Weighing a microservices approach means covering all architecture bases

Is a microservices approach to architecture right for your organization? Consider microservices vs. monolithic architecture, SOA, and migration challenges.

Many organizations are taking a microservices approach to IT architecture. It’s easy to see why, with benefits such as better testing, easier deployment, faster performance, and more. But nothing is perfect — and microservices is no exception.

A microservices approach enables DevOps teams to develop an application as a suite of small services. Then, they can split these services into functional application programming interfaces (APIs), rather than shipping applications as one large, collective unit. However, in some cases, an organization may be better suited to another architecture approach. While microservices vs. monolithic architecture is a common debate, organizations have other considerations, like service-oriented architecture (SOA), tools, monitoring solutions, and potential migration issues.

Therefore, it’s critical to weigh the advantages of microservices against its potential issues, other architecture approaches, and your unique business needs.

What is the monolithic architecture approach?

Microservices is a development approach in which a large application is broken down into modular services, which support specific functions and communicate with other services. In contrast, monolithic architecture structures software in a single tier. The resulting product is large, uniform, and rigid.

Monolithic software systems employ one large codebase, which includes collections of tools, software development kits, and associated development dependencies. There are many common use cases for building services as a single application, such as automating a shipping label or charging a credit card. In fact, giants like Google and Microsoft once employed monolithic architectures almost exclusively.

With monolithic architecture, components all coexist in a single deployment. One large team generally maintains the source code in a centralized repository that’s visible to all engineers, who commit their code in a single build. These teams typically use standardized tools and follow a sequential process to build, review, test, deliver, and deploy code.

Common problems with monolithic architecture

Problems arise when the application’s demand and requirements grow. Because monolithic software systems employ one large codebase repository, the service becomes a massive piece of software that is labor-intensive to manage. One team may build it, but three separate DevOps and IT teams must maintain it.

Even in heavily regulated industries, such as banking and government agencies, most organizations find the monolithic approach too slow to meet demand and too restrictive for developers. Additionally, a massive codebase can suffer from instability issues and bugs that directly affect other shared systems.

Comparing microservices vs. monolithic architecture

As the demand for services grows and containerized applications deploy across multicloud environments, organizations need a more flexible, cohesive approach for designing and developing a distributed systems architecture.

Microservice design patterns allow developers to use their preferred programming language or framework, which helps to prevent employee churn and the need for outsourced talent. Therefore, designated teams within an organization own various APIs and applications throughout their lifecycles and become leading in-house experts on the technologies they develop.

Smaller teams can launch services much faster using flexible containerized environments, such as Kubernetes, or serverless functions, such as AWS Lambda, Google Cloud Functions, and Azure Functions. So applications don’t have to wrestle over shared resources during runtime.

As organizations choose between microservices vs. monolithic architecture, it’s critical to consider the following:

  • Monolithic software only communicates to systems within its own boundaries, whereas microservices-based software can direct calls to multiple services and repositories simultaneously. This distributed microservices approach gives applications more flexibility and enables them to spread processing requests across many resources, reducing bottlenecks.
  • Monolithic development can be painfully slow and cumbersome, especially when the application codebase becomes unmanageable.
  • Microservices development is intrinsically optimized for continuous integration/continuous deployment (CI/CD) processes that apply agile development best practices for the rapid delivery of reliable code.

Tips for migrating from monolithic to microservices architecture

Together with cloud deployment technologies, monitoring, API management, and integration technologies, microservices are an agile and efficient way to deploy large, complex enterprise applications.

Although monolithic applications can be easier to work with initially, this changes quickly as applications evolve and become more tangled. For example, isolating services for independent scaling or code maintainability becomes challenging.

As an organization grows, the applications and services become more complex. To successfully support a growing business, apps and services need to scale with it. Dynatrace offers Custom Service Entry Points, which virtually breaks the monolith and provides valuable information about whether organizations should break out certain pieces of the monolith. This approach enables continuous experimentation and provides fast feedback without changing a single line of code.

What are the challenges of microservices migration?

While microservices have several key benefits, switching from a monolithic architecture to a microservices approach can pose the following challenges:

  • Transition hurdles. If an organization is accustomed to working with monolithic applications, it might struggle to develop distributed applications. Therefore, it’s important to assess tools and development practices before making the move to microservices. Remember, refactoring an application that was built on monolithic architecture is a massive undertaking for large enterprise applications.
  • Testing can be more complicated. In a microservices architecture, there isn’t just one executable. Testing requires several additional services and application pieces.
  • Deployments can become more complicated. If an application spans multiple services, it will require careful cross-team coordination to deploy the application properly. There is also the operational complexity of deploying and managing a system in production comprising many different microservice types.
  • Increased resource consumption. Rather than one application instance, an organization has to provision resources — such as memory, CPU, and disk — to accommodate each cluster or service requirement.
  • Additional complexity and monitoring challenges. Developers need to adjust to working with the complexity of a distributed system. As part of that complexity, monitoring microservices can become a challenge. Therefore, it’s crucial to have the right microservices monitoring solution in place.

