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.

This implies that a unit, either a class, a function, or a service, should have only one responsibility. At no point should two units share one responsibility or one unit have 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 others. In the preceding scenario, Customer, Product, and Order will be 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 library dependencies, and execution environments such as web servers and containers or virtual machines that abstract physical resources.

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

In traditional application developments, we build a WAR or an EAR, then deploy it into a JEE application server, such as with JBoss, WebLogic, WebSphere, and so on. 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, lightweight runtime environments. Container technologies, such as Docker, are an ideal choice for microservices deployment.

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 so on) are completely hidden behind the service API.

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

In a Customer Profile microservice, internals such as the data structure, technologies, business logic, and so on are hidden. They aren't exposed or visible to any external entities. Access is restricted through the service endpoints or APIs. For instance, Customer Profile microservices may expose Register Customer and Get Customer as two APIs for others to interact with.

Well-designed microservices are aligned to a single business capability, so 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 compared to more complex traditional application servers such as WebLogic or WebSphere.

Container technologies such as Docker also help 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 help us quickly replicate the entire setup on a new machine or on a completely different hosting environment or even to move across different cloud providers. As there is no physical infrastructure dependency, containerized microservices are easily portable.

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

In the preceding example, as Hotel Search is expected to have high transaction volumes with stringent performance requirements, it is implemented using Erlang. In order to support predictive searching, Elasticsearch 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 from development to production.

As microservices break monolithic applications into a number of smaller services, large enterprises may see a proliferation of microservices. A large number of microservices is hard to manage until and unless automation is in place. The smaller footprint of microservices also helps us automate the microservices development to the 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 preceding diagram, automations are typically applied during the development, test, release, and deployment phases:

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 and 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 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 is that they do not relay on heavyweight enterprise-level products, such as Enterprise Service Bus (ESB). Instead, the business logic and intelligence are embedded as a part of the services themselves.

A typical SOA implementation is shown in the preceding diagram. Shopping logic is fully implemented in 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 heavily relay 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 at Netflix. It is one of the most powerful approaches to building 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 at withstanding these challenges. Amazon's GameDay exercise and Netflix' 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 should be given to how quickly the system can fail and if it fails, how quickly it can recover from this 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 downstream functions aren't 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.

In the first example, we will review a holiday portal, Fly By Points. Fly By Points collects points that are accumulated when a customer books a hotel, flight, or car through the online website. When the customer logs in to the Fly By Points website, he/she is able to see the points accumulated, personalized offers that can be availed of by redeeming the points, and upcoming trips if any.

Let's assume that the preceding page is the home page after login. There are two upcoming trips for Jeo, four personalized offers, and 21,123 loyalty points. When the user clicks on each of the boxes, the details are queried and displayed.

The holiday portal has a Java Spring-based traditional monolithic application architecture, as shown in the following:

As shown in the preceding diagram, the holiday portal's architecture is web-based and modular, with a clear separation between layers. Following the usual practice, the holiday portal is also deployed as a single WAR file on a web server such as Tomcat. Data is stored on an all-encompassing backing relational database. This is a good fit for the purpose architecture when the complexities are few. As the business grows, the user base expands, and the complexity also increases. This results in a proportional increase in transaction volumes. At this point, enterprises should look to rearchitecting the monolithic application to microservices for better speed of delivery, agility, and manageability.

Examining the simple microservices version of this application, we can immediately note a few things in this architecture:

It is assumed that the presentation layer is developed using a client-side JavaScript MVC framework such as Angular JS. These client-side frameworks are capable of invoking REST calls directly.

When the web page is loaded, all the three boxes, Trips, Offers, and Points will be displayed with details such as points, the number of offers, and the number of trips. This will be done by each box independently making asynchronous calls to the respective backend microservices using REST. There is no dependency between the services at the service layer. When the user clicks on any of the boxes, the screen will be transitioned and will load the details of the item clicked on. This will be done by making another call to the respective microservice.

