What is Microservice Architecture?
Microservices is an architectural approach where a large application is divided into smaller, independent modules called microservices. These microservices are loosely coupled and focused on specific functionalities, with each having its own data store and a simple interface for communication, such as a REST API. They are typically deployed and managed in a container environment, like Kubernetes with Docker, allowing for independent development, deployment, and maintenance of each microservice.
Microservices are usually organised and managed in a container environment such as Kubernetes with Docker, although, there are numerous alternatives out there. Containers allow each microservice to be developed, deployed and maintained independently without as much interference to other services or the application as a whole. Each microservice also has an isolated environment and technology stack separate from each other.
Quicker deployment: Microservices enable individual services to be updated and deployed independently without affecting the entire application, resulting in faster release cycles.
Ease of understanding: With the application divided into smaller parts, developers can have a better understanding of how each microservice functions, leading to improved development and maintenance.
Technology design and upgrades: Containers facilitate easy technology stack upgrades or replacements for individual services, without impacting other services.
Easier bug isolation: When a service breaks, it only affects that specific service, making it easier to isolate and fix bugs without disrupting the rest of the application.
Quicker continuous integration: Teams can focus on specific services, enabling faster and more frequent feature rollouts and improved continuous integration.
Overhead and resource utilization: While containers are more lightweight than traditional virtual machines, managing multiple containers can still require significant memory overhead and resource consumption.
Complex distributed system design: Microservices introduce complexities in designing distributed systems, including inter-service communication and ensuring transactional consistency across multiple services.
Testing difficulties: Global integration testing can become more challenging in microservices architecture compared to monolithic architectures.
Debugging complexities: Each microservice generates its own set of logs, making it more challenging to trace and debug issues across different services.
Microservice Framework Challenges
Increased complexity: Dividing an application into autonomous components can increase overall complexity, requiring careful management and coordination.
Database management and data consistency: Managing multiple databases within microservices and ensuring data consistency across services can be challenging.
Security vulnerabilities: Microservice APIs introduce additional security risks and require robust security measures to mitigate potential breaches.
Demand for resources and expertise: Implementing and maintaining microservices may require additional resources and specialized knowledge, which can increase costs.
Agility challenges for smaller businesses: Setting up and iterating rapidly in a microservices environment can be more complex and resource-intensive, posing challenges for smaller organizations.
Interface design and test coverage: As the system becomes distributed, ensuring a tight and well-defined interface between microservices becomes crucial, along with comprehensive test coverage.
What is Serverless Architecture?
Serverless computing allows developers to focus on their application logic rather than managing the underlying infrastructure. While the term "serverless" can be misleading, as there are still servers involved, it means that developers only need to worry about writing the code and not the server-side infrastructure. The cloud provider takes care of provisioning and managing the compute containers where the code runs.
For instance, let's consider creating an order using AWS. As a developer, you would write the necessary code to process the input and store it in a data store. This code is then uploaded to AWS as a Lambda function along with configurations, permissions, environment variables, and a runtime. AWS supports various runtimes such as Node.js, Python, Ruby, Java, .NET, and Go.
When the Lambda function is invoked, an instance of the code is loaded and executed within a micro container somewhere in AWS. Once the execution is complete, the instance is stopped. To optimize performance, the code instance is kept in cache for a certain period so that it can be quickly loaded and executed when triggered again. However, if the function is out of cache and triggered, it may take a few seconds to load and execute. This delay is known as a "cold start" and can be a concern in some scenarios.
Lambda functions in AWS can be used for various purposes beyond just APIs. However, focusing on APIs, you can create APIs using Lambda functions combined with an API Gateway instance. The API Gateway maps different HTTP methods like GET, POST, etc., to specific Lambda functions that handle the corresponding requests. These Lambda functions typically perform CRUD operations on databases and other data stores. The following diagram illustrates this setup:
At Fathom, we have a preference for utilizing serverless architecture when developing APIs for our projects. We leverage technologies such as Terraform to create the necessary infrastructure. With Terraform, we can easily define and provision resources ranging from API Gateway instances to the serverless functions themselves.
