should see a successful build similar to this:

[INFO] --- fabric8-maven-plugin:apply (default-cli) @ hola-
springboot ---
[INFO] Using https://10.1.2.2:8443/ in namespace microservice-
book
[INFO] Kubernetes JSON: /Users/ceposta/dev/sandbox
[INFO] /microservices-by-example/source/spring-boot/hola-
springboot
[INFO] /target/classes/kubernetes.json
[INFO] OpenShift platform detected
[INFO] Using namespace: microservice-book
[INFO] Creating a Template from kubernetes.json namespace
[INFO] microservice-book name hola-springboot
[INFO] Created Template: target/fabric8/applyJson/microservice-
book/
[INFO] template-hola-springboot.json
[INFO] Looking at repo with directory /Users/ceposta/dev/
sandbox/
[INFO] microservices-by-example/.git
[INFO] Creating a Service from kubernetes.json namespace
[INFO] Created Service: target/fabric8/applyJson/microservice-
book
[INFO] /service-hola-springboot.json
[INFO] Creating a ReplicationController from kubernetes.json
namespace
[INFO] Created ReplicationController: target/fabric8/applyJson
[INFO] /microservice-book/replicationcontroller-hola-
springboot.json
[INFO] Creating Route microservice-book:hola-springboot host:
[INFO] -------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] -------------------------------------------------------
[INFO] Total time: 19.101 s
[INFO] Finished at: 2016-04-04T09:05:02-07:00
[INFO] Final Memory: 52M/726M
[INFO] -------------------------------------------------------
Let’s take a quick look at what the fabric8-maven-plugin plug-in
did for us.
First, Kubernetes exposes a REST API that allows us to manipulate
the cluster (what’s deployed, how many, etc.). Kubernetes follows a
“reconciliation of end state” model where you describe what you
want your deployment to look like and Kubernetes makes it happen.
This is similar to how some configuration management systems
work where you declaratively express what should be deployed and
not how it should be accomplished. When we post data to the
Kubernetes REST API, Kubernetes will reconcile what needs to hap‐

pen inside the cluster. For example, if “we want a pod running hola-
springboot" we can make an HTTP POST to the REST API with a
JSON/YAML manifest file, and Kubernetes will create the pod, cre‐
ate the Docker containers running inside that pod, and schedule the
pod to one of the hosts in the cluster. A Kubernetes pod is an atomic
unit that can be scheduled within a Kubernetes cluster. It typically
consists of a single Docker container, but it can contain many
Docker containers. In our code samples, we will treat our hello-
world deployments as a single Docker container per pod.
The fabric8-maven-plugin we used in the preceding code example
will automatically generate the REST objects inside a JSON/YAML
manifest file for us and POST this data to the Kubernetes API. After
running the mvn -Pf8-local-deploy command successfully, we
should be able to navigate to the webconsole (https://10.1.2.2:8443)
or using the CLI tooling to see our new pod running our hello-
springboot application:
$ oc get pod
NAME READY STATUS RESTARTS AGE
hola-springboot-8xtdm 1/1 Running 0 3d
At this point we have a single pod running in our cluster. What
advantage does Kubernetes bring as a cluster manager? Let’s start by
exploring the first of many. Let’s kill the pod and see what happens:
$ oc delete pod/hola-springboot-8xtdm
pod "hola-springboot-8xtdm" deleted
Now let’s list our pods again:
$ oc get pod
hola-springboot-42p89 0/1 Running 0 3d
Wow! It’s still there! Or, more correctly, another pod has been cre‐
ated after we deleted the previous one. Kubernetes can start/stop/
auto-restart your microservices for you. Can you imagine what a
headache it would be to determine whether a service is started/stop‐
ped at any kind of scale? Let’s continue exploring some of the other
valuable cluster management features Kubernetes brings to the table
for managing microservices.
Scaling
One of the advantages of deploying in a microservices architecture
is independent scalability. We should be able to replicate the number

of services in our cluster easily without having to worry about port
conflicts, JVM or dependency mismatches, or what else is running
on the same machine. With Kubernetes, these types of scaling con‐
cerns can be accomplished with the ReplicationController. Let’s see
what replication controllers exist in our deployment:
$ oc get replicationcontroller
CONTROLLER CONTAINER(S) IMAGE(S)
hola-springboot hola-springboot fabric8/hola-springboot:1.0
SELECTOR
group=com.redhat.examples,project=hola-springboot,
provider=fabric8,version=1.0
REPLICAS AGE
1 3d
We can also abbreviate the command:
$ oc get rc
One of the objects that the fabric8-maven-plugin created for us is
the ReplicationController with a replica value of 1. This means
we want to always have one pod/instance of our microservice at all
times. If a pod dies (or gets deleted), then Kubernetes is charged
with reconciling the desired state for us, which is replicas=1. If the
cluster is not in the desired state, Kubernetes will take action to
make sure the desired configuration is satisfied. What happens if we
want to change the desired number of replicas and scale up our ser‐
vice?
$ oc scale rc hola-springboot --replicas=3
replicationcontroller "hola-springboot" scaled
Now if we list the pods, we should see three pods of our hello-
springboot application:
$$ oc get pod
hola-springboot-42p89 1/1 Running 0 3d
hola-springboot-9s6a6 1/1 Running 0 3d
hola-springboot-np2l1 1/1 Running 0 3d
Now if any of our pods dies or gets deleted, Kubernetes will do what
it needs to do to make sure the replica count is 3. Notice, also, that
we didn’t have to change ports on these services or do any unnatural
port remapping. Each one of the services is listening on port 8080
and does not collide with the others.

