Software architecture-patterns

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Mark Richards
Software Architecture
Patterns
Understanding Common Architecture
Patterns and When to Use Them

978-1-491-92424-2
[LSI]
Software Architecture Patterns
by Mark Richards
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Table of Contents
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
1. Layered Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Pattern Description 1
Key Concepts 3
Pattern Example 5
Considerations 7
Pattern Analysis 8
2. Event-Driven Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Mediator Topology 11
Broker Topology 14
Considerations 17
Pattern Analysis 18
3. Microkernel Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Pattern Examples 23
Considerations 24
Pattern Analysis 25
4. Microservices Architecture Pattern. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Pattern Topologies 29
Avoid Dependencies and Orchestration 32
Considerations 33
Pattern Analysis 34
iii

5. Space-Based Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Pattern Dynamics 39
Considerations 42
Pattern Analysis 43
A. Pattern Analysis Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
iv | Table of Contents

Introduction
It’s all too common for developers to start coding an application
without a formal architecture in place. Without a clear and well-
defined architecture, most developers and architects will resort to
the de facto standard traditional layered architecture pattern (also
called the n-tier architecture), creating implicit layers by separating
source-code modules into packages. Unfortunately, what often
results from this practice is a collection of unorganized source-code
modules that lack clear roles, responsibilities, and relationships to
one another. This is commonly referred to as the big ball of mud
architecture anti-pattern.
Applications lacking a formal architecture are generally tightly cou‐
pled, brittle, difficult to change, and without a clear vision or direc‐
tion. As a result, it is very difficult to determine the architectural
characteristics of the application without fully understanding the
inner-workings of every component and module in the system.
Basic questions about deployment and maintenance are hard to
answer: Does the architecture scale? What are the performance
characteristics of the application? How easily does the application
respond to change? What are the deployment characteristics of the
application? How responsive is the architecture?
Architecture patterns help define the basic characteristics and
behavior of an application. For example, some architecture patterns
naturally lend themselves toward highly scalable applications,
whereas other architecture patterns naturally lend themselves
toward applications that are highly agile. Knowing the characteris‐
tics, strengths, and weaknesses of each architecture pattern is neces‐
v

sary in order to choose the one that meets your specific business
needs and goals.
As an architect, you must always justify your architecture decisions,
particularly when it comes to choosing a particular architecture pat‐
tern or approach. The goal of this report is to give you enough infor‐
mation to make and justify that decision.
vi | Introduction

CHAPTER 1
Layered Architecture
The most common architecture pattern is the layered architecture
pattern, otherwise known as the n-tier architecture pattern. This
pattern is the de facto standard for most Java EE applications and
therefore is widely known by most architects, designers, and devel‐
opers. The layered architecture pattern closely matches the tradi‐
tional IT communication and organizational structures found in
most companies, making it a natural choice for most business appli‐
cation development efforts.
Pattern Description
Components within the layered architecture pattern are organized
into horizontal layers, each layer performing a specific role within
the application (e.g., presentation logic or business logic). Although
the layered architecture pattern does not specify the number and
types of layers that must exist in the pattern, most layered architec‐
tures consist of four standard layers: presentation, business, persis‐
tence, and database (Figure 1-1). In some cases, the business layer
and persistence layer are combined into a single business layer, par‐
ticularly when the persistence logic (e.g., SQL or HSQL) is embed‐
ded within the business layer components. Thus, smaller
applications may have only three layers, whereas larger and more
complex business applications may contain five or more layers.
Each layer of the layered architecture pattern has a specific role and
responsibility within the application. For example, a presentation
layer would be responsible for handling all user interface and
1

browser communication logic, whereas a business layer would be
responsible for executing specific business rules associated with the
request. Each layer in the architecture forms an abstraction around
the work that needs to be done to satisfy a particular business
request. For example, the presentation layer doesn’t need to know
or worry about how to get customer data; it only needs to display
that information on a screen in particular format. Similarly, the
business layer doesn’t need to be concerned about how to format
customer data for display on a screen or even where the customer
data is coming from; it only needs to get the data from the persis‐
tence layer, perform business logic against the data (e.g., calculate
values or aggregate data), and pass that information up to the pre‐
sentation layer.
Figure 1-1. Layered architecture pattern
One of the powerful features of the layered architecture pattern is
the separation of concerns among components. Components within
a specific layer deal only with logic that pertains to that layer. For
example, components in the presentation layer deal only with pre‐
sentation logic, whereas components residing in the business layer
deal only with business logic. This type of component classification
makes it easy to build effective roles and responsibility models into
your architecture, and also makes it easy to develop, test, govern,
and maintain applications using this architecture pattern due to
well-defined component interfaces and limited component scope.
2 | Chapter 1: Layered Architecture

Key Concepts
Notice in Figure 1-2 that each of the layers in the architecture is
marked as being closed. This is a very important concept in the lay‐
ered architecture pattern. A closed layer means that as a request
moves from layer to layer, it must go through the layer right below it
to get to the next layer below that one. For example, a request origi‐
nating from the presentation layer must first go through the busi‐
ness layer and then to the persistence layer before finally hitting the
database layer.
Figure 1-2. Closed layers and request access
So why not allow the presentation layer direct access to either the
persistence layer or database layer? After all, direct database access
from the presentation layer is much faster than going through a
bunch of unnecessary layers just to retrieve or save database infor‐
mation. The answer to this question lies in a key concept known
as layers of isolation.
The layers of isolation concept means that changes made in one
layer of the architecture generally don’t impact or affect components
in other layers: the change is isolated to the components within that
layer, and possibly another associated layer (such as a persistence
layer containing SQL). If you allow the presentation layer direct
access to the persistence layer, then changes made to SQL within the
Key Concepts | 3

persistence layer would impact both the business layer and the pre‐
sentation layer, thereby producing a very tightly coupled application
with lots of interdependencies between components. This type of
architecture then becomes very hard and expensive to change.
The layers of isolation concept also means that each layer is inde‐
pendent of the other layers, thereby having little or no knowledge of
the inner workings of other layers in the architecture. To understand
the power and importance of this concept, consider a large refactor‐
ing effort to convert the presentation framework from JSP (Java
Server Pages) to JSF (Java Server Faces). Assuming that the contracts
(e.g., model) used between the presentation layer and the business
layer remain the same, the business layer is not affected by the refac‐
toring and remains completely independent of the type of user-
interface framework used by the presentation layer.
While closed layers facilitate layers of isolation and therefore help
isolate change within the architecture, there are times when it makes
sense for certain layers to be open. For example, suppose you want
to add a shared-services layer to an architecture containing com‐
mon service components accessed by components within the busi‐
ness layer (e.g., data and string utility classes or auditing and logging
classes). Creating a services layer is usually a good idea in this case
because architecturally it restricts access to the shared services to the
business layer (and not the presentation layer). Without a separate
layer, there is nothing architecturally that restricts the presentation
layer from accessing these common services, making it difficult to
govern this access restriction.
In this example, the new services layer would likely reside below the
business layer to indicate that components in this services layer are
not accessible from the presentation layer. However, this presents a
problem in that the business layer is now required to go through the
services layer to get to the persistence layer, which makes no sense at
all. This is an age-old problem with the layered architecture, and is
solved by creating open layers within the architecture.
As illustrated in Figure 1-3, the services layer in this case is marked
as open, meaning requests are allowed to bypass this open layer and
go directly to the layer below it. In the following example, since the
services layer is open, the business layer is now allowed to bypass it
and go directly to the persistence layer, which makes perfect sense.