While Terraform is our go-to choice for infrastructure provisioning, there are other alternatives available. For example, AWS CloudFormation provides a comprehensive way to describe and deploy infrastructure resources in an automated manner. AWS SAM (Serverless Application Model) is another option that specifically caters to serverless applications on AWS, offering higher-level abstractions and simplified deployment.
Cost-effectiveness: Serverless architectures, such as AWS Lambda, offer a pay-per-use model where you are billed only for the actual execution time of your functions. This can result in cost savings compared to running and maintaining a dedicated cluster for microservices.
Scalability: With serverless, the cloud provider takes care of the scaling for you. AWS Lambda, for example, can automatically handle scaling based on the incoming workload, allowing you to focus on the application logic without worrying about capacity planning.
Development focus: Serverless allows developers to concentrate on writing code and building functionality, rather than dealing with infrastructure management. You can upload your code and let the cloud provider handle the underlying infrastructure and operational aspects.
Vendor lock-in: Serverless platforms tie you to the options and limitations provided by the specific cloud provider. Customizing backend infrastructure resources, such as CPU power or RAM limits, may be restricted.
Runtime limitations: Each serverless function has a maximum runtime, typically around 15 minutes. This may not be suitable for heavy or time-consuming processing tasks that require longer execution times.
Cold starts: When a serverless function is inactive for a certain period, its instance may be removed from cache. The first invocation or a long period of inactivity can result in a "cold start," where the function takes longer to execute as it needs to be loaded and initialized again.
Testing challenges: Running serverless functions locally for testing purposes can be more complex compared to traditional microservices. Various tools like Lambda Local, Localstack, AWS SAM, and Serverless Framework aim to replicate the local environment but may have limitations or unpredictable behavior.
Serverless Framework Challenges
Long-term contracts: Depending on the third-party provider or framework chosen, committing to a specific serverless framework may introduce long-term contractual obligations.
Provider dependency: Making significant changes to business logic or technology may require transitioning to another serverless provider, which can be challenging and time-consuming.
Multi-tenancy performance: Multi-tenant serverless platforms can suffer from performance issues or defects if neighboring tenants deploy poorly optimized or defective code.
Cold start overhead: Applications or services that remain inactive for an extended period may experience a cold start, which requires additional time and effort to establish the necessary resources for execution.
Microservices vs Serverless Architecture:
Which One Should Be Used for The Creation of Applications?
Both microservices and serverless architectures offer distinct advantages and limitations, and choosing the appropriate architecture depends on the specific needs and objectives of your business.
If cost-efficiency and rapid deployment are crucial factors, serverless architecture is a favorable choice. Serverless allows you to pay only for the execution time and eliminates the need for managing infrastructure, resulting in cost savings. Additionally, serverless architectures enable quick deployment and scalability, making them suitable for applications with fluctuating workloads.
On the other hand, if your goal is to build a large, complex application that requires extensive customization and adaptability, microservices architecture may be a more viable solution. Microservices provide flexibility in designing and evolving the application, allowing independent development and deployment of modular services. They also offer greater control over the infrastructure and resource allocation, enabling fine-grained customization.
In some cases, a hybrid approach that combines microservices and serverless technologies can be advantageous. By leveraging the strengths of both architectures, you can create a cloud-native environment that suits your specific requirements. However, it is important to consider the trade-offs and complexities associated with integration testing, debugging, and maintenance when adopting a hybrid approach.
When making an informed decision, it is crucial to assess factors such as the granularity of serverless functions, the complexity of integration testing, and the availability of supporting tools and frameworks. Serverless architectures may introduce challenges in testing and debugging due to their fine-grained nature, while microservices benefit from mature tools and established processes.
Ultimately, the choice between microservices and serverless architectures should be based on a careful analysis of your business objectives, scalability requirements, customization needs, and the expertise of your development team.