Let’s go ahead and scale down to 0 to get ready for the next section;
we can just run the same command:
$ oc scale rc hola-springboot --replicas=0
Kubernetes also has the ability to do autoscaling by watching met‐
rics like CPU, memory usage, or user-defined triggers, to scale the
number of replicas up or down. Autoscaling is outside the scope of
this book but is a very valuable piece of the cluster-management
puzzle.
Service discovery
One last concept in Kubernetes that we should understand is Ser
vice. In Kubernetes, a Service is a simple abstraction that provides
a level of indirection between a group of pods and an application
using the service represented by that group of pods. We’ve seen how
pods are managed by Kubernetes and can come and go. We’ve also
seen how Kubernetes can easily scale up the number of instances of
a particular service. In our example, we’re going to start our backend
service from the previous chapters to play the role of service pro‐
vider. How will our hola-springboot communicate with the back
end?
Let’s run the backend service by navigating to our source code to the
folder backend and deploy it locally to our locally running Kuber‐
netes cluster running in the CDK:
Let’s take a look at what Kubernetes services exist:
$ oc get service
NAME CLUSTER_IP EXTERNAL_IP PORT(S)
backend 172.30.231.63 80/TCP
hola-springboot 172.30.202.59 80/TCP
SELECTOR AGE
component=backend,provider=fabric8 3d
group=com.redhat.examples,project=hola-springboot,
provider=fabric8 3d
Note the Service objects get automatically created by the fabric8-
maven-plugin just like the ReplicationController objects in the
previous section. There are two interesting attributes of a service
that appear in the preceding code example. One is the CLUSTER_IP.
This is a virtual IP that is assigned when a Service object is created

and never goes away. It’s a single, fixed IP that is available to any
applications running within the Kubernetes cluster and can be used
to talk to backend pods. The pods are “selected” with the SELECTOR
field. Pods in Kubernetes can be “labeled” with whatever metadata
you want to apply (like “version” or “component” or “team”) and can
subsequently be used in the selector for a Service. In this example,
we’re selecting all the pods with label component=backend and pro
vider=fabric8. This means any pods that are “selected” by the
selector can be reached just by using the cluster IP. No need for
complicated distributed registries (e.g., Zookeeper, Consul, or Eur‐
eka) or anything like that. It’s all built right into Kubernetes. Cluster-
level DNS is also built into Kubernetes. Using DNS in general for
microservice service discovery can be very challenging and down‐
right painful. In Kubernetes, the cluster DNS points to the cluster
IP; and since the cluster IP is a fixed IP and doesn’t go away, there
are no issues with DNS caching and other gremlins that can pop up
with traditional DNS.
Let’s add a couple environment variables to our hola-springboot
project to use our backend service when running inside a Kuber‐
netes cluster:
<fabric8.env.GREETING_BACKENDSERVICEHOST>
backend<
/fabric8.env.GREETING_BACKENDSERVICEHOST>
<fabric8.env.GREETING_BACKENDSERVICEPORT>
80
</fabric8.env.GREETING_BACKENDSERVICEPORT>
Let’s build the Kubernetes manifest and verify we’re passing in these
environment variables to our pod. Note that Spring Boot will resolve
configuration from application.properties but can be overridden
with system properties and environment variables at runtime:
$ mvn fabric8:json
Inspect file target/classes/kubernetes.json:
"containers" : [ {
"args" : [ ],
"command" : [ ],
"env" : [ {
"name" : "GREETING_BACKENDSERVICEHOST",
"value" : "backend"
}, {
"name" : "GREETING_BACKENDSERVICEPORT",
"value" : "80"