Figure 1-3. Open layers and request flow
Leveraging the concept of open and closed layers helps define the
relationship between architecture layers and request flows and also
provides designers and developers with the necessary information to
understand the various layer access restrictions within the architec‐
ture. Failure to document or properly communicate which layers in
the architecture are open and closed (and why) usually results in
tightly coupled and brittle architectures that are very difficult to test,
maintain, and deploy.
Pattern Example
To illustrate how the layered architecture works, consider a request
from a business user to retrieve customer information for a particu‐
lar individual as illustrated in Figure 1-4. The black arrows show
the request flowing down to the database to retrieve the customer
data, and the red arrows show the response flowing back up to the
screen to display the data. In this example, the customer informa‐
tion consists of both customer data and order data (orders placed by
the customer).
Pattern Example | 5

The customer screen is responsible for accepting the request and dis‐
playing the customer information. It does not know where the data
is, how it is retrieved, or how many database tables must be queries
to get the data. Once the customer screen receives a request to get
customer information for a particular individual, it then forwards
that request onto the customer delegate module. This module is
responsible for knowing which modules in the business layer can
process that request and also how to get to that module and what
data it needs (the contract). The customer object in the business layer
is responsible for aggregating all of the information needed by the
business request (in this case to get customer information). This
module calls out to the customer dao (data access object) module in
the persistence layer to get customer data, and also the order dao
module to get order information. These modules in turn execute
SQL statements to retrieve the corresponding data and pass it back
up to the customer object in the business layer. Once the customer
object receives the data, it aggregates the data and passes that infor‐
mation back up to the customer delegate, which then passes that
data to the customer screen to be presented to the user.
Figure 1-4. Layered architecture example
From a technology perspective, there are literally dozens of ways
these modules can be implemented. For example, in the Java plat‐
form, the customer screen can be a (JSF) Java Server Faces screen

coupled with the customer delegate as the managed bean compo‐
nent. The customer object in the business layer can be a local Spring
bean or a remote EJB3 bean. The data access objects illustrated in
the previous example can be implemented as simple POJO’s (Plain
Old Java Objects), MyBatis XML Mapper files, or even objects
encapsulating raw JDBC calls or Hibernate queries. From a Micro‐
soft platform perspective, the customer screen can be an ASP (active
server pages) module using the .NET framework to access C# mod‐
ules in the business layer, with the customer and order data access
modules implemented as ADO (ActiveX Data Objects).
Considerations
The layered architecture pattern is a solid general-purpose pattern,
making it a good starting point for most applications, particularly
when you are not sure what architecture pattern is best suited for
your application. However, there are a couple of things to consider
from an architecture standpoint when choosing this pattern.
The first thing to watch out for is what is known as the architecture
sinkhole anti-pattern. This anti-pattern describes the situation where
requests flow through multiple layers of the architecture as simple
pass-through processing with little or no logic performed within
each layer. For example, assume the presentation layer responds to a
request from the user to retrieve customer data. The presentation
layer passes the request to the business layer, which simply passes
the request to the persistence layer, which then makes a simple SQL
call to the database layer to retrieve the customer data. The data is
then passed all the way back up the stack with no additional pro‐
cessing or logic to aggregate, calculate, or transform the data.
Every layered architecture will have at least some scenarios that fall
into the architecture sinkhole anti-pattern. The key, however, is to
analyze the percentage of requests that fall into this category. The
80-20 rule is usually a good practice to follow to determine whether
or not you are experiencing the architecture sinkhole anti-pattern. It
is typical to have around 20 percent of the requests as simple pass-
through processing and 80 percent of the requests having some
business logic associated with the request. However, if you find that
this ratio is reversed and a majority of your requests are simple pass-
through processing, you might want to consider making some of the
Considerations | 7

architecture layers open, keeping in mind that it will be more diffi‐
cult to control change due to the lack of layer isolation.
Another consideration with the layered architecture pattern is that it
tends to lend itself toward monolithic applications, even if you split
the presentation layer and business layers into separate deployable
units. While this may not be a concern for some applications, it does
pose some potential issues in terms of deployment, general robust‐
ness and reliability, performance, and scalability.
Pattern Analysis
The following table contains a rating and analysis of the common
architecture characteristics for the layered architecture pattern. The
rating for each characteristic is based on the natural tendency
for that characteristic as a capability based on a typical implementa‐
tion of the pattern, as well as what the pattern is generally known
for. For a side-by-side comparison of how this pattern relates to
other patterns in this report, please refer to Appendix A at the end
of this report.
Overall agility
Rating: Low
Analysis: Overall agility is the ability to respond quickly to a
constantly changing environment. While change can be isolated
through the layers of isolation feature of this pattern, it is still
cumbersome and time-consuming to make changes in this
architecture pattern because of the monolithic nature of most
implementations as well as the tight coupling of components
usually found with this pattern.
Ease of deployment
Rating: Low
Analysis: Depending on how you implement this pattern,
deployment can become an issue, particularly for larger applica‐
tions. One small change to a component can require a
redeployment of the entire application (or a large portion of the
application), resulting in deployments that need to be planned,
scheduled, and executed during off-hours or on weekends.
As such, this pattern does not easily lend itself toward a contin‐
uous delivery pipeline, further reducing the overall rating for
deployment.