Let's examine another microservices example: an online retail website. In this case, we will focus more on the backend services, such as the Order Service which processes the Order Event generated when a customer places an order through the website:

This microservices system is completely designed based on reactive programming practices.

When an event is published, a number of microservices are ready to kick-start upon receiving the event. Each one of them is independent and does not rely on other microservices. The advantage of this model is that we can keep adding or replacing microservices to achieve specific needs.

In the preceding diagram, there are eight microservices shown. The following activities take place upon the arrival of Order Event:

In this approach, each service is responsible for only one function. Services accept and generate events. Each service is independent and is not aware of its neighborhood. Hence, the neighborhood can organically grow as mentioned in the honeycomb analogy. New services can be added as and when necessary. Adding a new service does not impact any of the existing services.

This third example is a simple travel agent portal application. In this example, we will see both synchronous REST calls as well as asynchronous events.

In this case, the portal is just a container application with multiple menu items or links in the portal. When specific pages are requested—for example, when the menu or a link is clicked on—they will be loaded from the specific microservices.

When a customer requests a booking, the following events take place internally:

The interesting factor here is that we can change the user interface, logic, and data of a microservice without impacting any other microservices.

This is a clean and neat approach. A number of portal applications can be built by composing different screens from different microservices, especially for different user communities. The overall behavior and navigation will be controlled by the portal application.

The approach has a number of challenges unless the pages are designed with this approach in mind. Note that the site layouts and static content will be loaded by the Content Management System (CMS) as layout templates. Alternately, this could be stored in a web server. The site layout may have fragments of UIs that will be loaded from the microservices at runtime.

With microservices, architects and developers can choose fit for purpose architectures and technologies for each microservice. This gives the flexibility to design better-fit solutions in a more cost-effective way.

As microservices are autonomous and independent, each service can run with its own architecture or technology or different versions of technologies.

The following shows a simple, practical example of a polyglot architecture with microservices.

There is a requirement to audit all system transactions and record transaction details such as request and response data, the user who initiated the transaction, the service invoked, and so on.

As shown in the preceding diagram, while core services such as the Order and Products microservices use a relational data store, the Audit microservice persists data in Hadoop File System (HDFS). A relational data store is neither ideal nor cost effective in storing large data volumes such as in the case of audit data. In the monolithic approach, the application generally uses a shared, single database that stores Order, Products, and Audit data.

In this example, the audit service is a technical microservice using a different architecture. Similarly, different functional services could also use different architectures.

In another example, there could be a Reservation microservice running on Java 7, while a Search microservice could be running on Java 8. Similarly, an Order microservice could be written on Erlang, whereas a Delivery microservice could be on the Go language. None of these are possible with a monolithic architecture.

Modern enterprises are thriving towards quick wins. Microservices are one of the key enablers for enterprises to do disruptive innovation by offering the ability to experiment and fail fast.

As services are fairly simple and smaller in size, enterprises can afford to experiment new processes, algorithms, business logics, and so on. With large monolithic applications, experimentation was not easy; nor was it straightforward or cost effective. Businesses had to spend huge money to build or change an application to try out something new. With microservices, it is possible to write a small microservice to achieve the targeted functionality and plug it into the system in a reactive style. One can then experiment with the new function for a few months, and if the new microservice does not work as expected, we can change or replace it with another one. The cost of change will be considerably less compared to that of the monolithic approach.

In another example of an airline booking website, the airline wants to show personalized hotel recommendations in their booking page. The recommendations must be displayed on the booking confirmation page.

As shown in the preceding diagram, it is convenient to write a microservice that can be plugged into the monolithic applications booking flow rather than incorporating this requirement in the monolithic application itself. The airline may choose to start with a simple recommendation service and keep replacing it with newer versions till it meets the required accuracy.

As microservices are smaller units of work, they enable us to implement selective scalability.

