The benefits of lightweight integration, Part 1
The fate of the ESB
From its origins in the SOA era to the challenges that inspired the
search for a better approach
This content is part # of # in the series: The benefits of lightweight integration, Part 1
Stay tuned for additional content in this series.
This content is part of the series:The benefits of lightweight integration, Part 1
Stay tuned for additional content in this series.
This two-part series explores the approach that modern integration
architectures are taking to ensure they can be as agile as the
applications that they interconnect. The pattern of the centralized
enterprise service bus (ESB) has served its purpose, and still has its
place, but many enterprises are exploring a more containerized and
decentralized approach to integration architecture.
Part 1 explores the fate of the ESB. We will briefly look at how and why
the centralized ESB pattern arose in the era of service-oriented
architecture (SOA), but also at the challenges that came as a result of
it. We’ll also consider where APIs fit into this picture and what
relationship there is between all this and microservices architecture.
Without a clear understanding of this history, we cannot make confident
statements about how to do things better in the future.
“SOA has an enterprise scope and looks at how integration
occurs between applications. Microservice architecture has an
application scope, dealing with how the internals of an application
Part 2 will describe lightweight integration, looking at how integration
architecture can benefit from the technologies and principles behind
microservices in order to ensure that new applications can perform the
integration they need at the pace and scale of modern innovation. We will
also explore how integration could be more fundamentally decentralized in
order to enable greater autonomy and productivity to lines of
Let’s begin by painting a clear picture of what a centralized ESB looks
like and where it came from.
Differentiating the ESB pattern from its predecessors
The term ESB is often used very loosely to describe integration runtimes in
general, but this is deeply inaccurate, both historically and
If you look back to somewhere just before the year 2000, integration was
almost exclusively asynchronous, using files and messages to integrate
between systems of record. Writing code in or around each system for each
and every point-to-point integration was expensive and resulted in a
complex web of interactions.
Figure 1. Point-to-point vs. hub-and-spoke
The inevitable result was to introduce an integration hub that sat between
all systems, and provided tools that made connectivity much simpler,
enabling some amount of re-use of the integration work performed.
It is critically important to note that this primarily asynchronous
hub-and-spoke architecture significantly predates the ESB pattern. There
are no services being exposed yet. The event-based interaction pattern of
hub and spoke is still very common today and, as you’ll see later, is
perhaps gaining a resurgence due to the increased preference for modern
applications to receive data via events. So, to be clear: The
hub-and-spoke pattern is not the same as the ESB pattern, even though it
may well be implemented using the same integration runtime.
The forming of the
As we started the millennium, we saw the beginnings of the first truly
cross-platform protocol for interfaces. The internet, and with it HTTP,
had become ubiquitous, XML was limping its way into existence off the back
of HTML, and the SOAP protocols for exposing synchronous web service
interfaces were just taking shape. Relatively wide acceptance of these
standards hinted at a brighter future where any system could discover and
talk to any other system via a real-time synchronous remote procedure
call, without reams of integration code as had been required in the
From this series of events, service-oriented architecture (SOA) was born.
The core purpose of SOA was to expose data and functions buried in systems
of record over well-formed, simple-to-use, synchronous interfaces, such as
web services. Clearly, SOA was about more than just exposing those
services, and often involved some significant re-engineering to align the
back-end systems with the business needs, but the end goal was a suite of
re-usable services. This would enable new applications to be implemented
without the burden of deep integration every time, as once the integration
was done for the first time and exposed as a service, it could be re-used
by the next application.
However, this simple integration was a one-sided equation. We might have
been able to standardize the exposure protocols and data formats, but the
back-end systems of record were typically old and had antiquated protocols
and data formats for their current interfaces. Something needed to mediate
between the old system and the new cross-platform protocols.
Figure 2. Synchronous exposure pattern
This synchronous exposure pattern via web services was what the
enterprise services bus (ESB) term was introduced for. It’s all in the
name—a centralized “bus” that could expose web “services” across
the “enterprise.” We already had the technology (the integration runtime)
to connect to the back-end systems, coming from the hub-and-spoke pattern.
These integration runtimes could simply be taught to expose integrations
synchronously via SOAP/HTTP, and we’d have our ESB.
A common source of confusion around the term ESB comes from the fact that
at this stage, there was only one component implementing the
pattern—the integration runtime. As a result, that integration
runtime was often simply referred to as an ESB. Although the integration
runtime was in fact performing two separate patterns (hub and spoke, and
service exposure), these looked similar enough on an architectural diagram
to be merged into a single thing. From that point on, the term ESB was
used indiscriminately to refer to the integration runtime itself,
regardless of what pattern it was performing.
What went wrong for the centralized ESB pattern?
