Microservices

In today's world of distributed systems and microservices, it is crucial to maintain consistency. Microservice architecture is considered almost a standard for building modern, flexible, and reliable high-loaded systems. But at the same time introduces additional complexities. Monolith vs Microservices In monolithic applications, consistency can be achieved using transactions. Within a transaction, we can modify data in multiple tables. If an error occurred during the modification process, the transaction would roll back and the data would remain consistent. Thus consistency was achieved by the database tools. In a microservice architecture, things get much more complicated. At some point, we will have to change data not only in the current microservice but also in other microservices. Imagine a scenario where a user interacts with a web application and creates an order on the website. When the order is created, it is necessary to reduce the number of items in stock. In a monolithic application, this could look like the following: In a microservice architecture, such tables can change within different microservices. When creating an order, we need to call another service using, for example, REST or Kafka. But there are many problems here: the request may fail, the network or the microservice may be temporarily unavailable, the microservice may stop immediately after creating a record in the orders table and the message will not be sent, etc. Transactional Outbox One solution to this problem is to use the transactional outbox pattern. We can create an order and a record in the outbox table within one transaction, where we will add all the necessary data for a future event. A specific handler will read this record and send the event to another microservice. This way we ensure that the event will be sent if we have successfully created an order. If the network or microservice is unavailable, then the handler will keep trying to send the message until it receives a successful response. This will result in eventual consistency. It is worth noting here that it is necessary to support idempotency because, in such architectures, request processing may be duplicated. Implementation Let's consider an example of implementation in a Spring Boot application. We will use a ready solution transaction-outbox. First, let's start PostgreSQL in Docker: Shell docker run -d -p 5432:5432 --name db \ -e POSTGRES_USER=admin \ -e POSTGRES_PASSWORD=password \ -e POSTGRES_DB=demo \ postgres:12-alpine Add a dependency to build.gradle: Groovy implementation 'com.gruelbox:transactionoutbox-spring:5.3.370' Declare the configuration: Java @Configuration @EnableScheduling @Import({ SpringTransactionOutboxConfiguration.class }) public class TransactionOutboxConfig { @Bean public TransactionOutbox transactionOutbox(SpringTransactionManager springTransactionManager, SpringInstantiator springInstantiator) { return TransactionOutbox.builder() .instantiator(springInstantiator) .initializeImmediately(true) .retentionThreshold(Duration.ofMinutes(5)) .attemptFrequency(Duration.ofSeconds(30)) .blockAfterAttempts(5) .transactionManager(springTransactionManager) .persistor(Persistor.forDialect(Dialect.POSTGRESQL_9)) .build(); } } Here we specify how many attempts should be made in case of unsuccessful request sending, the interval between attempts, etc. For the functioning of a separate thread that will parse records from the outbox table, we need to call outbox.flush() periodically. For this purpose, let's declare a component: Java @Component @AllArgsConstructor public class TransactionOutboxWorker { private final TransactionOutbox transactionOutbox; @Scheduled(fixedDelay = 5000) public void flushTransactionOutbox() { transactionOutbox.flush(); } } The execution time of flush should be chosen according to your requirements. Now we can implement the method with business logic. We need to create an Order in the database and send the event to another microservice. For demonstration purposes, I will not implement the actual call but will simulate the error of sending the event by throwing an exception. The method itself should be marked @Transactional, and the event sending should be done not directly, but using the TransactionOutbox object: Java @Service @AllArgsConstructor @Slf4j public class OrderService { private OrderRepository repository; private TransactionOutbox outbox; @Transactional public String createOrderAndSendEvent(Integer productId, Integer quantity) { String uuid = UUID.randomUUID().toString(); repository.save(new OrderEntity(uuid, productId, quantity)); outbox.schedule(getClass()).sendOrderEvent(uuid, productId, quantity); return uuid; } void sendOrderEvent(String uuid, Integer productId, Integer quantity) { log.info(String.format("Sending event for %s...", uuid)); if (ThreadLocalRandom.current().nextBoolean()) throw new RuntimeException(); log.info(String.format("Event sent for %s", uuid)); } } Here randomly the method may throw an exception. However, the key feature is that this method is not called directly, and the call information is stored in the Outbox table within a single transaction. Let's start the service and execute the query: Shell curl --header "Content-Type: application/json" \ --request POST \ --data '{"productId":"10","quantity":"2"}' \ http://localhost:8080/order {"id":"6a8e2960-8e94-463b-90cb-26ce8b46e96c"} If the method is successful, the record is removed from the table, but if there is a problem, we can see the record in the table: Shell docker exec -ti <CONTAINER ID> bash psql -U admin demo psql (12.16) Type "help" for help. demo=# \x Expanded display is on. demo=# SELECT * FROM txno_outbox; -[ RECORD 1 ]---+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ id | d0b69f7b-943a-44c9-9e71-27f738161c8e invocation | {"c":"orderService","m":"sendOrderEvent","p":["String","Integer","Integer"],"a":[{"t":"String","v":"6a8e2960-8e94-463b-90cb-26ce8b46e96c"},{"t":"Integer","v":10},{"t":"Integer","v":2}]} nextattempttime | 2023-11-19 17:59:12.999 attempts | 1 blocked | f version | 1 uniquerequestid | processed | f lastattempttime | 2023-11-19 17:58:42.999515 Here we can see the parameters of the method call, the time of the next attempt, the number of attempts, etc. According to your settings, the handler will try to execute the request until it succeeds or until it reaches the limit of attempts. This way, even if our service restarts (which is considered normal for cloud-native applications), we will not lose important data about the external service call, and eventually the message will be delivered to the recipient. Conclusion Transactional outbox is a powerful solution for addressing data consistency issues in distributed systems. It provides a reliable and organized approach to managing transactions between microservices. This greatly reduces the risks associated with data inconsistency. We have examined the fundamental principles of the transactional outbox pattern, its implementation, and its benefits in maintaining a coherent and synchronized data state. The project code is available on GitHub.

Many sources provide explanations of microservices in a general context, but there is a lack of domain-specific examples. Newcomers or those unsure of where to begin may find it challenging to grasp how to transition their legacy systems into a microservices architecture. This guide is primarily intended for individuals who are struggling to initiate their migration efforts, and it offers business-specific examples to aid in understanding the process. There is another pattern I wanted to talk about - the Strangler Pattern - which is a migration pattern used to transition from a legacy system to a new system incrementally while minimizing risk. Let's take an example of a legacy grocery billing system. Now it is time to upgrade to microservice architecture to leverage its benefits. Strangler is a pattern that is for gradually decommissioning the old system while incrementally developing the new system. That way, users can start using the newer system sooner rather than waiting for the whole system migration to be complete. In this first article, I am going to focus on microservices needed by a grocery store. For example, consider a scenario where you currently have a legacy system for a grocery store, and you're interested in upgrading it to a microservices architecture and migrating it to the cloud. Overview of Grocery Store Legacy System First, the modules a grocery store online might have are: Shopping cart service Payment processing service with refund Inventory management service: The quantity of the product should be subtracted when the item is sold, and added back when the order is refunded. As per the Strangler Pattern, you should be able to replace one module with a new microservice while you continue using the other modules until newer services are ready. Here, you can first replace the shopping cart with a newer service. Since the shopping cart service is dependent on a payment processing service, you need to develop that one, too. Assume that we are going to develop these services incrementally. For demonstration purposes, I will just focus on the above three services only. But in a real-world scenario, you might need the other services as illustrated below to complete the whole e-commerce site for the grocery store: Now let's consider the essential model classes and operations required for each service. For the shopping cart service, you'll need the following model classes and operations: product, product category, items added to the shopping cart, shopping cart, and order. It can be structured as follows: Shopping Cart Service C# public class Product { public Guid Id { get; set; } public string Name { get; set; } public decimal Price { get; set; } public int StockQuantity { get; set; } public Category ProductCategory { get; set; } } public class Category { public Guid Id { get; set; } public string Name { get; set; } } public class ShoppingCartItem { public Product Product { get; set; } public int Quantity { get; set; } } public class ShoppingCart { public Guid Id { get; set; } public List<ShoppingCartItem> Items { get; set; } public Customer Customer { get; set; } public DateTime CreatedAt { get; set; } } public class Order { public Guid Id { get; set; } public List<ShoppingCartItem> Items { get; set; } public Customer Customer { get; set; } public decimal TotalAmount { get; set; } public DateTime CreatedAt { get; set; } } Ideally, you should create a shared project to house all models and interfaces. It's essential to begin by identifying the necessary models and operations first. When considering the operations that a customer can perform in the shopping cart, it typically involves just one primary action, CreateOrder, when adding items to the cart. However, other operations, such as payment processing, refunds, and inventory adjustments, should be implemented as separate microservices. This modular approach allows for more flexibility and scalability in managing different aspects of the business process. C# public class BillingService : IBillingService { public Order CreateOrder(Customer customer, List<ShoppingCartItem> items) { return new Order { Id = Guid.NewGuid(), //Create a new order id Items = items, Customer = customer, TotalAmount = CalculateTotalAmount(items), CreatedAt = DateTime.Now }; } private decimal CalculateTotalAmount(List<ShoppingCartItem> items) { decimal totalAmount = 0; foreach (var item in items) { totalAmount += item.Product.Price * item.Quantity; } return totalAmount; } } Ideally in the shared project, you have to create an interface for IBillingService. It should look as below: C# public interface IBillingService { public Order CreateOrder(Customer customer, List<ShoppingCartItem> items); } Now you can unit-test the CreateOrder operation. In the real world, it's common practice to create an IBillingRepository to save orders in the database. This repository should encompass methods for storing orders in a database, or you can opt to use a downstream service to handle the order creation process. I won't be addressing user authentication, security, hosting, monitoring, proxy, and other related topics in this discussion, as they are distinct subjects. My primary focus remains on the design aspects of microservices tailored to your specific requirements. After creating the shopping cart, the next step involves customer payment. Let's proceed by creating the Payment Service project and its associated models. Payment Processing Service C# public class Payment { public Guid Id { get; set; } public decimal Amount { get; set; } public PaymentStatus Status { get; set; } public DateTime PaymentDate { get; set; } public PaymentMethod PaymentMethod { get; set; } } public enum PaymentStatus { Pending, Approved, Declined, } public enum PaymentMethod { CreditCard, DebitCard, PayPal, } public class Receipt { public Guid Id { get; set; } public Order Order { get; set; } public decimal TotalAmount { get; set; } public DateTime IssuedDate { get; set; } } public class PaymentService : IPaymentService { private PaymentGateway paymentGateway; public PaymentService() { this.paymentGateway = new PaymentGateway(); } public Payment MakePayment(decimal amount, PaymentMethod paymentMethod, string paymentDetails) { // In a real system, you would handle the payment details and validation before calling the payment gateway. return paymentGateway.ProcessPayment(amount, paymentMethod, paymentDetails); } } public class ReceiptService : IReceiptService { public Receipt GenerateReceipt(Order order) { var receipt = new Receipt { Id = Guid.NewGuid(), Order = order, TotalAmount = order.TotalAmount, IssuedDate = DateTime.Now }; return receipt; } } In this Service project, you have to create and implement the following interfaces: C# public Interface IPaymentService { public Payment MakePayment(decimal amount, PaymentMethod paymentMethod, string paymentDetails); } public Interface IReceiptService { public Receipt GenerateReceipt(Order order); } public Interface IPaymentRepository { public Payment ProcessPayment(decimal amount, PaymentMethod paymentMethod, string paymentDetails) } public class PaymentGateway : IPaymentRepository { public Payment ProcessPayment(decimal amount, PaymentMethod paymentMethod, string paymentDetails) { // Simplified payment processing logic for demonstration var payment = new Payment { Id = Guid.NewGuid(), Amount = amount, Status = PaymentStatus.Pending, PaymentDate = DateTime.Now, PaymentMethod = paymentMethod }; // In a real system, you would connect to a payment gateway and process the payment, updating the payment status accordingly. // For example, you might use an external payment processing library or API to handle the transaction. // Simulating a successful payment here for demonstration purposes. payment.Status = PaymentStatus.Approved; return payment; } } With all of those services created, we can easily decommission the shopping cart with the new system (assuming that you have a new user interface also done in parallel). Next, we must address inventory management following the placement of an order. The Inventory Management Service is responsible for restocking when a purchase order is created. The structure of this service project will appear as follows: Inventory Management Service C# public class Product { public Guid Id { get; set; } public string Name { get; set; } public decimal Price { get; set; } public int QuantityInStock { get; set; } public Category ProductCategory { get; set; } } public class Category { public Guid Id { get; set; } public string Name { get; set; } } public class Supplier { public Guid Id { get; set; } public string Name { get; set; } public string ContactEmail { get; set; } } public class PurchaseOrder { public Guid Id { get; set; } public Supplier Supplier { get; set; } public List<PurchaseOrderItem> Items { get; set; } public DateTime OrderDate { get; set; } public bool IsReceived { get; set; } } public class PurchaseOrderItem { public Product Product { get; set; } public int QuantityOrdered { get; set; } public decimal UnitPrice { get; set; } } public interface IInventoryManagementService { void ReceivePurchaseOrder(PurchaseOrder purchaseOrder); void SellProduct(Product product, int quantitySold); } public class InventoryManagementService : IInventoryManagementService { public void ReceivePurchaseOrder(PurchaseOrder purchaseOrder) { if (purchaseOrder.IsReceived) { throw new InvalidOperationException("The order is already placed."); } foreach (var item in purchaseOrder.Items) { item.Product.QuantityInStock += item.QuantityOrdered; } purchaseOrder.IsReceived = true; } public void SellProduct(Product product, int quantitySold) { if (product.QuantityInStock < quantitySold) { throw new InvalidOperationException("Item not in stock."); } product.QuantityInStock -= quantitySold; } } As I mentioned, this guide is primarily intended for individuals who are struggling to initiate their migration efforts, and it offers business-specific examples to aid in understanding the process. I trust that this article has provided valuable insights on how to initiate your migration project within a microservices architecture. If you are working on a grocery or any online shopping cart system, this information should be particularly useful to you. I hope you can take it from here. In my next article, I will present another domain-specific example, as you can always explore more general information on microservices elsewhere.