Scalability requirements may be different for different functions in an application. A monolithic application, packaged as a single WAR or an EAR, can only be scaled as a whole. An I/O-intensive function when streamed with high velocity data could easily bring down the service levels of the entire application.

In the case of microservices, each service could be independently scaled up or down. As scalability can be selectively applied at each service, the cost of scaling is comparatively less with the microservices approach.

In practice, there are many different ways available to scale an application and is largely subject to the architecture and behavior of the application. Scale Cube defines primarily three approaches to scaling an application:

When y axis scaling is applied to monolithic applications, it breaks the monolithic to smaller units aligned with business functions. Many organizations successfully applied this technique to move away from monolithic applications. In principle, the resulting units of functions are in line with the microservices characteristics.

For instance, in a typical airline website, statistics indicate that the ratio of flight searching to flight booking could be as high as 500:1. This means one booking transaction for every 500 search transactions. In this scenario, the search needs 500 times more scalability than the booking function. This is an ideal use case for selective scaling.

The solution is to treat search requests and booking requests differently. With a monolithic architecture, this is only possible with z scaling in the scale cube. However, this approach is expensive because in the z scale, the entire code base is replicated.

In the preceding diagram, Search and Booking are designed as different microservices so that Search can be scaled differently from Booking. In the diagram, Search has three instances, and Booking has two instances. Selective scalability is not limited to the number of instances, as shown in the diagram, but also in the way in which the microservices are architected. In the case of Search, an in-memory data grid (IMDG) such as Hazelcast can be used as the data store. This will further increase the performance and scalability of Search. When a new Search microservice instance is instantiated, an additional IMDG node is added to the IMDG cluster. Booking does not require the same level of scalability. In the case of Booking, both instances of the Booking microservice are connected to the same instance of the database.

Microservices are self-contained, independent deployment modules enabling the substitution of one microservice with another similar microservice.

Many large enterprises follow buy-versus-build policies to implement software systems. A common scenario is to build most of the functions in house and buy certain niche capabilities from specialists outside. This poses challenges in traditional monolithic applications as these application components are highly cohesive. Attempting to plug in third-party solutions to the monolithic applications results in complex integrations. With microservices, this is not an afterthought. Architecturally, a microservice can be easily replaced by another microservice developed either in-house or even extended by a microservice from a third party.

A pricing engine in the airline business is complex. Fares for different routes are calculated using complex mathematical formulas known as the pricing logic. Airlines may choose to buy a pricing engine from the market instead of building the product in house. In the monolithic architecture, Pricing is a function of Fares and Booking. In most cases Pricing, Fares, and Booking are hardwired, making it almost impossible to detach.

In a well-designed microservices system, Booking, Fares, and Pricing would be independent microservices. Replacing the Pricing microservice will have only a minimal impact on any other services as they are all loosely coupled and independent. Today, it could be a third-party service; tomorrow, it could be easily substituted by another third-party or home-grown service.

Microservices help us build systems that are organic in nature. This is significantly important when migrating monolithic systems gradually to microservices.

Organic systems are systems that grow laterally over a period of time by adding more and more functions to it. In practice, an application grows unimaginably large in its lifespan, and in most cases, the manageability of the application reduces dramatically over this same period of time.

Microservices are all about independently manageable services. This enable us to keep adding more and more services as the need arises with minimal impact on the existing services. Building such systems does not need huge capital investments. Hence, businesses can keep building as part of their operational expenditure.

A loyalty system in an airline was built years ago, targeting individual passengers. Everything was fine until the airline started offering loyalty benefits to their corporate customers. Corporate customers are individuals grouped under corporations. As the current systems core data model is flat, targeting individuals, the corporate environment needs a fundamental change in the core data model, and hence huge reworking, to incorporate this requirement.

As shown in the preceding diagram, in a microservices-based architecture, customer information would be managed by the Customer microservice and loyalty by the Loyalty Points microservice.

