A microservices architecture treats applications as a set of loosely coupled services. In a microservices architecture, services are highly granular, serving only a specific purpose, and lightweight protocols enable communication between them.
The goal of microservices is to enable small teams to work on services independently of other teams. This reduces the complexity of each service, makes changes easier, and avoids complex dependencies between components within an application. There is less need for different teams to communicate and coordinate, dramatically eases deployment, and improves reliability, because changes in one component can no longer break another.
Microservices allow organizations to quickly scale software projects and easily make use of off-the-shelf or open source components. However, a microservices architecture can be challenging to build and operate. Interfaces between services must be carefully designed and treated as public APIs. And new technologies are needed to orchestrate fleets of independent microservices, typically deployed as containers or serverless functions.
is a sum of its parts, and operates as a result of interaction and data exchange between services.
The general characteristics of microservices design and architecture are:
Related content: Read our guide to microservices architecture
A monolith is a large system constructed with a single code base and deployed as a single unit, typically behind a load balancer. It usually comprises four main components: a user interface, business logic, a data interface, and a database.
Monolithic architectures: pros and cons
The primary allure of monolithic structures stems from their operational simplicity and minimal overhead requirements. They are easy to build, test, and deploy, while scalability can be achieved by running multiple application instances behind a load balancer. Furthermore, thanks to a unified codebase, they handle cross-cutting concerns such as logging, configuration management, and performance monitoring more effectively. Memory sharing within monolithic components facilitates faster communication, enhancing overall performance.
Despite these advantages, monoliths come with a significant pitfall: tight coupling. Over time, monolithic components grow increasingly intertwined, posing challenges in management, scalability, and continuous deployment. This tight coupling also raises reliability issues; any malfunction within an application module can potentially crash the entire system. Updates become an arduous task as a minor change requires the deployment of the entire application. Additionally, the monolithic system’s insistence on a single technology stack throughout adds time and cost to any technological modifications.
Microservices architectures: pros and cons
On the other hand, microservices architectures break down the application into autonomous, loosely-coupled services. These services can be deployed and scaled independently, optimizing resource utilization. Their low coupling not only enables individual testing but also fosters flexibility and adaptability over time.
Nevertheless, a shift to microservices poses its unique set of challenges, primarily around application monitoring and developer workload. The efficiency of microservices hinges on the skill level of the team handling them. Hence, an assessment of the team’s capabilities is crucial before making the transition. Moreover, dividing applications into components gives rise to additional elements that need monitoring and maintenance. Without suitable testing and monitoring tools, managing these components can become a daunting task.
Choosing the Right Architecture
When it comes to choosing between these two architectures, consider the size and complexity of your project. For small-scale, uncomplicated applications, monolithic architectures serve well. However, if your team lacks experience with distributed architectures, transitioning to microservices may not prove advantageous. For larger, more complex systems, a microservices architecture is often a better choice, especially if your developers are well-versed in working with microservices and enthusiastic about the transition.
The circuit breaker pattern is a design pattern in software development that is used to prevent a network or service from being overwhelmed by requests. This pattern is particularly useful when a service is temporarily unavailable or experiencing high latency.
In a distributed system, services often need to communicate with one another. If one of these services fails and the other services continue to make requests to it, this can lead to a cascading failure throughout the system. The circuit breaker pattern can help to prevent this.
The circuit breaker pattern is named after the electrical circuit breaker that switches off the current to prevent the wires from overheating due to excessive current flow. Similarly, in software, the circuit breaker prevents excessive communication that might be harmful.
Circuit breakers have three possible states:
The pattern is frequently used in microservice architectures to improve system resilience and fault tolerance. For example, it is a key component in Netflix’s Hystrix library, which is used to isolate points of access between the services, stop cascading failures across them and provide fallback options.
The Backend for Frontend (BFF) pattern is a design approach in microservices architectures that suggests creating separate backend services for different types of clients, like mobile, desktop, and public API.
In more traditional approaches, there’s often a single backend service that all types of clients interact with. However, this can lead to complexity and inefficiency, as different clients often have different needs in terms of data format, data volume, and operations available.