In the vast and ever-evolving domain of software development, the architecture of software systems stands as a pivotal aspect, shaping not only how applications are built and maintained but also how they adapt to changing technological landscapes and business needs. This paper embarks on an exploratory journey through the evolution of software architecture, tracing its progression from the early days of monolithic designs to the contemporary era of microservices and serverless architectures. We delve into the fundamental shifts in architectural patterns, examining how each has been influenced by and has responded to the advancements in technology, the growing complexity of applications, and the evolving requirements of businesses. Our exploration begins with monolithic architectures, the bedrock of early software development, characterized by their unified and indivisible nature. We then transition to modular designs, heralding a new era of software architecture that emphasizes separation of concerns and encapsulation. Following this, we explore the emergence of Service-Oriented Architecture (SOA), a paradigm shift that underscores service reuse and interoperability. The narrative progresses to the rise of microservices architecture, a fine-grained approach building on the principles of SOA but with a greater emphasis on independence and scalability. Our journey extends to the realm of serverless computing, a paradigm that further abstracts and simplifies (or not ?) architectural complexities. Throughout this exploration, we also address a critical aspect of modern software architecture — the escalating infrastructure costs associated with increasingly decentralized systems that become acceptable with the emergence of powerful modern infrastructure. This paper aims not only to provide a historical perspective on software architecture but also to highlight the importance of context-driven decision-making in the face of these evolving paradigms. By understanding the strengths, limitations, and suitable application contexts of each architectural style, we can better navigate the complex landscape of software development, ensuring that our architectural choices are both technologically sound and aligned with the strategic goals of the organizations and projects they serve. In sum, this paper offers an overview of the evolution of software architecture, illuminating the path from monolithic simplicity to the complex yet flexible world of microservices and beyond while emphasizing the need for thoughtful, context-aware architectural decisions in the face of ever-changing technological and business environments. What Is Software Architecture? In the context of this paper, “Software Architecture” refers to the fundamental organization of a software system, encompassing its components, the relationships between them, and the principles guiding its design and evolution. It is a blueprint that defines the structure, behavior, and, more importantly, the logical integrity of the software, ensuring that it meets both technical and business requirements. Software architecture goes beyond the mere selection of technological tools; it involves strategic decision-making about how to best structure and interconnect various parts of an application to achieve desired performance, scalability, maintainability, and other critical attributes. This includes considerations like how data flows through the system, how components communicate, how they are deployed, and how they can be scaled and maintained over time. In essence, software architecture is about creating a cohesive and coherent framework that not only supports the functional needs of the software but also aligns with broader business goals and adapts to the ever-evolving technological landscape. It is the foundation upon which software’s reliability, efficiency, and adaptability are built, and it plays a crucial role in determining a software project’s long-term success and viability. The Early Days — Monolithic Architectures Timeframe Roughly from the 1960s to the late 1980s. Key Contributor Monolithic architectures have been the default since the early days of computing, so it’s challenging to attribute them to a single person. However, companies like IBM were instrumental in defining early software architecture through their development of mainframe computers in the 1960s and 1970s. What Is Monolithic Architecture? Monolithic architecture represents an early approach in software design where an application is developed as a single, indivisible unit. It combines various components, such as the user interface, business logic, and data access layers, into a unified codebase. This structure, prevalent in the initial phase of software development, necessitated deploying the entire application as one cohesive entity. Link With Early Software Development Technology During the early stages of software engineering, technology was markedly different, characterized by simpler and less powerful hardware, limited networking capabilities, and nascent development tools. Applications were generally smaller and less complex, managed by smaller, centralized teams. The monolithic architecture fits well within this context, where modular programming and distributed systems were still in their infancy.Security concerns were also not a big issue because there were fewer people using it, and the data was not as valuable as it is today. Benefits of Monolithic Architecture The monolithic approach offered several advantages in its time: Simplicity in Development and Deployment: The unified codebase and development environment simplified the development, testing, and deployment processes, particularly important when tools and operational expertise were limited. It is also necessary that we are not talking about a “web application,” which means that you would have to deploy directly on devices, taking care of OS management, etc. Performance Efficiency: Adapted to Limited Resources: Monolithic architectures were efficient because they were designed to operate within the constraints of less powerful hardware. They had to ensure that systems worked effectively with limited memory and CPU resources. Simpler Applications: Applications were simpler and less demanding, so a single, unified application structure was sufficient and practical for the available technology. Trade-Offs of Monolithic Architecture Despite its benefits, monolithic architecture came with trade-offs: Scalability Issues: As applications grew in complexity, scaling monolithic applications became challenging. Flexibility Limitations: Implementing new technologies or making significant changes within a monolithic application could be cumbersome. Deployment Challenges: With all components tied into a single unit, even small changes required redeploying the entire application, leading to longer downtime and potential risks. The Shift to Modular Design Timeframe Gaining traction in the 1970s, with a significant rise in the 1980s and 1990s. Key Contributor The concept of modularity in software design gained traction in the 1970s with the publication of David Parnas’ seminal paper on modular program structure in 1972, which laid the theoretical foundation for this approach. What Is Modular Design in Software Architecture? Modular design in software architecture is a forward-thinking approach that revolutionizes the structure and development of software systems. It involves breaking down a system into distinct, manageable modules, each responsible for a specific functionality. This philosophy is anchored on principles like modularity, encapsulation, and separation of concerns. Modularity divides software into smaller parts; encapsulation conceals each module’s internal operations, and separation of concerns ensures each module uniquely addresses an aspect of the software’s functionality. Evolving Software Complexity and Practices The move toward modular design was primarily driven by the growing complexity of software systems and the inherent limitations of monolithic architectures. As software applications grew in size and complexity, the need for more manageable, maintainable, and scalable architectures became evident. Early Software Engineering Practices During this period, the focus was on improving software design principles and methodologies. Concepts like structured programming and later object-oriented programming (which became widely adopted in the 1980s and 1990s) played a significant role in shaping modular design. These practices emphasized breaking down software into manageable, logically distinct components, thus paving the way for modular architectures. It is important to understand that most of the concepts that seem obvious today had to be invented, conceptualized, and tools created to implement them. Benefits of Modular Design Enhanced Maintainability: The modular structure makes it easier to update and maintain different parts of the software independently. Improved Scalability: Modules can be scaled independently, allowing for more efficient resource utilization and system growth. Increased Flexibility: Modular designs allow for easier integration of new technologies and quick adaptation to changing requirements. Simplified Debugging and Testing: Isolated modules simplify the process of identifying and fixing issues. Trade-Offs in Adopting Modular Design Complexity in Integration: Ensuring seamless interaction between different modules can be complex. Overhead in Communication: Communication between modules, especially in distributed systems, can introduce latency and complexity. Design Challenges: Designing a system with well-defined, independent modules requires careful planning and expertise. In conclusion, the transition to modular design has been a crucial evolution in software architecture, addressing the challenges of increasing complexity and maintenance demands. This shift has laid the groundwork for more robust, scalable, and maintainable software architectures and represents a significant milestone in the maturation of software development practices. Service-Oriented Architecture (SOA) Timeframe SOA emerged in the late 1990s and early 2000s, a period that coincided with the rapid expansion and democratization of the internet. Key Contributors The concept of “Service-Oriented Architecture” was popularized by Gartner in a 1996 report. During the early 2000s, IBM and Microsoft were notable advocates, integrating SOA principles into their products and services. What Is Service-Oriented Architecture (SOA)? SOA is a software design paradigm that focuses on service reuse and interoperability, essential in a networked world boosted by the internet’s growth. It represents an architectural pattern where applications are built to provide services to other applications across a network, utilizing communication protocols. SOA’s hallmarks include reusability, loose coupling, interoperability, and discoverability, facilitating diverse services to interlink and address various business needs. Link With Technological Developments SOA’s ascendancy is linked to business needs, technological advancements, and the widespread adoption of the internet. As businesses sought agility and flexibility to quickly respond to market changes, SOA provided an apt framework. The internet’s democratization played a pivotal role, offering a global platform for interconnected services and applications. This technological backdrop made SOA a natural fit, enabling seamless integration of diverse systems and promoting a collaborative IT environment in an increasingly internet-centric world. Benefits Enhanced Business Agility: Leveraging the internet’s reach, SOA enables swift adaptation to new opportunities and challenges by reconfiguring services. Cost-Effectiveness: By reusing services, SOA cuts down repetitive software development costs. Simplified Maintenance and Scalability: SOA’s modular nature eases maintenance and enhances scalability, aligning with the dynamic nature of the internet. Trade-Offs Complexity in Design and Governance: The challenge lies in designing and managing SOA frameworks, requiring robust systems. Legacy System Integration: Incorporating older systems into an SOA framework, especially in an internet-driven environment, often requires substantial changes. Cultural Shifts: Adopting SOA demands a shift from traditional, isolated approaches to collaborative, networked mindsets, mirroring the interconnectedness fostered by the internet. The Rise of Microservices Timeframe Gaining momentum in the early 2010s. Key Contributor Dr. Peter Rogers is credited with first using the term “micro web services” during a cloud computing conference in 2005. The term “Microservices” gained widespread attention in 2011 at a workshop for software architects, where many were experimenting with this style of architecture. What Is Microservices Architecture? Microservices architecture represents an evolutionary development in software architecture, building on SOA principles but introducing finer granularity in service decomposition. It involves breaking down applications into small, independently deployable services, each running unique processes and communicating typically via HTTP-based APIs. Link With Cloud Computing, DevOps, and Containerization The ascent of microservices is closely tied to advancements in cloud computing, DevOps, and containerization. Cloud computing offers scalable infrastructure ideal for deploying microservices, while DevOps practices align with the philosophy of rapid, reliable delivery of complex applications. Containerization technologies like Docker and Kubernetes provide consistent environments for development and simplify service management. Benefits Improved Scalability: Microservices allow for scaling individual components of an application rather than the entire application, facilitating efficient resource usage. Enhanced Flexibility and Agility: The modular nature of microservices enables faster development cycles and the ability to update or add new features without overhauling the entire system. Resilience and Isolation of Faults: Failures in one service do not necessarily cause system-wide failures, enhancing overall resilience. This isolation simplifies troubleshooting and recovery. Technological Diversity: Teams can use the best technology stack for each service, leading to optimized performance and ease of integration with various tools and technologies. Continuous Deployment and Delivery: Aligning well with DevOps practices, microservices facilitate continuous integration and continuous deployment, allowing for frequent and reliable updates. Improved Scalability with Cloud Compatibility: Microservices are particularly compatible with cloud environments, benefiting from cloud infrastructure’s flexibility and scalability. Trade-Offs Complexity in Management and Coordination: Managing multiple services can be more complex than managing a monolithic application. This includes challenges in service discovery, load balancing, and inter-service communication. Network Latency and Communication Overhead: As services communicate over the network, there can be latency issues, especially if not properly managed. Data Management Complexity: Handling data consistency across different services can be challenging, especially when each service has its database. Increased Resource Requirements: While each service might be small, the cumulative resource requirement for running many services can be significant. Testing Complexity: Testing a microservices-based application can be more complex than testing a monolithic application due to the interactions between services. Overhead in Deployment and Operations: The need for automated tools and processes to manage deployment, monitoring, and logging across numerous services can add operational overhead. Security Challenges: Ensuring security across multiple services and their interactions adds complexity, requiring robust security strategies and tools. Cultural and Organizational Adjustments: Adopting microservices often requires changes in team structure and communication, moving towards a more decentralized approach. Serverless Architectures — The Next Frontier Timeframe Began to surface prominently around 2014, following the launch of AWS Lambda. Key Contributor The concept of “serverless” computing was popularized by Amazon Web Services with the launch of AWS Lambda in 2014, a platform which executes code in response to events without requiring the user to manage the underlying compute resources. What Is Serverless Architecture? Serverless architecture is a transformation in application building, deployment, and management, abstracting away servers and infrastructure management. In this model, developers focus on writing and deploying code without managing underlying hardware. Core components include Function as a Service (FaaS) and Backend as a Service (BaaS). Serverless as an Extension of Microservices Serverless can be seen as an extension of microservices, reducing service granularity to individual functions. It’s driven by the need for scalability and cost-efficiency, allowing automatic scaling and ensuring payment only for used resources. Benefits Cost Efficiency: Serverless computing typically operates on a pay-as-you-go model, meaning businesses only pay for the resources they use. This can lead to significant cost savings compared to traditional cloud hosting models. Scalability: Serverless architectures can automatically scale up or down based on demand, eliminating the need for manual scaling. This makes it ideal for applications with variable workloads. Simplified Operations: The abstraction of servers and infrastructure management reduces operational complexity. Developers can focus on writing code without worrying about server maintenance and configuration. Faster Time-to-Market: The ease of deploying applications in a serverless environment leads to quicker development cycles, facilitating faster time-to-market for new features and updates. Enhanced Productivity: By offloading infrastructure concerns, developers can focus more on the business logic and user experience, enhancing overall productivity. Event-driven and Modern Workflow Compatibility: Serverless architectures are inherently event-driven, making them well-suited for modern application workflows like IoT applications, real-time file processing, and more. Trade-Offs Vendor Lock-in: Most serverless services are provided by specific cloud providers. This can lead to vendor lock-in, restricting flexibility and potentially complicating future migrations. Cold Start Issue: Serverless functions may experience a ‘cold start’ — a delay during the initial execution when the function is not already running, affecting performance. Limited Control: The abstraction that simplifies operations also means less control over the underlying infrastructure, which can be a disadvantage for certain specific requirements. Debugging and Monitoring Challenges: Traditional debugging and monitoring tools are often less effective in a serverless environment due to its distributed and ephemeral nature. Security Concerns: Security in serverless architectures can be complex, as it requires a different approach to traditional server-based environments. Ensuring secure function execution and data transfer is crucial. Time and Memory Constraints: Serverless functions typically have time and memory limits, which might not be suitable for long-running processes or applications requiring significant computational resources. Complexity in Large-Scale Applications: While serverless is excellent for microservices and small, independent functions, managing a large-scale application with numerous functions can become complex. Tools Adapt to Architectures That Adapt to the Underlying Network It’s fascinating to observe how the evolution of software architecture is characterized by a trend toward decentralization, where the overarching theme is ‘decoupling.’ This shift is a response to the advancements in network capabilities and hardware. As networks become more capable and efficient, they demand enhanced interoperability and interconnectedness. In this context, microservices and serverless architectures represent the latest step in addressing these evolving constraints. Concurrently, as software architecture has adapted to these contextual constraints, tools have evolved in tandem to support these architectural changes. The rise of DevOps, along with tools such as Docker and Kubernetes, aligns perfectly with the needs of microservices architecture. Similarly, the advent of Agile methodology coincided with the Internet revolutionizing business and software development. This shift was essential to meet the new demands of a rapidly changing market and user behavior, necessitating reduced time-to-market for software products. The Dangers of Blindly Following Architectural Trends In the rapidly evolving field of software architecture, it’s tempting for architects and developers to gravitate toward the latest trends and practices. However, this inclination, if not tempered with critical evaluation, can lead to significant pitfalls. While architectural styles such as microservices, serverless computing, and others have evolved to address specific constraints and requirements of modern software development, the blind adoption of these trends without a thorough understanding of their implications can be detrimental. Context-Driven Decision Making Every architectural decision should be driven by the specific context and requirements of the project at hand. For example, while microservices offer scalability and flexibility, they also introduce complexity in deployment and management. Similarly, serverless architectures, despite their cost-efficiency and scalability, might not be suitable for all types of workloads, especially those requiring long-running processes. The key is to understand that there is no one-size-fits-all solution in software architecture. What works for one project or organization might not be appropriate for another. The Pitfalls of Trend-Driven Architecture The trend-driven approach to software architecture often overlooks critical factors such as organizational readiness, team skill sets, and the actual needs of the business or application. This can lead to several issues: Overengineering: Implementing a complex architectural style like microservices for a simple application can lead to unnecessary complexity and resource drain. Mismatch with Business Needs: Choosing an architecture style that doesn’t align with business goals or operational capabilities can hinder rather than help the organization. Skill Gaps: Adopting a new architectural trend without having the necessary expertise in the team can lead to poor implementation and maintenance challenges. Increased Costs and Maintenance Overheads: Without proper consideration, the adoption of a new architecture can lead to increased costs, both in terms of development and ongoing maintenance. Balancing Innovation and Pragmatism While it’s important to stay abreast of new trends and technologies, architects must balance innovation with pragmatism. This involves a careful assessment of the benefits and drawbacks of a particular architectural style in the context of the specific requirements, capabilities, and constraints of the project and organization. It also entails a commitment to continuous learning and adaptation, ensuring that decisions are made based on a solid understanding of both the current technological landscape and the specific needs of the business. Increasing Infrastructure Costs in Decentralized Architectures As we delve deeper into the realm of decentralized architectures, like microservices and serverless computing, a critical aspect that emerges is the escalating infrastructure costs. This phenomenon is a direct consequence of the shift from monolithic to more fragmented, distributed systems. Why Infrastructure Costs are Rising in Decentralized Systems Greater Complexity in Deployment and Management: Decentralized architectures typically involve a multitude of services, each potentially running on separate instances or containers. Managing such a distributed setup requires sophisticated orchestration and monitoring tools, which adds to the infrastructure overhead. Resource Redundancy: In a microservices architecture, each service might need its own set of resources, including databases, caching, and networking capabilities. This redundancy can lead to increased usage of computational resources, thereby raising costs. Network Traffic and Data Transfer Costs: The inter-service communication in decentralized systems often occurs over the network. As the volume of this inter-service traffic increases, so does the cost associated with data transfer, especially in cloud-based environments where network usage incurs charges. Scaling Costs: While decentralized architectures offer better scalability, the cost of scaling numerous small services can be higher than scaling a single monolithic application. Each service may need to be scaled independently, requiring more compute and storage resources. Maintenance and Monitoring Tools: The need for specialized tools to monitor and maintain a distributed system also contributes to higher costs. These tools are essential for ensuring system health and performance but come with their own set of licensing and operational expenses. Strategies to Mitigate Infrastructure Costs in Decentralized Systems Efficient Resource Utilization: Leveraging containerization and orchestration tools like Kubernetes can optimize resource usage, ensuring that services use only what they need and scale down when not in use. Cost-effective Service Design: Designing services to be lightweight and resource-efficient can significantly reduce costs. This includes optimizing the codebase and choosing the right set of tools and technologies that balance performance with cost. Intelligent Scaling Strategies: Implementing auto-scaling policies that accurately reflect usage patterns can prevent over-provisioning of resources, thus reducing costs. Monitoring and Optimization: Continuous monitoring of resource usage and performance can help identify and eliminate inefficiencies, leading to cost savings. Hybrid Architectural Approaches: Sometimes, a hybrid approach that combines elements of both monolithic and microservices architectures can offer a more cost-effective solution. This approach allows for leveraging the benefits of microservices where it makes sense while maintaining simpler, cost-effective monolithic structures for other parts of the application. This is the actual rise of the “Modulith” approach. As decentralized architectures continue to gain popularity, understanding and managing the associated infrastructure costs becomes increasingly important. By adopting strategic approaches to resource utilization, service design, and scaling, organizations can enjoy the benefits of decentralized systems while keeping infrastructure costs in check. This balance is crucial for achieving not just technological efficiency but also financial prudence in the modern era of software development. Conclusion The comprehensive exploration of software architecture from monolithic structures to microservices and serverless paradigms underscores a dynamic and ever-evolving field. This evolution, closely mirroring advancements in technology and shifting business needs, highlights a continual movement towards decentralization and flexibility in software design. The journey from the unified simplicity of monolithic systems to the granular specificity of serverless computing illustrates a keen response to the growing complexity of applications and the pressing need for scalable, efficient solutions. However, this progression is not without its challenges. As architectures have become more decentralized, there has been a corresponding rise in infrastructure costs and operational complexities. This necessitates a balanced approach, blending innovation with pragmatism. The adoption of any architectural style — be it microservices, serverless, or others — should be a deliberate choice, driven by the specific context and requirements of the project rather than a reflexive following of prevailing trends. Moreover, the shift in architectural paradigms also brings to light the significance of supportive tools and methodologies. The synergy between evolving architectures and advancements in tools like Docker, Kubernetes, and Agile practices reflects a cohesive maturation of the software development ecosystem. It is a testament to the industry’s adaptability and responsiveness to the changing technological landscape. In navigating this complex terrain, architects and developers must exercise discernment, aligning architectural choices with strategic business objectives and operational capabilities. Balancing innovation with a practical understanding of costs, benefits, and organizational readiness is crucial. As the field continues to advance, the focus should remain on creating architectures that are not only technologically robust but also pragmatically aligned with the unique needs and goals of each project. In conclusion, the evolution of software architecture is a journey of adaptation and refinement. It is a reflection of the software industry’s relentless pursuit of solutions that are more scalable, resilient, and aligned with the rapid pace of technological change. By understanding and thoughtfully applying these architectural paradigms, we can continue to forge software that is not only functionally superior but also strategically advantageous in an increasingly complex and interconnected digital world.