In this situation, it is easy to add a new Corporate Customer microservice to manage corporate customers. When a corporation is registered, individual members will be pushed to the Customer microservice to manage them as usual. The Corporate Customer microservice provides a corporate view by aggregating data from the Customer microservice. It will also provide services to support corporate-specific business rules. With this approach, adding new services will have only a minimal impact on the existing services.

As microservices are smaller in size and have minimal dependencies, they allow the migration of services that use end-of-life technologies with minimal cost.

Technology changes are one of the barriers in software development. In many traditional monolithic applications, due to the fast changes in technologies, today's next-generation applications could easily become legacy even before their release to production. Architects and developers tend to add a lot of protection against technology changes by adding layers of abstractions. However, in reality, this approach does not solve the issue but, instead, results in over-engineered systems. As technology upgrades are often risky and expensive with no direct returns to business, the business may not be happy to invest in reducing the technology debt of the applications.

With microservices, it is possible to change or upgrade technology for each service individually rather than upgrading an entire application.

Upgrading an application with, for instance, five million lines written on EJB 1.1 and Hibernate to the Spring, JPA, and REST services is almost similar to rewriting the entire application. In the microservices world, this could be done incrementally.

As shown in the preceding diagram, while older versions of the services are running on old versions of technologies, new service developments can leverage the latest technologies. The cost of migrating microservices with end-of-life technologies is considerably less compared to enhancing monolithic applications.

As microservices package the service runtime environment along with the service itself, this enables having multiple versions of the service to coexist in the same environment.

There will be situations where we will have to run multiple versions of the same service at the same time. Zero downtime promote, where one has to gracefully switch over from one version to another, is one example of a such a scenario as there will be a time window where both services will have to be up and running simultaneously. With monolithic applications, this is a complex procedure because upgrading new services in one node of the cluster is cumbersome as, for instance, this could lead to class loading issues. A canary release, where a new version is only released to a few users to validate the new service, is another example where multiple versions of the services have to coexist.

With microservices, both these scenarios are easily manageable. As each microservice uses independent environments, including service listeners such as Tomcat or Jetty embedded, multiple versions can be released and gracefully transitioned without many issues. When consumers look up services, they look for specific versions of services. For example, in a canary release, a new user interface is released to user A. When user A sends a request to the microservice, it looks up the canary release version, whereas all other users will continue to look up the last production version.

Care needs to be taken at the database level to ensure the database design is always backward compatible to avoid breaking the changes.

As shown in the preceding diagram, version 1 and 2 of the Customer service can coexist as they are not interfering with each other, given their respective deployment environments. Routing rules can be set at the gateway to divert traffic to specific instances, as shown in the diagram. Alternatively, clients can request specific versions as part of the request itself. In the diagram, the gateway selects the version based on the region from which the request is originated.

Microservices help us build self-organizing systems. A self-organizing system support will automate deployment, be resilient, and exhibit self-healing and self-learning capabilities.

In a well-architected microservices system, a service is unaware of other services. It accepts a message from a selected queue and processes it. At the end of the process, it may send out another message, which triggers other services. This allows us to drop any service into the ecosystem without analyzing the impact on the overall system. Based on the input and output, the service will self-organize into the ecosystem. No additional code changes or service orchestration is required. There is no central brain to control and coordinate the processes.

Imagine an existing notification service that listens to an INPUT queue and sends notifications to an SMTP server, as shown in the following figure:

Let's assume, later, a personalization engine, responsible for changing the language of the message to the customer's native language, needs to be introduced to personalize messages before sending them to the customer, the personalization engine is responsible for changing the language of the message to the customer's native language.

With microservices, a new personalization microservice will be created to do this job. The input queue will be configured as INPUT in an external configuration server, and the personalization service will pick up the messages from the INPUT queue (earlier, this was used by the notification service) and send the messages to the OUTPUT queue after completing process. The notification services input queue will then send to OUTPUT. From the very next moment onward, the system automatically adopts this new message flow.