}, {
"name" : "KUBERNETES_NAMESPACE",
"valueFrom" : {
"fieldRef" : {
"fieldPath" : "metadata.namespace"
}
}
} ],
Let’s delete all of the pieces of the hola-springboot project and
redeploy:
$ oc delete all -l project=hola-springboot
We should now be able to list the pods and see our hola-
springboot pod running as well as our backend service pod:
$ oc get pod
backend-nk224 1/1 Running 5 3d
hola-springboot-r5ykr 1/1 Running 0 2m
Now, just to illustrate a handy debugging technique, we’re going to
set up port-forwarding between our local machine and our hola-
springboot-r5ykr pod and verify that our service is working cor‐
rectly and we can call the backend. Let’s set up portforward to port
9000 on our local machine:
$ oc port-forward -p hola-springboot-r5ykr 9000:8080
We should not be able to communicate with the pod over our local‐
host port 9000. Note this technique works great even across a
remote Kubernetes cluster, not just on our local CDK. So instead of
having to try and find which host our pod is running and how to
ssh into it, we can just use oc port-forward to expose it locally.
So now we should be able to navigate locally using our browser or a
CLI command line:
$ curl http://localhost:9000/api/hola
Hola Spring Boot de 172.17.0.9
We can see the /api/hola endpoint at http://localhost:9000 using
our port-forwarding! We also see that the /api/hola endpoint is
returning the IP address of the pod in which it’s running. Let’s call
the /api/greeting API, which is supposed to call our backend:
$ curl http://localhost:9000/api/greeting
Hola Spring Boot from cluster Backend at host: 172.17.0.5

We can see that the backend pod returns its IP address in this call!
So our service was discovered correctly, and all it took was a little bit
of DNS and the power of Kubernetes service discovery. One big
thing to notice about this approach is that we did not specify any
extra client libraries or set up any registries or anything. We happen
to be using Java in this case, but using Kubernetes cluster DNS pro‐
vides a technology-agnostic way of doing basic service discovery!
Fault Tolerance
Complex distributed systems like a microservice architecture must
be built with an important premise in mind: things will fail. We can
spend a lot of energy preventing things from failing, but even then
we won’t be able to predict every case where and how dependencies
in a microservice environment can fail. A corollary to our premise
of “things will fail” is that “we design our services for failure.”
Another way of saying that is “figure out how to survive in an envi‐
ronment where there are failures.”
Cluster Self-Healing
If a service begins to misbehave, how will we know about it? Ideally
our cluster management solution can detect and alert us about fail‐
ures and let human intervention take over. This is the approach we
typically take in traditional environments. When running microser‐
vices at scale, where we have lots of services that are supposed to be
identical, do we really want to stop and troubleshoot every possible
thing that can go wrong with a single service? Long-running serv‐
ices may experience unhealthy states. An easier approach is to
design our microservices such that they can be terminated at any
moment, especially when they appear to be behaving incorrectly.
Kubernetes has a couple of health probes we can use out of the box
to allow the cluster to administer and self-heal itself. The first is a
readiness probe, which allows Kubernetes to determine whether or
not a pod should be considered in any service discovery or load-
balancing algorithms. For example, some Java apps may take a few
seconds to bootstrap the containerized process, even though the pod
is technically up and running.
If we start sending traffic to a pod in this state, users may experience
failures or inconsistent states. With readiness probes, we can let
Kubernetes query an HTTP endpoint (for example) and only con‐

sider the pod ready if it gets an HTTP 200 or some other response.
If Kubernetes determines a pod does not become ready within a
specified period of time, the pod will be killed and restarted.
Another health probe we can use is a liveness probe. This is similar
to the readiness probe; however, it’s applicable after a pod has been
determined to be “ready” and is eligible to receive traffic. Over the
course of the life of a pod or service, if the liveness probe (which
could also be a simple HTTP endpoint) starts to indicate an unheal‐
thy state (e.g., HTTP 500 errors), Kubernetes can automatically kill
the pod and restart it.
When we used the JBoss Forge tooling and the fabric8-setup com‐
mand from the Fabric8 addon, a readiness probe was automatically
added to our Kubernetes manifest by adding the following Maven
properties to the respective pom.xml. If it wasn’t, you can use the
command fabric8-readiness-probe or fabric8-liveness-probe
to add it to an existing project:
<fabric8.readinessProbe.httpGet.path>
/health
</fabric8.readinessProbe.httpGet.path>
<fabric8.readinessProbe.httpGet.port>
8080
</fabric8.readinessProbe.httpGet.port>
<fabric8.readinessProbe.initialDelaySeconds>
5
</fabric8.readinessProbe.initialDelaySeconds>
<fabric8.readinessProbe.timeoutSeconds>
30
</fabric8.readinessProbe.timeoutSeconds>
The Kubernetes JSON that gets generated for including these Maven
properties includes:
"readinessProbe" : {
"httpGet" : {
"path" : "/health",
"port" : 8080
},
This means the “readiness” quality of the hola-springboot pod will
be determined by periodically polling the /health endpoint of our
pod. When we added the actuator to our Spring Boot microservice
earlier, a /health endpoint was added which returns:
{
"diskSpace": {
"free": 106880393216,
Fault Tolerance | 103

