Microservices is one of the increasingly popular architecture patterns next to Service Oriented Architecture (SOA), complemented by DevOps and Cloud. Its evolution has been greatly influenced by the disruptive digital innovation trends in modern business and the evolution of technology in the last few years. We will examine these two catalysts--business demands and technology--in this section.

In this era of digital transformation, enterprises are increasingly adopting technologies as one of the key enablers for radically increasing their revenue and customer base. Enterprises are primarily using social media, mobile, cloud, big data, and Internet of Things (IoT) as vehicles for achieving the disruptive innovations. Using these technologies, enterprises are finding 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 where businesses invested in large application developments with turnaround times of a few years. Enterprises are no longer interested in developing consolidated applications for managing their end-to-end business functions as they did a few years ago.

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

Today, for instance, airlines are not investing in rebuilding their core mainframe reservation systems as another monolithic monster. Financial institutions are not rebuilding their core banking systems. Retailers and other industries are not rebuilding heavyweight supply chain management applications, such as their traditional ERP's. Focus has been shifted from building large applications to building quick win, point solutions that cater to the 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 their sales by offering their products personalized to a customer based on the customer's past shopping preferences and much more, or they want to enlighten customers by offering products to customer based on propensity to buy a product.

In such cases, enterprises want to quickly develop a personalization engine or an offer engine based on their immediate needs, and plug them into their legacy application, as shown here:

As shown in the preceding diagram, rather than investing on rebuilding the Core Legacy System, this will either be done by passing the responses through the new functions, as shown in the diagram marked A, or 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. Business can later validate key performance indicators change or replace these implementations if required.

Emerging technologies have made us rethink the way we build software systems. For example, a few decades ago, 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 layers of software architecture.

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

With cloud adoptions steamed into the mainstream, Platform as a Services (PaaS) providers, such as Pivotal CF, AWS, Sales Force, IBM Bluemix, Redhat OpenShift, and more, made us rethink the way we build middleware components. The container revolution created by Docker radically influenced the infrastructure space. Container orchestration tools, such as Mesosphere DCOS, made infrastructure management much easier. Serverless added further easiness in application managements.

Integration landscape has also changed with the emerging Integration Platform as a Services (iPaaS), such as Dell Boomi, Informatica, MuleSoft, and more. These tools helped organizations stretch integration boundaries beyond the traditional enterprise.

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

Application architecture has always been evolving alongside with demanding business requirements and evolution of technologies.

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

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

Microservices are an architectural style used by many organizations today as a game changer to achieve high degrees of agility, speed of delivery, and scale. Microservices gives us a way to develop physically separated modular applications.

Microservices are not invented. Many organizations, such as Netflix, Amazon, and eBay had successfully used the divide and conquer technique for functionally partitioning their monolithic applications into smaller atomic units, each performing a single function. These organizations solved a number of prevailing issues they were experiencing with their monolithic application. Following the success of these organizations, many other organizations started adopting this as a common pattern for refactoring their monolithic applications. Later, evangelists termed this pattern microservices architecture.

Microservices originated from the idea of Hexagonal Architecture, which was coined by Alister Cockburn back in 2005. Hexagonal Architecture, or Hexagonal pattern, is also known as the Ports and Adapters pattern.

In simple terms, Hexagonal architecture advocates to encapsulate business functions from the rest of the world. These encapsulated business functions are unaware of their surroundings. For example, these business functions are not even aware of input devices or channels and message formats used by those devices. Ports and adapters at the edge of these business functions convert messages coming from different input devices and channels to a format that is known to the business function. When new devices are introduced, developers can keep adding more and more ports and adapters to support those channels without touching business functions. One may have as many ports and adapters to support their needs. Similarly, external entities are not aware of business functions behind these ports and adapters. They will always interface with these ports and adapters. By doing so, developers enjoy the flexibility to change channels and business functions without worrying too much about future proofing interface designs.