Microservices enable us to develop transparent software systems. Traditional systems communicate with each other through native protocols and hence behave like a black box application. Business events and system events, unless published explicitly, are hard to understand and analyze. Modern applications require data for business analysis, to understand dynamic system behaviors, and analyze market trends, and they also need to respond to real-time events. Events are useful mechanisms for data extraction.

A well-architected microservice always works with events for both input and output. These events can be tapped by any service. Once extracted, events can be used for a variety of use cases.

For example, the business wants to see the velocity of orders categorized by product type in real time. In a monolithic system, we need to think about how to extract these events. This may impose changes in the system.

In the microservices world, Order Event is already published whenever an order is created. This means that it is just a matter of adding a new service to subscribe to the same topic, extract the event, perform the requested aggregations, and push another event for the dashboard to consume.

Microservices are one of the key enablers of DevOps. DevOps is widely adopted as a practice in many enterprises, primarily to increase the speed of delivery and agility. A successful adoption of DevOps requires cultural changes, process changes, as well as architectural changes. DevOps advocates to have agile development, high-velocity release cycles, automatic testing, automatic infrastructure provisioning, and automated deployment.

Automating all these processes is extremely hard to achieve with traditional monolithic applications. Microservices are not the ultimate answer, but microservices are at the center stage in many DevOps implementations. Many DevOps tools and techniques are also evolving around the use of microservices.

Consider a monolithic application that takes hours to complete a full build and 20 to 30 minutes to start the application; one can see that this kind of application is not ideal for DevOps automation. It is hard to automate continuous integration on every commit. As large, monolithic applications are not automation friendly, continuous testing and deployments are also hard to achieve.

On the other hand, small footprint microservices are more automation-friendly and therefore can more easily support these requirements.

Microservices also enable smaller, focused agile teams for development. Teams will be organized based on the boundaries of microservices.

SOA and microservices follow similar concepts. Earlier in this chapter, we discussed that microservices are evolved from SOA, and many service characteristics are common in both approaches.

However, are they the same or are they different?

As microservices are evolved from SOA, many characteristics of microservices are similar to SOA. Let's first examine the definition of SOA.

The definition of SOA from The Open Group consortium is as follows:

We observed similar aspects in microservices as well. So, in what way are microservices different? The answer is: it depends.

The answer to the previous question could be yes or no, depending upon the organization and its adoption of SOA. SOA is a broader term, and different organizations approached SOA differently to solve different organizational problems. The difference between microservices and SOA is in a way based on how an organization approaches SOA.

In order to get clarity, a few cases will be examined.

Service-oriented integration

Service-oriented integration refers to a service-based integration approach used by many organizations.

Many organizations would have used SOA primarily to solve their integration complexities, also known as integration spaghetti. Generally, this is termed as Service-Oriented Integration (SOI). In such cases, applications communicate with each other through a common integration layer using standard protocols and message formats such as SOAP/XML-based web services over HTTP or JMS. These types of organizations focus on Enterprise Integration Patterns (EIP) to model their integration requirements. This approach strongly relies on heavyweight ESB such as TIBCO Business Works, WebSphere ESB, Oracle ESB, and the likes. Most ESB vendors also packed a set of related products such as rules engines, business process management engines, and so on as an SOA suite. Such organizations' integrations are deeply rooted into their products. They either write heavy orchestration logic in the ESB layer or the business logic itself in the service bus. In both cases, all enterprise services are deployed and accessed via ESB. These services are managed through an enterprise governance model. For such organizations, microservices are altogether different from SOA.

Legacy modernization

SOA is also used to build service layers on top of legacy applications.

Another category of organizations would use SOA in transformation projects or legacy modernization projects. In such cases, the services are built and deployed in the ESB layer connecting to backend systems using ESB adapters. For these organizations, microservices are different from SOA.

