2020 cemented the reality that modern software development practices require rapid, scalable delivery in response to unpredictable conditions. To keep pace with the need for innovation and increasing demand, developers need to divvy up resources into “microservices” based on requirements and distribute applications accordingly — as opposed to maintaining a monolithic codebase and resource pool.
What are microservices?
Microservices are flexible, lightweight, modular software services of limited scope that fit together with other services to deliver full applications. This method of structuring, developing, and operating complex, multi-function software as a collection of smaller independent services is known as microservice architecture.
Using a microservices approach, DevOps teams split services into functional APIs instead of shipping applications as one collective unit. Easy to leverage API interfaces connect services with core functionality, allowing applications to communicate and share data.
The teams required to develop and maintain microservices over time can be smaller since the scope of the projects is more manageable. Applications, in turn, become collections of services. Microservices are:
- Easily testable
- Loosely coupled
- Independently deployable
- Focused on delivering business value
Microservices are run using container-based orchestration platforms like Kubernetes and Docker or cloud-native function-as-a-service (FaaS) offerings like AWS Lambda, Azure Functions, and Google Cloud Functions, all of which help automate the process of managing microservices.
To fully answer “What are microservices?” it helps to understand the monolithic architectures that preceded them.
Understanding monolithic architectures
Monolithic architectures structure software in a single tier. Like a stone pillar, the resulting product is large, uniform, and rigid. Accordingly, monolithic software systems employ one large codebase (or repository), which includes collections of tools, SDKs, and associated development dependencies. Giants like Google and Microsoft once employed monolithic architectures almost exclusively.
With monolithic architectures, components all coexist together in a single deployment. One large team generally maintains the source code in a centralized repository visible to all engineers, who commit their code in a single large build. These teams generally use standardized tools and follow a sequential process to build, review, test, deliver, and deploy code.
A centralized, sequential process may be a benefit depending on your organization’s makeup, services catalog, or team expertise. However, most organizations, even in heavily regulated industries and government agencies, find the monolithic approach to be too slow to meet demand, and too restrictive to developers. A massive codebase can suffer from instability issues and bugs can impact other shared systems.
Microservices vs. monoliths: a more agile approach
As demand has increased and applications have spread out into containerized and multi-cloud environments, organizations needed a more agile way of architecting and developing apps.
The microservices approach gives developers more flexibility, and different teams within an organization own various APIs and applications throughout their lifecycles. Teams can become leading experts on the technologies they develop. Microservices architecture also makes it possible for teams to use preferred development stacks simultaneously. On the deployment side, smaller teams can launch services much faster using flexible containerized environments like Kubernetes or serverless functions like AWS Lambda, Google Cloud Functions, and Azure Functions. Accordingly, applications don’t have to wrestle over shared resources during runtime.
Whereas monolithic software only communicates to systems within its own boundaries, microservices-based software can direct calls to multiple services and repositories simultaneously. This distributed approach affords applications more flexibility and the ability to spread processing requests across many resources, which can reduce bottlenecks.
In short, microservices are intended for quickly building and managing apps. The architecture is optimized for continuous integration and continuous deployment (CI/CD) — processes that define operating principles, development best practices, and rapid delivery of reliable code.
Microservices architecture helps teams become more flexible and bring highly scalable apps to market faster. This approach gives agile teams an advantage over teams that use monolithic architecture. Specifically, microservices offer the following advantages:
- Flexible architecture. Microservices architecture enables developers to use many different images, containers, and management engines in assembling a microservices ecosystem. This gives developers tremendous latitude and configuration options.
- Use fewer resources. Microservices tend to use fewer resources overall during runtime thanks to the cluster manager, which divvies up memory and CPU capacity among services.
- More resilient. Microservices are more resilient than their monolithic counterparts. Services have clear boundaries and resources. Should one fail, the rest keep working. This isn’t typical in a monolith, where one service failure can take out many adjacent services, hurting overall app functionality.
- Greater up-time. With microservices, it’s easier to maintain uptime. Because there are more, smaller pieces, it’s much easier for a developer to jump in and tackle a problem without affecting the other services.
- Simple network calls. Because their functionality is limited, calls made using microservices are simple and lightweight. They commonly leverage HTTP and REST API technologies to communicate with other services.
Because it does take some time to acclimate to these technologies, the benefits of microservices don’t come without cost. A microservices architecture can pose the following challenges:
- Steep learning curve. The learning curve and logistics for initial setup can be a challenge as configuring images and containers can be tricky when starting from zero.
- Complexity. Fragmentation, while a unique benefit of microservices, means there are more pieces to manage and own. In response, teams must adopt a common language and implement automated solutions to help them manage and coordinate the complexity.
- Potential for decreased reliability. Performance-wise, long call chains over the network can potentially decrease reliability. When more services can make more calls simultaneously, the potential for failure compounds for each service — especially when handling large call volumes. To avoid this, automated detection and testing can help streamline and optimize these API backends appropriately.
- Limited observability. With many dynamic services managed throughout disparate environments, maintaining adequate observability into systems can make monitoring a pain point. Manually pulling metrics from a managed system like Kubernetes can be laborious.
- Cultural shift. The culture shift required of microservices architecture is an advantage in that it requires team members to think more efficiently and modularly. But it also requires time and commitment for teams to make the transition. Advancing any microservices initiative requires sharing answers and collaboration to keep its many moving parts up and running.
A few best practices
To keep things simple, there are some common tips to keep in mind while developing microservices.
- Ownership of each component is highly important; each team member plays a critical role.
- Sharpening up (or clearly defining) CI/CD processes can help development processes run much more smoothly.
- Test early and often using multiple methods.
- Security is also critical since numerous calls will be made over the network, and more intermediary systems are involved.
DevOps teams need a solution that puts automation and observability at the forefront of microservices management.
To manage the complexity that accompanies microservices architecture, observability is crucial. The Dynatrace platform delivers broad end-to-end observability into microservices and the systems and platforms that host and orchestrate them, including Kubernetes, cloud-native platforms, and open-source technologies. The Dynatrace AI engine, Davis, and continuous automation help DevOps teams implement the automatic detection and testing required to mitigate or eliminate reliability issues with complex call chains. With AI and continuous automation, teams can easily discover where streamlining is needed.
Learn today how Dynatrace works to help your team seamlessly monitor the microservices and containers that keep your applications agile, stable, and scalable.