Modernizing Legacy Systems: Migrating to .NET Framework

Businesses often handle complex software systems that become difficult to manage as they grow. These systems, called monolithic applications, pose challenges. They have internal structures that make it hard for developers to update the code, which leads to higher costs. Coordinating large releases with multiple teams becomes a challenge too. As a company expands, these systems might struggle to keep up with the increasing number of users. To solve these issues, businesses should consider updating their applications in the AWS Cloud to better meet their needs.



Your modernization journey

Your business's way of updating impacts how well you can grow with what your customers need. Let's explore a step-by-step plan that helps you achieve a modern setup. It aims to make things easier to manage, scale up smoothly, release updates quickly, and save costs.

This involves four steps:

  • Break down the monolith

  • Containerize your application

  • Refactor to .NET 6

  • Migrate to a purpose-built, lower-cost database engine.


1. Break down the monolith

Moving your applications to Amazon Web Services (AWS) Cloud offers many benefits like quicker market entry, improved business flexibility, increased revenue possibilities, and cost reductions. But to make the most of this, it's essential to update your applications regularly. This includes breaking down large applications into smaller, more manageable parts called microservices.

This transformation from big to small applications involves understanding the current code and how different parts of the business work. There are various ways to gradually change a large app into smaller ones, but this process is manual, risky, and takes a lot of time.


2. Containerize

Why shift to containers? Containers offer a way to bundle, test, put out, and re-put out applications across different setups. Specifically, Docker Containers provide a dependable method to gather all the parts of your application and put them together into one single package. This is vital because today's applications include various elements beyond just code, like dependencies, tools, or system libraries. Moving older .NET Framework apps to containers helps use the operating system better and ensures consistent performance.


For faster results, use AWS App2Container (A2C) to convert these apps into Windows containers. A2C is a tool that turns .NET and Java apps into containerized versions. It examines and lists all applications, whether on virtual machines, on-premises, or in the cloud. Choose the app you want, and A2C packs it into a container image along with its dependencies. Here's a detailed guide and workshop to help you start using A2C.


Once your app is in a container, you can manage it yourself using Amazon EC2 for Docker with Windows containers. Or you can go for Amazon Elastic Container Service (ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). These are managed services that handle container organization, allowing you to focus on developing and managing apps instead of dealing with your infrastructure. Check out Amazon ECS vs Amazon EKS to decide between AWS container services.


In the following part, we'll cover two main areas for reducing expenses in our modernization plan:


  1. Licensing costs of running workloads on Windows servers.

  2. SQL Server licensing cost.


3. Refactor to .NET 6

Consider transitioning from Windows to Linux to address Windows licensing costs. This involves adopting .NET Core and using Dockerfile for a Linux Container. By updating applications to .NET 6 and leveraging AWS, there are significant performance enhancements, particularly a 30–40% boost in socket performance on Linux. AWS Graviton supports ARM64-specific optimizations in .NET libraries.

Another cost-efficient approach is moving to serverless options like AWS Lambda supporting .NET 6 runtime or using ECS with Fargate, a serverless compute engine. Fargate, powered by AWS Graviton2 processors, can cut costs by up to 20% and enhance performance by up to 40% compared to x86 Intel-based instances. For more control over applications, consider running .NET 6 on Amazon EC2 Linux instances with the latest-gen Intel and AMD processors.

AWS has introduced .NET 6 support in Porting Assistant for .NET, an analysis tool. It evaluates .NET Framework applications (version 3.5+) to produce a compatibility assessment for .NET Core or .NET 6. This assessment helps prioritize applications for porting and identifies incompatible APIs and packages, offering replacements. You can watch a demo video for more clarity on this process.


4. Migrate from SQL Server to a lower-cost database engine


AWS recommends building scalable, specialized applications based on your needs. They offer various database engines (over 15) to support different data models. In microservices architectures, each service can handle its own data through independent data stores. This approach, known as the database-per-service pattern, allows choosing the best data stores for your app's needs.


For SQL Server alternatives, consider moving to open-source relational database engines. AWS offers Amazon 

Relational Database Service (Amazon RDS) supporting MySQL, MariaDB, and PostgreSQL. This blog will focus on PostgreSQL, which has a clear migration path. Amazon RDS provides two types of Postgres databases: Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL-Compatible Edition. To assist in your decision, check out "Is Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL a better choice for me?


Once you've chosen the type of Amazon RDS you want, the next question is, How should I migrate?


  1. Convert your schema

  2. Migrate the data

  3. Refactor your application


Schema conversion

AWS Schema Conversion Tool (SCT) is a free tool that assists in changing your existing database from one type to another. It supports several source databases like Microsoft SQL Server, Oracle, and MySQL. You can opt for target database engines like Amazon Aurora PostgreSQL-Compatible Edition or even set up a data lake using Amazon S3. SCT offers a user-friendly interface that connects to both the source and target databases to fetch the current schema objects. Once connected, you can generate a report that summarizes the conversion effort and necessary actions.



Data migration

After completing the schema migration, you'll need to transfer your data from the original database to the new one. Depending on your application's needs, you can perform a direct copy of the data from the old to the new database in a single go. Alternatively, you can use a tool that continuously copies data and updates changes until you're prepared to switch to the new database. AWS Database Migration Service (AWS DMS) is one such tool that aids in migrating various types of databases, including relational databases and data warehouses.


AWS DMS facilitates both one-time migrations and continuous data replication to keep the source and target databases synchronized. Once both databases are aligned, you can stop using the original database and transition to the new one. Refer to the Microsoft SQL Server To Amazon Aurora with PostgreSQL Compatibility playbook or utilize the self-guided workshop to migrate to a PostgreSQL compatible database using SCT and DMS.



Application refactoring


Each database system has its own unique traits, and shifting from one database like MSSQL Server to PostgreSQL involves modifying code. Once the initial database move is done, manually changing application code, updating database drivers, and ensuring the application works as expected demands a lot of effort. This process carries a risk of mistakes when making extensive code modifications.


To simplify this transition, AWS created Babelfish for Aurora PostgreSQL. It's a feature for Amazon Aurora PostgreSQL-Compatible Edition that allows Aurora to interpret commands from applications designed for Microsoft SQL Server. Babelfish enables Aurora PostgreSQL to understand T-SQL, the specialized SQL language of SQL Server. It uses the same communication method, allowing applications originally built for SQL Server to function with Aurora. Learn more about migrating from SQL Server to Babelfish for Aurora PostgreSQL. Use the Babelfish Compass tool to check if the application uses any SQL features not currently supported by Babelfish.


Conclusion

When considering a migration from MSSQL Server to PostgreSQL for improved efficiency, it's crucial to understand the differences between these databases. The transition involves code alterations and potential risks of errors during the modification process. However, with tools like Babelfish for Aurora PostgreSQL offered by AWS, the process is streamlined, enabling PostgreSQL to comprehend SQL Server-based applications. This simplification allows applications originally designed for SQL Server to seamlessly operate with Aurora PostgreSQL. Explore these migration options when availing dotnet development services for a smooth transition and enhanced database functionality.
Location: United States

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