Service-oriented application

Some organizations adopt SOA at an application level.

In this approach, lightweight integration frameworks, such as Apache Camel or Spring Integration, are embedded within applications to handle service-related cross-cutting capabilities such as protocol mediation, parallel execution, orchestration, and service integration. As some of the lightweight integration frameworks have native Java object support, such applications would even use native Plain Old Java Objects (POJO) services for integration and data exchange between services. As a result, all services have to be packaged as one monolithic web archive. Such organizations could see microservices as the next logical step of their SOA.

Monolithic migration using SOA

The last possibility is transforming a monolithic application into smaller units after hitting the breaking point with the monolithic system. They would break the application into smaller, physically deployable subsystems, similar to the y axis scaling approach explained earlier, and deploy them as web archives on web servers or as JARs deployed on some home-grown containers. These subsystems as service would use web services or other lightweight protocols to exchange data between services. They would also use SOA and service design principles to achieve this. For such organizations, they may tend to think that microservices are the same old wine in a new bottle.

Cloud computing is one of the rapidly evolving technologies. Cloud computing promises many benefits, such as cost advantage, speed, agility, flexibility, and elasticity. There are many cloud providers offering different services. They lower the cost models to make it more attractive to the enterprises. Different cloud providers such as AWS, Microsoft, Rackspace, IBM, Google, and so on use different tools, technologies, and services. On the other hand, enterprises are aware of this evolving battlefield and, therefore, they are looking for options for de-risking from lockdown to a single vendor.

Many organizations do lift and shift their applications to the cloud. In such cases, the applications may not realize all the benefits promised by cloud platforms. Some applications need to undergo overhaul, whereas some may need minor tweaking before moving to cloud. This by and large depends upon how the application is architected and developed.

For example, if the application has its production database server URLs hardcoded as part of the applications WAR, it needs to be modified before moving the application to cloud. In the cloud, the infrastructure is transparent to the application, and especially, the physical IP addresses cannot be assumed.

How do we ensure that an application, or even microservices, can run seamlessly across multiple cloud providers and take advantages of cloud services such as elasticity?

It is important to follow certain principles while developing cloud native applications.

Twelve-Factor App, forwarded by Heroku, is a methodology describing the characteristics expected from modern cloud-ready applications. Twelve-Factor App is equally applicable for microservices as well. Hence, it is important to understand Twelve-Factor App.

A single code base

The code base principle advises that each application has a single code base. There can be multiple instances of deployment of the same code base, such as development, testing, and production. Code is typically managed in a source control system such as Git, Subversion, and so on.

Extending the same philosophy for microservices, each microservice should have its own code base, and this code base is not shared with any other microservice. It also means that one microservice has exactly one code base.

Bundling dependencies

As per this principle, all applications should bundle their dependencies along with the application bundle. With build tools such as Maven and Gradle, we explicitly manage dependencies in a pom.xml or the .gradle file and link them using a central build artifact repository such as Nexus or Archiva. This ensures that the versions are managed correctly. The final executables will be packaged as a WAR file or an executable JAR file, embedding all the dependencies.

In the context of microservices, this is one of the fundamental principles to be followed. Each microservice should bundle all the required dependencies and execution libraries such as the HTTP listener and so on in the final executable bundle.

Externalizing configurations

This principle advises the externalization of all configuration parameters from the code. An application's configuration parameters vary between environments, such as support to the e-mail IDs or URL of an external system, username, passwords, queue name, and so on. These will be different for development, testing, and production. All service configurations should be externalized.

The same principle is obvious for microservices as well. The microservices configuration parameters should be loaded from an external source. This will also help to automate the release and deployment process as the only difference between these environments is the configuration parameters.

Backing services are addressable

All backing services should be accessible through an addressable URL. All services need to talk to some external resources during the life cycle of their execution. For example, they could be listening or sending messages to a messaging system, sending an e-mail, persisting data to database, and so on. All these services should be reachable through a URL without complex communication requirements.