Testability
Rating: High
Analysis: Because components belong to specific layers in the
architecture, other layers can be mocked or stubbed, making
this pattern is relatively easy to test. A developer can mock a
presentation component or screen to isolate testing within a
business component, as well as mock the business layer to test
certain screen functionality.
Performance
Rating: Low
Analysis: While it is true some layered architectures can per‐
form well, the pattern does not lend itself to high-performance
applications due to the inefficiencies of having to go through
multiple layers of the architecture to fulfill a business request.
Scalability
Rating: Low
Analysis: Because of the trend toward tightly coupled and mon‐
olithic implementations of this pattern, applications build using
this architecture pattern are generally difficult to scale. You can
scale a layered architecture by splitting the layers into separate
physical deployments or replicating the entire application into
multiple nodes, but overall the granularity is too broad, making
it expensive to scale.
Ease of development
Rating: High
Analysis: Ease of development gets a relatively high score,
mostly because this pattern is so well known and is not overly
complex to implement. Because most companies develop appli‐
cations by separating skill sets by layers (presentation, business,
database), this pattern becomes a natural choice for most
business-application development. The connection between a
company’s communication and organization structure and the
way it develops software is outlined is what is called Conway’s
law. You can Google “Conway’s law" to get more information
about this fascinating correlation.
Pattern Analysis | 9

CHAPTER 2
Event-Driven Architecture
The event-driven architecture pattern is a popular distributed
asynchronous architecture pattern used to produce highly scalable
applications. It is also highly adaptable and can be used for small
applications and as well as large, complex ones. The event-driven
architecture is made up of highly decoupled, single-purpose event
processing components that asynchronously receive and process
events.
The event-driven architecture pattern consists of two main topolo‐
gies, the mediator and the broker. The mediator topology is com‐
monly used when you need to orchestrate multiple steps within an
event through a central mediator, whereas the broker topology is
used when you want to chain events together without the use of a
central mediator. Because the architecture characteristics and imple‐
mentation strategies differ between these two topologies, it is impor‐
tant to understand each one to know which is best suited for your
particular situation.
Mediator Topology
The mediator topology is useful for events that have multiple steps
and require some level of orchestration to process the event. For
example, a single event to place a stock trade might require you to
first validate the trade, then check the compliance of that stock trade
against various compliance rules, assign the trade to a broker, calcu‐
late the commission, and finally place the trade with that broker. All
of these steps would require some level of orchestration to deter‐
11

mine the order of the steps and which ones can be done serially and
in parallel.
There are four main types of architecture components within the
mediator topology: event queues, an event mediator, event channels,
and event processors. The event flow starts with a client sending an
event to an event queue, which is used to transport the event to the
event mediator. The event mediator receives the initial event and
orchestrates that event by sending additional asynchronous events
to event channels to execute each step of the process. Event process‐
ors, which listen on the event channels, receive the event from the
event mediator and execute specific business logic to process the
event. Figure 2-1 illustrates the general mediator topology of the
event-driven architecture pattern.
Figure 2-1. Event-driven architecture mediator topology
It is common to have anywhere from a dozen to several hundred
event queues in an event-driven architecture. The pattern does
not specify the implementation of the event queue component; it
can be a message queue, a web service endpoint, or any combination
thereof.
There are two types of events within this pattern: an initial event and
a processing event. The initial event is the original event received by
12 | Chapter 2: Event-Driven Architecture

the mediator, whereas the processing events are ones that
are generated by the mediator and received by the event-processing
components.
The event-mediator component is responsible for orchestrating
the steps contained within the initial event. For each step in the ini‐
tial event, the event mediator sends out a specific processing event
to an event channel, which is then received and processed by the
event processor. It is important to note that the event mediator
doesn’t actually perform the business logic necessary to process the
initial event; rather, it knows of the steps required to process the ini‐
tial event.
Event channels are used by the event mediator to asynchronously
pass specific processing events related to each step in the initial
event to the event processors. The event channels can be either mes‐
sage queues or message topics, although message topics are most
widely used with the mediator topology so that processing events
can be processed by multiple event processors (each performing a
different task based on the processing event received).
The event processor components contain the application business
logic necessary to process the processing event. Event processors are
self-contained, independent, highly decoupled architecture compo‐
nents that perform a specific task in the application or system.
While the granularity of the event-processor component can vary
from fine-grained (e.g., calculate sales tax on an order) to coarse-
grained (e.g., process an insurance claim), it is important to keep in
mind that in general, each event-processor component should per‐
form a single business task and not rely on other event processors to
complete its specific task.
The event mediator can be implemented in a variety of ways. As an
architect, you should understand each of these implementation
options to ensure that the solution you choose for the event media‐
tor matches your needs and requirements.
The simplest and most common implementation of the event medi‐
ator is through open source integration hubs such as Spring Integra‐
tion, Apache Camel, or Mule ESB. Event flows in these open source
integration hubs are typically implemented through Java code or a
DSL (domain-specific language). For more sophisticated mediation
and orchestration, you can use BPEL (business process execution
language) coupled with a BPEL engine such as the open source
Mediator Topology | 13

Apache ODE. BPEL is a standard XML-like language that describes
the data and steps required for processing an initial event. For very
large applications requiring much more sophisticated orchestration
(including steps involving human interactions), you can implement
the event mediator using a business process manager (BPM) such
as jBPM.
Understanding your needs and matching them to the correct event
mediator implementation is critical to the success of any event-
driven architecture using this topology. Using an open source inte‐
gration hub to do very complex business process management
orchestration is a recipe for failure, just as is implementing a BPM
solution to perform simple routing logic.
To illustrate how the mediator topology works, suppose you are
insured through an insurance company and you decide to move. In
this case, the initial event might be called something like relocation
event. The steps involved in processing a relocation event are con‐
tained within the event mediator as shown in Figure 2-2. For each
initial event step, the event mediator creates a processing event (e.g.,
change address, recalc quote, etc.), sends that processing event to the
event channel and waits for the processing event to be processed by
the corresponding event processor (e.g., customer process, quote
process, etc.). This process continues until all of the steps in the ini‐
tial event have been processed. The single bar over the recalc quote
and update claims steps in the event mediator indicates that these
steps can be run at the same time.
Broker Topology
The broker topology differs from the mediator topology in that
there is no central event mediator; rather, the message flow is dis‐
tributed across the event processor components in a chain-like
fashion through a lightweight message broker (e.g., ActiveMQ,
HornetQ, etc.). This topology is useful when you have a relatively
simple event processing flow and you do not want (or need) central
event orchestration.
There are two main types of architecture components within the
broker topology: a broker component and an event processor compo‐
nent. The broker component can be centralized or federated and
contains all of the event channels that are used within the event flow.

The event channels contained within the broker component can be
message queues, message topics, or a combination of both.
Figure 2-2. Mediator topology example
This topology is illustrated in Figure 2-3. As you can see from the
diagram, there is no central event-mediator component controlling
and orchestrating the initial event; rather, each event-processor
component is responsible for processing an event and publishing a
new event indicating the action it just performed. For example, an
event processor that balances a portfolio of stocks may receive an
initial event called stock split. Based on that initial event, the event
processor may do some portfolio rebalancing, and then publish a
new event to the broker called rebalance portfolio, which would then
be picked up by a different event processor. Note that there may be
times when an event is published by an event processor but not
picked up by any another event processor. This is common when
you are evolving an application or providing for future functionality
and extensions.
Broker Topology | 15

Figure 2-3. Event-driven architecture broker topology
To illustrate how the broker topology works, we’ll use the same
example as in the mediator topology (an insured person moves).
Since there is no central event mediator to receive the initial event in
the broker topology, the customer-process component receives the
event directly, changes the customer address, and sends out an event
saying it changed a customer’s address (e.g., change address event).
In this example, there are two event processors that are interested in
the change address event: the quote process and the claims process.
The quote processor component recalculates the new auto-
insurance rates based on the address change and publishes an event
to the rest of the system indicating what it did (e.g., recalc quote
event). The claims processing component, on the other hand,
receives the same change address event, but in this case, it updates an
outstanding insurance claim and publishes an event to the system as
an update claim event. These new events are then picked up by other
event processor components, and the event chain continues through
the system until there are no more events are published for that par‐
ticular initiating event.

