As software developers, there is nothing new in how we think about factoring an application into component parts. It is the central paradigm of object orientation, software abstractions, and componentization. Today, this factorization tends to take the form of classes and interfaces between shared libraries and technology layers. Typically, a tiered approach is taken with a back-end store, middle-tier business logic, and a front-end user interface (UI). What has changed over the last few years is that we, as developers, are building distributed applications that are for the cloud and driven by the business.

The changing business needs are:

  • A service that’s built and operates at scale to reach customers in new geographical regions (for example).
  • Faster delivery of features and capabilities to be able to respond to customer demands in an agile way.
  • Improved resource utilization to reduce costs.
  • These business needs are affecting how we build applications.

All applications evolve over time. Successful applications evolve by being useful to people. Unsuccessful applications do not evolve and eventually are deprecated. The question becomes: How much do you know about your requirements today, and what will they be in the future? For example, let’s say that you are building a reporting application for a department. You are sure that the application remains within the scope of your company and that the reports are short-lived. Your choice of approach is different from, say, building a service that delivers video content to tens of millions of customers.

Sometimes, getting something out the door as proof of concept is the driving factor, while you know that the application can be redesigned later. There is little point in over-engineering something that never gets used. It’s the usual engineering trade-off. On the other hand, when companies talk about building for the cloud, the expectation is growth and usage. The issue is that growth and scale are unpredictable. We would like to be able to prototype quickly while also knowing that we are on a path to deal with future success. This is the lean startup approach: build, measure, learn, and iterate.2

During the client-server era, we tended to focus on building tiered applications by using specific technologies in each tier. The term monolithic application has emerged for these approaches. The interfaces tended to be between the tiers, and a more tightly coupled design was used between components within each tier. Developers designed and factored classes that were compiled into libraries and linked together into a few executables and DLLs.1

There are benefits to such a monolithic design approach. It’s often simpler to design, and it has faster calls between components, because these calls are often over interprocess communication (IPC). Also, everyone tests a single product, which tends to be more people-resource efficient. The downside is that there’s a tight coupling between tiered layers, and you cannot scale individual components. If you need to perform fixes or upgrades, you have to wait for others to finish their testing. It is more difficult to be agile.

Microservices address these downsides and more closely align with the preceding business requirements, but they also have both benefits and liabilities. The benefits of microservices are that each one typically encapsulates simpler business functionality, which you scale up or down, test, deploy, and manage independently. One important benefit of a microservice approach is that teams are driven more by business scenarios than by technology, which the tiered approach encourages. In practice, smaller teams develop a microservice based on a customer scenario and use any technologies they choose.

In other words, the organization doesn’t need to standardize tech to maintain microservice applications. Individual teams that own services can do what makes sense for them based on team expertise or what’s most appropriate to solve the problem. In practice, a set of recommended technologies, such as a particular NoSQL store or web application framework, is preferable.

The downside of microservices comes in managing the increased number of separate entities and dealing with more complex deployments and versioning. Network traffic between the microservices increases as well as the corresponding network latencies. Lots of chatty, granular services are a recipe for a performance nightmare. Without tools to help view these dependencies, it is hard to “see” the whole system.

Standards make the microservice approach work by agreeing on how to communicate and being tolerant of only the things you need from a service, rather than rigid contracts. It is important to define these contracts up front in the design, because services update independently of each other. Another description coined for designing with a microservices approach is “fine-grained service-oriented architecture (SOA).”

At its simplest, the microservices design approach is about a decoupled federation of services, with independent changes to each, and agreed-upon standards for communication.

As more cloud apps are produced, people discover that this decomposition of the overall app into independent, scenario-focused services is a better long-term approach.

What is Microservices?

Microservices is an approach to application development in which a large application is built as a suite of modular services. Each module supports a specific business goal and uses a simple, well-defined interface to communicate with other sets of services. 

Applications built as a set of modular components are easier to understand, easier to test, and most importantly easier to maintain over the life of the application. It enables organizations to achieve much higher agility and be able to vastly improve the time it takes to get working improvements to production. This approach has proven to be superior, especially for large enterprise applications which are developed by teams of geographically and culturally diverse developers.

There are other benefits:

  • Developer independence: Small teams work in parallel and can iterate faster than large teams.
  • Isolation and resilience: If a component dies, you spin up another while and the rest of the application continues to function.
  • Scalability: Smaller components take up fewer resources and can be scaled to meet increasing demand of that component only.
  • Lifecycle automation: Individual components are easier to fit into continuous delivery pipelines and complex deployment scenarios not possible with monoliths.
  • Relationship to the business: Microservice architectures are split along business domain boundaries, increasing independence and understanding across the organization.

“The microservice architectural style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API. These services are built around business capabilities and independently deployable by fully automated deployment machinery.”

How microservices work?

In a microservices architecture, each service runs a unique process and usually manages its own database. This not only provides development teams with a more decentralized approach to building software, it also allows each service to be deployed, rebuilt, redeployed and managed independently.

Microservices leans toward business enhancements in order to improve the overall product, not just the project at hand. In monoliths, work is complete once the project is finished. In microservices, work is complete over the span of the lifetime of a product. Companies such as Amazon and Netflix employ this business model.

Microservices pros and cons

Among their advantages for software development, microservices:

  • Are easily deployed.
  • Require less production time.
  • Can scale quickly.
  • Can be reused among different projects.
  • Work well with containers, such as Docker.
  • Complement cloud activities.

However, there are also drawbacks with microservices, such as:

  • Potentially too granular;
  • Latency during heavy use; and
  • Testing can be complex.
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