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An Overview of Microservices Architecture & Design


Microservices are becoming an increasingly common architectural choice for building cloud-based enterprise software applications as they are highly scalable, easily extensible, selectively deployable, and designed for the cloud from the start. Microservices, unlike traditional applications, provide a migration path to modernize legacy applications and move them to the cloud. A microservices application must be designed and architected according to specific principles to realize all of its advantages.

What is a Microservices Architecture

A microservices architecture (MSA) — also referred to simply as microservices — comprises a set of small, autonomous services. The software team designs the application as a set of services. The architecture provides a framework to write, update, and deploy services independently.

Within this architecture, every microservice is self-contained and implements one business feature. Examples of business features are order entry, updating customer details, and net price calculation. The different microservices communicate and work in unison to accomplish business goals.


Monolith Architecture Versus Microservice Architecture

For decades, we’ve built applications in a traditional, 3-tier style of architecture. Today known as monoliths because of their tightly-coupled, highly interdependent service structure, this type of application is now under scrutiny: they cannot easily be deployed to new, cloud-based infrastructure that enterprises are adopting to better meet customer demands at scale. Microservices architecture emerged as a way to create more modular services and came into being to address the shortcomings of monoliths. Here are the critical differences between the monolithic and microservices architectures.

  • In a monolithic application, a single executable includes all functionality and runs as a single process. Microservices architecture is a collection of lightweight applications (microservices). They work cooperatively and provide the required business functionality.
  • A monolith has tightly-coupled features. Hence, making changes is difficult and often risky because a change in one module can impact a seemingly unrelated module somewhere else. Microservices are decoupled and isolated, so developers understand them better and it’s easier for them to update.
  • Monoliths are often huge applications with millions of lines of code and tens of thousands of classes–this makes them complex. Microservices handle this complexity better by breaking up the same functionality into smaller and manageable chunks.
  • Monolithic applications have a high risk of failure because they have many dependent and tightly coupled processes. So, a single point of failure can bring down the entire system. Microservices are independent of each other, which makes them easier to fix in the case of errors or failures. Hence, they confine faults to a small part of the system. This means that you can rectify issues handily and quickly.
  • You can only scale a monolith vertically, that is, by running more instances of the full application. This is both expensive and inefficient. But you can scale a microservice application merely by running more instances of heavily used services. This is effective and a lot less expensive.
  • Teams working on a monolithic application are organized by technology-an Oracle team, an Angular team, a Java team, etc. Teams working on a microservices architecture are organized by business feature, with each team being completely responsible for developing a microservice from top to bottom and also running it.
  • Deploying a monolith is a time-consuming and difficult operation–for decades, minor releases came a few times a year, whereas a major release was done annually or even less frequent than that. Any changes must first be painstakingly tested to check for side effects and breakages. Microservices are lightweight and should utilize CI/CD pipelines from the get-go. This makes deployments and rollbacks in case of failure much easier. Elite organizations commonly deploy many times a day.
  • Teams working on a monolith work on a limited set of technologies prescribed centrally by a CTO or architect. Microservices are independent of each other. So, crews working on microservices have more freedom in selecting the tech stack and tools that are most appropriate for them.

Microservices’ Relationship to Cloud-Native

Bill Wilder first used the phrase “cloud-native” in his book Cloud Architecture Patterns.

‘Cloud Native’ refers to a set of technologies that enable businesses to develop and operate software applications in the public, private, or hybrid cloud. Developers use these technologies to design and build loosely-coupled systems that are fault-tolerant, observable, and maintainable.

Cloud-native applications are fast, massively scalable, inexpensive (pay as you go billing), and either self-service or full-service based on business maturity and requirements. This leads to agility, efficiency, and faster time-to-market. Hence, being cloud-native is the Holy Grail of many businesses.

Several technologies enable this approach — containers, service meshes, automation, and CI/CD — but the most important of them all is microservices.