The following diagram shows the conceptual view of Hexagonal Architecture:

In the preceding diagram, the application is completely isolated and exposed through a set of frontend adapters, as well as a set of backend adapters. Frontend adaptors are generally used for integrating UI and other APIs, whereas backend adapters are used for connecting to various data sources. Ports and adapters on both sides are responsible for converting messages coming in and going out to appropriate formats expected by external entities. Hexagonal architecture was the inspiration for microservices.

When we look for a definition for microservices, there is no single standard way of describing them. Martin Fowler defines microservices as follows:

The definition used in this book is as follows:

The preceding diagram depicts a traditional n-tier application architecture, having a Presentation Layer, Business Layer, and Database Layer. Modules A, B, and C represent three different business capabilities. The layers in the diagram represent separation of architecture concerns. Each layer holds all three business capabilities pertaining to that layer. The presentation layer has web components of all three modules, the business layer has business components of all three modules, and the database host tables of all three modules. In most cases, layers are physically spreadable, whereas modules within a layer are hardwired.

Let's now examine a microservices-based architecture:

As we can see in the preceding diagram, the boundaries are inversed in the microservices architecture. Each vertical slice represents a microservice. Each microservice will have its own presentation layer, business layer, and database layer. Microservices are aligned towards business capabilities. By doing so, changes to one microservice does not impact others.

There is no standard for communication or transport mechanisms for microservices. In general, microservices communicate with each other using widely adopted lightweight protocols, such as HTTP and REST, or messaging protocols, such as JMS or AMQP. In specific cases, one might choose more optimized communication protocols, such as Thrift, ZeroMQ, Protocol Buffers, or Avro.

Since microservices are more aligned to business capabilities and have independently manageable life cycles, they are the ideal choice for enterprises embarking on DevOps and cloud. DevOps and cloud are two facets of microservices.

A honeycomb is an ideal analogy for representing the evolutionary microservices architecture:

In the real world, bees build a honeycomb by aligning hexagonal wax cells. They start small, using different materials to build the cells. Construction is based on what is available at the time of building. Repetitive cells form a pattern, and result in a strong fabric structure. Each cell in the honeycomb is independent, but also integrated with other cells. By adding new cells, the honeycomb grows organically to a big, solid structure. The content inside the cell is abstracted and is not visible outside. Damage to one cell does not damage other cells, and bees can reconstruct those cells without impacting the overall honeycomb.

In this section, we will examine some of the principles of the microservices architecture. These principles are a must have when designing and developing microservices. The two key principles are single responsibility and autonomous. 

The single responsibility principle is one of the principles defined as part of the SOLID design pattern. It states that a unit should only have one responsibility.

It implies that a unit, either a class, a function, or a service, should have only one responsibility. At no point do two units share one responsibility, or one unit perform more than one responsibility. A unit with more than one responsibility indicates tight coupling:

As shown in the preceding diagram, Customer, Product, and Order are different functions of an e-commerce application. Rather than building all of them into one application, it is better to have three different services, each responsible for exactly one business function, so that changes to one responsibility will not impair the others. In the preceding scenario, Customer, Product, and Order were treated as three independent microservices.

Microservices are self-contained, independently deployable, and autonomous services that take full responsibility of a business capability and its execution. They bundle all dependencies including the library dependencies; execution environments, such as web servers and containers; or virtual machines that abstract the physical resources.

One of the major differences between microservices and SOA is in its level of autonomy. While most of the SOA implementations provide the service-level abstraction, microservices go further and abstract the realization and the execution environment.

In traditional application developments, we build a war or a ear, then deploy it into a JEE application server, such as JBoss, Weblogic, WebSphere, and more. We may deploy multiple applications into the same JEE container. In the microservices approach, each microservice will be built as a fat jar embedding all dependencies and run as a standalone Java process:

Microservices may also get their own containers for execution, as shown in the preceding diagram. Containers are portable, independently manageable, and lightweight runtime environments. Container technologies, such as Docker, are an ideal choice for microservices deployments.

The microservices definition discussed earlier in this chapter is arbitrary. Evangelists and practitioners have strong, but sometimes, different opinions on microservices. There is no single, concrete, and universally accepted definition for microservices. However, all successful microservices implementations exhibit a number of common characteristics. Therefore, it is important to understand these characteristics rather than sticking to theoretical definitions. Some of the common characteristics are detailed in this section.

