DevOps teams are under incredible pressure to migrate monolithic applications to distributed containerized architectures using Kubernetes to optimize the software delivery life cycle (SDLC). They seek to shorten release cycles, ease deployment changes, and reduce dependency vulnerabilities.
These demands are driving a shift away from monolithic applications. Monolithic applications have difficulty keeping up with modern requirements because a single change requires rebuilding the entire stack. According to the Cloud Native Computing Foundation (CNCF) 2022 Annual Survey, 79% of organizations are moving to a microservices architecture that allows teams to easily iterate on individual services.
For many organizations, adopting a lift-and-shift approach is the first step in migrating monolithic applications to Kubernetes and microservices. This involves lifting the monolith directly onto cloud-hosted hardware and then gradually breaking the app into microservices. However, the lift-and-shift philosophy presents challenges, as organizations must refactor their monoliths to optimize them for the cloud. Therefore, it is often more cost-effective to refactor your application services into a per-service containerized architecture.
Below are four best practices that DevOps teams can follow to efficiently migrate monoliths to microservices in a containerized Kubernetes environment.
1. Understand the monolith
Monolithic applications are often easily broken by breaking a complex and fragile web of dependencies. As a result, sudden and unexpected interruptions are almost inevitable when migrating monoliths to the cloud or containers, especially if his DevOps team proceeds without a clear plan. Thoroughly plan your monolith dependencies and business functionality before your migration project to avoid surprises.
Manually mapping monolith dependencies has a high risk of human error given its complexity. Therefore, to understand the relationship between the backend and frontend components of your application, it is helpful to use an automated solution that allows you to visualize your app in real time. Application topology mapping using telemetry data from transaction traces is essential, allowing teams to build accurate visual representations of monolithic applications and their components.
2. Take a step-by-step approach
Refactoring a monolithic application for a containerized Kubernetes environment is a huge undertaking and often requires rebuilding and starting from scratch. With this in mind, it’s important to break down your migration into smaller, gradual, more manageable jobs.
After mapping a monolith, DevOps teams can replace its components with microservices one at a time. As teams create individual microservices, they can test them and compare them to the monolith to see how the new service impacts performance and functionality. Then, once the microservice successfully replicates the functionality of the monolith, the team can remove each component’s dependency on the monolith and move on to the next component.
3. Make microservices loosely coupled
Dependencies within a monolithic app are deeply intertwined. These tight relationships between components prevent flexible changes and deployments and are one of the driving forces behind the move to Kubernetes and microservices.
When moving an application to a microservices architecture, it is important for teams to understand all dependencies between services and reduce and streamline those dependencies as much as possible. Asynchronous messaging is key, allowing services to communicate by sending and receiving messages using queues. By employing asynchronous messaging, communication between microservices is less likely to be bottlenecked, while making it much easier to edit or replace individual microservices.
4. Implement end-to-end observability
Moving from a monolith to containerized services on Kubernetes means your applications will have more services and supporting technologies running independently of each other, potentially making your applications more complex. Given the number of components, it can be difficult for DevOps teams to manually track all dependencies between components. Just as teams need to map their monoliths before migration, they also need to maintain a map of their microservices environment with end-to-end observability.
In practice, this means using observability data such as logs, metrics, and traces from components in your cloud technology stack to understand service relationships and application dependencies. This observability should also extend to each Kubernetes cluster, node, and pod, and the workloads running on them. When a problem occurs, DevOps teams can use observability data to identify the root cause of the problem and resolve it quickly.
To be most effective, teams should use a platform that integrates observability and security data from across the application infrastructure. This integrated platform should enable teams to automate much of the work around triaging, explaining, and remediating issues, leveraging AI capabilities that provide accurate answers about the health of the environment.
Modern technology is essential for migrating to Kubernetes-based microservices
Migrating an application from a monolith to containerized microservices can be complex and time-consuming. However, once the migration is complete, DevOps teams will have more freedom to act more iteratively and flexibly, while getting the most out of cloud services.
Much of the work your team completes to enable the transition will reap benefits down the road. By employing the latest technologies to accelerate migration, such as end-to-end observability and AI, teams can continuously monitor and optimize their microservices environment for the best possible user experience and business outcomes. will be able to provide it. These techniques can enhance change efforts and help organizations achieve lasting competitive advantage.
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