Key Microservice Architecture Concepts

Every microservice architecture is unique. Yet, all such architectures share some common characteristics:


Organization of Teams by Business Features

Traditionally, software development teams working on large projects have organized themselves by technology. For example, the group may be split into front-end, back-end, database, quality assurance, and DevOps teams.

This approach has some major disadvantages. There needs to be communication and negotiation across teams even for small changes. And some teams may find that it is easier to just make changes in the part of the code they own, even though that may not be the cleanest approach.

Teams working on a microservices architecture tend to take a different tack. They organize teams by business capability or feature. A single squad develops a feature from the top (UI) to the bottom (database), and is also responsible for testing, deploying, and supporting it. This leads to improved ownership, faster development, and better quality.

This method of developing microservices based on business features is called Domain-Driven Design (DDD), a term popularized by Eric Evans in his eponymous book. Check out this free summary.

Automated Deployment

Software developers will readily vouch for the fact that deploying monolithic code changes to a production environment can be a nerve-wracking, stressful, and time-consuming experience. Several packages must be built, tested, and deployed, which requires a lot of time. After deploying the build, the team must track the application closely to ensure that nothing broke. And if any issues crop up, they must fix the failures immediately or roll back the changes.

Teams working on microservices avoid some of these problems by automating much of their build and deployment pipelines. The pipeline comprises a set of machines, each belonging to a different environment-build, integration, test, or production. Automated build and test scripts run on these machines. If all tests pass, then the build is “promoted” to the next environment. If any tests fail, then the scripts abort the process and notify the developer.

So, the only thing that developers have to do is to check their code changes into a designated branch. The automated pipeline builds, tests, and deploys the code. There is a saving of effort. There is also high confidence that the changes are of acceptable quality.

Intelligence in the Endpoints

Traditional applications include significant functionality and intelligence in the systems (“pipes”) that help communicate between processes. One example is the Enterprise Service Bus (ESB), which includes complex functions like message routing, transformation, and business logic.

Microservices gravitate toward intelligent endpoints and dumb pipes. This makes each service logically decoupled and cohesive. The endpoints have the intelligence to receive requests, apply business logic, and then respond. This approach is more similar to the World Wide Web or UNIX.

Another approach used by microservices is to use lightweight message buses, like RabbitMQ, which offer only basic routing facilities. In this case, also, intelligence lives in the endpoints.

Decentralized Control of Languages and Data

Monolithic development teams lean toward mandating common development platforms. This is suboptimal, as the same tools and languages may not work well in every situation. Microservices development encourages decentralization. In a microservices setup, teams consist of members who all work on the same service.

Each microservice team selects the language and tools best suited for the job. If performance is critical, they can opt for C or C++. For data crunching or machine learning, Python may be a good choice. And if the services need to be lightweight, then Golang may be the best option. This architecture encourages polyglot programming.

The decentralization of microservices leads to a greater sense of ownership and accountability. Teams are fully and solely responsible for the microservices that they have developed — from design and development to deployment and support.

Design for Failure

Applications built on microservices need to be fault-tolerant. This is because the system comprises many services, and individual services can fail at any time. You must build and test the microservices to minimize the risk of failure, and to recover automatically in case of failure. Therefore, microservices need to have logging and monitoring in place. Sophisticated dashboards should provide a real-time view of the health of each service. The system should alert when an issue occurs or appears imminent.


What Is Microservices Architecture Used For?

Microservices are commonly used to speed up application development and deployment. Typical use cases for Microservices include:

  • Monolith to Microservices: An enterprise application developed as a monolith is broken into microservices and migrated to be cloud native.
  • Media servers: Media servers employ a microservices architecture to store audio, video, and image assets digitally, and serve them efficiently and at scale to web and mobile apps.
  • Separation of functions: Each Microservice works independently. So, in an e-commerce app, even if the checkout service is not working, users can still use the shopping cart service to select products for purchase.