Figure 2-4. Broker topology example
As you can see from Figure 2-4, the broker topology is all about the
chaining of events to perform a business function. The best way to
understand the broker topology is to think about it as a relay race.
In a relay race, runners hold a baton and run for a certain distance,
then hand off the baton to the next runner, and so on down the
chain until the last runner crosses the finish line. In relay races, once
a runner hands off the baton, she is done with the race. This is also
true with the broker topology: once an event processor hands
off the event, it is no longer involved with the processing of that spe‐
cific event.
Considerations
The event-driven architecture pattern is a relatively complex pattern
to implement, primarily due to its asynchronous distributed nature.
When implementing this pattern, you must address various dis‐
tributed architecture issues, such as remote process availability, lack
of responsiveness, and broker reconnection logic in the event of a
broker or mediator failure.
Considerations | 17

One consideration to take into account when choosing this architec‐
ture pattern is the lack of atomic transactions for a single business
process. Because event processor components are highly decoupled
and distributed, it is very difficult to maintain a transactional
unit of work across them. For this reason, when designing your
application using this pattern, you must continuously think about
which events can and can’t run independently and plan the granu‐
larity of your event processors accordingly. If you find that you need
to split a single unit of work across event processors—that is, if
you are using separate processors for something that should be an
undivided transaction—this is probably not the right pattern for
your application.
Perhaps one of the most difficult aspects of the event-driven archi‐
tecture pattern is the creation, maintenance, and governance of the
event-processor component contracts. Each event usually has a spe‐
cific contract associated with it (e.g., the data values and data format
being passed to the event processor). It is vitally important when
using this pattern to settle on a standard data format (e.g., XML,
JSON, Java Object, etc.) and establish a contract versioning policy
right from the start.
Pattern Analysis
architecture characteristics for the event-driven architecture pattern.
The rating for each characteristic is based on the natural tendency
of this report.
Overall agility
Rating: High
constantly changing environment. Since event-processor com‐
ponents are single-purpose and completely decoupled from
other event processor components, changes are generally iso‐
lated to one or a few event processors and can be made quickly
without impacting other components.

Ease of deployment
Rating: High
Analysis: Overall this pattern is relatively easy to deploy due to
the decoupled nature of the event-processor components. The
broker topology tends to be easier to deploy than the
mediator topology, primarily because the event mediator com‐
ponent is somewhat tightly coupled to the event processors: a
change in an event processor component might also require a
change in the event mediator, requiring both to be deployed for
any given change.
Testability
Rating: Low
Analysis: While individual unit testing is not overly difficult, it
does require some sort of specialized testing client or testing
tool to generate events. Testing is also complicated by the asyn‐
chronous nature of this pattern.
Performance
Rating: High
Analysis: While it is certainly possible to implement an event-
driven architecture that does not perform well due to all the
messaging infrastructure involved, in general, the pattern ach‐
ieves high performance through its asynchronous capabili‐
ties; in other words, the ability to perform decoupled, parallel
asynchronous operations outweighs the cost of queuing and
dequeuing messages.
Scalability
Rating: High
Analysis: Scalability is naturally achieved in this pattern through
highly independent and decoupled event processors. Each event
processor can be scaled separately, allowing for fine-grained
scalability.
Ease of development
Rating: Low
Analysis: Development can be somewhat complicated due to
the asynchronous nature of the pattern as well as contract cre‐
ation and the need for more advanced error handling condi‐
tions within the code for unresponsive event processors and
failed brokers.

CHAPTER 3
Microkernel Architecture
The microkernel architecture pattern (sometimes referred to as the
plug-in architecture pattern) is a natural pattern for implementing
product-based applications. A product-based application is one that
is packaged and made available for download in versions as a typical
third-party product. However, many companies also develop and
release their internal business applications like software products,
complete with versions, release notes, and pluggable features. These
are also a natural fit for this pattern. The microkernel architecture
pattern allows you to add additional application features as plug-ins
to the core application, providing extensibility as well as feature sep‐
aration and isolation.
Pattern Description
The microkernel architecture pattern consists of two types of archi‐
tecture components: a core system and plug-in modules. Application
logic is divided between independent plug-in modules and the basic
core system, providing extensibility, flexibility, and isolation of
application features and custom processing logic. Figure 3-1 illus‐
trates the basic microkernel architecture pattern.
The core system of the microkernel architecture pattern tradition‐
ally contains only the minimal functionality required to make the
system operational. Many operating systems implement the micro‐
kernel architecture pattern, hence the origin of this pattern’s name.
From a business-application perspective, the core system is often
21

defined as the general business logic sans custom code for special
cases, special rules, or complex conditional processing.
Figure 3-1. Microkernel architecture pattern
The plug-in modules are stand-alone, independent components that
contain specialized processing, additional features, and custom code
that is meant to enhance or extend the core system to produce addi‐
tional business capabilities. Generally, plug-in modules should be
independent of other plug-in modules, but you can certainly design
plug-ins that require other plug-ins to be present. Either way, it is
important to keep the communication between plug-ins to a mini‐
mum to avoid dependency issues.
The core system needs to know about which plug-in modules are
available and how to get to them. One common way of implement‐
ing this is through some sort of plug-in registry. This registry con‐
tains information about each plug-in module, including things like
its name, data contract, and remote access protocol details (depend‐
ing on how the plug-in is connected to the core system). For exam‐
ple, a plug-in for tax software that flags high-risk tax audit items
might have a registry entry that contains the name of the
service (AuditChecker), the data contract (input data and output
data), and the contract format (XML). It might also contain a WSDL
(Web Services Definition Language) if the plug-in is accessed
through SOAP.
Plug-in modules can be connected to the core system through a
variety of ways, including OSGi (open service gateway initiative),
messaging, web services, or even direct point-to-point binding (i.e.,
object instantiation). The type of connection you use depends on
the type of application you are building (small product or large busi‐
ness application) and your specific needs (e.g., single deploy or dis‐
22 | Chapter 3: Microkernel Architecture

