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A monolith results from legacy code that has endured years of collaborative coding, rendering it nearly impossible to restructure due to its ongoing usage and interdependence. Of course, we can design microservices to tackle monoliths’ unreliability. But the transition from one to another doesn’t always go down smoothly. In this article, we have prepared a detailed plan for your migration process:
1. First, we must unravel the monolith’s intricacies of function, behavior, and data schemas. Instead of going over and over its source codes, their coupling, and dependencies to decipher the application’s operations, it’s best to adopt a bird’s eye view of the application’s data model. Examining the interrelationships between the data schemes allows you to unearth vital insights into the data requirements and the application’s logic.
Understanding the existing data schemes will help clarify the persistent data structures and models required for the apps’ functionality.
2. Next, you can walk through the UI and functionality with the user to learn exactly how the user data inputs are collected, and the expected reports are generated. If different users have different UI, note separations of functionalities. By correlating the UIs with the data structures you have figured out in the previous step, you can create a behavioral/functional map that allows you to see if there are gaps in the data model to support or affect the system’s information entropy.
You have created a data-oriented view of the application. Now, you can correlate the monolith’s logical sections based on basic design principles and data clustering entropy to form a modular function/data map. It can help visualize what functional modules are absolutely necessary and the associated data structures as a module.
Some modules will share common data structures at this point – they can be part of a bounded context (in DDD’s parlance). In some cases, they might not be part of it but still link elements between separate bounded-contexts.
By correlating the usage-functionality and the data process-flow with the data schemes, you can create a high-level functional-module/data map. That data is then clustered into its relevant bounded-context to aid clarity in the visual inspection and to ensure that information entropy has been kept during the decomposition of the functionalities into modules. Voila! You have modularized the monolith system.
Once you have figured out what part of the monolith needs to be replaced with a microservice, the next step is determining the optimal approach for executing the transformation. Incremental integration tends to be the most efficient method, as it minimizes the need for retracing steps.
For less complex services, Â starting from the outermost layers and progressively introducing simple functionality can offer valuable insight into the codebase while swiftly advancing the process. For more complex services and their interdependencies, targeted integrations are often more efficient. Â These techniques enable early identification of obstacles and scope modification without impeding velocity.
For example:
When the goal is to migrate a database table, you might want to start from a simple read by primary key endpoint. Once established, all corresponding calls within the monolith could be redirected to the newly created service. However, when it comes to migrating a group of related database tables, you need to rewrite one controller to use the microservice. With the initial implementation in place, subsequent deconstruction can be tackled easier.
Regardless of the complexity of the microservice, feature flagging will serve you as a vital tool.
The functional modules/data map generated above creates a blueprint for microservice architecture by separating the application into its relevant bounded context. The aim is to have one or multiple microservices powering each bounded-context, delivering business logic and functionality via APIs. Every module can become a microservice. It involves componentization based on single-responsibility attributes per module and information entropy from the data schemes, as they are separated appropriately.
Moving from a monolithic design to a microservice architecture has several issues that need to be considered beforehand. That includes:
As you fragment data schemes, it may become necessary to duplicate data fields in other microservice nodes. This is because each microservice is intended to be self-contained with its own database, ensuring the isolation and loose coupling that is characteristic of microservices. However, managing the same data fields in different microservices requires careful handling, particularly when it comes to updates and changes that impact all microservices sharing those fields. Various design patterns, such as CQRS, event sourcing, and Saga patterns, have been employed to ensure data consistency across all microservices.
Fundamentally, microservices are a type of distributed system, with each microservice acting as a self-contained, autonomous system. And distributed systems often mean inevitable issues. Since no microservice exists in isolation (otherwise, it would be no different from a monolithic system), it must communicate and interact with other microservices. This requires each microservice to have its own communication stack to interface with other nodes. Although there are no standardized methods for this communication mechanism, it’s vital that all nodes implement the same method to ensure proper and effective communication.
These require significant attention. Debugging and testing a distributed system is more complicated than working with a monolith, and monitoring a network of microservice nodes’ health and performance requires proper tools.
Wherever you are on your monolith to microservices journey, AINSYS team is ready to help you. Our integration framework syncs data between every tool and platform your team employs in their work, helping you get an accurate picture of your software. By using these tips and AINSYS tools together, any IT specialist can ensure that they are making the right decisions for their organization. Contact us to learn more.
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