SOA turned out to be a little more complex than just the implementation of
an ESB, for a host of reasons—not the least of which was the
question of who would fund such an enterprise-wide program. Implementing
the ESB pattern itself also turned out to be no small task.
The ESB pattern often took the “E” in ESB very literally, and implemented a
single ESB infrastructure for the whole enterprise, or at least one for
each significant part of the enterprise. Tens or even hundreds of
integrations might have been installed on a production server cluster, and
if that was scaled up, they would be present on every clone within that
cluster. Although this heavy centralization isn’t required by the ESB
pattern itself, it was almost always present in the resultant topology.
There were good reasons for this, at least initially: Hardware and
software costs were shared, provisioning of the servers only had to be
performed once, and due to the relative complexity of the software, only
one dedicated team of integration specialists needed to be skilled up to
perform the development work.
The centralized ESB pattern had the potential to deliver significant
savings in integration costs, if interfaces could be re-used from one
project to the next (the core benefit proposition of SOA). However,
coordinating such a cross-enterprise initiative and ensuring that it would
get continued funding—and that that funding only applied to
services that would be sufficiently re-usable to cover their creation
costs—proved to be very difficult indeed. Standards and tooling
were maturing at the same time as the ESB patterns were being implemented,
so the implementation cost and time for exposing a single service were
Often, line-of-business teams that were expecting a greater pace of
innovation in their new applications became increasingly frustrated with
SOA, and by extension the ESB pattern.
Some of the challenges of a centralized ESB pattern were:
- Deploying changes to the interfaces could potentially destabilize
other unrelated interfaces, so complex regression testing across a
wide range of interfaces was often needed.
- Runtimes were substantial in size due to the number of integrations
they contained, so starting and stopping them was highly undesirable.
They had to be kept running and patched live wherever possible. This
made it hard to track server configurations, and therefore hard to
replicate environments for testing and diagnosis, and caused great
resistance to adding changes.
- Creating topologies with high availability and disaster recovery for
these large servers was costly. Scaling up required up-front planning,
and additional servers were expensive.
- Applying the latest middleware fixes and features introduced in new
versions was risky, as it could affect existing integrations, so
servers typically ran many versions back. This required integration
developers to create work-arounds for features that were otherwise
available in newer versions.
- The integration specialist teams knew the integration tooling, but
often didn’t understand the applications they were trying to expose,
which added further lead time to the implementation cycle.
- Integration was still a complex field. Few systems exposed good
interfaces, so deep skills were required. Only a small handful of
integration specialists could be trusted to create, maintain, and
administer integrations. In order to pool these specialist resources,
they often formed separate “SOA” teams, with strict procedures for
receiving work in a waterfall style, introducing a separate dependency
to any application development project.
- Service discovery was immature at that time. There were very few
options that integrated a service registry with the runtime component.
This led to one of two outcomes: Either documentation was stored
separately and became quickly outdated, or documentation wasn’t stored
at all and each re-use required human-to-human interaction, which
eroded the time-to-market promise of re-use.
The result was that creation of services by this specialist SOA team became
a bottleneck for projects rather than the enabler that it was intended to
be. This typically gave by association the centralized ESB pattern a bad
Formally, as we’ve described, ESB is an architectural pattern that refers
to the exposure of services. However, as mentioned above, the term is
often over-simplified and applied to the integration engine that’s used to
implement the pattern. This erroneously ties the static and aging
centralized ESB pattern with integration engines that have changed
radically over the intervening time.
Integration engines of today are significantly more lightweight, easier to
install and use, and can be deployed in ways unimaginable at the time the
ESB pattern was born. Let’s take a look at how these modern runtimes
enable completely different architectural patterns that are more
lightweight and decentralized.
Supplementing the ESB pattern with a formal exposure
Exposure of request/response-based services is the key differentiator
between the ESB pattern and the more event-driven hub-and-spoke pattern
that preceded it. SOAP-style RPC interfaces proved complex to understand
and use, and simpler and more consistent RESTful services exposed using
JSON/HTTP became a popular mechanism for exposure. But the end goal was
the same: to make functions and data available via standardized interfaces
so that new applications could be built on top of them more quickly.
With the broadening usage of these service interfaces, both within and
beyond the enterprise, more formal mechanisms for exposure were required.
It quickly became clear that simply making something available over a web
service interface, or latterly as a RESTful JSON/HTTP API, was only part
of the story. That service needed to be easily discovered by potential
consumers, who needed a path of least resistance for gaining access to it
and learning how to use it. Additionally, the providers of the service or
API needed to be able to place controls on its usage, such as traffic
control and an appropriate security model.
Figure 3. Enhancing the ESB pattern with a separate service
Some of these capabilities could be introduced into the integration
runtime, but due to the heavyweight and complex nature of the centralized
ESB pattern, this meant adding even more responsibilities to the already
overburdened ESB team. A common alternative approach was to separate the
role of service/API exposure out into a separate gateway.