tributed deployment). The architecture pattern itself does not
specify any of these implementation details, only that the plug-in
modules must remain independent from one another.
The contracts between the plug-in modules and the core system can
range anywhere from standard contracts to custom ones. Custom
contracts are typically found in situations where plug-in compo‐
nents are developed by a third party where you have no control over
the contract used by the plug-in. In such cases, it is common to cre‐
ate an adapter between the plug-in contact and your standard con‐
tract so that the core system doesn’t need specialized code for each
plug-in. When creating standard contracts (usually implemented
through XML or a Java Map), it is important to remember to create
a versioning strategy right from the start.
Pattern Examples
Perhaps the best example of the microkernel architecture is the
Eclipse IDE. Downloading the basic Eclipse product provides you
little more than a fancy editor. However, once you start adding
plug-ins, it becomes a highly customizable and useful product.
Internet browsers are another common product example using the
microkernel architecture: viewers and other plug-ins add additional
capabilities that are not otherwise found in the basic browser (i.e.,
core system).
The examples are endless for product-based software, but what
about large business applications? The microkernel architecture
applies to these situations as well. To illustrate this point, let’s use
another insurance company example, but this time one involving
insurance claims processing.
Claims processing is a very complicated process. Each state has dif‐
ferent rules and regulations for what is and isn’t allowed in an insur‐
ance claim. For example, some states allow free windshield
replacement if your windshield is damaged by a rock, whereas other
states do not. This creates an almost infinite set of conditions for a
standard claims process.
Not surprisingly, most insurance claims applications leverage large
and complex rules engines to handle much of this complexity. How‐
ever, these rules engines can grow into a complex big ball of mud
where changing one rule impacts other rules, or making a
Pattern Examples | 23

simple rule change requires an army of analysts, developers, and
testers. Using the microkernel architecture pattern can solve many
of these issues.
The stack of folders you see in Figure 3-2 represents the core system
for claims processing. It contains the basic business logic required
by the insurance company to process a claim, except without any
custom processing. Each plug-in module contains the specific rules
for that state. In this example, the plug-in modules can be imple‐
mented using custom source code or separate rules engine instances.
Regardless of the implementation, the key point is that state-specific
rules and processing is separate from the core claims system and can
be added, removed, and changed with little or no effect on the rest
of the core system or other plug-in modules.
Figure 3-2. Microkernel architecture example
Considerations
One great thing about the microkernel architecture pattern is that it
can be embedded or used as part of another architecture pattern.
For example, if this pattern solves a particular problem you have
with a specific volatile area of the application, you might find that
you can’t implement the entire architecture using this pattern. In this
case, you can embed the microservices architecture pattern in
another pattern you are using (e.g., layered architecture). Similarly,
the event-processor components described in the previous section
on event-driven architecture could be implemented using the
microservices architecture pattern.
The microservices architecture pattern provides great support for
evolutionary design and incremental development. You can first
produce a solid core system, and as the application evolves incre‐

mentally, add features and functionality without having to make sig‐
nificant changes to the core system.
For product-based applications, the microkernel architecture pat‐
tern should always be your first choice as a starting architecture,
particularly for those products where you will be releasing addi‐
tional features over time and want control over which users
get which features. If you find over time that the pattern doesn’t sat‐
isfy all of your requirements, you can always refactor your applica‐
tion to another architecture pattern better suited for your specific
requirements.
Pattern Analysis
architecture characteristics for the microkernel architecture pattern.
of this report.
Overall agility
Rating: High
constantly changing environment. Changes can largely be iso‐
lated and implemented quickly through loosely coupled plug-in
modules. In general, the core system of most microkernel archi‐
tectures tends to become stable quickly, and as such is fairly
robust and requires few changes over time.
Ease of deployment
Rating: High
Analysis: Depending on how the pattern is implemented,
the plug-in modules can be dynamically added to the core sys‐
tem at runtime (e.g., hot-deployed), minimizing downtime dur‐
ing deployment.

Testability
Rating: High
Analysis: Plug-in modules can be tested in isolation and can be
easily mocked by the core system to demonstrate or prototype a
particular feature with little or no change to the core system.
Performance
Rating: High
Analysis: While the microkernel pattern does not naturally lend
itself to high-performance applications, in general, most appli‐
cations built using the microkernel architecture pattern perform
well because you can customize and streamline applications to
only include those features you need. The JBoss Application
Server is a good example of this: with its plug-in architecture,
you can trim down the application server to only those features
you need, removing expensive non-used features such as
remote access, messaging, and caching that consume memory,
CPU, and threads and slow down the app server.
Scalability
Rating: Low
Analysis: Because most microkernel architecture implementa‐
tions are product based and are generally smaller in size, they
are implemented as single units and hence not highly scalable.
Depending on how you implement the plug-in modules, you
can sometimes provide scalability at the plug-in feature level,
but overall this pattern is not known for producing highly scala‐
ble applications.
Ease of development
Rating: Low
Analysis: The microkernel architecture requires thoughtful
design and contract governance, making it rather complex to
implement. Contract versioning, internal plug-in registries,
plug-in granularity, and the wide choices available for plug-in
connectivity all contribute to the complexity involved with
implementing this pattern.

CHAPTER 4
Microservices Architecture Pattern
The microservices architecture pattern is quickly gaining ground in
the industry as a viable alternative to monolithic applications and
service-oriented architectures. Because this architecture pattern is
still evolving, there’s a lot of confusion in the industry about what
this pattern is all about and how it is implemented. This section of
the report will provide you with the key concepts and foundational
knowledge necessary to understand the benefits (and trade-offs) of
this important architecture pattern and whether it is the right pat‐
tern for your application.
Pattern Description
Regardless of the topology or implementation style you chose, there
are several common core concepts that apply to the general architec‐
ture pattern. The first of these concepts is the notion of separately
deployed units. As illustrated in Figure 4-1, each component of the
microservices architecture is deployed as a separate unit, allowing
for easier deployment through an effective and streamlined delivery
pipeline, increased scalability, and a high degree of application and
component decoupling within your application.
Perhaps the most important concept to understand with this pattern
is the notion of a service component. Rather than think about serv‐
ices within a microservices architecture, it is better to think about
service components, which can vary in granularity from a single
module to a large portion of the application. Service components
contain one or more modules (e.g., Java classes) that represent either
27

