Spring Microservices: Internet-scale architecture with Spring framework, Spring Cloud, Spring Boot

In this era of digital transformation, enterprises increasingly adopt technologies as one of the key enablers for radically increasing their revenue and customer base. Enterprises primarily use social media, mobile, cloud, big data, and Internet of Things as vehicles to achieve the disruptive innovations. Using these technologies, enterprises find new ways to quickly penetrate the market, which severely pose challenges to the traditional IT delivery mechanisms.

The following graph shows the state of traditional development and microservices against the new enterprise challenges such as agility, speed of delivery, and scale.

Gone are the days when businesses invested in large application developments with the turnaround time of a few years. Enterprises are no longer interested in developing consolidated applications to manage their end-to-end business functions as they did a few years ago.

The following graph shows the state of traditional monolithic applications and microservices in comparison with the turnaround time and cost.

Today, for instance, airlines or financial institutions do not invest in rebuilding their core mainframe systems as another monolithic monster. Retailers and other industries do not rebuild heavyweight supply chain management applications, such as their traditional ERPs. Focus has shifted to building quick-win point solutions that cater to specific needs of the business in the most agile way possible.

Let's take an example of an online retailer running with a legacy monolithic application. If the retailer wants to innovate his/her sales by offering their products personalized to a customer based on the customer's past shopping, preferences, and so on and also wants to enlighten customers by offering products based on their propensity to buy them, they will quickly develop a personalization engine or offers based on their immediate needs and plug them into their legacy application.

As shown in the preceding diagram, rather than investing in rebuilding the core legacy system, this will be either done by passing the responses through the new functions, as shown in the diagram marked A, or by modifying the core legacy system to call out these functions as part of the processing, as shown in the diagram marked B. These functions are typically written as microservices.

This approach gives organizations a plethora of opportunities to quickly try out new functions with lesser cost in an experimental mode. Businesses can later validate key performance indicators and alter or replace these implementations if required.

Emerging technologies have also made us rethink the way we build software systems. For example, a few decades back, we couldn't even imagine a distributed application without a two-phase commit. Later, NoSQL databases made us think differently.

Similarly, these kinds of paradigm shifts in technology have reshaped all the layers of the software architecture.

The emergence of HTML 5 and CSS3 and the advancement of mobile applications repositioned user interfaces. Client-side JavaScript frameworks such as Angular, Ember, React, Backbone, and so on are immensely popular due to their client-side rendering and responsive designs.

With cloud adoptions steamed into the mainstream, Platform as a Services (PaaS) providers such as Pivotal CF, AWS, Salesforce.com, IBMs Bluemix, RedHat OpenShift, and so on made us rethink the way we build middleware components. The container revolution created by Docker radically influenced the infrastructure space. These days, an infrastructure is treated as a commodity service.

The integration landscape has also changed with Integration Platform as a Service (iPaaS), which is emerging. Platforms such as Dell Boomi, Informatica, MuleSoft, and so on are examples of iPaaS. These tools helped organizations stretch integration boundaries beyond the traditional enterprise.

NoSQLs have revolutionized the databases space. A few years ago, we had only a few popular databases, all based on relational data modeling principles. We have a long list of databases today: Hadoop, Cassandra, CouchDB, and Neo 4j to name a few. Each of these databases addresses certain specific architectural problems.

Application architecture has always been evolving alongside demanding business requirements and the evolution of technologies. Architectures have gone through the evolution of age-old mainframe systems to fully abstract cloud services such as AWS Lambda.

Different architecture approaches and styles such as mainframes, client server, N-tier, and service-oriented were popular at different timeframes. Irrespective of the choice of architecture styles, we always used to build one or the other forms of monolithic architectures. The microservices architecture evolved as a result of modern business demands such as agility and speed of delivery, emerging technologies, and learning from previous generations of architectures.

Microservices help us break the boundaries of monolithic applications and build a logically independent smaller system of systems, as shown in the preceding diagram.