Benefits of Microservices Architecture

Microservices is only one among several options historically attempted to alleviate software challenges. There have beenother paradigms in addition to monolithic system design, such as service-oriented architecture (SOA). So why are microservices more attractive than these other attempted options? It is because they offer more benefits for deploying to the cloud.

We covered some of the benefits in the section “Monolith Architecture versus Microservice Architecture.” To summarize, microservices are better because they:

  • Are loosely coupled, lightweight, autonomous
  • Enable agile development and releases
  • Provide flexibility in scaling
  • Are easy to deploy
  • Are not tied to one tech stack
  • Are resilient

Key Technologies Supporting Microservices

More complexities and issues crop up as more businesses move to a microservices architecture. Several new technologies have come to the fore to help deal with these issues. We look at some of them here.

Containers

Containerization is an approach to software development where an application or independent service and its dependencies (libraries, configurations, etc.) are all packaged together in a single container image. You can deploy the containerized image as one instance to the host server and test it in standalone mode.

Containers, such as Docker and other participants in the Open Container Initiative (OCI), allow developers to test and deploy their apps in isolation. As the container includes all dependencies, there are no surprises after deployment. The application can be conveniently scaled by deploying more container instances.

Can you deploy monolithic applications in containers? Sure. But in this case, if there are performance bottlenecks in some parts of the application, you wouldn’t be able to scale those parts. Instead, you will need to scale the entire application. This is not an optimum solution because the costs of scaling everything all the time will become prohibitive. In an e-commerce application, some features are used more — browsing available products or adding products to a shopping cart. Some parts are comparatively less used, like submitting reviews or changing account details.

With microservices, you can scale up the required services alone. This makes container usage a natural use case for microservices.

Kubernetes

Containers provide several advantages, as we have just seen. But when containers deploy microservices applications, the sheer number of containers to be managed can be frustrating. So how do we mitigate this situation?

Enter Kubernetes to the rescue. Kubernetes (or K8s) is a portable, extensible, and open-source container deployment and orchestration system. Developed by Google, Kubernetes provides a simple, automated process for deploying, scaling, and managing containerized applications by using individually deployed ‘pods’. You can run your systems resiliently, and if a container goes down, Kubernetes automatically spins up a replacement.

A typical use-case is to develop microservices (or applications) in Docker containers, then use Kubernetes to deploy and manage the containers. Kubernetes supports all the familiar container runtimes like Docker.

Some other advantages that Kubernetes provides include load balancing among your servers, automated rollouts and rollbacks, and secrets (password) management.

Kubernetes’ usage frees developers to focus on their applications instead of the infrastructure.

Service Mesh

A service mesh is a layer of code that sits on top of the infrastructure layer and is designed to improve traffic control out of the service layer in Kubernetes by means of ‘sidecar’ proxy — allowing for more elasticity. It takes care of some routine tasks associated with running microservices, like monitoring, networking, and security. It manages and controls communications between services.

A service mesh isolates the application or service from network functions. So, developers can focus on application features rather than on communications issues.

There are special security requirements because authorization and authentication flow across microservices. Service meshes support this by providing a Central Security Certificate Authority that provides certificates for each service, and by giving developers the ability to enforce access policies. They encrypt and decrypt all requests and responses between microservices.

Service meshes provide observability by keeping inbound and outbound traffic metrics, allowing a single request to be traced across all services, and monitoring workloads to detect security policy violations.

Note that a service mesh is an example of the Ambassador design pattern, described later.

Serverless

Serverless is becoming a more popular option. In serverless technologies, server infrastructure management is handled by the cloud services provider instead of the development shop.

Contrary to expectations, serverless functions, if properly configured, are equally fast. They cost less than traditional models because there is no 24X7 server running – it is strictly pay-by-use, and there are no sysops or infra management overhead. They are equally secure. Most programming languages support them.