a single-purpose function (e.g., providing the weather for a specific
city or town) or an independent portion of a large business applica‐
tion (e.g., stock trade placement or determining auto-insurance
rates). Designing the right level of service component granularity is
one of the biggest challenges within a microservices architecture.
This challenge is discussed in more detail in the following service-
component orchestration subsection.
Figure 4-1. Basic Microservices architecture pattern
Another key concept within the microservices architecture pattern
is that it is a distributed architecture, meaning that all the compo‐
nents within the architecture are fully decoupled from one other
and accessed through some sort of remote access protocol (e.g.,
JMS, AMQP, REST, SOAP, RMI, etc.). The distributed nature of this
architecture pattern is how it achieves some of its superior scalabil‐
ity and deployment characteristics.
One of the exciting things about the microservices architecture is
that it evolved from issues associated with other common architec‐
ture patterns, rather than being created as a solution waiting for a
problem to occur. The microservices architecture style naturally
evolved from two main sources: monolithic applications developed
using the layered architecture pattern and distributed applications
developed through the service-oriented architecture pattern.
The evolutionary path from monolithic applications to a microser‐
vices architecture style was prompted primarily through the devel‐
opment of continuous delivery, the notion of a continuous
28 | Chapter 4: Microservices Architecture Pattern

deployment pipeline from development to production which
streamlines the deployment of applications. Monolithic applications
typically consist of tightly coupled components that are part of a sin‐
gle deployable unit, making it cumbersome and difficult to change,
test, and deploy the application (hence the rise of the common
“monthly deployment” cycles typically found in most large IT
shops). These factors commonly lead to brittle applications that
break every time something new is deployed. The microservices
architecture pattern addresses these issues by separating the applica‐
tion into multiple deployable units (service components) that can be
individually developed, tested, and deployed independent of other
service components.
The other evolutionary path that lead to the microservices architec‐
ture pattern is from issues found with applications implementing
the service-oriented architecture pattern (SOA). While the SOA pat‐
tern is very powerful and offers unparalleled levels of abstraction,
heterogeneous connectivity, service orchestration, and the promise
of aligning business goals with IT capabilities, it is nevertheless com‐
plex, expensive, ubiquitous, difficult to understand and implement,
and is usually overkill for most applications. The microservices
architecture style addresses this complexity by simplifying the
notion of a service, eliminating orchestration needs, and simplifying
connectivity and access to service components.
Pattern Topologies
While there are literally dozens of ways to implement a microservi‐
ces architecture pattern, three main topologies stand out as the most
common and popular: the API REST-based topology, application
REST-based topology, and the centralized messaging topology.
The API REST-based topology is useful for websites that expose
small, self-contained individual services through some sort of
API (application programming interface). This topology, which is
illustrated in Figure 4-2, consists of very fine-grained service com‐
ponents (hence the name microservices) that contain one or two
modules that perform specific business functions independent from
the rest of the services. In this topology, these fine-grained service
components are typically accessed using a REST-based interface
implemented through a separately deployed web-based API layer.
Examples of this topology include some of the common single-
Pattern Topologies | 29

purpose cloud-based RESTful web services found by Yahoo, Google,
and Amazon.
Figure 4-2. API REST-based topology
The application REST-based topology differs from the API REST-
based approach in that client requests are received through tradi‐
tional web-based or fat-client business application screens rather
than through a simple API layer. As illustrated in Figure 4-3, the
user-interface layer of the application is deployed as a separate web
application that remotely accesses separately deployed service com‐
ponents (business functionality) through simple REST-based inter‐
faces. The service components in this topology differ from those in
the API-REST-based topology in that these service components tend
to be larger, more coarse-grained, and represent a small portion of
the overall business application rather than fine-grained, single-
action services. This topology is common for small to medium-sized
business applications that have a relatively low degree of complexity.

Figure 4-3. Application REST-based topology
Another common approach within the microservices architecture
pattern is the centralized messaging topology. This topology (illus‐
trated in Figure 4-4) is similar to the previous application REST-
based topology except that instead of using REST for remote access,
this topology uses a lightweight centralized message broker (e.g.,
ActiveMQ, HornetQ, etc.). It is vitally important when looking at
this topology not to confuse it with the service-oriented architecture
pattern or consider it “SOA-Lite." The lightweight message broker
found in this topology does not perform any orchestration, transfor‐
mation, or complex routing; rather, it is just a lightweight transport
to access remote service components.
The centralized messaging topology is typically found in larger busi‐
ness applications or applications requiring more sophisticated con‐
trol over the transport layer between the user interface and the
service components. The benefits of this topology over the simple
REST-based topology discussed previously are advanced queuing
mechanisms, asynchronous messaging, monitoring, error handling,
and better overall load balancing and scalability. The single point of
failure and architectural bottleneck issues usually associated with a
centralized broker are addressed through broker clustering and
broker federation (splitting a single broker instance into multiple
broker instances to divide the message throughput load based on
functional areas of the system).
Pattern Topologies | 31

Figure 4-4. Centralized messaging topology
Avoid Dependencies and Orchestration
One of the main challenges of the microservices architecture pattern
is determining the correct level of granularity for the service compo‐
nents. If service components are too coarse-grained you may not
realize the benefits that come with this architecture pattern (deploy‐
ment, scalability, testability, and loose coupling). However, service
components that are too fine-grained will lead to service orchestra‐
tion requirements, which will quickly turn your lean microservices
architecture into a heavyweight service-oriented architecture, com‐
plete with all the complexity, confusion, expense, and fluff typically
found with SOA-based applications.
If you find you need to orchestrate your service components from
within the user interface or API layer of the application, then chan‐
ces are your service components are too fine-grained. Similarly, if
you find you need to perform inter-service communication between
service components to process a single request, chances are your
service components are either too fine-grained or they are not parti‐
tioned correctly from a business functionality standpoint.
Inter-service communication, which could force undesired cou‐
plings between components, can be handled instead through a

shared database. For example, if a service component handing Inter‐
net orders needs customer information, it can go to the database to
retrieve the necessary data as opposed to invoking functionality
within the customer-service component.
The shared database can handle information needs, but what about
shared functionality? If a service component needs functionality
contained within another service component or common to all ser‐
vice components, you can sometimes copy the shared functionality
across service components (thereby violating the DRY princi‐
ple: don’t repeat yourself). This is a fairly common practice in most
business applications implementing the microservices architecture
pattern, trading off the redundancy of repeating small portions of
business logic for the sake of keeping service components independ‐
ent and separating their deployment. Small utility classes might fall
into this category of repeated code.
If you find that regardless of the level of service component granu‐
larity you still cannot avoid service-component orchestration, then
it’s a good sign that this might not be the right architecture pattern
for your application. Because of the distributed nature of this pat‐
tern, it is very difficult to maintain a single transactional unit of
work across (and between) service components. Such a practice
would require some sort of transaction compensation framework
for rolling back transactions, which adds significant complexity to
this relatively simple and elegant architecture pattern.
Considerations
The microservices architecture pattern solves many of the common
issues found in both monolithic applications as well as service-
oriented architectures. Since major application components are
split up into smaller, separately deployed units, applications built
using the microservices architecture pattern are generally more
robust, provide better scalability, and can more easily support con‐
tinuous delivery.
Another advantage of this pattern is that it provides the capability to
do real-time production deployments, thereby significantly reducing
the need for the traditional monthly or weekend “big bang” produc‐
tion deployments. Since change is generally isolated to specific ser‐
vice components, only the service components that change need
to be deployed. If you only have a single instance of a service com‐
Considerations | 33

