Transforming a Monolithic On-Prem System into Scalable AWS Microservices with CDK

Transitioning IT infrastructure from on-premises to the cloud can be a daunting task, but with the right tools and strategies, it can lead to significant improvements in efficiency and scalability. In this case study, we’ll explore how one of our clients successfully used AWS Cloud Development Kit (CDK) to automate and transform their monolithic on-premises IT infrastructure into a scalable microservices architecture on AWS.

Introduction

Moving from an on-premises monolithic system to a cloud-based microservices architecture is a complex yet rewarding journey. Our client, a mid-sized enterprise, sought to leverage the power of AWS to enhance their IT capabilities. They aimed to achieve automation, scalability, and cost efficiency by transitioning to AWS using the AWS CDK.

Project Overview

Client: Mid-sized Enterprise in the E-commerce Sector
Objective: Transform IT infrastructure from a monolithic on-premises system to AWS microservices
Tools Used: AWS CDK, AWS CloudFormation, AWS Lambda, AWS S3, AWS EC2, AWS RDS, Amazon ECS, AWS Fargate, Amazon API Gateway

Goals and Objectives

The primary goals of the project were to:

1. Automate infrastructure deployment
2. Break down the monolithic application into microservices
3. Improve scalability and reliability
4. Reduce operational costs
5. Enhance security and compliance

Initial Challenges

Before diving into the transformation, the client faced several challenges:

• Monolithic Architecture: The on-premises system was a single, large codebase with tightly coupled components.
• Manual Processes: The infrastructure required significant manual intervention for deployments and updates.
• Scalability Issues: Scaling the infrastructure to meet peak demands was time-consuming and costly.
• Maintenance Overhead: Regular maintenance of physical servers and network equipment was burdensome.
• Security Concerns: Ensuring security and compliance with industry standards was complex and resource-intensive.

The Solution: AWS CDK

AWS CDK (Cloud Development Kit) provided a robust framework for defining cloud infrastructure in code, making the transition smoother and more manageable.

Step 1: Assessment and Planning

The first step was a thorough assessment of the existing monolithic infrastructure. We identified all critical components, dependencies, and performance requirements. This assessment helped in planning the migration strategy effectively.

Step 2: Designing the Architecture

Using AWS CDK, we designed a cloud architecture that broke down the monolithic system into microservices while leveraging AWS services. Key components included:

• Amazon ECS with Fargate: For running containerized microservices
• Amazon API Gateway: For managing API endpoints and routing traffic to microservices
• Amazon S3: For secure and durable storage
• Amazon RDS: For managed database services
• AWS Lambda: For serverless compute
• AWS CloudFormation: For infrastructure as code

Step 3: Implementing Infrastructure as Code (IaC)

The core of our strategy was implementing Infrastructure as Code using AWS CDK. This approach allowed us to define and provision AWS resources using familiar programming languages.

import * as cdk from '@aws-cdk/core';
import * as ecs from '@aws-cdk/aws-ecs';
import * as ec2 from '@aws-cdk/aws-ec2';
import * as s3 from '@aws-cdk/aws-s3';
import * as rds from '@aws-cdk/aws-rds';
import * as apigateway from '@aws-cdk/aws-apigateway';

class MyStack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    // Define an S3 bucket
    const bucket = new s3.Bucket(this, 'MyBucket', {
      versioned: true,
      removalPolicy: cdk.RemovalPolicy.DESTROY
    });

    // Define a VPC
    const vpc = new ec2.Vpc(this, 'MyVpc', {
      maxAzs: 3
    });

    // Define an RDS instance
    const rdsInstance = new rds.DatabaseInstance(this, 'MyRdsInstance', {
      engine: rds.DatabaseInstanceEngine.mysql({
        version: rds.MysqlEngineVersion.VER_8_0_19,
      }),
      vpc: vpc,
      instanceType: ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE2, ec2.InstanceSize.MICRO),
      removalPolicy: cdk.RemovalPolicy.DESTROY,
    });

    // Define an ECS cluster
    const cluster = new ecs.Cluster(this, 'MyCluster', {
      vpc: vpc
    });

    // Define a Fargate service
    const fargateService = new ecs.FargateService(this, 'MyFargateService', {
      cluster: cluster,
      taskDefinition: new ecs.FargateTaskDefinition(this, 'TaskDef', {
        memoryLimitMiB: 512,
        cpu: 256,
      }),
      desiredCount: 2,
    });

    // Define API Gateway
    const api = new apigateway.RestApi(this, 'MyApi', {
      restApiName: 'MyService',
    });

    const items = api.root.addResource('items');
    items.addMethod('GET', new apigateway.LambdaIntegration(new aws_lambda.Function(this, 'GetItemsFunction', {
      runtime: aws_lambda.Runtime.NODEJS_12_X,
      handler: 'index.handler',
      code: aws_lambda.Code.fromAsset('lambda'),
    })));
  }
}

const app = new cdk.App();
new MyStack(app, 'MyStack');
app.synth();

Step 4: Breaking Down the Monolith

We refactored the monolithic application into smaller, independent microservices. Each microservice was containerized using Docker and deployed on Amazon ECS with AWS Fargate. This ensured that services were isolated, making the system more scalable and easier to manage.

Step 5: Migration

With the architecture defined, we proceeded with migrating the on-premises applications and data to AWS. This phase involved:

• Data Transfer: Using AWS DataSync for efficient and secure data transfer.
• Service Deployment: Deploying containerized microservices on ECS and serverless functions on Lambda.
• Database Migration: Migrating databases to Amazon RDS with minimal downtime.

Step 6: Setting Up API Gateway

We used Amazon API Gateway to create, publish, maintain, monitor, and secure APIs at any scale. This allowed the microservices to communicate efficiently and securely.

Step 7: Automation and Monitoring

Post-migration, we set up automated deployment pipelines using AWS CodePipeline and monitoring solutions using Amazon CloudWatch. This ensured continuous delivery and real-time monitoring of the infrastructure.

Results and Benefits

The transformation yielded several significant benefits for the client:

• Automation: Automated deployment reduced manual intervention, leading to faster and more reliable updates.
• Scalability: The infrastructure could now easily scale to meet demand, thanks to AWS’s elastic services.
• Cost Efficiency: By leveraging AWS’s pay-as-you-go model, the client achieved significant cost savings.
• Enhanced Security: AWS’s built-in security features and compliance certifications ensured robust security.
• Improved Agility: The microservices architecture allowed for more flexible and faster development cycles.

Wrapping Up

Transitioning from a monolithic on-premises system to AWS microservices using AWS CDK was a game-changer for our client. It not only automated their infrastructure but also provided the scalability, reliability, and cost efficiency they needed. This case study underscores the importance of strategic planning and the right tools in achieving successful cloud transformations.

FAQs

Q: What is AWS CDK?
A: AWS CDK (Cloud Development Kit) is a framework for defining cloud infrastructure using familiar programming languages.

Q: Why choose AWS CDK over other tools?
A: AWS CDK allows for more flexible and maintainable infrastructure as code, using programming languages like TypeScript, Python, and JavaScript.

Q: How does AWS CDK improve scalability?
A: By defining infrastructure as code, AWS CDK makes it easier to replicate and scale resources as needed.

Further Reading

• AWS CDK Documentation
• Migrating to AWS: Best Practices and Strategies