In particular, serverless offerings benefit microservices hosting. Infrastructure sharing is an anti-pattern. Hence, several application and database servers will be required for an application comprising several microservices. It is hard to manage this setup. It also leads to an escalation of cost. The serverless option–especially when incorporating application state–attempts to solve this problem.


Microservices and Java

Cloud-native applications are now an accepted concept. The software development lifecycle has been moving faster to the cloud. The advantages of being in the cloud are already well known to recount here.

There are good reasons why applications written in Java are especially amenable to being cloud-native. These include the sheer volume of supporting tools and frameworks that are available. Some examples of tools that support microservices design in Java  Akka, Quarkus, GraalVM, Eclipse Vert.x, Spring Boot plus accessories, and OpenJDK.

We will take a closer look at cloud-native Java applications in this section.


Cloud-Native Java

Cloud-native applications are built using a set of principles that reduce the time spent on tasks that do not add value to the customer. Cloud-native apps implicitly admit their inability to forecast future demand and capacity accurately. So, they enable scaling up on the fly as needed.

There are many enablers of cloud-native applications. Key among them are a microservices architecture, containerization, and Continuous Integration/Continuous Deployment (CI/CD).

The microservices approach results in numerous independent services, managed by disparate teams, that need to be deployed separately. Containers help do this efficiently.

CI/CD pipelines enable code changes to be pushed to production frequently (often several times a day) with minimal human intervention in a repeatable manner.

Java in Cloud-Native Applications

Java consistently ranks among the most popular programming languages for enterprise software development. There are good reasons for this. Java is owned by Oracle Corporation, which belongs to the top echelon of blue-blood Silicon Valley companies, thereby gaining instant credibility. It has been around for over 25 years, so there is a large installed base of working Java applications, tools, and frameworks–including multiple Java Development Kits (JDKs) from not only Oracle but also Amazon, Microsoft, Red Hat, IBM, Azul Systems and Alibaba.

These days, there are frequent updates to the Java language, keeping it current and relevant. These updates have enriched the language and made it faster and leaner. Many of the changes are to enable Java applications to work seamlessly on a cloud platform.

Typical Cloud-Native Java Application Stack

Here is a simplified view of a Java cloud native applications stack using the most popular tools out there–Spring, Maven, Gradle, JUnit, Docker, and others. This is not comprehensive. Only one option is mentioned at every step, but several alternatives exist:

Spring Boot and Microservices

Spring Boot is one of the simplest and most commonly used frameworks to create microservices. Spring is arguably the most popular framework used by developers to write standalone as well as enterprise Java applications. It simplifies the creation of high-performing and scalable Java EE (Java Enterprise Edition) applications using POJOs (Plain Old Java Objects). It is open source.

You can think of Spring as a framework of frameworks because it supports many other legacy frameworks, such as Struts and Hibernate. The advantages of using Spring include loose coupling between modules, readymade templates for other frameworks, testability, and a powerful abstraction model.

A Spring application is a collection of objects (beans). Spring manages the objects, their relationships, and their services.

Using Spring Boot, which uses Spring, you can build microservices smoothly in a flash. It helps you create production-ready Spring applications that “just run.” One reason Spring Boot makes things easy is its ability to add smart defaults: it figures out what classes and configurations are required, and adds them on its own.

Spring Boot comes with a starter set of Gradle build files. It automatically sets up connections to available databases. It also includes several plug-ins to develop and test applications rapidly. The term “opinionated” is used to describe tools that make choices based on established best practices without input from developers. This allows users to get started with minimum effort.


When to Use Microservices

You might think that with so many advantages, microservices would be the only way to go. But this need not be the case, particularly if you already have a working monolith that meets most of your business goals. The old adage “If it ain’t broke, don’t fix it” is memorable here. Software gurus and Microservices experts Martin Fowler and Sam Newman advise that you should only opt for microservices when you have some specific needs that are not being currently met.