In the microservices world, microservices either talk to a messaging system to send or receive messages, or they could accept or send messages to other service APIs. In a regular case, these are either HTTP endpoints using REST and JSON or TCP- or HTTP-based messaging endpoints.

Isolation between build, release, and run

This principle advocates a strong isolation between the build, release, and run stages. The build stage refers to compiling and producing binaries by including all the assets required. The release stage refers to combining binaries with environment-specific configuration parameters. The run stage refers to running application on a specific execution environment. The pipeline is unidirectional, so it is not possible to propagate changes from the run stages back to the build stage. Essentially, it also means that it is not recommended to do specific builds for production; rather, it has to go through the pipeline.

In microservices, the build will create executable JAR files, including the service runtime such as an HTTP listener. During the release phase, these executables will be combined with release configurations such as production URLs and so on and create a release version, most probably as a container similar to Docker. In the run stage, these containers will be deployed on production via a container scheduler.

Externalizing logs

A Twelve-Factor application never attempts to store or ship log files. In a cloud, it is better to avoid local I/Os. If the I/Os are not fast enough in a given infrastructure, it could create a bottleneck. The solution to this is to use a centralized logging framework. Splunk, Greylog, Logstash, Logplex, and Loggly are some examples of log shipping and analysis tools. The recommended approach is to ship logs to a central repository by tapping the logback appenders and write to one of the shippers' endpoints.

In a microservices ecosystem, this is very important as we are breaking a system into a number of smaller services, which could result in decentralized logging. If they store logs in a local storage, it would be extremely difficult to correlate logs between services.

In development, the microservice may direct the log stream to stdout, whereas in production, these streams will be captured by the log shippers and sent to a central log service for storage and analysis.

Many organizations have already successfully embarked on their journey to the microservices world. In this section, we will examine some of the frontrunners on the microservices space to analyze why they did what they did and how they did it. We will conduct some analysis at the end to draw some conclusions:

When we analyze the preceding enterprises, there is one common theme. All these enterprises started with monolithic applications and transitioned to a microservices architecture by applying learning and pain points from their previous editions.

Even today, many start-ups begin with monolith as it is easy to start, conceptualize, and then slowly move to microservices when the demand arises. Monolithic to microservices migration scenarios have an added advantage: they have all the information upfront, readily available for refactoring.

Though, for all these enterprises, it is monolithic transformation, the catalysts were different for different organizations. Some of the common motivations are a lack of scalability, long development cycles, process automation, manageability, and changes in the business models.

While monolithic migrations are no-brainers, there are opportunities to build microservices from the ground up. More than building ground-up systems, look for opportunities to build smaller services that are quick wins for business—for example, adding a trucking service to an airline's end-to-end cargo management system or adding a customer scoring service to a retailer's loyalty system. These could be implemented as independent microservices exchanging messages with their respective monolithic applications.

Another point is that many organizations use microservices only for their business-critical customer engagement applications, leaving the rest of the legacy monolithic applications to take their own trajectory.

Another important observation is that most of the organizations examined previously are at different levels of maturity in their microservices journey. When eBay transitioned from a monolithic application in the early 2000s, they functionally split the application into smaller, independent, and deployable units. These logically divided units are wrapped with web services. While single responsibility and autonomy are their underpinning principles, the architectures are limited to the technologies and tools available at that point in time. Organizations such as Netflix and Airbnb built capabilities of their own to solve the specific challenges they faced. To summarize, all of these are not truly microservices, but are small, business-aligned services following the same characteristics.

There is no state called "definite or ultimate microservices". It is a journey and is evolving and maturing day by day. The mantra for architects and developers is the replaceability principle; build an architecture that maximizes the ability to replace its parts and minimizes the cost of replacing its parts. The bottom line is that enterprises shouldn't attempt to develop microservices by just following the hype.