In the microservices world, services are first class citizens. Microservices expose service endpoints as APIs and abstract all their realization details. The internal implementation logic, architecture, and technologies, including programming language, database, quality of services mechanisms, and more, are completely hidden behind the service API.

Moreover, in the microservices architecture, there is no more application development, instead organizations will focus on service development. In most enterprises, this requires a major cultural shift in the way applications are built.

In a customer profile microservice, the internals, such as data structure, technologies, business logic, and so on, will be hidden. It wont be exposed or visible to any external entities. Access will be restricted through the service endpoints or APIs. For instance, customer profile microservices may expose register customer and get customers as two APIs for others to interact.

Since microservices are more or less like a flavor of SOA, many of the service characteristics defined in the SOA are applicable to microservices as well.

The following are some of the characteristics of services that are applicable to microservices as well:

• Service contract: Similar to SOA, microservices are described through well-defined service contracts.  In the microservices world, JSON and REST are universally accepted for service communication. In case of JSON/REST, there are many techniques used to define service contracts. JSON Schema, WADL, Swagger, and RAML are a few examples. 
• Loose coupling: Microservices are independent and loosely coupled. In most cases, microservices accept an event as input and respond with another event.  Messaging, HTTP, and REST are commonly used for interaction between microservices. Message-based endpoints provide higher levels of decoupling.  
• Service abstraction: In microservices, service abstraction is not just abstraction of service realization, but also provides complete abstraction of all libraries and environment details, as discussed earlier.
• Service reuse: Microservices are course grained reusable business services. These are accessed by mobile devices and desktop channels, other microservices, or even other systems.
• Statelessness: Well-designed microservices are a stateless, shared nothing with no shared state, or conversational state maintained by the services. In case there is a requirement to maintain state, they will be maintained in a database, perhaps in-memory.
• Services are discoverable: Microservices are discoverable. In a typical microservices environment, microservices self-advertise their existence and make themselves available for discovery. When services die, they automatically take themselves out from the microservices ecosystem.
• Service interoperability: Services are interoperable as they use standard protocols and message exchange standards. Messaging, HTTP, and more are used as the transport mechanism. REST/JSON is the most popular method to develop interoperable services in the microservices world. In cases where further optimization is required on communications, then other protocols such as Protocol Buffers, Thrift, Avro, or Zero MQ could be used. However, use of these protocols may limit the overall interoperability of the services.
• Service Composeability: Microservices are composeable. Service composeability is achieved either through service orchestration or service choreography.

Well-designed microservices are aligned to a single business capability; therefore, they perform only one function. As a result, one of the common characteristics we see in most of the implementations are microservices with smaller footprints.

When selecting supporting technologies, such as web containers, we will have to ensure that they are also lightweight so that the overall footprint remains manageable. For example, Jetty or Tomcat are better choices as application containers for microservices as compared to more complex traditional application servers, such as Weblogic or WebSphere.

Container technologies such as Docker also helps us keep the infrastructure footprint as minimal as possible compared to hypervisors such as VMware or Hyper-V.

As shown in the preceding diagram, microservices are typically deployed in Docker containers, which encapsulate the business logic and needed libraries. This helps us quickly replicate the entire setup on a new machine, a completely different hosting environment, or even move across different cloud providers. Since there is no physical infrastructure dependency, containerized microservices are easily portable.

Since microservices are autonomous and abstract everything behind the service APIs, it is possible to have different architectures for different microservices. A few common characteristics that we see in microservices implementations are as follows:

• Different services use different versions of the same technologies. One microservice may be written on Java 1.7 and another one could be on Java 1.8.
• Different languages for developing different microservices, such as one microservice in Java and another one in Scala.
• Different architectures such as one microservice using Redis cache to serve data while another microservice could use MySQL as a persistent data store.

A polyglot language scenario is depicted in the following diagram: 

In the preceding example, since Hotel Search is expected to have high transaction volumes with stringent performance requirements, it is implemented using Erlang. In order to support predictive search, Elastic Search is used as the data store. At the same time, Hotel Booking needs more ACID transactional characteristics. Therefore, it is implemented using MySQL and Java. The internal implementations are hidden behind service endpoints defined as REST/JSON over HTTP.