As you may know, many orchestration tools exist to manage and scale microservices. But, in this case, we will talk about the two most extensive mechanisms: Kubernetes vs. Amazon ECS. In this article, we will review each of them individually. We’re going to talk about their pros and cons. Ultimately, depending on your company's needs, we’ll decide which one is the right container orchestration tool for your web application. Let’s start! Kubernetes vs. Amazon ECS: Which Would Win? These two cluster management systems help microservice applications manage, deploy, autoscale, and network among containers. On the one hand, Kubernetes is a container orchestration service. Developed by Google and hosted in the Cloud, this service also functions with Docker. It is relevant to highlight that Kubernetes has a vital community. On the other hand, Amazon ECS is a container orchestration tool that enables applications to scale up. Continuously, it creates more containers to run the application processes as the demand increases. Both tools have positive and negative sides when adopting one of them, hence the importance of reviewing them to make a good choice depending on what you are looking for in your business. Even with Kubernetes in the cloud (Azure Kubernetes Service or Amazon Kubernetes service), managing it will take around 20% more time. Amazon ECS, a service that doesn’t have a cost, except for the costs associated with the instance assigned to the service, can be as small as a small instance. Features: Kubernetes vs. Amazon ECS Multi-Cloud Well, this is obvious, and the winner is Kubernetes. A compelling reason is that it can be deployed on-prem or any cloud provider, including Azure, Google Cloud, or Amazon. In the case of ECS, the platform is closed code; consequently, it has a vendor lock-in and is not cloud-agnostic. Easy to Operate In this case, Amazon ECS is your best option. The ECS ecosystem is already preconfigured. It is an Amazon service that doesn’t require a full setup. Additionally, it takes the most challenging parts so that you can focus on a few configurations. Alternatively, Kubernetes has an intense configuration process, requiring an appropriate amount of hours to make it work. Availability and Scalability Both platforms cover the features at the same level, but clearly, Amazon ECS has benefits inherently. They can be deployed in different availability zones versus Kubernetes on-prem. This will take you a fair amount of time to replicate a similar approach with multi-region/zones, etc. Deployments With Amazon ECS, the native deployment system is rolling updates. As well as the other deployment strategies. For example, Canary and blue-green deployments. They can be incorporated in your CI-CD process but with the reinforcement of Amazon Code deployment. On the other hand, Kubernetes per se doesn’t have multiple deployment systems, such as rolling updates, canary deployments, etc. Except for blue/green deployments, which work flawlessly with Kubernetes. Costs Organizations aspire to reduce IT costs without compromising quality or agility, right? So, Kubernetes usually is more expensive than Amazon ECS. One strong argument from Kubernetes is that it requires at least two servers. And that will cost you a lot of money from the hosting side. And not just that, if we go deeper into your organization, with Kubernetes on-prem, the likely work efforts are 2X. That’s because of its configuration, deployment, and maintenance complexity. To Conclude Now that we have taken a closer look at each tool and compared Kubernetes vs. Amazon ECS, the time has come to decide which container orchestration tool is your best option! If you’re looking for multi-cloud, Kubernetes can be the right choice. But if you want to reduce IT labor hosting costs and management, then you should consider Amazon ECS.

In the ever-evolving landscape of database technology, staying ahead of the curve is not just an option, it’s a necessity. As modern applications continue to grow in complexity and global reach, the role of the underlying database becomes increasingly critical. It’s the backbone that supports the seamless functioning of applications and the storage and retrieval of vast amounts of data. In this era of global-scale applications, having a high-performance, flexible, and efficient database is paramount. As the demands of modern applications surge, the need for a database that can keep pace has never been greater. The “ultra-database” has become a key player in ensuring that applications run seamlessly and efficiently globally. These databases need to offer a unique combination of speed, versatility, and adaptability to meet the diverse requirements of various applications, from e-commerce platforms to IoT systems. They need to be more than just data repositories. They must serve as intelligent hubs that can quickly process, store, and serve data while facilitating real-time analytics, security, and scalability. The ideal ultra-database is not just a storage facility; it’s an engine that drives the dynamic, data-driven applications that define the modern digital landscape. The latest release of HarperDB 4.2 introduces a unified development architecture for enterprise applications, providing an approach to building global-scale applications. HarperDB 4.2 HarperDB 4.2 is a comprehensive solution that seamlessly combines an ultra-fast database, user-programmable applications, and data streaming into a cohesive technology. The result is a development environment that simplifies the complex, accelerates the slow, and reduces costs. HarperDB 4.2 offers a unified platform that empowers developers to create applications that can span the globe, handling data easily and quickly. In this tutorial, we will explore the features of HarperDB 4.2 and show you how to harness its power in conjunction with Java Quarkus. We will take you through the steps to leverage HarperDB’s new capabilities to build robust and high-performance applications with Quarkus, demonstrating the impressive potential of this unified development architecture. So, join us on this enlightening journey and revolutionize your application development process. Creating a Quarkus Microservice API With HarperDB, Part 1: Setting up the Environment This section will guide you through configuring your development environment and creating the necessary project setup to get started. Step 1: Configuring the Environment Before diving into the development, you need to set up your environment. We’ll start by running HarperDB in a Docker container. To do this, open your terminal and run the following command: Shell docker run -d -e HDB_ADMIN_USERNAME=root -e HDB_ADMIN_PASSWORD=password -e HTTP_THREADS=4 -p 9925:9925 -p 9926:9926 harperdb/harperdb This command downloads and runs the HarperDB Docker container with the specified configuration. It exposes the necessary ports for communication. Step 2: Creating a Schema and Table With HarperDB up and running, the next step is to create a schema and define a table to store animal data. We will use the “curl” commands to interact with HarperDB’s RESTful API. Create a schema named “dev” by executing the following command: Shell curl --location --request POST 'http://localhost:9925/' \ --header 'Authorization: Basic cm9vdDpwYXNzd29yZA==' \ --header 'Content-Type: application/json' \ --data-raw '{ "operation": "create_schema", "schema": "dev" }' This command sends a POST request to create the “dev” schema. Next, create a table named “animal” with “scientificName” as the hash attribute using the following command: Shell curl --location 'http://localhost:9925' \ --header 'Authorization: Basic cm9vdDpwYXNzd29yZA==' \ --header 'Content-Type: application/json' \ --data '{ "operation": "create_table", "schema": "dev", "table": "animal", "hash_attribute": "scientificName" }' This command establishes the “animal” table within the “dev” schema. 3. Now, add the required attributes for the “animal” table by creating “name,” “genus,” and “species” attributes: Shell curl --location 'http://localhost:9925' \ --header 'Authorization: Basic cm9vdDpwYXNzd29yZA==' \ --header 'Content-Type: application/json' \ --data '{ "operation": "create_attribute", "schema": "dev", "table": "animal", "attribute": "name" }' curl --location 'http://localhost:9925' \ --header 'Authorization: Basic cm9vdDpwYXNzd29yZA==' \ --header 'Content-Type: application/json' \ --data '{ "operation": "create_attribute", "schema": "dev", "table": "animal", "attribute": "genus" }' curl --location 'http://localhost:9925' \ --header 'Authorization: Basic cm9vdDpwYXNzd29yZA==' \ --header 'Content-Type: application/json' \ --data '{ "operation": "create_attribute", "schema": "dev", "table": "animal", "attribute": "species" }' These commands add the “name”, “genus”, and “species” attributes to the “animal” table within the “dev” schema. With HarperDB configured and the schema and table set up, you can start building your Quarkus-based microservice API to manage animal data. Stay tuned for the next part of the tutorial, where we’ll dive into the development process. Building Quarkus Application We configured HarperDB and prepared the environment. Now, we’ll start building our Quarkus application to manage animal data. Quarkus makes it easy with a handy project generator, so let’s begin. Quarkus offers an intuitive web-based project generator that simplifies the initial setup. Visit Quarkus Project Generator, and follow these steps: Select the extensions you need for your project. Add “JAX-RS” and “JSON” for this tutorial to handle REST endpoints and JSON serialization. Click the “Generate your application” button. Download the generated ZIP file and extract it to your desired project directory. With your Quarkus project generated, you’re ready to move on. Our project will use the DataFaker library and the HarperDB Java driver to generate animal data to interact with the HarperDB database. To include the HarperDB Java Driver, please read the previous article. In your Quarkus project, create a Java record to represent the Animal entity. This record will have fields for the scientific name, name, genus, and species, allowing you to work with animal data efficiently. Java public record Animal(String scientificName, String name, String genus, String species) { public static Animal of(Faker faker) { var animal = faker.animal(); return new Animal( animal.scientificName(), animal.name(), animal.genus(), animal.species() ); } } This record includes a factory method, of, that generates an Animal instance with random data using the DataFaker library. We’ll use this method to populate our database with animal records. In your Quarkus project, we’ll set up CDI (Contexts and Dependency Injection) to handle database connections and data access. Here’s an example of how to create a ConnectionSupplier class that manages database connections: Java @ApplicationScoped public class ConnectionSupplier { private static final Logger LOGGER = Logger.getLogger(ConnectionSupplier.class.getName()); @Produces @RequestScoped public Connection get() throws SQLException { LOGGER.info("Creating connection"); // Create and return the database connection, e.g., using DriverManager.getConnection } public void dispose(@Disposes Connection connection) throws SQLException { LOGGER.info("Closing connection"); connection.close(); } } The ConnectionSupplier class uses CDI annotations to produce and dispose of database connections. This allows Quarkus to manage the database connection lifecycle for you. Let’s create the AnimalDAO class to interact with the database using JDBC. This class will have methods for inserting and querying animal data. Java @ApplicationScoped public class AnimalDAO { private final Connection connection; public AnimalDAO(Connection connection) { this.connection = connection; } public void insert(Animal animal) { try { // Prepare and execute the SQL INSERT statement to insert the animal data } catch (SQLException exception) { throw new RuntimeException(exception); } } public Optional<Animal> findById(String id) { try { // Prepare and execute the SQL SELECT statement to find an animal by ID } catch (SQLException exception) { throw new RuntimeException(exception); } } // Other methods for data retrieval and manipulation } In the AnimalDAO class, you’ll use JDBC to perform database operations. You can add more methods to handle various database tasks, such as updating and deleting animal records. The AnimalService class will generate animal data and utilize the AnimalDAO for database interaction. Java @ApplicationScoped public class AnimalService { private final Faker faker; private final AnimalDAO dao; @Inject public AnimalService(Faker faker, AnimalDAO dao) { this.faker = faker; this.dao = dao; } // Implement methods for generating and managing animal data } In the AnimalService, you’ll use the DataFaker library to generate random animal data and the AnimalDAO for database operations. With these components in place, you’ve set up the foundation for your Quarkus-based Microservice API with HarperDB. In the next part of the tutorial, we’ll dive into developing RESTful endpoints and data management. Create AnimalResource Class In this final part of the tutorial, we will create an AnimalResource class to expose our animal service through HTTP endpoints. Additionally, we will provide sample curl commands to demonstrate how to consume these endpoints locally. Create an AnimalResource class with RESTful endpoints for managing animal data. This class will interact with the AnimalService to handle HTTP requests and responses. Java @Path("/animals") @Produces(MediaType.APPLICATION_JSON) @Consumes(MediaType.APPLICATION_JSON) public class AnimalResource { private