Here are some circumstances in which you could seriously consider adopting a microservices architecture approach:

  • Specific goals: These could include a better way to scale, independent deployments, faster time to market, or improved resiliency.
  • Need to Independently Deploy New Functionality with Limited Downtime: With microservices, you need to deploy only the services that have changed. Because of the fast deployment cycles, you can make releases with little downtime. This is a must for most SaaS-based businesses.
  • Need for Data Partitioning: Your application may process sensitive data, such as health records or credit card information. They will need to conform to industry-mandated data handling guidelines like GDPR, SOC2, or PCI. It is easier to conform to industry regulations if you have localized the sensitive data and the logic that operates on them, to a few microservices.
  • Better Team Organization: Microservices lends itself to forming 2-pizza teams (this is a somewhat antiquated concept from Amazon to mean a small team that you can feed with just two pizzas). The advantage over larger teams is better communication, improved coordination, and a stronger sense of ownership.

How to Decompose an App Into Microservices

Converting a monolithic application to a microservices architecture is called app modernization. While there are many ways of doing this, broadly, the process followed would be:

  • When building new functionality, don’t add it to the monolith. Create a distinct microservice instead. Over time, identify logical components in the monolith that you can extract out. Convert these components to microservices. Keep your monolith intact. The monolith will shrink slowly, and you will be left with microservices.
  • Flatten and refactor components. Refactor the code to remove dependencies between components. Flattening a class refers to the process of explicitly including all inherited members.
  • Identify dependencies between components.
  • Group similar components.
  • Create public APIs for clients to call the microservices, and the backend APIs for the microservices to talk to each other.
  • Migrate groups to macro services. A macro service is like a microservice but shares a database with the monolith or other macro services.
  • Migrate macro services to microservices.
  • Repeat the last two steps to satisfaction until you’ve been able to decompose most monolithic services into individual microservices.
  • Plan the transition to be iterative and incremental.

Best Practices in Microservices Development

We have seen that microservices architecture can provide several benefits. But those benefits will accrue only if you follow good design and coding principles. Let’s take a look at some of these practices.

  • You should model your services on business features and not technology. Every service should only have a single responsibility.
  • Decentralize. Give teams the autonomy to design and build services.
  • Don’t share data. Data ownership is the responsibility of each microservice, and should not be shared across multiple services to avoid high latency.
  • Don’t share code. Avoid tight coupling between services to avoid inefficiencies in the cloud
  • Services should have loose logical coupling but high functional cohesion. Functions that are likely to change together should be part of the same service.
  • Use a distributed message bus. There should be no chatty calls between microservices.
  • Use asynchronous communication to handle errors and isolate failures within a service and prevent them from cascading into broader issues.
  • Determine the correct level of abstraction for each service. If too coarse, then you will not reap the benefits of microservices. If too fine, then the resulting overabundance of services will lead to a deployment nightmare.

How Big Should a Microservice Be?

There is often a debate among software designers about how big a microservice should be. But size–i.e. lines of code–is not relevant here. We have seen that one service should have only one responsibility, i.e., it should handle only one business feature. So, it needs to be as big or as small as needed to make this happen.

That leads to another discussion: what exactly should the business feature contain? This brings up the concept of the boundaries of a service. It has now become common to use a Domain-Driven Design (mentioned previously) to analyze the business domain and define the bounded context of the domain.

Bounded Context is an important concept in DDD. A bounded context defines a logical boundary for a business feature. It is used to define the scope of a service. If you have correctly defined the bounded context of a microservice, you can safely update it with no knowledge of the internal workings of any other microservice.


Microservices Design Patterns

Microservices architecture is difficult to implement, even for experienced programmers. Use the following design patterns to make your development easier.

Ambassador

The Ambassador design pattern is used for handling common supporting tasks, such as logging, monitoring, and security.

Anti-corruption

This is an interface between legacy and modern applications. It ensures that the limitations of a legacy system do not hinder the optimum design of a new system.