Here’s how the BFF pattern works:
The Ambassador Pattern is a structural design pattern for microservices, often used to offload common client connectivity tasks into a reusable module. This pattern can be thought of as an out-of-process proxy that is co-located with the client.
Here’s how the Ambassador Pattern works:
The Saga Pattern is a design pattern in microservices architecture which provides a solution to maintain data consistency across multiple services in a distributed transaction scenario.
In a monolithic service, data consistency is typically managed by a single database through ACID (Atomicity, Consistency, Isolation, Durability) transactions. However, in a microservices architecture, each service has its own database to ensure loose coupling and service autonomy. This makes it difficult to maintain data consistency across multiple services.
Here’s how the Saga Pattern works:
Microservices architectures can incorporate various languages and tools, but there is a core handful of tools required to enable microservices.
A container is a lightweight mechanism to move application components between different environments. Container objects contain everything required to run an application, including code, libraries, and dependencies. Containers are popular for microservices because they are portable, secure, and start faster than VMs.
Container runtimes are software components that run containers on a host operating system and manage their lifecycle. They work with the operating system kernel to launch and support containerization, and can be controlled and automated by container orchestrators like Kubernetes.
Serverless is an architectural pattern where server infrastructure is fully managed by a cloud provider. Developers create serverless functions, which are simple pieces of code, and the serverless runtime executes them fully automatically.
Running microservices on serverless platforms combines the advantages of both architectural patterns, resulting in a highly scalable and cost-efficient system. By deploying each microservice as a serverless function, you can leverage the benefits of the serverless infrastructure while maintaining the modularity and flexibility of the microservices architecture.
Microservices typically use APIs to communicate, with an API gateway as the intermediary layer between the client and a service. The gateway can route requests and increase security, which is especially useful when there are a growing number of services.
A service mesh is an infrastructure layer for facilitating service-to-service communication between microservices, often used in cloud-based applications. It’s designed to handle a high volume of network-based interprocess communication via APIs.
A service mesh offers several advantages for a microservices architecture:
A service mesh handles these concerns at the infrastructure level, allowing developers to focus on business logic.
Microservices must be able to find each other to operate. Service discovery tools help identify the location and state of microservices in real time, making it easier for developers to write code and avoid issues arising from the rapidly changing architecture. A dynamic database acts as a microservices registry specifying the location of instances, allowing developers to discover services.
Services must be state-aware, and API calls are not effective for keeping track of state information. API calls that establish state must be coupled with alerts or event streams to transmit state data to the relevant parties automatically. Some organizations use a general-purpose alerting system or message broker, while others build event-driven systems.
Edge computing is an architectural paradigm that focuses on processing data closer to the source of data generation, typically at the “edge” of the network. This approach can reduce latency, minimize data transfer costs, and increase data privacy.
When combining edge computing and microservices, you deploy your microservices at the edge of the network, closer to the data sources and end-users.
Distributed tracing is a monitoring and observability technique that is particularly useful for microservices-based applications. Microservices architectures involve multiple independent services interacting with each other, and so it can be challenging to monitor, debug, and troubleshoot issues when they arise. Distributed tracing helps address these challenges by providing visibility into the flow of requests across services and enabling developers to understand the performance and behavior of the entire system.
Application mapping is a technology used to visualize and understand the relationships between the components of a distributed system, such as a microservices-based application. In a microservices architecture, an application is composed of multiple, loosely-coupled services that communicate with each other, making it essential to understand the dependencies and interactions between these services.
There are several options for deploying microservices:
Cloud computing is the delivery of computing services, such as servers, storage, databases, networking, software, and analytics, over the internet. Cloud platforms enable on-demand access to these resources, offering flexibility, scalability, and cost-efficiency.
Using microservices in the cloud allows developers to leverage the benefits of both microservices and cloud computing. This combination provides:
There are several cloud platforms that offer a range of services and tools to support the development, deployment, and management of microservices-based applications. Some of the most popular cloud platforms for microservices are:
AWS offers a wide range of services that can be used to build, deploy, and manage microservices-based applications. Here are some key AWS services that can help you implement a microservices architecture:
Microsoft Azure offers a variety of services that can help you build, deploy, and manage microservices-based applications. Here are some key Azure services that can support a microservices architecture:
Google Cloud Platform offers a variety of services that can help you build, deploy, and manage microservices-based applications. Here are some key GCP services that can support a microservices architecture:
Microservices testing is becoming an integral part of the continuous integration/continuous delivery (CI/CD) pipeline managed by modern DevOps teams.