"status": "UP",
"threshold": 10485760,
"total": 107313364992
},
"status": "UP"
}
The same thing can be done with Dropwizard and WildFly Swarm!
Circuit Breaker
As a service provider, your responsibility is to your consumers to
provide the functionality you’ve promised. Following promise
theory, a service provider may depend on other services or down‐
stream systems but cannot and should not impose requirements
upon them. A service provider is wholly responsible for its promise
to consumers. Because distributed systems can and do fail, there will
be times when service promises can’t be met or can be only partly
met. In our previous examples, we showed our Hola apps reaching
out to a backend service to form a greeting at the /api/greeting end‐
point. What happens if the backend service is not available? How do
we hold up our end of the promise?
We need to be able to deal with these kinds of distributed systems
faults. A service may not be available; a network may be experienc‐
ing intermittent connectivity; the backend service may be experienc‐
ing enough load to slow it down and introduce latency; a bug in the
backend service may be causing application-level exceptions. If we
don’t deal with these situations explicitly, we run the risk of degrad‐
ing our own service, holding up threads, database locks, and resour‐
ces, and contributing to rolling, cascading failures that can take an
entire distributed network down. To help us account for these fail‐
ures, we’re going to leverage a library from the NetflixOSS stack
named Hystrix.
Hystrix is a fault-tolerant Java library that allows microservices to
hold up their end of a promise by:
• Providing protection against dependencies that are unavailable
• Monitoring and providing timeouts to guard against unexpec‐
ted dependency latency
• Load shedding and self-healing
• Degrading gracefully

• Monitoring failure states in real time
• Injecting business-logic and other stateful handling of faults
With Hystrix, you wrap any call to your external dependencies with
a HystrixCommand and implement the possibly faulty calls inside the
run() method. To help you get started, let’s look at implementing a
HystrixCommand for the hola-wildflyswarm project. Note for this
example, we’re going to follow the Netflix best practices of making
everything explicit, even if that introduces some boilerplate code.
Debugging distributed systems is difficult and having exact stack
traces for your code without too much magic is more important
than hiding everything behind complicated magic that becomes
impossible to debug at runtime. Even though the Hystrix library has
annotations for convenience, we’ll stick with implementing the Java
objects directly for this book and leave it to the reader to explore the
more mystical ways to use Hystrix.
First let’s add the hystrix-core dependency to our Maven pom.xml:
<dependency>
<groupId>com.netflix.hystrix</groupId>
<artifactId>hystrix-core</artifactId>
<version>${hystrix.version}</version>
</dependency>
Let’s create a new Java class called BackendCommand that extends
from HystrixCommand in our hola-wildflyswarm project shown in
Example 6-1.
BackendCommand
public class BackendCommand extends HystrixCommand<BackendDTO> {
private String host;
private int port;
public BackendCommand(String host, int port) {
super(HystrixCommandGroupKey.Factory
.asKey("wfswarm.backend"));
this.host = host;
this.port = port;
}
public BackendCommand withSaying(String saying) {

return this;
}
@Override
protected BackendDTO run() throws Exception {
String.format("http://%s:%d", host, port);
return client.target(backendServiceUrl)
.path("api")
.path("backend")
}
}
You can see here we’ve extended HystrixCommand and provided our
BackendDTO class as the type of response our command object will
return. We’ve also added some constructor and builder methods for
configuring the command. Lastly, and most importantly, we’ve
added a run() method here that actually implements the logic for
making an external call to the backend service. Hystrix will add
thread timeouts and fault behavior around this run() method.
What happens, though, if the backend service is not available or
becomes latent? You can configure thread timeouts and rate of fail‐
ures which would trigger circuit-breaker behavior. A circuit breaker
in this case will simply open a circuit to the backend service by not
allowing any calls to go through (failing fast) for a period of time.
The idea with this circuit-breaker behavior is to allow any backend
remote resources time to recover or heal without continuing to take
load and possibly further cause it to persist or degrade into unheal‐
thy states.
You can configure Hystrix by providing configuration keys, JVM
system properties, or by using a type-safe DSL for your command
object. For example, if we want to enable the circuit breaker (default
true) and open the circuit if we get five or more failed requests
(timeout, network error, etc.) within five seconds, we could pass the
following into the constructor of our BackendCommand object:

super(Setter.withGroupKey(
HystrixCommandGroupKey.Factory
.asKey("wildflyswarm.backend"))
.andCommandPropertiesDefaults(
HystrixCommandProperties.Setter()
.withCircuitBreakerEnabled(true)
.withCircuitBreakerRequestVolumeThreshold(5)
.withMetricsRollingStatistical
WindowInMilliseconds(5000)
))
;
this.host = host;
this.port = port;
}
Please see the Hystrix documentation for more advanced configura‐
tions as well as for how to externalize the configurations or even
configure them dynamically at runtime.
If a backend dependency becomes latent or unavailable and Hystrix
intervenes with a circuit breaker, how does our service keep its
promise? The answer to this may be very domain specific. For
example, if we consider a team that is part of a personalization ser‐
vice, we want to display custom book recommendations for a user.
We may end up calling the book-recommendation service, but what
if it isn’t available or is too slow to respond? We could degrade to a
book list that may not be personalized; maybe we’d send back a book
list that’s generic for users in a particular region. Or maybe we’d not
send back any personalized list and just a very generic “list of the
day.” To do this, we can use Hystrix’s built-in fallback method. In our
example, if the backend service is not available, let’s add a fallback
method to return a generic BackendDTO response:
public class BackendCommand extends HystrixCommand<BackendDTO> {
<rest of class here>
@Override
protected BackendDTO getFallback() {
BackendDTO rc = new BackendDTO();
rc.setGreeting("Greeting fallback!");
rc.setIp("127.0.0,1");
rc.setTime(System.currentTimeMillis());
return rc;
}

}
Our /api/greeting-hystrix service should not be able to service a
client and hold up part of its promise, even if the backend service is
not available.
Note this is a contrived example, but the idea is ubiquitous. How‐
ever, the application of whether to fallback or gracefully degrade
versus breaking a promise is very domain specific. For example, if
you’re trying to transfer money in a banking application and a back‐
end service is down, you may wish to reject the transfer. Or you may
wish to make only a certain part of the transfer available while the
backend gets reconciled. Either way, there is no one-size-fits-all fall‐
back method. In general, coming up with the fallback is related to
what kind of customer experience gets exposed and how best to
gracefully degrade considering the domain.
Bulkhead
Hystrix offers some powerful features out of the box, as we’ve seen.
One more failure mode to consider is when services become latent
but not latent enough to trigger a timeout or the circuit breaker.
This is one of the worst situations to deal with in distributed systems
as latency like this can quickly stall (or appear to stall) all worker
threads and cascade the latency all the way back to users. We would
like to be able to limit the effect of this latency to just the depend‐
ency that’s causing the slowness without consuming every available
resource. To accomplish this, we’ll employ a technique called the
bulkhead. A bulkhead is basically a separation of resources such that
exhausting one set of resources does not impact others. You often
see bulkheads in airplanes or trains dividing passenger classes or in
boats used to stem the failure of a section of the boat (e.g., if there’s a
crack in the hull, allow it to fill up a specific partition but not the
entire boat).
Hystrix implements this bulkhead pattern with thread pools. Each
downstream dependency can be allocated a thread pool to which it’s
assigned to handle external communication. Netflix has bench‐
marked the overhead of these thread pools and has found for these
types of use cases, the overhead of the context switching is minimal,
but it’s always worth benchmarking in your own environment if you
have concerns. If a dependency downstream becomes latent, then

the thread pool assigned to that dependency can become fully uti‐
lized, but other requests to the dependency will be rejected. This has
the effect of containing the resource consumption to just the degra‐
ded dependency instead of cascading across all of our resources.
If the thread pools are a concern, Hystrix also can implement the
bulkhead on the calling thread with counting semaphores. Refer to
the Hystrix documentation for more information.
The bulkhead is enabled by default with a thread pool of 10 worker
threads with no BlockingQueue as a backup. This is usually a suffi‐
cient configuration, but if you must tweak it, refer to the configura‐
tion documentation of the Hystrix component. Configuration would
look something like this (external configuration is possible as well):
super(Setter.withGroupKey(
HystrixCommandGroupKey.Factory
.asKey("wildflyswarm.backend"))
.andThreadPoolPropertiesDefaults(
HystrixThreadPoolProperties.Setter()
.withCoreSize(10)
.withMaxQueueSize(-1))
.andCommandPropertiesDefaults(
HystrixCommandProperties.Setter()
.withCircuitBreakerEnabled(true)
.withCircuitBreakerRequestVolumeThreshold(5)
.withMetricsRollingStatisticalWindow
InMilliseconds(5000)
))
;
this.host = host;
this.port = port;
}
To test out this configuration, let’s build and deploy the hola-
wildflyswarm project and play around with the environment.
Build Docker image and deploy to Kubernetes:
Let’s verify the new /api/greeting-hystrix endpoint is up and func‐
tioning correctly (this assumes you’ve been following along and still
have the backend service deployed; refer to previous sections to get
that up and running):
$ oc get pod
backend-pwawu 1/1 Running 0 18h

hola-dropwizard-bf5nn 1/1 Running 0 19h
hola-springboot-n87w3 1/1 Running 0 19h
hola-wildflyswarm-z73g3 1/1 Running 0 18h
Let’s port-forward the hola-wildflyswarm pod again so we can
reach it locally. Recall this is a great benefit of using Kubernetes that
you can run this command regardless of where the pod is actually
running in the cluster:
$ oc port-forward -p hola-wildflyswarm-z73g3 9000:8080
Now let’s navigtate to http://localhost:9000/api/greeting-hystrix:
Now let’s take down the backend service by scaling its Replication
Controller replica count down to zero:
$ oc scale rc/backend --replicas=0
By doing this, there should be no backend pods running:
$ oc get pod
backend-pwawu 1/1 Terminating 0 18h
Now if we refresh our browser pointed at http://localhost:9000/api/
greeting-hystrix, we should see the service degrade to using the Hys‐
trix fallback method:
Load Balancing
In a highly scaled distributed system, we need a way to discover and
load balance against services in the cluster. As we’ve seen in previous
examples, our microservices must be able to handle failures; there‐