final AnimalService service; public AnimalResource(AnimalService service) { this.service = service; } @GET public List<Animal> findAll() { return this.service.findAll(); } @POST public Animal insert(Animal animal) { this.service.insert(animal); return animal; } @DELETE @Path("{id}") public void delete(@PathParam("id") String id) { this.service.delete(id); } @POST @Path("/generate") public void generateRandom() { this.service.generateRandom(); } } In this class, we’ve defined several RESTful endpoints, including: GET /animals: Returns a list of all animals. POST /animals: Inserts a new animal. DELETE /animals/{id}: Deletes an animal by its ID. POST /animals/generate: Generates random animal data. Here are curl commands to test the HTTP endpoints locally using http://localhost:8080/animals/ as the base URL: Retrieve All Animals (GET) Shell curl -X GET http://localhost:8080/animals/ Insert a New Animal (POST) Shell curl -X POST -H "Content-Type: application/json" -d '{ "scientificName": "Panthera leo", "name": "Lion", "genus": "Panthera", "species": "Leo" }' http://localhost:8080/animals/ Delete an Animal by ID (DELETE) Replace {id} with the ID of the animal you want to delete: Shell curl -X DELETE http://localhost:8080/animals/{id} Generate Random Animal Data (POST) This endpoint doesn’t require any request data: Shell curl -X POST http://localhost:8080/animals/generate These curl commands allow you to interact with the Quarkus-based microservice API, performing actions such as retrieving, inserting, and deleting animal data. The generated random data endpoint is valuable for populating your database with test data. With these RESTful endpoints, you have a fully functional Quarkus application integrated with HarperDB to manage animal data over HTTP. You can extend and enhance this application further to meet your specific requirements. Congratulations on completing this tutorial! Conclusion In this tutorial, we embarked on a journey to build a Quarkus-based Microservice API integrated with HarperDB, a robust, high-performance database. We started by setting up our environment and creating a Quarkus project with the necessary extensions. Leveraging the DataFaker library, we generated random animal data to populate our HarperDB database. The core of our application was the seamless integration with HarperDB, showcasing the capabilities of the HarperDB Java driver. We used CDI to manage database connections efficiently and created a structured data access layer with the AnimalDAO class. Through this, we performed database operations, such as inserting and querying animal data. With the implementation of the AnimalService class, we combined the generated data with database operations, bringing our animal data management to life. Finally, we exposed our animal service through RESTful endpoints in the AnimalResource class, allowing us to interact with the service through HTTP requests. You can explore the complete source code of this project on GitHub. Feel free to fork, modify, and extend it to suit your needs. As you continue your journey into the world of HarperDB and Quarkus, remember to consult the comprehensive HarperDB documentation available at HarperDB Documentation to dive deeper into the capabilities and features of HarperDB. Stay informed about the latest updates, release notes, and news on HarperDB’s official website to ensure you’re always working with the most up-to-date information. Check out the latest release notes to discover what’s new and improved in HarperDB. By combining Quarkus and HarperDB, you’re well-equipped to build efficient and scalable applications that meet the demands of the modern digital landscape. Happy coding!

By Otavio Santana CORE

Multi-divisional organizations and team-building web applications with distributed teams are adopting micro-frontend architectures for front-end development similar to microservices. Large enterprises are seeing massive value in shifting from sequential to parallel development by architecting web experiences as independent, re-deployable, micro-frontend components. What Are Micro-Frontends? Micro-frontends are frontend components packaged as a mini-app to deliver a functional capability and web experience to end users. Each micro-frontend is deployable on its own and comprises user interactions and events representing a set of logical use cases for a given ecosystem. Micro-frontends share standard building blocks in the form of design system elements and dependencies in the form of self-contained packaged libraries. Micro-frontends are modular and contribute to scaling development to build parallel web experiences within an ecosystem. From a high-level skeleton code standpoint, here is a simple Micro-frontend with its package.json reflecting dependencies or scripts consumed by the micro-frontend as below. JSON //package.json { "name": "micro-frontend1", "version": "1.0.0", "description": "Micro frontend1 with shared dependencies", "main": "index.js", "scripts": { "start": "http-server" }, "dependencies": { "http-server": "^0.12.3", // Library for a local server "redux": "^4.1.1", // Library for state management "lerna": "^4.0.0", // Library for managing multiple packages "storybook": "^6.4.3", // Library for shared UI component library "jest": "^27.4.0", // Library for testing "cypress": "^9.1.0", // Library for end-to-end testing "auth0-js": "^9.16.2", // Library for authentication "webpack": "^5.62.3", // Library for building and code splitting "babel-core": "^6.26.3" // Library for transpiling code } } index.html HTML <!DOCTYPE html> <html> <head> <title>Micro-Frontend 1</title> <link rel="stylesheet" type="text/css" href="styles.css"> </head> <body> <div id="micro-frontend1-container"> <h1>Hello from Micro-Frontend 1!</h1> </div> <script src="mfe1.js"></script> </body> </html> mfe1.js JavaScript // Micro-Frontend1 JavaScript console.log('Micro-Frontend1 Loaded.'); //Micro-frontend1 functionality goes here. styles.css CSS /* Micro-Frontend1 Styles */ #micro-frontend1-container { background-color: #f0f0f0; padding: 20px; border: 1px solid #ddd; } From a high-level diagram, a simple web page with multiple micro-frontends will look like the one below. Figure 1 Common Dependencies With Micro-Frontends In a real-world ecosystem, most micro-frontends depend on multiple dependencies and packages. These include state management tools like Redux or Mobx communication mechanisms like web sockets or custom event buses, UI component libraries (design system elements) like Lerna, or storybooks testing frameworks like enzyme, jest, cypress, or lighthouse—additionally, authentication/authorization libraries such as Okta, Auth0, or Firebase. Build tools like Babel and Webpack also play a role alongside UI frameworks such as Semantic UI, Bootstrap, or Material UI. Figure 2 Risks With Multiple Dependency Injections With Micro-Frontends When an ecosystem use cases spread across multiple cross-functional teams, and each use case has logical workflows that render on a web page, micro-frontends aid in vertical development, which results in embedding multiple micro-frontends on the same page and eventually results in various dependencies injections leading to: Dependency conflicts Version challenges Performance overheads Debugging complexity and Maintainability issues. These are common pitfalls leading to longer testing cycles and time to market and pose a security risk due to complicated dependency management. Addressing the Risks With Orchestrators A micro-frontend architecture enables a loose pattern of rules of communication, integration, and interaction between each of these micro-frontends if we treat one of the micro-frontends as a container. An "Orchestrator" is a central micro-frontend that serves as the entry point for a web application built using the micro-frontend architecture. It's sometimes referred to as the "shell" micro-frontend. The primary role of the shell micro-frontend is to coordinate and manage the integration of other micro-frontends, resulting in a cohesive user experience but majorly eliminating the risks of multiple dependency injections. An orchestrator micro-frontend oversees and coordinates the operations of micro-frontends. This approach offers benefits: Consistent user experience: The orchestrator ensures that users have a consistent experience by providing a layout, navigation, and overall structure for the application. Users perceive an interface even though different micro-frontends contribute to the content. Seamless visual appeal: By centralizing components such as the header, footer, and navigation menus, the orchestrator maintains an appeal throughout the application. This aspect is crucial for branding purposes and enhancing user experience. Efficient code reusability: The orchestrator efficiently manages shared dependencies, libraries, and code to minimize duplication and promote reusability. Child micro-frontends can rely on these shared resources effectively. Isolated development: Child micro-frontends can be maintained independently without worrying about the application's structure and layout. Development teams can focus on features or sections of the application with ease. Encapsulation: The orchestrator handles the routing and integration logic, providing a structure for the child micro-frontends. This modularity significantly improves maintainability and testability. Parallel development: Different development teams can work simultaneously on application components. Parallel development speeds up development and release cycles since modifications in one micro-frontend typically do not affect others. Scalability: The orchestrator efficiently manages micro-frontends as the application expands. This scalability is crucial for handling intricate web applications. Managing state: The orchestrator effectively handles state management, ensuring that different application parts can access shared state or interact with each other through a system. Error handling and logging: Error handling and logging mechanisms can be implemented within the orchestrator, allowing for reporting and debugging throughout the application. Performance improvements: The orchestrator incorporates performance optimization techniques such as loading and code splitting to enhance the application's performance and provide a user experience. A/B testing: The orchestrator allows for the management of experimentation, feature flagging, enabling controlled feature rollouts, and A/B testing across different application sections. Authentication and authorization: The orchestrator handles User authentication and authorization, ensuring access control and session management throughout the application. Flexibility: The orchestrator is adaptable to frontend frameworks, libraries, and technologies utilized in parts of the application. It allows for flexibility in choosing the technologies. Isolation: By isolating child micro-frontends, the orchestrator reduces the risk of conflicts or issues arising when different application components interact directly. Ecosystem management: It provides an entry point to oversee the entire micro-frontend ecosystem, making it easier to understand, document, and manage effectively. In a frontend architecture, an orchestrator micro-frontend holds a position as it brings several advantages to large-scale web applications. It effectively combines the benefits of micro-frontends and modular development while ensuring a user experience. Applying the orchestrator method to the existing micro-frontends listed in Figure 2 will now look as below. Figure 3 And here is how an orchestrator micro-frontend map offers a method to handle the overall structure of the application and ensure a seamless user experience. Orchestrators are particularly useful when having a cohesive and unified user interface is essential and when there is a need for shared resources and consistency across micro-frontends. HTML <!DOCTYPE html> <html> <head> <title>Orchestrator Micro Frontend</title> </head> <body> <header> <nav> <ul> <li><a href="#/micro-frontend1">Home</a></li> <li><a href="#/micro-frontend2">Account</a></li> <li><a href="#/micro-frontend3">Products</a></li> <li><a href="#/micro-frontend4">Dashboard</a></li> <li><a href="#/micro-frontend5">Search</a></li> <li><a href="#/micro-frontend6">Navigation</a></li> </ul> </nav> </header> <div id="micro-frontend-orchestrator"></div> <script> const mfeContainer = document.getElementById('micro-frontend-orchestrator'); // Client-side routing to load child micro-frontends function loadMfe(route) { const mfeMap = { '/home': 'home-micro-frontend1.html', '/accounts': 'accounts-micro-frontend2.html', '/products': 'products-micro-frontend3.html', }; const mfe = mfeMap[route]; if (mfe) { mfeContainer.innerHTML = ''; // Clear the container const script = document.createElement('script'); script.src = mfe; mfeContainer.appendChild(script); } else { mfeContainer.innerHTML = 'Page not found'; } } // Initial load based on the current route loadMicroFrontend(window.location.hash); // Listen for route changes window.addEventListener('hashchange', () => { loadMicroFrontend(window.location.hash); }); </script> </body> </html> A traditional micro-frontend provides the opportunity for independence and isolation among micro-frontends, making it an excellent fit for situations where each requires autonomy and separation. The decision on which approach to choose depends on the application's needs. Traditional micro-frontends provide modularity and independence, whereas orchestrator micro-frontends prioritize user experience and shared resources. Specific projects may combine both approaches in real-world scenarios to find ground between independence and consistency.

