In today’s fast-paced digital landscape, building agile, scalable and maintainable software systems is paramount. Organizations often start with monolithic applications for their simplicity, but soon face challenges as these systems grow more complex. A distributed monolith, where the application is split into components but remains tightly coupled and interdependent, can hamper agility and scalability. Transitioning from this state to a composable architecture on AWS empowers teams to deliver resilient, scalable and business-aligned software solutions.

This article walks through the conceptual and practical journey of moving from a distributed monolith to a composable architecture on AWS, highlighting key principles, architectural patterns, AWS services and operational practices.

Understanding the distributed monolith problem

A distributed monolith is a system composed of multiple services or components, deployed independently but tightly coupled through synchronous dependencies such as direct API calls or shared databases. Unlike a true microservices architecture, where services are autonomous and loosely coupled, distributed monoliths share many pitfalls of monoliths, including:

• Tight coupling: Components depend heavily on the internals of others, creating fragile dependencies.
• Deployment friction: Changes require coordinated deployments across services.
• Operational complexity: Dysfunctional distributed components make troubleshooting and scaling difficult.
• Slow innovation: Teams struggle to iterate rapidly due to the interconnected system nature.

These issues arise when teams attempt to scale monolithic applications prematurely without fully embracing decoupling and domain-driven principles.

Principles of composable architecture

Composable architecture embraces modularity and loose coupling by treating every component as an independent building block. The focus lies in business alignment and agility rather than just code decomposition.

Key characteristics

• Independent deployability: Each component or microservice can be developed, deployed and scaled independently.
• Domain-driven design (DDD): Using bounded contexts and ubiquitous language to define clear service boundaries aligned with business domains.
• API-led communication: Interactions happen via well-defined APIs or event-driven messaging, avoiding direct code or database sharing.
• Data decentralization: Each service manages its own data to prevent tight coupling through shared databases.

These principles enable systems where components can be composed, replaced or upgraded without impacting the whole system.

AWS services enabling composable architecture

AWS offers a rich ecosystem tailored for building composable systems:

• AWS Lambda: Serverless compute enabling event-driven, stateless functions as microservices.
• Amazon API Gateway: Creating and managing APIs for service communication.
• Amazon DynamoDB: A NoSQL database that supports single-table design for efficient data access per service.
• Amazon EventBridge: Event bus enabling loosely coupled event-driven architectures.
• AWS Step Functions: Orchestrating workflows across microservices.
• AWS Cloud Development Kit (CDK): Infrastructure as code for automated, repeatable service deployments.
• Amazon SNS/SQS: Messaging services for asynchronous communication between components.

These services help build fully decoupled architectures with scalable and maintainable infrastructure.

The transformation journey, step-by-step

1. Assess and identify boundaries

Start by analyzing the existing application to find natural business or functional boundaries. Use Domain-Driven Design to define bounded contexts that encapsulate specific business capabilities.

Aim to reduce inter-service dependencies by identifying:

• Shared databases that need decoupling.
• Synchronous calls that can be turned into asynchronous messaging.
• Code or library dependencies crossing service boundaries.

2. Separate and modularize codebase

Refactor the code into separate repositories or modules, each representing a bounded context or microservice. This clear separation supports independent deployment pipelines and ownership.

import * as cdk from 'aws-cdk-lib';
import * as lambda from 'aws-cdk-lib/aws-lambda';
import * as apigw from 'aws-cdk-lib/aws-apigateway';

export class OrderServiceStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const orderLambda = new lambda.Function(this, 'OrderHandler', {
      runtime: lambda.Runtime.NODEJS_18_X,
      handler: 'order.handler',
      code: lambda.Code.fromAsset('lambda/order-service'),
    });

    new apigw.LambdaRestApi(this, 'OrderAPI', {
      handler: orderLambda,
      restApiName: 'Order Service',
    });
  }
}

Consolidate infra-as-code with AWS CDK to manage each microservice’s infrastructure, including APIs, storage and permissions.

3. Implement API-first communication

Replace direct code or database calls with API calls or events. For example:

• Use REST or GraphQL APIs via API Gateway.
• Emit business events via EventBridge or SNS for asynchronous processing.

Use SQS for message queuing to handle transient workloads.

const AWS = require('aws-sdk');
const eventBridge = new AWS.EventBridge();

exports.handler = async (event) => {
  const orderDetails = event.detail;

  const params = {
    Entries: [
      {
        Source: 'order.service',
        DetailType: 'OrderCreated',
        Detail: JSON.stringify(orderDetails),
        EventBusName: 'default',
      },
    ],
  };

  await eventBridge.putEvents(params).promise();

  return { statusCode: 200, body: 'Order event sent' };
};

This shift fosters loose coupling and enables scalability.

4. Decentralize data ownership

Assign each microservice its own DynamoDB table or data store. Avoid cross-service database joins or queries.

Adopt a single-table design in DynamoDB to optimize data retrieval patterns within each service boundary. This approach improves scalability and performance at the data layer.

5. Migrate incrementally

Migrating a distributed monolith at once is risky. Instead:

• Start with migrating low-risk or well-bounded components.
• Implement a strangler pattern to route traffic gradually to new microservices.
• Refactor and evolve incrementally while monitoring performance and errors.

Use Case: Migrating an e-commerce platform

An e-commerce platform began as a large monolith with intertwined order processing and customer management. Source

Challenge: Frequent downtime due to deployment coupling; slow feature delivery.

Approach

• Applied Domain-Driven Design to split Order, Customer and Payment domains.
• Used AWS Lambda and API Gateway for new microservices.
• Deployed EventBridge for asynchronous events like OrderPlaced, triggering inventory updates.
• Adopted DynamoDB single-table design per microservice.

Outcome: Reduced deployment risk, improved team autonomy and scaled individual services based on demand.

Benefits of Composable Architecture on AWS

• Improved agility: Teams can develop and deploy services independently, accelerating release cycles.
• Scalability: Scale services on demand based on their usage patterns.
• Resilience: Failures remain isolated, reducing system-wide outages.
• Operational simplicity: Decoupling simplifies troubleshooting and makes infrastructure management easier.
• Business alignment: Services reflect real-world domains, improving code understanding and maintenance.

Challenges and Considerations

A composable architecture also introduces complexity:

• Increased operational overhead: Managing many services requires sophisticated CI/CD, monitoring and automation.
• Proper observability: Monitoring your services, application and infrastructure is also very critical and you can use some full-stack observability tools such as those reviewed and rated by Gartner’s Peer Insights.
• Security: More communication points demand stringent security measures.
• Data consistency: Eventual consistency and distributed transactions need careful design.
• Skill requirements: Teams must be proficient with distributed systems, cloud-native patterns and AWS tooling.

These challenges can be mitigated with proper tooling, automation and training.

Conclusion

Transitioning from a distributed monolith to a composable architecture on AWS is a strategic journey that demands careful planning and execution. By applying domain-driven design principles, leveraging AWS’s serverless and managed services and embracing modular, loosely coupled components, organizations can build scalable, resilient and flexible software systems aligned with evolving business needs. This transformation not only solves traditional monolithic pain points but also unlocks agility and innovation potential critical for today’s competitive environment.

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