fore, we have to be able to load balance against services that exist,
services that may be joining or leaving the cluster, or services that
exist in an autoscaling group. Rudimentary approaches to load bal‐
ancing, like round-robin DNS, are not adequate. We may also need
sticky sessions, autoscaling, or more complex load-balancing algo‐
rithms. Let’s take a look at a few different ways of doing load balanc‐
ing in a microservices environment.
Kubernetes Load Balancing
The great thing about Kubernetes is that it provides a lot of
distributed-systems features out of the box; no need to add any extra
components (server side) or libraries (client side). Kubernetes Serv
ices provided a means to discover microservices and they also pro‐
vide server-side load balancing. If you recall, a Kubernetes Service
is an abstraction over a group of pods that can be specified with
label selectors. For all the pods that can be selected with the speci‐
fied selector, Kubernetes will load balance any requests across them.
The default Kubernetes load-balancing algorithm is round robin,
but it can be configured for other algorithms such as session affinity.
Note that clients don’t have to do anything to add a pod to the Ser
vice; just adding a label to your pod will enable it for selection and
be available. Clients reach the Kubernetes Service by using the clus‐
ter IP or cluster DNS provided out of the box by Kubernetes. Also
recall the cluster DNS is not like traditional DNS and does not fall
prey to the DNS caching TTL problems typically encountered with
using DNS for discovery/load balancing. Also note, there are no
hardware load balancers to configure or maintain; it’s all just built
in.
To demonstrate load balancing, let’s scale up the backend services in
our cluster:
$ oc scale rc/backend --replicas=3
Now if we check our pods, we should see three backend pods:
$ oc get pod
backend-8ywcl 1/1 Running 0 18h
backend-d9wm6 1/1 Running 0 18h
backend-vt61x 1/1 Running 0 18h
Load Balancing | 111

If we list the Kubernetes services available, we should see the back
end service as well as the selector used to select the pods that will be
eligible for taking requests. The Service will load balance to these
pods:
$ oc get svc
NAME CLUSTER_IP PORT(S)
backend 172.30.231.63 80/TCP
hola-dropwizard 172.30.124.61 80/TCP
hola-springboot 172.30.55.130 80/TCP
hola-wildflyswarm 172.30.198.148 80/TCP
We can see here that the backend service will select all pods with
labels component=backend and provider=fabric8. Let’s take a quick
moment to see what labels are on one of the backend pods:
$ oc describe pod/backend-8ywcl | grep Labels
Labels: component=backend,provider=fabric8
We can see that the backend pods have the labels that match what
the service is looking for; so any time we communicate with the ser‐
vice, we will be load balanced over these matching pods.
Let’s make a call to our hola-wildflyswarm service. We should see
the response contain different IP addresses for the backend service:
$ oc port-forward -p hola-wildflyswarm-z73g3 9000:8080
Hola from cluster Backend at host: 172.17.0.45
Here we enabled port forwarding so that we can reach our hola-
wildflyswarm service and tried to access the http://localhost:
9000/api/greeting endpoint. I used curl here, but you can use your
favorite HTTP/REST tool, including your web browser. Just refresh
your web browser a few times to see that the backend, which gets
called is different each time. The Kubernetes Service is load balanc‐
ing over the respective pods as expected.

Do We Need Client-Side Load Balancing?
Client-side load balancers can be used inside Kubernetes if you need
more fine-grained control or domain-specific algorithms for deter‐
mining which service or pod you need to send to. You can even do
things like weighted load balancing, skipping pods that seem to be
faulty, or some custom-based Java logic to determine which
service/pod to call. The downside to client-side load balancing is
that it adds complexity to your application and is often language
specific. In a majority of cases, you should prefer to use the
technology-agnostic, built-in Kubernetes service load balancing. If
you find you’re in a minority case where more sophisticated load
balancing is required, consider a client-side load balancer like
SmartStack, bakerstreet.io, or NetflixOSS Ribbon.
In this example, we’ll use NetflixOSS Ribbon to provide client-side
load balancing. There are different ways to use Ribbon and a few
options for registering and discovering clients. Service registries like
Eureka and Consul may be good options in some cases, but when
running within Kubernetes, we can just leverage the built-in Kuber‐
netes API to discover services/pods. To enable this behavior, we’ll
use ribbon-discovery project from Kubeflix. Let’s enable the
dependencies in our pom.xml that we’ll need:
<dependency>
<artifactId>ribbon</artifactId>
</dependency>
<dependency>
<groupId>io.fabric8.kubeflix</groupId>
<artifactId>ribbon-discovery</artifactId>
<version>${kubeflix.version}</version>
</dependency>
For Spring Boot we could opt to use Spring Cloud, which provides
convenient Ribbon integration, or we could just use the NetflixOSS
dependencies directly:
<dependency>
<groupId>com.netflix.ribbon</groupId>
<artifactId>ribbon-core</artifactId>
<version>${ribbon.version}</version>
</dependency>
<dependency>
<groupId>com.netflix.ribbon</groupId>
<artifactId>ribbon-loadbalancer</artifactId>
Load Balancing | 113