The Initial Need Leading to CQRS The traditional CRUD (Create, Read, Update, Delete) pattern has been a mainstay in system architectures for many years. In CRUD, reading and writing operations are usually handled by the same data model and often by the same database schema. While this approach is straightforward and intuitive, it becomes less effective as systems scale and as requirements become more complex. For instance, consider a large-scale e-commerce application with millions of users. This system may face conflicting demands: it needs to quickly read product details, reviews, and user profiles, but it also has to handle thousands of transactions, inventory updates, and order placements efficiently. As both reading and writing operations grow, using a single model for both can lead to bottlenecks, impacting performance and user experience. Basics of the CQRS Pattern CQRS was introduced to address these scaling challenges. The essence of the pattern lies in its name — Command Query Responsibility Segregation. Here, commands are responsible for any change in the system’s state (like placing an order or updating a user profile), while queries handle data retrieval without any side effects. In a CQRS system, these two operations are treated as entirely distinct responsibilities, often with separate data models, databases, or even separate servers or services. This allows each to be tuned, scaled, and maintained independently of the other, aligning with the specific demands of each operation. CQRS Components Commands Commands are the directive components that perform actions or changes within the system. They should be named to reflect the intent and context, such as PlaceOrder or UpdateUserProfile. Importantly, commands should be responsible for changes, and therefore should not return data. Further exploration into how commands handle validation, authorization, and business logic would illuminate their role within the CQRS pattern. Queries Queries, on the other hand, handle all request-for-data operations. The focus could be on how these are constructed to provide optimized, denormalized views of the data tailored for specific use cases. You may delve into different strategies for structuring and optimizing query services to deal with potentially complex read models. Command and Query Handlers Handlers serve as the brokers that facilitate the execution of commands and queries. Command handlers are responsible for executing the logic tied to data mutations while ensuring validations and business rules are adhered to. Query handlers manage the retrieval of data, potentially involving complex aggregations or joins to form the requested read model. Single Database vs Dual Database Approach Single Database In this model, both command and query operations are performed on a single database, but with distinct models or schemas. Even though both operations share the same physical storage, they might utilize different tables, views, or indexes optimized for their specific requirements. It could be represented as follows: Single database representation Benefits Simplified infrastructure and reduced overhead Immediate consistency, as there’s no lag between write and read operations Trade-Offs The shared resource can still become a bottleneck during heavy concurrent operations. Less flexibility in tuning and scaling operations independently Dual Database Approach Here, command and query operations are entirely separated, using two distinct databases or storage systems. The write database is dedicated to handling commands, while the read database serves query operations. A handle synchronization system must be added. It could be represented as follows: Multi database representation Benefits Individual tuning, scaling, and optimization for each operation Potential for each database to be hosted on different servers or services, distributing load; database solutions could also be different to allow more modularity for specific needs. Trade-Offs Introduces complexity in ensuring data consistency between the two databases Requires synchronization mechanisms to bridge the potential misalignment or latency between the write and read databases: For instance, a write operation may update the command database, but the read database might not immediately reflect these changes. To address this, synchronization techniques can range from simple polling to more intricate methods like Event Sourcing, where modifications are captured as a series of events and replayed to update the read database. The single database approach offers simplicity; the dual database configuration provides more flexibility and scalability at the cost of added complexity. The choice between these approaches depends on the specific requirements and challenges of the system in question, particularly around performance needs and consistency requirements. Benefits and Trade-Offs of CQRS Pattern Benefits Performance optimization: By separating read and write logic, you can independently scale and optimize each aspect. For instance, if a system is read-heavy, you can allocate more resources to handling queries without being bogged down by the demands of write operations. Flexibility and scalability: With separate models for reading and writing, it’s easier to introduce changes in one without affecting the other. This segregation not only allows for more agile development and easier scalability but also offers protection against common issues associated with eager entity loading. By distinctly handling reads and writes, systems can be optimized to avoid unnecessary data loading, thereby improving performance and reducing resource consumption. Simplified codebase: Separating command and query logic can lead to a more maintainable and less error-prone codebase. Each model has a clear responsibility, reducing the likelihood of bugs introduced due to intertwined logic. Trade-Offs Data consistency: As there might be different models for read and write, achieving data consistency can be challenging. CQRS often goes hand-in-hand with the “eventual consistency” model, which might not be suitable for all applications. Complexity overhead: Introducing CQRS can add complexity, especially if it’s paired with other patterns like Event Sourcing. It’s crucial to assess whether the benefits gained outweigh the added intricacy. Increased development effort: Having separate models for reading and writing means, in essence, maintaining two distinct parts of the system. This can increase the initial development effort and also the ongoing maintenance overhead. Conclusion CQRS offers a transformative approach to data management, bringing forth significant advantages in terms of performance, scalability, and code clarity. However, it’s not a silver bullet. Like all architectural decisions, adopting CQRS should be a measured choice, considering both its benefits and the challenges it introduces. For systems where scalability and performance are paramount, and where the trade-offs are acceptable, CQRS can be a game-changer, elevating the system’s robustness and responsiveness to new heights.

In the dynamic world of microservices architecture, efficient service communication is the linchpin that keeps the system running smoothly. To maintain the reliability, security, and performance of your microservices, you need a well-structured service mesh. This dedicated infrastructure layer is designed to cater to service-to-service communication, offering essential features like load balancing, security, monitoring, and resilience. In this comprehensive guide, we’ll delve into the world of service meshes and explore best practices for their effective management within a microservices environment. Understanding Service Mesh A service mesh is essentially the invisible backbone of a network, connecting and empowering the various components of a microservices ecosystem. It comprises a suite of capabilities, such as managing traffic, enabling service discovery, enhancing security, ensuring observability, and fortifying resilience. To execute these tasks, service meshes employ a set of proxy instances seamlessly integrated alongside application code. These proxies act as vigilant guardians, adept at intercepting and directing incoming and outgoing traffic between services. A service mesh typically consists of two primary components: 1. Data Plane The data plane, also known as the sidecar proxy, is responsible for handling the actual network traffic between microservices. Each microservice in your architecture is paired with a sidecar proxy. These sidecar proxies intercept, route, and manage the network traffic to and from the microservices they are associated with. Some popular sidecar proxies used in service meshes are Envoy, Linkerd’s proxy, and Nginx. 2. Control Plane The control plane is the central management and configuration component of the service mesh. It’s where you define and enforce the policies that govern traffic management, security, observability, and other aspects of service-to-service communication. The control plane also provides tools for monitoring, managing, and analyzing the performance and behavior of the service mesh. Why Service Mesh Matters in Microservices In the intricate realm of microservices, service-to-service communication poses multifaceted challenges. Services must interact seamlessly, but issues like load balancing, authentication, encryption, and service discovery can become formidable hurdles. Here’s why a service mesh stands out as a centralized solution: Traffic Management Example: Imagine a scenario where you’re implementing a blue-green deployment strategy to roll out a new version of a service. A service mesh can be configured to intelligently route a fraction of incoming traffic to the new version, ensuring a smooth transition and minimizing potential downtime and risks. Service Discovery Example: As your microservices dynamically scale in and out, they need to locate and communicate with one another in real time. Service meshes provide the magic of automatic service discovery, making the chore of services finding their peers as effortless as pie, regardless of their ever-changing locations. Security Example: In the security-conscious landscape of microservices, safeguarding communications between services is paramount. Service meshes come equipped with robust security features, including mutual TLS (mTLS) encryption. This ensures that every interaction is not only encrypted but also authenticated, enhancing the overall security posture. Observability Example: Clear insights into how requests flow through your microservices are crucial for diagnosing issues and optimizing performance. Service meshes often include tools for collecting metrics and distributed tracing data. You can visualize this data in real time using tools like Grafana and Prometheus or delve deep into the intricacies of distributed tracing analysis with tools like Jaeger. Resilience Example: Failures are inevitable in any system. However, service meshes offer a lifeline by implementing resilience patterns like circuit breakers and retries. In the face of a service failure, a service mesh can skillfully reroute traffic to healthy instances or provide informative error messages, saving the day with finesse. Best Practices for Managing Your Service Mesh To master the art of managing your service mesh in a microservices environment, consider these tried-and-tested best practices: Choose the Right Service Mesh Technology Example: Service mesh technologies like Istio, Linkerd, and Consul are your allies. Evaluate their features and community support to align your choice with your specific project needs. For large-scale deployments, Istio shines with its comprehensive feature set and widespread community backing. Define and Enforce Service Mesh Policies Example: The success of your service mesh largely depends on the policies you define. From traffic management to security and observability, these policies need to be crystal clear. Use the built-in tools provided by the service mesh technology to enforce these policies effectively. For instance, configure mutual TLS (mTLS) to ensure encrypted and authenticated communications within your service mesh. Monitor and Analyze Mesh Performance Example: Utilize the monitoring and tracing capabilities woven into the service mesh. Keep your finger on the pulse of your system’s performance with real-time visualizations through Grafana and Prometheus. Dive deeper into the mysteries of distributed tracing analysis with the likes of Jaeger. Implement Fine-Grained Access Control Example: Keep control at your fingertips by implementing role-based access control (RBAC) within your service mesh. Define who can communicate with whom. For instance, restrict the payment service from directly accessing the customer database and mandate it to go through the customer service for a finer level of control. Regularly Update and Patch Service Mesh Components Example: Stay ahead of the curve by keeping your service mesh components up-to-date. Regularly update the control plane components to benefit from bug fixes, new features, and enhanced security. For instance, keeping your Istio control plane up-to-date ensures that your service mesh stays secure and efficient. Conclusion In the world of microservices, the adept management of service communication is non-negotiable. A well-tamed service mesh streamlines these interactions, guaranteeing secure, observable, and resilient microservices. By adhering to these best practices, you can unlock the true potential of your service mesh, facilitating the efficient operation and maintenance of your microservices ecosystem while laying a solid foundation for future growth and innovation.