Testing microservices applications requires a strategy that considers both service dependencies and the isolated nature of microservices. The microservices testing process usually isolates each microservice to make sure it works, and then tests the microservices together.
There are four types of tests commonly used in microservices applications:
In a traditional monolith, you could simply find all the logs in the server filesystem. But with microservices, each instance of each service has its own logs, which can get deleted when an instance shuts down. This requires a centralized system that collects and analyzes logs. Microservices testing requires robust logging on all microservices, with unique IDs for each microservice instance that can be used to correlate requests.
Monitoring and managing microservices can be particularly challenging, given the need to track and maintain each service component of an application and their interactions. These functions include observability, failure detection, and gathering metrics from logs to identify performance and stability issues.
If an issue arises within one of your services, it is crucial to identify it as quickly as possible so that you can address it. However, it’s not enough to simply know that the problem occurred. You need to understand why it happened, when it happened, and under what conditions it occurred. This knowledge will enable you to make the necessary changes to maintain that functionality as swiftly as possible.
This highlights one of the significant advantages that microservices offer – the fact that you don’t have to worry about how troubleshooting-related changes to one service affect the rest of the app. You can go in, make any required adjustments, and keep everything online and functioning as intended.
Here are best practices that can help you make your microservices application a success.
High availability is essential for cloud and container-based workloads. Containerized applications should not have to manage infrastructure or environmental layers. New containers should be available for automatic re-instantiation when another container fails.
It is important to design microservices for failure and test the services to see how they cope under various conditions. This design approach enables the infrastructure to repair itself, minimizing emergency calls and preventing attrition. The failure-tolerant design also helps ensure uptime and prevent outages.
Organizations often have to add or update functionalities as their applications mature. Development shops usually require a versioning mechanism to ensure consistent updates. Versioning methods are particularly important for microservices because development teams update services individually. It is also harder to version microservices applications than conventional applications.
Developers should keep API versions and service names independent from the versioning of the entire microservices app. It is essential to keep proper documentation and update it with each version of individual APIs and services. It should be easy to configure every service’s URL and version number—this is important to avoid hard-coding them into the backend.
Continuous delivery enables fast, frequent deployments, a key benefit of microservices. This approach automatically tests and pushes each build through the pipeline to production. The pipeline’s steps depend on the amount and type of testing required before releasing changes to production. Several steps may cover staging and integration, component, and load tests. Release policies determine which steps to implement and when.
Choosing the communication mechanism is a major microservices design challenge. Most microservice architectures use synchronous communication based on REST APIs. While this approach is the simplest to implement, it also allows latencies to build up and can result in cascading failures. Asynchronous communication works better for some scenarios, but is also more difficult to debug. You can implement it in various ways, including via message queues (Kafka), CQRS, or asynchronous REST.
The domain-driven design approach might work well for some teams, but may be overkill for smaller organizations. It applies object-oriented programming to business models, with rules to design the model around business domains. Large platforms like Netflix use this principle to deliver and track content using multiple servers.
Microservices typically cover specific business domains, with dedicated teams for each domain. Different teams may use different technologies, meaning that the communication protocols should be technology-agnostic. Common protocols used to enable requests to various microservices serving a single client include REST, GraphQL, and gRPC.
REST is suitable for static, but not dynamic, APIs. GraphQL is extremely customizable and supports graph-based APIs to give users a high degree of flexibility and control over data. However, GraphQL is also more difficult to implement than REST.
gRPC is a Google-developed, open source communication framework that handles most aspects of communication between services, enabling the integration of non-client-facing services. It provides high performance, and is suitable for collaborative projects due to its language neutrality.
It is possible to combine protocols—for example, using REST for edge services and gRPC for internal services.
Codefresh helps you answer important questions within your organization, whether you’re a developer or a product manager:
With Codefresh, you can answer all of these questions by viewing one dashboard, our Applications Dashboard that can help you visualize an entire microservices application in one glance:
The dashboard lets you view the following information in one place:
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