<version>${ribbon.version}</version>
</dependency>
Once we’ve got the right dependencies, we can configure Ribbon to
use Kubernetes discovery:
loadBalancer = LoadBalancerBuilder.newBuilder()
.withDynamicServerList(
new KubernetesServerList(config))
.buildDynamicServerListLoadBalancer();
Then we can use the load balancer with the Ribbon LoadBalancer
Command:
@Path("/greeting-ribbon")
@GET
public String greetingRibbon() {
BackendDTO backendDTO = LoadBalancerCommand.
<BackendDTO>builder()
.withLoadBalancer(loadBalancer)
.build()
.submit(new ServerOperation<BackendDTO>() {
@Override
public Observable<BackendDTO> call(Server server) {
"http://%s:%d",
server.getHost(), server.getPort());
System.out.println("Sending to: " +
backendServiceUrl);
return Observable.just(client
.target(backendServiceUrl)
.path("api")
.path("backend")
.get(BackendDTO.class));
}
}).toBlocking().first();
return backendDTO.getGreeting() + " at host: " +
backendDTO.getIp();
}
See the accompanying source code for the exact details.

Where to Look Next
In this chapter, we learned a little about the pains of deploying and
managing microservices at scale and how Linux containers can help.
We can leverage true immutable delivery to reduce configuration
drift, and we can use Linux containers to enable service isolation,
rapid delivery, and portability. We can leverage scalable container
management systems like Kubernetes and take advantage of a lot of
distributed-system features like service discovery, failover, health-
checking (and more!) that are built in. You don’t need complicated
port swizzling or complex service discovery systems when deploying
on Kubernetes because these are problems that have been solved
within the infrastructure itself. To learn more, please review the fol‐
lowing links:
• “An Introduction to Immutable Infrastructure” by Josha Stella
• “The Decline of Java Applications When Using Docker Con‐
tainers” by James Strachan
• Docker documentation
• OpenShift Enterprise 3.1 Documentation
• Kubernetes Reference Documentation: Horizontal Pod
Autoscaling
• Kubernetes Reference Documentation: Services
• Fabric8 Kubeflix on GitHub
• Hystrix on GitHub
• Netflix Ribbon on GitHub
• Spring Cloud

CHAPTER 7
Where Do We Go from Here?
We have covered a lot in this small book but certainly didn’t cover
everything! Keep in mind we are just scratching the surface here,
and there are many more things to consider in a microservices envi‐
ronment than what we can cover in this book. In this last chapter,
we’ll very briefly talk about a couple of additional concepts you
must consider. We’ll leave it as an exercise for the reader to dig into
more detail for each section!
Configuration
Configuration is a very important part of any distributed system and
becomes even more difficult with microservices. We need to find a
good balance between configuration and immutable delivery
because we don’t want to end up with snowflake services. For exam‐
ple, we’ll need to be able to change logging, switch on features for
A/B testing, configure database connections, or use secret keys or
passwords. We saw in some of our examples how to configure our
microservices using each of the three Java frameworks, but each
framework does configuration slightly differently. What if we have
microservices written in Python, Scala, Golang, NodeJS, etc?
To be able to manage configuration across technologies and within
containers, we need to adopt an approach that works regardless of
what’s actually running in the container. In a Docker environment
we can inject environment variables and allow our application to
consume those environment variables. Kubernetes allows us to do
that as well and is considered a good practice. Kubernetes also adds
117

