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DevOps teams are under incredible pressure to migrate monolithic applications to a distributed, containerized architecture using Kubernetes to optimize the software delivery life cycle (SDLC). They are trying to create shorter release cycles, ease deployment changes and reduce fragility through dependencies.
These demands have driven a shift away from monolithic applications, which struggle to keep up with modern requirements, because one change requires rebuilding the whole stack. According to the Cloud Native Computing Foundation (CNCF) 2022 Annual Survey, 79% of organizations have moved toward microservices architectures that allow teams to iterate on individual services with ease.
For many organizations, taking a lift-and-shift approach is the first step for migrating monolithic applications to Kubernetes and microservices. This involves directly lifting the monolith onto hardware hosted in the cloud, and then gradually breaking down the app into microservices. However, the lift-and-shift philosophy has its challenges, as organizations must refactor monoliths to optimize them for the cloud. Therefore, it’s often more cost-effective to refactor an application service by service into a containerized architecture.
The following are four best practices DevOps teams can follow to efficiently migrate their monoliths to microservices in containerized Kubernetes environments.
Monolithic applications are often easy to break by disrupting their complex and fragile webs of dependencies. As a result, sudden and unexpected disruptions are nearly inevitable when migrating a monolith to the cloud and containers — especially if DevOps teams go without a clear plan. To avoid unwanted surprises, map out the monolith’s dependencies and business functions in full before any migration project.
The risk of human error is high when manually mapping a monolith’s dependencies, given its complexity. Therefore, to understand the relationships across an application’s backend and frontend components, it helps to use an automated solution that can visualize the app in real time. Application topology mapping that uses telemetry data from transaction traces is essential, enabling teams to build a precise visual representation of a monolithic application and its components.
Refactoring a monolithic application for containerized Kubernetes environments is a huge task and often involves restructuring and rebuilding from scratch. With that in mind, it’s essential to break down a migration into small, incremental and 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 then test and compare them against the monolith to see how the new services affect performance and functionality. Then, once a microservice successfully replicates the monolith’s functionality, teams can remove the dependency on the monolith for that respective component and move on to the next one.
Dependencies within monolithic apps are deeply intertwined. These close relationships among components are one of the driving forces behind the move to Kubernetes and microservices, as they hinder flexible changes and deployment.
When migrating an application to a microservices architecture, it’s important for teams to understand all dependencies among services and to reduce and streamline them as much as possible. Asynchronous messaging is key, allowing services to communicate by sending and receiving messages using queues. By adopting asynchronous messaging, communications between microservices will be much less prone to bottlenecks, while also making it far easier to edit or replace an individual microservice.
Migrating from a monolith to containerized services on Kubernetes means applications have more services and supporting technologies running independently of one another, which can make them more complex. Given the number of components, it can be difficult for DevOps teams to manually track all the dependencies among them. Just as teams need to create a map of their monoliths before migrating them, they also need to maintain maps of their microservices environments with end-to-end observability.
In practice, this means using observability data — including logs, metrics and traces from components in the cloud technology stack — to understand service relationships and application dependencies. This observability must also extend into each Kubernetes cluster, node, and pod and the workloads running on them. When issues arise, observability data allows DevOps teams to identify the root causes of problems so that they can resolve them quickly.
To be most effective, teams should use a platform that unifies their observability and security data from the whole application infrastructure. This unified platform should harness AI capabilities that provide precise answers about the environments’ health so teams can automate much of the work around triaging, explaining and remediating a problem.
Migrating applications from monoliths to containerized microservices can be involved and time-consuming. However, once the migration is complete, DevOps teams are free to be more iterative and flexible, while also able to take full advantage of cloud services.
Much of the work that teams complete to enable the migration will pay dividends long into the future. Adopting modern technologies to facilitate that migration, such as end-to-end observability and AI, allows teams to continuously monitor and optimize their microservices environments, providing the best possible user experiences and business outcomes. These techniques can supercharge their transformation efforts and help their organization achieve a lasting competitive advantage.