These exposure capabilities evolved into what is now known as API
management, and enabled simple administration of service/API exposure. The
gateways could also be specialized to focus on API management-specific
capabilities, such as traffic management (rate/throughput limiting),
encryption/decryption, redaction, and security patterns.
The gateways could also be supplemented with portals that describe the
available APIs and enabling self-subscription in order to use the APIs,
and provisioning analytics for both users and providers of the APIs.
Increasingly, more modern systems of record already provided an HTTP-based
interface that only needed controlled exposure using the exposure gateway.
The integration runtime was only required when more complex integration
took place, such as more unusual protocols, data formats, compositions of
multiple requests—or perhaps in cases where transactionality was
The introduction of an API management layer led to the obvious question:
What now is the ESB? Many had come to see the integration runtime and the
ESB pattern as one and the same. But in fact, if the ESB pattern is all
about exposing services and APIs, then the boundaries of the pattern
really include both the integration runtime and the exposure gateway, and
in some cases just the gateway. However, due to the ESB’s incorrect
association with the integration runtime, we have to accept that this is
not how the ESB term is typically used.
spread outside the enterprise boundary
Once the mechanisms for effectively exposing APIs had matured, it became
clear that they could also be exposed outside the enterprise. Initially,
this was done to create the back-end-for-front-end (BFF) pattern
that’s still prevalent for mobile applications and single-page web
applications. In this pattern, APIs were created specifically for the
front-end application and perfectly suited to its needs with rationalized
data models, ideal granularity of operations, specialized security models,
and more. Soon it became clear that APIs could be exposed more broadly to
enable any partner to write applications, opening up a whole new set of
opportunities for collaboration.
Figure 4. The external service/API gateway and the
beginnings of the API Economy
It is hard to pin down the exact historical order here, as it varies by
enterprise. The concepts of API management for some enterprises
began with external exposure, and were only brought inside to
supplement the ESB pattern later. Whichever the sequence, external APIs
have become an essential part of the online persona of many companies, and
are at least as important as its websites and mobile applications.
Logically, the exposure of APIs outside the enterprise is just an extension
of the ESB pattern, with more focus on the gateway and aspects such as
security, discovery, and self-administration. I’ve covered the technical
differences between APIs and SOA services in-depth in a previous article. For example, it is immediately obvious that
the APIs are being used by consumers and devices that may exist anywhere
from a geographical and network point of view. As a result, it is
necessary to design the APIs differently to take into account the
bandwidth available and the capabilities of the devices used as consumers.
However, there are non-technical aspects to the differences, too. You
should not underestimate the difference in the business objectives of the
exposed APIs. External API exposure is much less focused on re-use, in the
same way that internal services were in SOA, and more focused on creating
services targeting specific niches of potential for new business. APIs
provide an enterprise with the opportunity to radically broaden the number
of innovation partners that it can work with (enabling crowd sourcing of
new ideas), and they play a significant role in the disruption of
industries that is so common today. This realization caused the birth of
what we now call the API Economy, and it is a well-covered topic in its own right.
The main takeaway here is that this progression exacerbated an already
growing divide between the older traditional systems of record
that still perform all the most critical transactions fundamental to the
business, and what became known as the systems of engagement,
where innovation occurred at a rapid pace, exploring new ways of
interacting with external consumers. This resulted in bi-modal
IT, where new decentralized, fast-moving areas of IT needed
much greater agility in their development, and led to the invention of new
ways of building applications using, for example, microservices
The rise of
Earlier, I covered the challenges of the heavily centralized integration
runtime—hard to safely and quickly make changes without affecting
other integrations, expensive and complex to scale, etc. Sound familiar?
It should. These were exactly the same challenges that application
development teams were facing at the same time: bloated, complex
application servers that contained too much interconnected and
cross-dependent code, on a fragile cumbersome topology that was hard to
replicate or scale. Ultimately, it was this common paradigm that led to
the emergence of the principles of microservices architecture. As
lightweight application servers such as IBM WAS Liberty were
introduced—servers that started in seconds and had tiny
footprints—it became easier to run them on smaller virtual
machines, and then eventually within container technologies such as
In order to meet the constant need for IT to improve agility and
scalability, a next logical step in application development was to break
up applications into smaller pieces and run them completely independently
of one another. Eventually, these pieces became small enough that they
deserved a name, and they were termed microservices.
Perhaps a better name would be microservice components as
the term often causes confusion (especially in integration circles, as
explained in previous articles and videos), but
“microservices” has now become pervasive.