ponent, you can write specialized code in the user interface applica‐
tion to detect an active hot-deployment and redirect users to an
error page or waiting page. Alternatively, you can swap multiple
instances of a service component in and out during a real-time
deployment, allowing for continuous availability during deployment
cycles (something that is very difficult to do with the layered archi‐
tecture pattern).
One final consideration to take into account is that since the micro‐
services architecture pattern is a distributed architecture, it shares
some of the same complex issues found in the event-driven architec‐
ture pattern, including contract creation, maintenance, and govern‐
ment, remote system availability, and remote access authentication
and authorization.
Pattern Analysis
architecture characteristics for the microservices architecture pat‐
tern. The rating for each characteristic is based on the natural ten‐
dency for that characteristic as a capability based on a typical
implementation of the pattern, as well as what the pattern is gener‐
ally known for. For a side-by-side comparison of how this pattern
relates to other patterns in this report, please refer to Appendix A at
the end of this report.
Overall agility
Rating: High
constantly changing environment. Due to the notion of sepa‐
rately deployed units, change is generally isolated to individual
service components, which allows for fast and easy deployment.
Also, applications build using this pattern tend to be very
loosely coupled, which also helps facilitate change.
Ease of deployment
Rating: High
Analysis: Overall this pattern is relatively easy to deploy due to
the decoupled nature of the event-processor components. The
broker topology tends to be easier to deploy than the mediator
topology, primarily because the event-mediator component is
somewhat tightly coupled to the event processors: a change in
an event processor component might also require a change in

the event mediator, requiring both to be deployed for any
given change.
Testability
Rating: High
Analysis: Due to the separation and isolation of business func‐
tionality into independent applications, testing can be scoped,
allowing for more targeted testing efforts. Regression testing for
a particular service component is much easier and more feasible
than regression testing for an entire monolithic application.
Also, since the service components in this pattern are loosely
coupled, there is much less of a chance from a development per‐
spective of making a change that breaks another part of the
application, easing the testing burden of having to test the entire
application for one small change.
Performance
Rating: Low
Analysis: While you can create applications implemented from
this pattern that perform very well, overall this pattern does not
naturally lend itself to high-performance applications due to the
distributed nature of the microservices architecture pattern.
Scalability
Rating: High
Analysis: Because the application is split into separately
deployed units, each service component can be individually
scaled, allowing for fine-tuned scaling of the application. For
example, the admin area of a stock-trading application may not
need to scale due to the low user volumes for that functionality,
but the trade-placement service component may need to scale
due to the high throughput needed by most trading applications
for this functionality.
Ease of development
Rating: High
Analysis: Because functionality is isolated into separate and dis‐
tinct service components, development becomes easier due to
the smaller and isolated scope. There is much less chance a
developer will make a change in one service component that
would affect other service components, thereby reducing the
coordination needed among developers or development teams.

CHAPTER 5
Space-Based Architecture
Most web-based business applications follow the same general
request flow: a request from a browser hits the web server, then an
application server, then finally the database server. While this pat‐
tern works great for a small set of users, bottlenecks start appearing
as the user load increases, first at the web-server layer, then at the
application-server layer, and finally at the database-server layer. The
usual response to bottlenecks based on an increase in user load is to
scale out the web servers. This is relatively easy and inexpensive, and
sometimes works to address the bottleneck issues. However, in most
cases of high user load, scaling out the web-server layer just moves
the bottleneck down to the application server. Scaling application
servers can be more complex and expensive than web servers and
usually just moves the bottleneck down to the database server,
which is even more difficult and expensive to scale. Even if you can
scale the database, what you eventually end up with is a triangle-
shaped topology, with the widest part of the triangle being the web
servers (easiest to scale) and the smallest part being the database
(hardest to scale).
In any high-volume application with an extremely large concurrent
user load, the database will usually be the final limiting factor in
how many transactions you can process concurrently. While various
caching technologies and database scaling products help to address
these issues, the fact remains that scaling out a normal application
for extreme loads is a very difficult proposition.
37

The space-based architecture pattern is specifically designed to
address and solve scalability and concurrency issues. It is also a use‐
ful architecture pattern for applications that have variable and
unpredictable concurrent user volumes. Solving the extreme and
variable scalability issue architecturally is often a better approach
than trying to scale out a database or retrofit caching technologies
into a non-scalable architecture.
Pattern Description
The space-based pattern (also sometimes referred to as the cloud
architecture pattern) minimizes the factors that limit application
scaling. This pattern gets its name from the concept of tuple
space, the idea of distributed shared memory. High scalability is
achieved by removing the central database constraint and using
replicated in-memory data grids instead. Application data is kept in-
memory and replicated among all the active processing units. Pro‐
cessing units can be dynamically started up and shut down as user
load increases and decreases, thereby addressing variable scalabil‐
ity. Because there is no central database, the database bottleneck is
removed, providing near-infinite scalability within the application.
Most applications that fit into this pattern are standard websites that
receive a request from a browser and perform some sort of action. A
bidding auction site is a good example of this. The site continually
receives bids from internet users through a browser request. The
application would receive a bid for a particular item, record that bid
with a timestamp, and update the latest bid information for the item,
and send the information back to the browser.
There are two primary components within this architecture pat‐
tern: a processing unit and virtualized middleware. Figure 5-1 illus‐
trates the basic space-based architecture pattern and its primary
architecture components.
The processing-unit component contains the application compo‐
nents (or portions of the application components). This includes
web-based components as well as backend business logic. The con‐
tents of the processing unit varies based on the type of application—
smaller web-based applications would likely be deployed into a sin‐
gle processing unit, whereas larger applications may split the appli‐
cation functionality into multiple processing units based on the
functional areas of the application. The processing unit typically
38 | Chapter 5: Space-Based Architecture

contains the application modules, along with an in-memory data
grid and an optional asynchronous persistent store for failover. It
also contains a replication engine that is used by the virtualized mid‐
dleware to replicate data changes made by one processing unit to
other active processing units.
Figure 5-1. Space-based architecture pattern
The virtualized-middleware component handles housekeeping and
communications. It contains components that control various
aspects of data synchronization and request handling. Included in
the virtualized middleware are the messaging grid, data grid, pro‐
cessing grid, and deployment manager. These components, which
are described in detail in the next section, can be custom written or
purchased as third-party products.
Pattern Dynamics
The magic of the space-based architecture pattern lies in the virtual‐
ized middleware components and the in-memory data grid con‐
tained within each processing unit. Figure 5-2 shows the typical
processing unit architecture containing the application modules, in-
memory data grid, optional asynchronous persistence store for fail‐
over, and the data-replication engine.
The virtualized middleware is essentially the controller for the archi‐
tecture and manages requests, sessions, data replication, distributed
request processing, and process-unit deployment. There are four
main architecture components in the virtualized middleware: the
Pattern Dynamics | 39