Back-ends for Front-ends

A microservices application can serve different front-ends (clients), such as mobile and web. This design pattern concerns itself with designing different back-ends to handle the conflicting requests coming in from different clients.

Bulkhead

The bulkhead design pattern describes how to allocate critical system resources such as processor, memory, and thread pools to each service. Further, it isolates the assigned resources so that none of the entities can monopolize the resources and starve other services.

Sidecar

A microservice may include some helper components that are not core to its business logic but help in coding, for instance, a specialized calendar class. The sidecar pattern specifies how to deploy these components in a separate container to enforce encapsulation.

The Strangler Pattern

When converting a monolith to microservices, the recommended path is to write the new service for the new function, make the monolith call the new service bypassing the old code, verify that the new service is working fine, and, finally, remove the old code. The Strangler design pattern–based off the lifecycle of the strangler fig plant described by Martin Fowler in this 2004 blog post–helps implement this approach.

Gateway Aggregation

This design pattern merges multiple requests to different microservices into a single request. This reduces traffic between clients and services.

Gateway Offloading

The gateway offloading pattern deals with microservices offloading common tasks (such as authentication) to an API gateway. Clients call the API Gateway instead of the service. This decouples the client from the service.

Gateway Routing

This enables several microservices to share the same endpoint, freeing up the operations team from having to manage a huge number of unique endpoints.

Adapter Pattern

The adapter pattern acts as a bridge between incompatible interfaces in different services. Developers implement an adapter class that joins two otherwise incompatible interfaces. For example, an adapter can ensure that all services provide the same monitoring interface. So, you need to use only one monitoring program. Another example is to make sure that all log files are written in the same format so that one logging application can read them.


Design of Communications for Microservices

Many microservices need to work cooperatively to deliver a single business functionality. They do this by exchanging messages containing data, ideally asynchronously to improve messaging reliability in a distributed system. So, communication must be fast, lightweight, and fault-tolerant. We will look at some issues related to microservices communication.

Synchronous Versus Asynchronous Messaging

Microservices can use two fundamental communication paradigms for exchanging messages: synchronous and asynchronous.

In synchronous communication, one service calls another service by invoking an API which the latter exposes. The API call uses a protocol such as HTTP or gRPC (Google Remote Procedure Call). The caller waits until it receives a response. In programming terms, the calling thread is blocked on the API call.

In asynchronous communication, one service sends a message to another service but does not wait for a response and is free to continue operations. Here, the calling thread is not blocked on the API call.

Both communication types have their pros and cons. Asynchronous messaging offers reduced coupling, isolation of a failing part, increased responsiveness, and better workflow management; however, if not set up with the understanding that system design will be different, you may experience disadvantages like increased latency, reduced throughput, and tighter coupling on a distributed message bus.

Distributed Transactions

Distributed transactions with several steps are common in a microservices application. This kind of transaction involves several microservices, with each service executing some steps. In some cases transactions are successful only if all the microservices correctly execute the steps they are responsible for–here, if even one microservice fails, the transaction results in a failure. In other cases, such as in asynchronous systems, sequence matters little.

A failure could be transient. An example is a timeout failure, which may be resolved by retrying.

A non-transient failure is more serious. In this case, an incomplete transaction results, and it may be necessary to roll back, or undo, the steps that have been executed so far. One way to do this is by using a Compensating Transaction.

Other Challenges

An enterprise application can have many microservices, each of which may have hundreds of running instances. An instance can fail for many reasons. So, resiliency should be built in by appropriately retrying API calls.

Kubernetes provides basic load-balancing using a simple randomizing algorithm. If this is inadequate, you can use a service mesh. A service mesh provides more sophisticated load-balancing based on observed metrics.

A single transaction may be executed across several microservices. Each microservice may keep its own logs and metrics. But if there is a failure, there must be a way of correlating these observations. This process is called distributed tracing.