APIs for mounting Secrets that allow us to safely decouple user‐
names, passwords, and private keys from our applications and inject
them into the Linux container when needed. Kubernetes also
recently added ConfigMaps which are very similar to Secrets in that
application-level configuration can be managed and decoupled from
the application Docker image but allow us to inject configuration via
environment variables and/or files on the container’s file system. If
an application can consume configuration files from the filesystem
(which we saw with all three Java frameworks) or read environment
variables, it can leverage Kubernetes configuration functionality.
Taking this approach, we don’t have to set up additional configura‐
tion services and complex clients for consuming it. Configuration
for our microservices running inside containers (or even outside),
regardless of technology, is now baked into the cluster management
infrastructure.
Logging, Metrics, and Tracing
Without a doubt, a lot of the drawbacks to implementing a micro‐
services architecture revolve around management of the services in
terms of logging, metrics, and tracing. The more you break a system
into individual parts, the more tooling, forethought, and insight you
need to invest to see the big picture. When you run services at scale,
especially assuming a model where things fail, we need a way to grab
information about services and correlate that with other data (like
metrics and tracing) regardless of whether the containers are still
alive. There are a handful of approaches to consider when devising
your logging, metrics, and tracing strategy:
• Developers exposing their logs
• Aggregation/centralization
• Search and correlate
• Visualize and chart
Kubernetes has addons to enable cluster-wide logging and metrics
collection for microservices. Typical technology for solving these
issues include syslog, Fluentd, or Logstash for getting logs out of
services and streamed to a centralized aggregator. Some folks use
messaging solutions to provide some reliability for these logs if
needed. ElasticSearch is an excellent choice for aggregating logs in a
central, scalable, search index; and if you layer Kibana on top, you
118 | Chapter 7: Where Do We Go from Here?

can get nice dashboards and search UIs. Other tools like Prome‐
theus, Zipkin, Grafana, Hawkular, Netflix Servo, and many others
should be considered as well.
Continuous Delivery
Deploying microservices with immutable images discussed earlier in
Chapter 5 is paramount. When we have many more smaller services
than before, our existing manual processes will not scale. Moreover,
with each team owning and operating its own microservices, we
need a way for teams to make immutable delivery a reality without
bottlenecks and human error. Once we release our microservices, we
need to have insight and feedback about their usage to help drive
further change. As the business requests change, and as we get more
feedback loops into the system, we will be doing more releases more
often. To make this a reality, we need a capable software-delivery
pipeline. This pipeline may be composed of multiple subpipelines
with gates and promotion steps, but ideally, we want to automate the
build, test, and deploy mechanics as much as possible.
Tools like Docker and Kubernetes also give us the built-in capacity
to do rolling upgrades, blue-green deployments, canary releases, and
other deployment strategies. Obviously these tools are not required
to deploy in this manner (places like Amazon and Netflix have done
it for years without Linux containers), but the inception of contain‐
ers does give us the isolation and immutability factors to make this
easier. You can use your CI/CD tooling like Jenkins and Jenkins
Pipeline in conjunction with Kubernetes and build out flexible yet
powerful build and deployment pipelines. Take a look at the Fabric8
and OpenShift projects for more details on an implementation of
CI/CD with Kubernetes based on Jenkins Pipeline.
Summary
This book was meant as a hands-on, step-by-step guide for getting
started with some popular Java frameworks to build distributed sys‐
tems following a microservices approach. Microservices is not a
technology-only solution as we discussed in the opening chapter.
People are the most important part of a complex system (a business)
and to scale and stay agile, you must consider scaling the organiza‐
tion structure as well as the technology systems involved.
Continuous Delivery | 119

After building microservices with either of the Java frameworks we
discussed, we need to build, deploy, and manage them. Doing this at
scale using our current techniques and primitives is overly complex,
costly, and does not scale. We can turn to new technology like
Docker and Kubernetes that can help us build, deploy, and operate
following best practices like immutable delivery.
When getting started with microservices built and deployed in
Docker and managed by Kubernetes, it helps to have a local envi‐
ronment used for development purposes. For this we looked at the
Red Hat Container Development Kit which is a small, local VM that
has Red Hat OpenShift running inside a free edition of Red Hat
Enterprise Linux (RHEL). OpenShift provides a production-ready
Kubernetes distribution, and RHEL is a popular, secure, supported
operating system for running production workloads. This allows us
to develop applications using the same technologies that will be run‐
ning in Production and take advantage of application packaging and
portability provided by Linux containers.
Lastly we touched on a few additional important concepts to keep in
mind like configuration, logging, metrics, and continuous, automa‐
ted delivery. We didn’t touch on security, self-service, and countless
other topics; but make no mistake: they are very much a part of the
microservices story.
We hope you’ve found this book useful. Please follow @openshift,
@kubernetesio, @fabric8io, @christianposta, and @RedHatNews for
more information, and take a look at the source code repository.
120 | Chapter 7: Where Do We Go from Here?

About the Author
Christian Posta (@christianposta) is a principal middleware special‐
ist and architect at Red Hat. He’s well known for being an author,
blogger, speaker, and open source contributor. He is a committer on
Apache ActiveMQ, Apache Camel, Fabric8, and others. Christian
has spent time at web-scale companies and now helps enterprise
companies creating and deploying large-scale distributed architec‐
tures, many of which are now microservice. He enjoys mentoring,
training, and leading teams to success through distributed-systems
concepts, microservices, DevOps, and cloud-native application
design. When not working, he enjoys time with his wife, Jackie, and
his two daughters, Madelyn and Claire.

Microservices for Java Developers

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