The service mesh has become popular lately, and many organizations seem to jump on the bandwagon. Promising enhanced observability, seamless microservice management, and impeccable communication, service mesh has become the talk of the town. But before you join the frenzy, it’s crucial to pause and reflect on whether your specific use case truly demands the adoption of a service mesh. In this blog post, we will try to scratch the surface to view beyond the service mesh hype. We’ll explore the factors you should consider when determining if it is the right fit for your architecture. We’ll navigate the complexities of service mesh adoption, dissect the advantages and drawbacks, and determine whether it’s a necessary addition to your technology stack or just a passing trend. So, let’s dive in and explore the service mesh from a neutral standpoint. What Is Service Mesh? Let’s first cover the basics of what service mesh is, the advantages and challenges that come with it, and then jump onto the part where we explore use cases where service mesh shines and where you can get around without one. A service mesh serves as a dedicated infrastructure layer designed to abstract away the complexities associated with service-to-service communication within a microservices architecture. Typically, it comprises a set of lightweight network proxies, commonly referred to as sidecars, which are deployed alongside each service in the cluster. These sidecars play a pivotal role in managing communication, providing features such as service discovery, load balancing, traffic management, and security. However, it’s important to acknowledge the evolving landscape of service mesh technologies. Recent advancements, such as Istio’s Ambient Mesh and Cilium, have introduced alternative deployment options that extend beyond the traditional sidecar model. These technologies offer the flexibility to operate as pods within a daemon set, presenting an alternative approach to orchestrating service-to-service communication and enhancing security. In essence, these proxies act as intermediaries, facilitating seamless inter-service communication while creating room for the implementation of essential features to optimize microservices deployments. To know more about service mesh, how it works, use cases, benefits, challenges, and popular service mesh out there, you can read this detailed blog on service mesh. Advantages of Service Mesh Besides simplifying and enhancing communication management between services in a distributed system, service mesh also adds multiple features to the network, including: Observability and monitoring: Service mesh offers valuable insights into the communication between services and effective monitoring to help in troubleshooting application errors. Traffic management: Service mesh offers intelligent request distribution, load balancing, and support for canary deployments. These capabilities enhance resource utilization and enable efficient traffic management. Resilience and reliability: By handling retries, timeouts, and failures, service mesh contributes to the overall stability and resilience of services, reducing the impact of potential disruptions. Security: Service mesh enforces security policies and handles authentication, authorization, and encryption – ensuring secure communication between services and, eventually, strengthening the overall security posture of the application. Service discovery: With service discovery features, service mesh can simplify the process of locating and routing services dynamically, adapting to system changes seamlessly. This enables easier management and interaction between services. Microservices communication: Adopting a service mesh can simplify the implementation of a microservices architecture by abstracting away infrastructure complexities. It provides a standardized approach to manage and orchestrate communication within the microservices ecosystem. A survey by TheNewStack revealed that security and observability are extremely essential to improving distributed systems operation. Among companies using service mesh in production Kubernetes environments: 60% consider service mesh crucial for enhancing application traffic control in distributed systems. 30% regard service mesh as important for enhancing application traffic control in distributed systems. 51% of those exploring service mesh perceive it as essential for improving security. 43% share the same sentiment for observability. These statistics indicate that security and observability are recognized as key technologies addressing the significant challenges faced by the DevOps and site reliability engineering communities. Drawbacks and Challenges of Service Mesh While service mesh offers significant advantages, it also comes with certain drawbacks and challenges: Complexity: When considering a service mesh, be aware that it introduces complexity to the infrastructure stack. Configuration, management, and operation can be challenging, particularly for organizations with limited expertise. You have to invest adequate resources and time to address the learning curve associated with implementation and maintenance. Performance overhead: Introducing a service mesh may lead to performance overhead due to the interception and routing of network traffic. Proxies and the associated data plane can impact latency and throughput. Thorough planning and optimization of the service mesh architecture are essential to minimize potential performance impacts. Operational overhead: Using service mesh requires ongoing operational management. There are many tasks, including updating, scaling, monitoring, and troubleshooting mesh components, that demand additional effort and resources from the operations team. You have to assess the team’s capacity to handle this operational overhead before adopting a service mesh. Increased network complexity: The introduction of a service mesh adds complexity to the network infrastructure. This can affect troubleshooting and debugging and necessitate adjustments to existing configurations and security policies. You must evaluate the impact of increased network complexity when making decisions about adopting a service mesh. Compatibility and Interoperability: Service mesh might not be compatible with various infrastructure components and tools within your ecosystem. You have to consider the effort required for integration with existing platforms, frameworks, and monitoring systems, as it may involve additional configuration and customization. Achieving interoperability across different cloud providers, container runtimes, and orchestrators may present challenges that need to be addressed. Vendor lock-in: Some solutions may come with vendor-specific features and dependencies, limiting flexibility and portability across environments. You have to take into account the potential vendor lock-in associated with specific service mesh implementations. We advise you to assess the long-term implications and evaluate trade-offs before committing to a particular service mesh solution. Increased complexity for developers: Adopting a service mesh means adding complexity for developers. They will need to familiarize themselves with the service mesh infrastructure and associated tools, increasing their cognitive load (which will increase the demand for Platform Engineering). There could be an impact on the development workflow, and you have to make adjustments according to application architectures and deployment processes. Addressing these challenges requires careful planning, expertise, and ongoing maintenance. It’s important to evaluate the specific needs of your organization and assess whether the benefits of a service mesh outweigh the drawbacks in your particular use case. Let’s see how you can evaluate your use case to identify the need for a service mesh. Evaluating Your Use Case When evaluating whether a service mesh is suitable for your project, you can consider the following factors: Microservices architecture: Service mesh is particularly beneficial in complex microservices architectures where services need to communicate with each other. If your application consists of multiple services that need to interact and require advanced traffic management, security, and observability, a service mesh can provide significant value. Scaling and performance requirements: If your application requires efficient load balancing, traffic shaping, and performance optimizations, a service mesh can help. Service mesh offers features like circuit breaking, request retries, and distributed tracing, which can improve the resilience and performance of your services. Security and compliance: When it comes to applications dealing with sensitive data or requiring strong security measures, a service mesh can enforce security policies, handle encryption, and provide mutual TLS authentication. It offers consistent security controls across services, reducing the risk of unauthorized access and data breaches. Observability and troubleshooting: A service mesh can provide observability features like metrics and distributed tracing in case you need detailed insights into the behavior and performance of your services. This can simplify troubleshooting, performance optimization, and capacity planning. Complex networking requirements: If your application requires complex networking configurations, such as service discovery, routing, and traffic splitting across multiple environments or cloud providers, a service mesh can help streamline these tasks and abstract away the underlying complexity. Operational scalability: You can use a service mesh to provide a centralized control plane to manage and automate the operational aspects of service-to-service communication if you anticipate a growing number of services or frequent updates and deployments. Existing infrastructure and tooling: Consider the compatibility of the service mesh with your existing infrastructure components, container runtimes, orchestration platforms, and monitoring systems. It would be smart to evaluate whether the service mesh integrates smoothly with your technology stack or if it requires additional configuration and customization efforts. Resource availability and expertise: Finally, assess the availability of resources and expertise within your organization to deploy, operate, and maintain a service mesh. If you don’t have the necessary skills and capacity to handle the complexity associated with a service mesh, it will be an additional investment of resources. It’s essential to carefully evaluate your specific requirements, priorities, and constraints before deciding to adopt a service mesh, as it is a long-term and heavy expenditure. You can conduct a proof-of-concept or pilot project to assess how well a service mesh aligns with your use case and whether it provides tangible benefits in terms of scalability, performance, security, and operational efficiency. Scenarios Where Service Mesh Shine There are some situations where service mesh fits best. Some of these scenarios are: Large-scale microservice deployments: Service mesh is highly beneficial in large-scale microservice architectures where numerous services need to communicate with each other. They provide a centralized control plane for managing service-to-service communication, enabling seamless interactions and simplifying the complexity of managing a large number of services. Multi-cloud and hybrid cloud environments: Service mesh is well-suited for scenarios where applications span multiple cloud providers or hybrid cloud environments. They offer consistent networking, security, and observability across different infrastructure environments, ensuring a unified experience for services regardless of the underlying cloud infrastructure. Complex network topologies: Service mesh excels in managing complex network topologies, especially when services need to communicate across various network boundaries, such as across data centers or regions. They provide the necessary abstraction and routing capabilities to simplify inter-service communication, regardless of the underlying network complexities. Compliance and regulatory requirements: Service mesh is advantageous in environments that have stringent compliance and regulatory requirements. They offer robust security features, such as authentication, authorization, and encryption, ensuring that communication between services meets the necessary compliance standards. Service mesh also provides observability features that aid in compliance audits and monitoring. There are many service mesh out there, and you might be confused about which one to use; you can check out our CNCF landscape navigator, which can help you choose the right service mesh for your needs. When Not To Use a Service Mesh? Service mesh is not always a good option to go with. It is possible that they can introduce more hassle to the system. Let’s see real-life use cases where you might consider not using a service mesh in an application that requires extremely low latency or real-time processing capabilities. Real-time analytics pipeline: In a real-time analytics pipeline, data is continuously streamed from multiple sources, processed in near real-time, and analyzed for insights. This type of application typically requires high data throughput and minimal processing latency to ensure timely analysis and decision-making. In such a scenario, introducing a service mesh with its additional layer of proxies and routing may introduce unnecessary overhead and potential bottlenecks in the data flow. The application focuses on efficiently processing and analyzing the streaming data rather than managing complex service communication patterns. Instead of a service mesh, alternative lightweight networking solutions like message queues, event-driven architectures, or specialized stream processing frameworks (explored later in the blog) can be used to optimize the data flow and ensure minimal latency. These solutions are often designed specifically for high-throughput data streaming scenarios and can provide better performance and scalability compared to a service mesh. Legacy enterprise application: If you are working with a legacy enterprise application that predates the microservices era and lacks the necessary architectural components for service mesh integration, trying to retrofit a service mesh may not be feasible. Instead, you can focus on other modernization efforts or consider containerization without a service mesh. Resource-constrained IoT devices: In Internet of Things (IoT) scenarios where devices have limited resources (CPU, memory, network), adding a service mesh can introduce too much overhead (undesirable amount of additional complexity, resource consumption, or operational