Modern standards for software development and user experience require developers to update their projects constantly. Deployment and integration have become continuous operations — a modern application is deployed daily. This is why having a working deployment technique is now more important than ever.
Today, systems are more flexible and scalable. They often use Cloud infrastructure, serverless approach, or microservices to offer a more adaptive functionality. There are many development teams involved in the process, and such diversity can make deployment tricky. In this article, you’ll find out how to deploy an application using the most viable application deployment strategies.
#1 Recreate deployment model
This is a standard deployment strategy example that requires developers to scale the previous version of the software before the app’s deployment. First, the developers shut down the initial version of the app to upload a new update. After the first version is scaled to zero and can be removed, the team uploads and deploys the next version. Every process is handled one at a time — it’s the main pattern requirement.
The recreate deployment strategy allows developers to deal with one scaling process at a time because it doesn’t allow parallel deployment. Therefore, the codebase is always organized and developers have full visibility of their workflows.
On the other hand, the app has to stop running temporarily. An active software can’t be deployed with this method. For critical applications that need to be available 24/7 (like medical platforms or server software), this strategy of deploying applications is not a good fit.
#2 Rolling deployment
With rolling updates, developers can upload several versions simultaneously — the number of active ones is called a window side. Developers can upload one instance at a time (setting up window size to 1) or deploying apps in simultaneous updates in clusters. For faster and safer deployment, developers use containers. Containers application deployment include Docker and Kubernetes, which are popular tools to isolate updated versions, enabling and disabling servers, and tracking changes.
A rolling update deployment pattern allows continuous deployment in the active application. The strategy reduces app downtime, which is crucial for critical applications.
Deployment teams can decide which window sizes will provide the highest efficiency and visibility. Developers can redirect traffic from the updated targets before the instance is ready to accept users, avoiding security and performance issues.
If a team updates too many instances at a time, they risk losing visibility of their deployment workflow. It’s better to start with smaller window sizes, gradually increasing the number of processed updates. Also, it’s important to set up the algorithms in which users are routed to old and updated instances not to cause compatibility issues of old and new versions.
#3 Blue/green deployment pattern
A blue/green pattern is a combined approach between a one-at-a-time and simultaneous approach to the Cloud application deployment. Developers run two versions of the application: the existing instance is blue and green is the updated one. They can choose which version is live, and keep another in a testing mode. Both versions are technically active, but only one is public (usually the blue one). After developers finish testing the new application’s edition, they redirect the traffic to the green zone.
The main distinction of this application deployment evaluation cycle from other strategies is that you can save the blue environment. The team is always free to execute a rollback or use the previous functionality to work on future updates.
This software deployment strategy allows switching between old and updated versions seamlessly. Developers use load balancers to redirect traffic streams between the blue and green editions. In this way, the deployment team minimizes the risks of downtimes.
If something goes wrong with uploading the green version, developers can always roll back to the blue one. Because both are separated, there are no risks of one version having a negative impact on another.
Even though downtime risks are low, the deployment team should make sure they use compatible data formats and stores to seamlessly shift between the blue and green environments.
Often developers need to run two versions of the application simultaneously which leads to the increase in storage space, computing power, hardware costs, etc.
#4 Canary deployment
Canary deployment and tests allow publishing features one by one, not the full update versions. Developers are keeping the previous version active and compare the performance of the updated instance to the original one.
The canary application deployment checklist is the following:
- Developers upload the new version (Version B) to the server.
- The majority of users are still redirected to Version A, while only a small portion interacts with Version B. Thus, developers can monitor performance and make improvements in Version B.
- Having made the necessary changes in Version B, developers redirect the majority of traffic to the updated instance. The team measures the performance and compares it with the original version.
- When Version B is stable enough, developers can cut off Version A and fully redirect the traffic to the updated codebase.
For canary deployment to be successful, developers have to set up clear performance metrics. The baseline version (the original one) and the canary version (the updated one) should be deployed simultaneously and under the same conditions (caching, connections, hash) for objective analysis.
Thanks to the live performance testing, developers can reduce deployment risks and create a better user experience. There are two app versions working, so users don’t experience downtimes. Moreover, the canary version receives enough traffic to provide an objective picture of its load-handling capacities and performance speed.
One of the main drawbacks of the canary method is its time-consuming nature. Canary testing and deployment is done in several stages and requires time for thorough monitoring and evaluation. Because of this, not all users will be able to benefit from the new features and upgrades right away.
As in many deployment strategies with both versions running simultaneously, developers need to keep in mind and ensure the compatibility of tech stack and databases.
#5 Shadow deployment
This is a strategy where both versions are active simultaneously. Version A, the older one, is initially presented to the user and is the one accepting the input. Version B receives traffic not directly from the load balancer but from Version A. This way, the risks of wrong redirects or poor performance are minimized — the operation is partially executed by the older version. Even if Version B fails, A can step in.
However, running operations through two versions prolongs the response time. You need to test the communication between A and B and minimize the delays.
How to execute a shadow strategy for software deployment:
- Application-level implementations. Developers write functions that send input both to a current and a new version of the application. Inputs and outputs can be processed at the same time or asynchronously in a queue for higher precision. The team can choose to divide inputs on the client-side, setting up distinctive API targets in a browser or mobile device.
- Infrastructure level implementations. Developers configure a load balancer to the fork format, supporting endpoints of both versions. Developers need to make sure that there is no duplication; the app must never request the same data twice. Version B should receive the user’s information from the first one; otherwise, users will have to enter payment or registration data twice.
- Shadow mode results evaluation. Teams pay attention to the data errors, compare the performance of both versions, check if the second version accepted the input from the first one correctly.
Thanks to real-time user and performance testing it’s easier for developers to achieve higher stability. If the new version performs poorly, the older one will back it. As users interact with both the tried-and-proven old version and the updated one, there is no impact on production by faults in services that process shadow data.
The fact that both Versions A and B run simultaneously doubles operations, requests, and uses twice as much computing power, storage space, and servers. Consequently, the communication between the two versions takes a long time to set up and test. As a result, the outcome will not be 100% accurate as there’s still an involvement from the original version.
Transferring to serverless engineering is an answer for established organisations and new businesses. It’s a method to lessen costs, improve item quality, abbreviate time to market, and spotlight on advancement. It is anything but a simple move — going serverless requires examination and mindfulness. This is why we at TechMagic always select a deployment strategy concerning the needs of the product. Small non-critical services can be deployed with a recreate pattern, whereas 24/7 platforms need a complex, no-downtime approach. Contact our experts to find out which deployment strategy works best for your software.