If you take a closer look at microservices concepts, you will see that it
has a much broader intent than simply breaking things up into smaller
pieces. There are implications for architecture, process, organization,
and more—all focused on enabling organizations to better use
cloud-native technology advances to increase their pace of innovation.
However, focusing back on the core technological difference, these small
independent microservice components can be changed independently to create
greater agility, scaled independently to make better use of cloud-native
infrastructure, and managed more ruthlessly (see my article “Cattle not pets”) to
provide the resilience required by 24/7 online applications.
Figure 5. Microservices architecture: A new way to build
In theory, these principles could be used anywhere. Where we see them most
commonly is in the systems of engagement layer, where greater agility is
essential. However, they could also be used to improve the agility,
scalability, and resilience of a system of record—or indeed
anywhere else in the architecture, as you will see shortly.
Without question, microservices principles can offer significant benefits
under the right circumstances. However, choosing the right time to use
these techniques is critical, and getting the design of highly distributed
components correct is non-trivial. You need only read Martin Fowler’s opinions on this to see the dilemma unfold. At
the end of the day, deciding the shape and size of your microservice
components is only part of the story; there is an equally critical set of
design choices around the extent to which you decouple them, and you need
to constantly balance practical reality with aspirations for
microservices-related benefits. Although decoupling is fundamental to
microservices, that doesn’t mean full decoupling is always good
microservice design. Good design is always a compromise. In short,
your microservice-based application is only as agile and scalable
as your design is good, and your methodology is mature.
A comparison of SOA and microservice architecture
It is tempting to compare microservices architecture with SOA, not least
because they share many words in common. However, as you will see, this
comparison is misleading at best, since the terms apply to two very
Figure 6. SOA is enterprise scoped, microservices
architecture is application scoped
Service-oriented architecture is an enterprise-wide initiative to
create re-usable, synchronously available services and APIs, such that new
applications can be created more quickly incorporating data from other
Microservices architecture, on the other hand, is an option for how you
might choose to write an individual application in a way that
makes that application more agile, scalable, and resilient.
It’s critical to recognize this difference in scope, as some of the core
principles of each approach could be completely incompatible if applied at
the same scope. For example:
- Re-use: In SOA, re-use of integrations is the primary
goal, and at an enterprise level, striving for some level of re-use is
essential. In microservices architecture, creating a microservice
component that is re-used at runtime throughout an application results
in dependencies that reduce agility and resilience. Microservice
components generally prefer to re-use code by copy and, as you’ll see
shortly, accept data duplication in order to improve decoupling
between the components.
- Synchronous calls: The re-usable services in SOA are
exposed across the enterprise using predominantly synchronous
protocols such as RESTful APIs. However, within a
microservice application, synchronous calls introduce real-time
dependencies, resulting in a loss of resilience, and also latency,
which impacts performance. Within a microservice application,
interaction patterns based on asynchronous communication are
preferred, such as event sourcing where a publish subscribe model is
used to enable a microservice component to remain up to date on
changes happening to the data in another component.
- Data duplication: A clear aim of exposing services in
an SOA is for all applications to get hold of, and make changes to,
data directly at its primary source, which reduces the need to
maintain complex data synchronization patterns. In microservice
applications, each microservice ideally has local access to all the
data it needs to ensure its independence from other microservices, and
indeed from other applications—even if this means some
duplication of data in other systems. Of course, this duplication adds
complexity, so it needs to be balanced against the gains in agility
and performance, but this is accepted as a reality of microservice
design. With data duplication so prevalent in microservices, there is
still the need to have an agreed-upon source of truth for each type of
So, in summary, SOA has an enterprise scope and looks at how
integration occurs between applications. Microservice architecture has an
application scope, dealing with how the internals of an
application are built. This is a relatively swift explanation of a much
more complex debate, which is thoroughly explored in a separate article, but it provides the key concepts we need
Making use of microservice principles in integration
So if it makes sense to build applications in a more granular fashion, why
couldn’t you apply this idea to integration, too? You could break up the
enterprise-wide centralized ESB component into smaller, more manageable,
and dedicated components. Perhaps you could go so far as to have one
integration runtime for each interface you expose.
This pattern is typically referred to as lightweight
integration, to differentiate it from full microservices
application architecture, and also to distinguish it from the ESB term,
which is strongly associated with the more cumbersome centralized
In Part 2, I’ll describe lightweight integration in more detail, looking at
how integration architecture can benefit from the technologies and
principles behind microservices. This ensures that new applications can
perform the integration they need at the pace and scale of modern
Thank you to the following people for their input and review of the
material in this article: Andy Garratt, Nick Glowacki, Rob Nicholson,
Brian Petrini, and Claudio Tagliabue.
via IBM developerWorks : Cloud computing https://ibm.co/2cihRPX
February 13, 2018 at 12:36PM