burden). In such cases, simple, lightweight communication protocols may be more suitable. Service Mesh Alternatives In our previous discussions, we explored scenarios where service meshes might not be the ideal solution. However, what if you desire to leverage the benefits of a service mesh without actually implementing one? Fortunately, there exist alternative tools that offer comparable functionalities without the added overhead and complexities of a service mesh. In this section, we will delve into these alternative options, examining their capabilities and exploring how they can fulfill your requirements effectively. API gateways: API gateways act as a single entry point for external clients to access services. They provide features like authentication, authorization, request routing, rate limiting, and caching. While API gateways primarily focus on client-facing APIs, they can also handle some inter-service communication aspects, especially in monolithic or simple microservices architectures. Service proxy: A service proxy is a lightweight intermediary component that sits between services and handles communication between them. It can provide load balancing, circuit breaking, and traffic management capabilities without the extensive feature set of a full-fledged service mesh. Service proxies like Envoy and HAProxy are commonly used for these purposes. Ingress controllers: Ingress controllers provide traffic routing and load balancing for incoming external traffic to a cluster or set of services. They handle the entry point into the system and can offer features like SSL termination, path-based routing, and request filtering. Ingress controllers like Nginx Ingress Controller and Traefik are popular choices. Message brokers: Message brokers facilitate asynchronous communication between services by decoupling senders and receivers. They provide reliable message queuing, publish-subscribe patterns, and event-driven architectures. Message brokers like Apache Kafka, RabbitMQ, and AWS Simple Queue Service (SQS) are commonly used for reliable messaging. Distributed tracing systems: Distributed tracing systems help monitor and trace requests as they propagate through a distributed system. They provide insights into request flows, latency analysis, and troubleshooting. Tools like Jaeger, Zipkin, and AWS X-Ray can be used for distributed tracing. Custom networking solutions: Depending on the specific requirements and constraints of your application, it may be more suitable to develop custom networking solutions tailored to your needs. This approach allows for fine-grained control and optimization of service communication while avoiding the additional complexity introduced by a service mesh. It’s important to note that these alternatives may not offer the same comprehensive feature set as a service mesh, but they can address specific aspects of service communication and provide lightweight solutions for specific use cases. The choice of an alternative depends on the specific requirements, architecture, and trade-offs you are willing to make in your application ecosystem. Do I Need a Service Mesh? Making the decision to adopt a service mesh involves careful evaluation and consideration of various factors. Here are some key steps to help you in your decision-making process: Weighing the pros and cons: You can begin by understanding the advantages and drawbacks of the service mesh, as well as their potential impact on your application architecture and development process. Assess how the benefits align with your specific needs and whether the drawbacks can be mitigated or outweighed by the advantages. Conducting a cost-benefit analysis: Evaluate the costs, both in terms of implementation effort and ongoing operational overhead, associated with adopting a service mesh. There would be hidden costs like a learning curve for your team, the need for additional infrastructure resources, and the impact on application performance. You have to analyze these costs against the expected benefits and determine if the investment is justified. Consulting with your team and stakeholders: Having a discussion with your development team, operations team, and other relevant stakeholders can help you to gather their perspectives and insights. Understand their requirements, concerns, and existing pain points related to service communication and management. Considering future scalability and flexibility: Any changes you make today would have an effect on the future. So, you must carefully evaluate the upcoming scalability and flexibility requirements of your application. Service mesh solution should accommodate your expected growth, handle increased traffic, and support evolving needs. Consider whether the service mesh architecture is flexible enough to adapt to changes in your application’s requirements or if it may introduce unnecessary constraints. Assessing existing networking capabilities: Assess whether your existing networking infrastructure, along with available solutions, can effectively meet your service communication and management requirements. Examine if your current setup can adequately address security, observability, and traffic management aspects or if adopting a service mesh could offer substantial enhancements. Piloting and testing: At the end of the day, one can run a pilot project or proof of concept (PoC) to evaluate the impact of a service mesh in a controlled environment. This can help validate the benefits, test the integration with your existing infrastructure, and identify any unforeseen challenges before fully committing to a service mesh implementation. It’s essential to align the decision with your organization’s goals and ensure that the adoption of a service mesh aligns with your long-term architectural vision and growth plans. That’s why you should not rush in and take your time to reach any decision. Service Mesh Decision Questionnaire Here are several questions that you may ask yourself to have a comprehensive perspective when deciding for and against the service mesh. Is my application architecture microservices-based? Do I require advanced traffic management and routing capabilities? Is observability and monitoring critical for my application? Do I have a highly distributed or multi-cloud environment? Is security a top concern for my application? Am I prepared to invest in the additional complexity and learning curve? Can I allocate sufficient resources for service mesh implementation and maintenance? If the answer to any of the questions is “yes,” it is advisable to consider using a service mesh. However, if the answer is “no” for any of the questions, it is recommended to explore the alternative options provided earlier. Conclusion It’s important to recognize that service mesh adoption may not be universally applicable. Factors such as application complexity, performance requirements, team size, available resources, existing networking solutions, and compatibility with legacy systems should be carefully considered. In some cases, alternative solutions like API gateways, service proxies, ingress controllers, message brokers, or custom networking solutions may be more appropriate. Ultimately, the decision to adopt a service mesh should align with your organization’s goals, architectural vision, and specific use case requirements. Each application and infrastructure is unique, and it’s crucial to evaluate the trade-offs and consider the long-term implications before deciding on the adoption of a service mesh. By taking a thoughtful and informed approach, you can determine whether a service mesh is the right choice for your application and leverage its capabilities to enhance the management, security, and observability of your distributed system. Once you decide whether service mesh fits your specific requirements or not, you may need external support to get started with its adoption. For that, you can check our service mesh consulting capabilities.

In the world of distributed systems, the likelihood of components failing or becoming unresponsive is higher compared to monolithic systems. Given the interdependence of microservices or modules in a distributed setup, the failure of one component can lead to cascading failures throughout the system, potentially causing the entire system to malfunction or shut down. Therefore, resilience — the ability of a system to handle and recover from failures — becomes critically important in distributed environments. Much like how an electrical circuit breaker prevents an overload by stopping the flow of electricity when excessive current is detected, the Circuit Breaker pattern in software engineering stops the flow of requests to a service when the number of failures exceeds a predefined threshold. This ensures that a failing service doesn’t continue receiving traffic until it recovers, preventing further strain and potential cascading failures. Conceptual Understanding The Circuit Breaker pattern is designed to detect failures and encapsulates the logic of preventing a system from executing an operation that’s set to fail. Instead of repeatedly making requests to a service that is likely unavailable or facing issues, the circuit breaker stops all attempts for a while, giving the troubled service time to recover. How It Works In order to manage this possible failure, the circuit breaker is composed of three possible states, allowing the system to understand the failure and react appropriately. Closed State: This is the default state of the circuit breaker. In this state, all requests to the service are allowed. If the service responds without errors, everything continues as normal. However, if errors start cropping up and cross a predefined threshold (which can be set based on the number of errors, response time, etc.), the circuit breaker transitions to the open state. Open State: In the open state, the circuit breaker prevents any requests to the failing service, providing an immediate fail mechanism. This state is maintained for a predefined time (reset interval). After this period, the circuit breaker transitions to the half-open state. Half-Open State: In this state, the circuit breaker allows a few test requests to determine the health and status of the failing service. If those requests succeed without errors, it’s an indication that the service might have recovered, and the circuit breaker transitions back to the closed state. If they fail, the circuit breaker goes back to the open state, continuing to block requests. The transition of a failure occurring on an external system that is then backed up: Benefits and Trade-Offs Benefits Resilience and Fail Fast Resilience in software refers to the ability of an application to bounce back from unforeseen failures and continue its operations. “Fail Fast” is a concept where the system promptly fails without prolonging the issue, ensuring it doesn’t waste resources or time. In the context of the Circuit Breaker pattern, this means that the system can quickly identify when a component or service is failing and halt the operations related to it. This instant detection and action prevent the application from repeatedly making futile requests, ensuring that it remains operational and responsive to other, unaffected parts of the system. Resource Optimization This is about making the best use of system resources, such as memory, CPU, and network bandwidth. If a component of the system is constantly failing and the system keeps making requests to it, it wastes valuable resources. By recognizing such failures and preventing further requests using the Circuit Breaker pattern, the system conserves resources, which can then be used for other operational tasks. System Protection This ensures that parts of the system have a chance to recover from failures without being overwhelmed with more requests. When a service is down or not performing optimally, continually sending traffic to it can exacerbate the problem. The Circuit Breaker pattern effectively “shields” the failing service, ensuring that it isn’t bogged down with more traffic, giving it breathing room to recover. Enhanced User Experience While a user might be initially disappointed with a service failing, they would prefer an immediate error message over a long, uncertain wait time. The Circuit Breaker pattern ensures that users get prompt feedback, allowing them to either retry or perform alternative actions rather than being left in the dark. Trade-Offs Flakiness in Circuit Breakers Flakiness denotes the inconsistent behavior of a system component. For the Circuit Breaker pattern, it’s the unpredictability in state transitions due to various influences. Threshold configuration: Improper thresholds can lead to the breaker opening too frequently for minor issues or not activating when necessary, causing perceived instability.Also, if the transition from Open to Half-Open is not managed properly, the service might never recover, which could lead to a system that never returns to normal behavior, even if it is working. False positives: Transient issues like brief network disruptions can trigger the breaker, making a healthy service appear unreliable. External dependencies: Inconsistencies in services or resources the protected service relies on can induce flakiness in the breaker’s behavior. Monitoring gaps: Insufficient monitoring makes it challenging to understand the breaker’s transitions, adding to its unpredictable nature. Retry mechanisms: Aggressive retries can intensify flakiness, especially when combined with temporary glitches in the service. Complexity Implementing a Circuit Breaker pattern introduces new logic, states, and transitions that the system has to handle. This can add complexity, both in terms of coding and monitoring the system. Configuration Challenges To work effectively, the Circuit Breaker needs to be tuned correctly. Deciding on the right thresholds for failure detection, the duration to keep the breaker open, and when to test for recovery (half-open state) can be tricky. Incorrect configurations can lead to suboptimal performance and unwanted system behaviors. Conclusion As distributed systems grow in complexity, the chances of a single point of failure bringing down the entire system increase. The Circuit Breaker pattern is an essential tool in a developer’s toolkit to prevent cascading failures and ensure system reliability. It’s important to balance the benefits of using a circuit breaker with the complexity it introduces. The correct configuration of a circuit breaker is critical to its effectiveness and, as with end-to-end testing, flakiness is its worst enemy. As with all patterns, it’s essential to monitor, gather data, and adjust configurations as needed to ensure that the Circuit Breaker serves its intended purpose.