Shadow deployment, at its core, is a software deployment practice where any changes to a software application are deployed in a parallel environment that mimics the production environment. The deployed changes are not visible to the end-users, hence the term “shadow.” This hidden deployment enables us to observe the behavior and impact of the changes without causing any disruption to the live application.
The primary purpose of shadow deployment is to test real-world application behavior under load, identify performance bottlenecks, and ensure that the application changes don’t affect the user experience negatively. It allows developers to test changes in an environment that closely replicates the live production environment, thereby reducing the risk of unforeseen issues cropping up post-deployment.
In essence, shadow deployment is akin to having a rehearsal before the actual performance. It provides a safety net to developers and aids in ensuring a smooth transition when the changes are finally deployed to the live environment.
One of the most significant benefits of shadow deployment is risk mitigation. In traditional deployment methodologies, code changes are often deployed directly to production, which may lead to unforeseen issues and disruptions.
With shadow deployment, the risk of such disruptions is significantly reduced as the changes are first deployed in a parallel environment. This allows for thorough testing and validation, ensuring any bugs or performance issues are identified and rectified before the changes hit the production environment. This is also useful for DevSecOps teams, as it can help them understand the security impact of a new deployment without exposing it to attackers.
Shadow deployment also offers the benefit of rollback. If any critical issue is identified during the shadow deployment, the changes can be rolled back without affecting the live environment. This provides a safety net to developers and businesses, ensuring that the user experience is not compromised.
Another significant advantage of shadow deployment is that it allows for real-world testing. In traditional testing methodologies, simulated data is used, which may not accurately represent the live environment. However, in shadow deployment, the parallel environment mirrors the live environment, ensuring testing is done under real-world conditions.
This offers a more accurate representation of how the changes will perform when deployed to production. It helps identify issues that might not be apparent in a simulated testing environment, ensuring a more robust and reliable deployment.
Finally, shadow deployment aids in efficient scaling. In today’s digital landscape, applications need to be scalable to handle varying loads. Shadow deployment enables developers to test the scalability of their changes in a real-world environment, thereby ensuring efficient scaling.
It allows for the testing of various aspects of scaling, such as database scaling, server load balancing and more. This ensures that when the changes are finally deployed to production, the application can efficiently scale based on user demand.
One of the primary use cases of shadow deployment is performance testing. It allows developers to test the performance of their changes under real-world conditions. This includes testing aspects such as response times, server loads, database queries and more. The insights gained from this testing can be used to optimize the changes, ensuring optimal performance when deployed to production.
Shadow deployment is also widely used during application migration. When migrating an application from one platform or technology stack to another, it is crucial to ensure that the migration does not negatively impact the user experience. Shadow deployment allows for the testing of the migrated application in a parallel environment, ensuring that any issues are identified and rectified before the migration is made live.
In today’s microservices architecture, applications often depend on various services to function correctly. Shadow deployment can be used to validate these service dependencies. It allows developers to test if their changes are compatible with the dependent services, ensuring smooth interoperability when deployed to production.
Finally, shadow deployment can be used to test new plugins or extensions. Plugins and extensions often add new functionalities to an application, and it is crucial to ensure that these additions do not impact the application negatively. Shadow deployment allows for the testing of these plugins and extensions in a parallel environment, ensuring that they work as expected when deployed to production.
Let’s compare the practice of shadow deployment with some of the other common deployment strategies used in the industry.
Blue-green deployment is a strategy that reduces downtime and risk by running two identical production environments. The blue environment is live, while the green environment is idle. When a new version of the application is ready, it’s deployed to the idle environment, tested and, once everything is confirmed to be working, traffic is switched from the blue to the green environment. This approach allows for instant rollback if issues arise.
Shadow deployment, on the other hand, takes a slightly different approach. It involves deploying a new version of the application alongside the live version and sending the same input to both. The new version doesn’t affect the live system, and its output is discarded. This strategy allows for extensive testing and performance comparisons without any risk to the live system.
Canary deployment is another strategy that involves rolling out changes to a small subset of users before deploying it to the entire infrastructure. This strategy allows teams to test the performance of new features on a small scale before a full-scale rollout.
In contrast, shadow deployment does not expose the new version to any users. Instead, it uses real production traffic to test the new version, discarding its output to ensure it doesn’t affect the live system. This allows for full-scale testing without risking user experience.
Feature flags, also known as feature toggles, allow developers to switch on or off certain features in their applications. This helps manage the rollout of new features and enables easy rollback if things go wrong.
Shadow deployment, however, focuses more on system performance rather than individual features. It uses duplicate traffic to test the entire system, providing valuable insights into how the new version will perform under real-world conditions.
Now that we understand how shadow deployment fits into the broader landscape of deployment strategies, let’s delve into the best practices to follow when implementing this strategy.
One of the foundational best practices for shadow deployment is the isolation of the real and shadow systems. This ensures that any problems in the shadow system do not affect the real one. The goal is to test how the new version of the software performs under real-world conditions without impacting the live system.
When setting up shadow deployment, it’s crucial to define clear duration and end criteria. This involves determining how long the shadow system will run alongside the real system and what conditions will mark the end of the testing phase. This helps in maintaining control over the process and ensures that you gather the necessary data to make informed decisions.
Efficient traffic duplication is another critical aspect of shadow deployment. This involves mirroring the traffic from the real system to the shadow system in real-time. It’s essential to ensure that this process is efficient to get accurate performance data without overloading your network resources.
Last but not least, prioritizing feedback loops is crucial in shadow deployment. This involves setting up mechanisms to gather and analyze data from the shadow system. The insights gleaned from this data can provide valuable feedback, helping you refine your deployment process and improve your system performance.
In conclusion, shadow deployment offers a unique approach to software deployment. By allowing for extensive testing under real-world conditions without affecting the live system, it provides invaluable insights into system performance. By understanding its benefits and following best practices, you can leverage this strategy to streamline your deployment process and improve your software systems.
Venkat Ramakrishnan talks with Mitch Ashley about platform engineering from Kubecon + CloudNativeCon 2023.
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