Microservices architecture vs. SOA

While many organizations ultimately choose between microservices vs. monolithic architecture, others could benefit from SOA. In this development architecture, each of SOA’s corresponding services leverages protocols. This allows users to combine functions from applications built from previous services.

Many developers consider microservices to be a more refined and mature form of SOA. Unlike microservices, which focuses on responsive-actor programming styles, SOA focuses on imperative programming. Additionally, typical SOA models use larger relational databases. In contrast, microservices typically uses NoSQL or a type of micro-SQL database.

However, one of the biggest differences is the architecture used to arrive at an integrated set of services. Experienced developers see microservice design patterns as more platform-agnostic. Therefore, any solution that deals with change more quickly potentially gives developers an advantage in creating agile solutions.

The demand for adaptable, highly scalable, and modular application designs has led many developers to move from SOA to a microservices approach. However, as with monoliths, a successful migration from SOA to microservices necessitates new tooling, adjusted team collaboration efforts, and refactoring the application.

Containers vs. VMs: How developers deliver microservices

While microservices vs. monolithic architecture is often debated, containers and microservices are quite different. A container is an executable package of software that includes all the necessary dependencies to function independently. Containers are isolated from other software pieces that surround them, and many containers can be deployed within the same environment. When working with microservices, each service is individually containerized under the same environment.

For example, a virtual machine (VM) can replace containers to design and architect microservices. Each individual service could use a VM to host a specific feature. However, using VMs to host microservices isn’t exactly ideal.

VMs require their own operating system and take up additional resources. Conversely, containers are much more resource-friendly, as they only need their underlying code and related dependencies to properly operate a given service.

What tools do you need to run a microservices architecture?

There is no set way to design a microservices architecture. And there is no mandatory set of tools to run it. However, most microservices environments commonly use a few core components, including the following:

  • Containers. Container development and orchestration environments — such as Docker and Kubernetes — are the preferred methods for running microservices, because they are more lightweight and portable than VMs. They also make it easier to manage containers at scale.
  • API gateways. These servers handle all requests from the client and route them to the appropriate microservices. This helps to keep individual services more lightweight.
  • Serverless platforms. Serverless, cloud-native platforms from Amazon Web Services, Microsoft Azure, and Google Cloud Platform provide server infrastructure and are options for building and deploying microservices.
  • Service mesh. A service mesh is a dynamic platform messaging layer built on top of the infrastructure layer that encrypts data, performs load balancing, and controls service-to-service communication requests.
  • Systems monitoring and alerting. Monitoring and alerting tools and protocols help simplify observability for all custom metrics. Three popular open source options include the following:
    • Prometheus stores metrics in a time-series database.
    • StatsD is a vendor-independent industry standard for real-time monitoring.
    • Telegraf is a plugin-driven server agent used to collect, process, aggregate, and write metrics.

How do you monitor microservices?

Effective microservices monitoring requires automatic and intelligent observability in all your cloud-native and container environments, as well as every resource with which your microservices interact. This observability provides insight into an application’s overall health by evaluating each service’s performance in context to other services and infrastructure. It also provides a comprehensive understanding of the availability and performance of each API transaction used to connect services, so teams can detect anomalies in near-real time.

After deciding which monitoring approach best meets your business objectives and what metrics to measure, keep in mind the following:

  • End-to-end observability. Implement observability across the full stack so cross-functional teams can identify and remediate performance issues in near-real time.
  • Real user monitoring (RUM). Record all user interactions to evaluate applications’ performance.
  • Auto-discovery. Automatically detect all applications, services, processes, and infrastructure at the start.
  • Automatic instrumentation. Harness automation to address the need for manual coding to avoid errors that occur when manually monitoring and measuring application performance.
  • Distributed tracing. Track requests as they flow through a distributed system to monitor, debug, and optimize services and microservices architecture.

Dynatrace’s distributed tracing and code-level analysis tool, PurePath, simplifies microservices monitoring with complete visibility across the entire application. PurePath automatically analyzes logs and traces in real time without changing the code. Additionally, the Dynatrace service can capture data from open source technologies, cloud-native platforms, containers, and more.

Join us for the on-demand webinar,Introducing next-gen PurePath: Distributed tracing for the latest cloud-native architectures,” for an in-depth look at PurePath and how Dynatrace can help your organization take your microservices approach and cloud-native architectures further.

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