messaging grid, the data grid, the processing grid, and the deploy‐
ment manager.
Figure 5-2. Processing-unit component
Messaging Grid
The messaging grid, shown in Figure 5-3, manages input request
and session information. When a request comes into the virtualized-
middleware component, the messaging-grid component determines
which active processing components are available to receive the
request and forwards the request to one of those processing
units. The complexity of the messaging grid can range from a simple
round-robin algorithm to a more complex next-available algorithm
that keeps track of which request is being processed by which pro‐
cessing unit.
Data Grid
The data-grid component is perhaps the most important and crucial
component in this pattern. The data grid interacts with the data-
replication engine in each processing unit to manage the data repli‐
cation between processing units when data updates occur. Since the
messaging grid can forward a request to any of the processing units
available, it is essential that each processing unit contains exactly the
same data in its in-memory data grid. Although Figure 5-4 shows a
synchronous data replication between processing units, in reality
this is done in parallel asynchronously and very quickly, sometimes
completing the data synchronization in a matter of microseconds
(one millionth of a second).

Figure 5-3. Messaging-grid component
Figure 5-4. Data-grid component
Processing Grid
The processing grid, illustrated in Figure 5-5, is an optional compo‐
nent within the virtualized middleware that manages distributed
request processing when there are multiple processing units, each
handling a portion of the application. If a request comes in that
requires coordination between processing unit types (e.g., an order
processing unit and a customer processing unit), it is the processing
Pattern Dynamics | 41

grid that mediates and orchestrates the request between those two
processing units.
Figure 5-5. Processing-grid component
Deployment Manager
The deployment-manager component manages the dynamic startup
and shutdown of processing units based on load conditions. This
component continually monitors response times and user loads, and
starts up new processing units when load increases, and shuts down
processing units when the load decreases. It is a critical component
to achieving variable scalability needs within an application.
Considerations
The space-based architecture pattern is a complex and expensive
pattern to implement. It is a good architecture choice for smaller
web-based applications with variable load (e.g., social media sites,
bidding and auction sites). However, it is not well suited for tradi‐
tional large-scale relational database applications with large amounts
of operational data.
Although the space-based architecture pattern does not require a
centralized datastore, one is commonly included to perform the ini‐
tial in-memory data grid load and asynchronously persist data
updates made by the processing units. It is also a common practice
to create separate partitions that isolate volatile and widely used

transactional data from non-active data, in order to reduce the
memory footprint of the in-memory data grid within each process‐
ing unit.
It is important to note that while the alternative name of this pattern
is the cloud-based architecture, the processing units (as well as the
virtualized middleware) do not have to reside on cloud-based hos‐
ted services or PaaS (platform as a service). It can just as easily
reside on local servers, which is one of the reasons I prefer the name
“space-based architecture.”
From a product implementation perspective, you can implement
many of the architecture components in this pattern through third-
party products such as GemFire, JavaSpaces, GigaSpaces, IBM
Object Grid, nCache, and Oracle Coherence. Because the imple‐
mentation of this pattern varies greatly in terms of cost and capabili‐
ties (particularly data replication times), as an architect, you should
first establish what your specific goals and needs are before making
any product selections.
Pattern Analysis
architecture characteristics for the space-based architecture pattern.
of this report.
Overall agility
Rating: High
constantly changing environment. Because processing units
(deployed instances of the application) can be brought up and
down quickly, applications respond well to changes related to an
increase or decrease in user load (environment changes).
Architectures created using this pattern generally respond well
to coding changes due to the small application size and dynamic
nature of the pattern.

Ease of deployment
Rating: High
Analysis: Although space-based architectures are generally not
decoupled and distributed, they are dynamic, and sophisticated
cloud-based tools allow for applications to easily be “pushed”
out to servers, simplifying deployment.
Testability
Rating: Low
Analysis: Achieving very high user loads in a test environment is
both expensive and time consuming, making it difficult to test
the scalability aspects of the application.
Performance
Rating: High
Analysis: High performance is achieved through the in-memory
data access and caching mechanisms build into this pattern.
Scalability
Rating: High
Analysis: High scalability come from the fact that there is little
or no dependency on a centralized database, therefore essen‐
tially removing this limiting bottleneck from the scalability
equation.
Ease of development
Rating: Low
Analysis: Sophisticated caching and in-memory data grid prod‐
ucts make this pattern relatively complex to develop, mostly
because of the lack of familiarity with the tools and products
used to create this type of architecture. Furthermore, special
care must be taken while developing these types of architectures
to make sure nothing in the source code impacts performance
and scalability.

APPENDIX A
Pattern Analysis Summary
Figure A-1 summarizes the pattern-analysis scoring for each of the
architecture patterns described in this report. This summary will
help you determine which pattern might be best for your situation.
For example, if your primary architectural concern is scalability, you
can look across this chart and see that the event-driven pattern,
microservices pattern, and space-based pattern are probably good
architecture pattern choices. Similarly, if you choose the layered
architecture pattern for your application, you can refer to the chart
to see that deployment, performance, and scalability might be risk
areas in your architecture.
45

Figure A-1. Pattern-analysis summary
While this chart will help guide you in choosing the right pattern,
there is much more to consider when choosing an architecture pat‐
tern. You must analyze all aspects of your environment, including
infrastructure support, developer skill set, project budget, project
deadlines, and application size (to name a few). Choosing the right
architecture pattern is critical, because once an architecture is in
place, it is very hard (and expensive) to change.
46 | Appendix A: Pattern Analysis Summary

About the Author
Mark Richards is an experienced, hands-on software architect
involved in the architecture, design, and implementation of micro‐
services architectures, service-oriented architectures, and dis‐
tributed systems in J2EE and other technologies. He has been in the
software industry since 1983 and has significant experience and
expertise in application, integration, and enterprise architecture.
Mark served as the president of the New England Java Users Group
from 1999 through 2003. He is the author of numerous technical
books and videos, including Software Architecture Fundamen‐
tals (O’Reilly video), Enterprise Messaging (O’Reilly video), Java
Message Service, 2nd Edition (O’Reilly), and a contributing author
to 97 Things Every Software Architect Should Know (O’Reilly).
Mark has a master’s degree in computer science and numerous
architect and developer certifications from IBM, Sun, The Open
Group, and BEA. He is a regular conference speaker at the No
Fluff Just Stuff (NFJS) Symposium Series and has spoken at more
than 100 conferences and user groups around the world on a vari‐
ety of enterprise-related technical topics. When he is not working,
Mark can usually be found hiking in the White Mountains or
along the Appalachian Trail.

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