Considerations for Microservices API Design

Many microservices “talk” directly to each other. All data exchanged between services happens via APIs or messages. So, well-designed APIs are necessary for the system to work efficiently.

Microservices apps support two types of APIs.

  • Public APIs are exposed by the microservices and called from client applications. An interface called the API Gateway handles this communication. The API Gateway is responsible for load balancing, monitoring, routing, caching, and API metering.
  • Private (or backend) APIs are used for inter-service communication.

Public APIs must be compatible with the client, so there may not be too many options here. In this discussion, we focus on private APIs.

Based on the number of microservices in the application, inter-services communication can generate a lot of traffic. This will slow the system down. Hence factors such as serialization speed, payload size, and chattiness must be considered in API design.

Here are some of the backend API recommendations and design options with their advantages and disadvantages:

REST versus RPC/gRPC:

REST is based on HTTP verbs, and is well-defined semantically. It is stateless and hence freely scalable, but does not always support the data-intensive needs of microservices. RPC/gRPC might lead to chatty API calls unless you design them correctly, yet this interface is potentially faster in many use cases than REST over HTTP.

Message Formats:

You can use a text-based message format like XML, JSON, or a binary format. Text-based formats are human-readable but verbose.

Response Handling:

Return appropriate HTTP Status Codes and helpful responses. Provide descriptive error messages.

Handle Large Data Intelligently:

Some requests may result in a large amount of data being returned from the database query. All the data may not be needed, hence processing power and bandwidth are wasted. This can be solved by passing a filter in the API query string.

API Versioning:

APIs evolve. A well-thought-out versioning strategy helps prevent client services from breaking because of API changes.


Microservices Architecture Patterns

We have seen some patterns that help in microservices design. Now let us look at some of the architectural best practices.

Dedicated Datastore per Service

You should not use the same datastore across microservices, because this will result in a situation where different teams share database elements and data. Each service team should use a database that is the best fit for it and not share it to ensure performance at scale.

Don’t Touch Stable and Mature Code

If you need to change a microservice that is working well, it is preferable to create a new microservice, leaving the old one untouched. After testing the new service and making it bug-free, you can either merge it into the existing service or replace it.

Version Each Microservice Independently

Build each microservice separately by pulling in dependencies at the appropriate revision level. This makes it easy to add new files without breaking anything.

Use Containers to Deploy

When you package microservices in containers, all you need is a single tool for deployment. It will know how to deploy the container.

Servers Are Stateless

All servers are the same: they perform the same function. Don’t rely on specific servers to perform specialized functions. This way, you can effortlessly replace a failing server and scale up when needed.

Other Patterns

We have covered the simplest and most widely used patterns here. Other patterns are also available – Auto Scaling, Horizontal Scaling Compute, Queue-Centric Workflow, MapReduce, Database Sharding, Co-locate, Multisite Deployment, and many more.


Converting Your Monolith Applications to Microservices

We have seen the many advantages that microservices architecture can bring. If your organization owns a monolithic application that is showing its age and holding back your business, it is time to take the first step toward converting it into a microservices architecture.

But moving toward a microservices architecture is not easy. You will need to consider a plethora of design, architecture, technological, and communications options. You will also have to resolve several knotty technical issues. A manual approach is failure-prone and hence strongly discouraged.

vFunction is aware of the cost, time, and risk constraints associated with the manual modernization of business applications. It has offset this by creating a platform to perform cloud-native modernization using a repeatable and scalable factory model.

Using automation, AI, and data science, vFunction’s platform allows developers and architects to intelligently convert complicated monolithic Java applications into microservices. It is the first and only platform of its kind.Request a demo to see how leading companies everywhere are leveraging vFunction to experience a faster, more predictable, and more productive digital transformation. Partnering with vFunction can help speed up the transformation of your legacy application into a high-performance, modern, scalable cloud-native one.

The vFunction platform is award-winning, patented technology that uses AI to reduce refactoring efforts in order to improve scale, reliability, and reduce costs.