Microservices architecture is the most popular trend in application development. It’s a great way to develop faster and deliver better solutions for your customers and users. Deploying microservices is critical for organizations. Microservices enable your development team to roll out software solutions more quickly and better react to customer needs.
This is possible because developers can also speed up their development and testing cycles, reduce errors, and fix bugs quickly. Although microservice architectures provide various benefits, they have some drawbacks due to the additional complexities involved.
Below are some key considerations related to microservices deployment that any organization must understand:
- Many services, dependencies, and interactions at runtime can make the project difficult to manage.
- Communication among multiple microservices brings more chances of failure. Your development teams must be very familiar with working with distributed systems. They must be able to address issues resulting from network latency and load balancing.
- Your development team will also need the right DevOps, networking, and security skills to deliver microservices. Your team will also have to understand the concepts, coding style, and test cases, which might take more time and effort.
- Upgrades and rollout of microservices would require lots of coordination between various engineering teams. In addition, your team must perform complex testing over distributed environments. Fortunately, microservices deployment strategies can help you overcome these challenges and minimize downtime.
Microservices Deployment Strategies and Patterns
The microservices deployment pattern is a technique for updating and modifying software components. A microservices deployment pattern or strategy enables easy deployments and allows you to modify microservices.
The following subsections list the microservice deployment patterns that help in improving microservices availability.
This is a well-known strategy in microservices deployment. A canary is a candidate version of microservices that get a small percentage of traffic.
This includes releasing the microservice with the new version to only a small percentage of load first and seeing if it works as expected. As the microservice passes through rigorous testing, it gradually encounters larger workloads. If canaries aren’t functioning correctly, traffic may be routed to a stable version while the problem is investigated and debugged. Canary rollback is the process of rolling back microservices regularly.
The canary deployment strategy releases only one microservice at a time. Microservices with higher criticality and risks involved can be made available before others.
How the Canary Deployment Strategy Reduces Downtime
It improves availability by detecting problems early, before a critical microservice is exposed to the entire system.
Pitfalls of Canary Deployment
The biggest potential pitfall in this approach is that it might release microservices too early. Canary releases are usually smaller microservices with limited traffic. Frequent issues during deployments can also slow down development.
The blue-green deployment strategy involves maintaining two microservice variations simultaneously in production. One microservice version (the blue microservice) is visible to the user and gets traffic. The other one (the green microservice) remains idle for developers to make updates. A microservice remains in a blue state until it passes tests and is ready to go out. After passing all tests, microservices move to the green state, where they get traffic and are visible to users. However, microservices are constantly being monitored to detect if the microservice is performing well or needs to revert back to the blue state.
How the Blue-Green Deployment Strategy Reduces Downtime
Blue-green deployment can improve availability by keeping the microservices available during development and deployment. There’s no downtime during development and deployment because there’s always another stable variation serving production traffic. In addition, if the new deployment isn’t working correctly, you can quickly roll back to the previous variation (i.e., the blue microservice).
Pitfalls of the Blue-Green Deployment Strategy
The possibility of microservice version mismatch is a potential pitfall in blue-green deployment. Another pitfall is that microservices need constant monitoring to detect issues. This leads to increased costs and time.
A dark launch is a technique that deploys updates to microservices catering to a small percentage of the user base. It does not affect the entire system. When you dark launch a new feature, you’ll initially hide it from most end users. The launch audience can vary depending on use case and business requirements. The process of dark launching involves building a new version of the microservices in an environment that’s separate from the production environment.
Once it has been tested, you deploy it in a pre-production or test environment, and gradually increase the rollout until all users are exposed to it. Subsequently, if you see the feature performing well, then you continue to deploy it to serve more users.
A dark launch is a technique that deploys updates to microservices catering to a small percentage of the user base. It does not affect the entire system. When you dark launch a new feature, you’ll initially hide it from most end users.
Feature toggles are a good way to release your updates gradually. You can easily turn the feature on or off. In this way, you can test to see its impact without having it be totally live. You can choose what traffic to route to the microservices that are behind feature toggles during the testing phase. When the microservice has been tested and found to be suitable under realistic loads, it is activated to serve traffic from the entire production environment.
How the Dark Launching Deployment Strategy Reduces Downtime
One of the biggest benefits of dark launching is that you can perform more tests before releasing your product. This lets you catch bugs early, thus saving time and cost associated with fixing bugs in production.
Dark launching also allows your development team to test the new system architecture before end-users can see it. This strategy allows controlled deployments to pre-determined audiences who are statistically likely to use the microservice. This will enable you to gain vital insights before deployment to production.
Pitfalls of Dark Launching
The biggest potential pitfall with dark launching is that it may release microservices early. Another drawback is that microservices are behind feature toggles, which can lead to increased costs and time for debugging microservices. Additionally, to enable continuous development, teams must be able to move microservices behind feature toggles during development itself.
The staged release deployment strategy for microservices involves gradually releasing microservices to one environment at a time. For example, your development team should first release microservices to the testing environment and later to production.
How the Staged Release Deployment Strategy Reduces Downtime
Don’t enable microservices in production until you know that they’re safe in a testing environment. The staged release strategy incrementally deploys each service. This ensures longer time between failures for each service than if they were simultaneously deployed. It maintains high availability by staggering failures across multiple services over time. This also provides better recovery capabilities.
Pitfalls of Staged Release
In this approach, there may be downtime while microservices get introduced in new environments. Deploying huge batches of change might make it difficult for the development team to diagnose and recover from failures. In addition, your development must deploy all the changes in a single batch. This means any errors will require them to roll back the entire release process for each microservice.
In this article, we went over various microservice deployment strategies for improving availability or reducing downtime. We also discussed various microservice deployment pitfalls.