Most of the microservices implementations are automated to a maximum, ranging from development to production.

Since microservices break monolithic applications into a number of smaller services, large enterprises may see a proliferation of microservices. Large numbers of microservices are hard to manage until and unless automation is in place. The smaller footprint of microservices also helps us automate the microservices development to deployment life cycle. In general, microservices are automated end to end, for example, automated builds, automated testing, automated deployment, and elastic scaling:

As indicated in the diagram, automations are typically applied during the development, test, release, and deployment phases. 

Different blocks in the preceding diagram are explained as follows:

• The development phase will be automated using version control tools, such as Git, together with continuous integration (CI) tools, such as Jenkins, Travis CI, and more. This may also include code quality checks and automation of unit testing. Automation of a full build on every code check-in is also achievable with microservices.
• The testing phase will be automated using testing tools such as Selenium, Cucumber, and other AB testing strategies. Since microservices are aligned to business capabilities, the number of test cases to automate will be fewer compared to the monolithic applications; hence, regression testing on every build also becomes possible.
• Infrastructure provisioning will be done through container technologies, such as Docker, together with release management tools, such as Chef or Puppet, and configuration management tools, such as Ansible. Automated deployments are handled using tools such as Spring Cloud, Kubernetes, Mesos, and Marathon.

Most of the large scale microservices implementations have a supporting ecosystem in place. The ecosystem capabilities include DevOps processes, centralized log management, service registry, API gateways, extensive monitoring, service routing, and flow control mechanisms:

Microservices work well when supporting capabilities are in place, as represented in the preceding diagram.

Successful microservices implementations encapsulate logic and data within the service. This results in two unconventional situations: 

• Distributed data and logic
• Decentralized governance

Compared to traditional applications, which consolidate all logic and data into one application boundary, microservices decentralize data and logic. Each service, aligned to a specific business capability, owns its own data and logic: 

The dotted line in the preceding diagram implies the logical monolithic application boundary. When we migrate this to microservices, each microservice, A, B, and C, creates its own physical boundaries.

Microservices don't typically use centralized governance mechanisms the way they are used in SOA. One of the common characteristics of microservices implementations are that they are not relaying on heavyweight enterprise-level products, such as an Enterprise Service Bus (ESB). Instead, the business logic and intelligence are embedded as a part of the services themselves.

A retail example with ESB is shown as follows: 

A typical SOA implementation is shown in the preceding diagram. Shopping Logic is fully implemented in the ESB by orchestrating different services exposed by Customer, Order, and Product. In the microservices approach, on the other hand, shopping itself will run as a separate microservice, which interacts with Customer, Product, and Order in a fairly decoupled way.

SOA implementations are heavily relaying on static registry and repository configurations to manage services and other artifacts. Microservices bring a more dynamic nature into this. Hence, a static governance approach is seen as an overhead in maintaining up-to-date information. This is why most of the microservices implementations use automated mechanisms to build registry information dynamically from the runtime topologies.

Antifragility is a technique successfully experimented with at Netflix. It is one of the most powerful approaches to build fail-safe systems in modern software development.

In the antifragility practice, software systems are consistently challenged. Software systems evolve through these challenges, and, over a period of time, get better and better to withstand these challenges. Amazon's Game Day exercise and Netflix's Simian Army are good examples of such antifragility experiments. 

Fail Fast is another concept used to build fault-tolerant, resilient systems. This philosophy advocates systems that expect failures versus building systems that never fail. Importance has to be given to how quickly the system can fail, and, if it fails, how quickly it can recover from that failure. With this approach, the focus is shifted from Mean Time Between Failures (MTBF) to Mean Time To Recover (MTTR). A key advantage of this approach is that if something goes wrong, it kills itself, and the downstream functions won’t be stressed.

Self-Healing is commonly used in microservices deployments, where the system automatically learns from failures and adjusts itself. These systems also prevent future failures.