Shadow Deployments: Benefits, Process, and 4 Tips for Success

What Is Shadow Deployment?

Shadow deployment is an innovative software deployment strategy. It allows an organization to test new software or updates in a production-like environment before going live. 

This strategy involves creating a ‘shadow’ or replica of the live environment, where the new software is deployed and tested under real-world conditions, without impacting the live system. To make the deployment more realistic, production traffic or data is mirrored to the shadow environment, but the outputs or responses are not shown to real users.

This method provides an effective way of identifying and addressing potential issues before rolling out a new version. It allows developers to observe the behavior and performance of the new software under realistic conditions, thereby significantly reducing the risk of unexpected issues once the software is live.

Benefits of Shadow Deployment 

Risk Mitigation

Shadow deployment substantially reduces the risk associated with deploying new software versions by allowing a parallel run in a shadow environment. This means any problematic behavior of the new software can be observed and corrected before it affects actual users. 

The shadow environment acts as a real-time testing ground where unexpected behaviors, bugs, or performance issues can be detected early. Since the mirrored traffic does not impact the live environment, the potential for downtime or user disruption is minimized. This level of risk control is particularly crucial for systems that require high availability and for organizations that cannot afford the reputational damage or financial loss of a failed deployment.

Real-World Testing

One of the key advantages of shadow deployment is the ability to test new software with production traffic. This means the software is subjected to the same conditions it will experience once it goes live, which includes variations in traffic volume, user behavior, and data input. 

This real-world testing is invaluable because it exposes the software to scenarios that may not have been anticipated during the development phase. It’s an effective way to observe the interaction between the new software and other systems or services it communicates with, ensuring compatibility and smooth operation.

Capacity Planning

Shadow deployment can also contribute to better capacity planning of a new software version. By observing the new software’s behavior with actual traffic, it’s possible to measure its resource utilization more accurately. Insights gathered regarding CPU, memory, and storage requirements under real-world conditions allow for better capacity planning. 

Organizations can use this information to optimize resource allocation, ensuring that the software scales efficiently under load. This preparation helps prevent overprovisioning or underprovisioning of resources when the new version is finally deployed to all users.

The Shadow Deployment Process 

Here is the general process involved in carrying out a shadow deployment:

  1. Planning phase: The first step in the shadow deployment strategy involves defining the scope of the deployment, determining the resources required, and setting a timeline for completion. It is important to identify the specific requirements of the deployment, including any necessary hardware and software, and expected traffic loads.
  2. Environment setup: This involves creating a replica of the live system, including the same hardware, software, and network conditions. The shadow environment should be as similar as possible to the live system.
  3. Deployment: Once the shadow environment is ready, the new software version can be deployed. This involves installing the software in the shadow environment and carrying out sanity checks to ensure it is functioning correctly.
  4. Traffic mirroring: This involves duplicating the live system’s traffic and directing it to the shadow environment. This allows the new software to be tested under the same conditions it will face once it is live.
  5. Monitoring and data collection: This final step involves continuously monitoring the software in the shadow environment, collecting data on its performance and behavior. 
  6. Deploying to production environment: Once the team is confident that the software is performing as expected, the new version can be deployed to production.

Learn more in our detailed guide to software deployment process 

Shadow Deployment vs. Other Deployment Strategies 

Blue-Green Deployment

Blue-green deployment is a popular deployment strategy that involves maintaining two identical production environments, known as Blue and Green. The Blue environment is live (serving real-time user traffic), while the Green is idle. When a new version of the software is ready, it’s deployed to the idle environment, and after successful testing, the traffic is switched over.

While both shadow deployment and blue-green deployment involve the use of a clone environment, the key difference lies in how the traffic is handled. In blue-green deployment, all the traffic is switched from the old version to the new one. In contrast, shadow deployment involves mirroring a portion of the real traffic to the shadow environment for testing purposes, without affecting the live environment. In addition, in blue-green deployments, the old version is typically destroyed, while in shadow environments it continues to run.

Canary Deployment

Canary deployment is a deployment strategy where the new version of the software is gradually rolled out to a small set of users before it’s made available to everyone. This allows the team to monitor the performance and gather user feedback before a full-scale rollout.

The primary difference between shadow deployment and canary deployment is the risk factor. While canary deployment exposes the new version to a small set of real users, shadow deployment doesn’t expose the new changes to any real users. The new version is tested with mirrored traffic in a shadow environment, significantly reducing the risk of negative user experiences. 

Another difference is that in a canary deployment, the new version is promoted to 100% of the traffic and the old version is discarded, while in a shadow deployment, the shadow environment continues to run.

Feature Flags

Feature flags, also known as feature toggles, is a technique that allows developers to enable or disable certain features in a software application. This is done without having to deploy or roll back the software, giving the team more control over the features that the users can access.

While feature flags offer greater flexibility in controlling feature access, they don’t provide the comprehensive testing environment that shadow deployment does. With shadow deployment, you can observe the full impact of the new changes on a clone of your production environment.

We should note that it is common to combine feature flags with shadow deployments. It is possible to use them together, for example to enable and disable certain features within the shadow environment. 

Best Practices for Shadow Deployment 

Implementing shadow deployment can be complex, and it’s important to follow certain best practices for maximum effectiveness.

1. Data Protection and Anonymization

When setting up a shadow deployment, ensuring the protection and anonymization of data is paramount. Because this strategy involves the use of real traffic to test new updates, sensitive data could be exposed. Therefore, it’s essential to apply data masking techniques to anonymize user data before it’s used in the shadow environment. 

Anonymization helps in protecting user privacy and complying with data protection regulations like GDPR. Additionally, access to the shadow environment should be tightly controlled and monitored to prevent data breaches.

2. Resource Monitoring

In a shadow deployment, it’s crucial to monitor resource usage continuously to ensure that the shadow environment does not adversely impact the live system’s performance. Tools and systems should be put in place to keep an eye on CPU, memory, disk I/O, and network bandwidth. 

If resource utilization in the shadow environment approaches critical limits, there should be automatic scaling or alerts to prevent any spillover effects on the production environment.

3. Selective Traffic Mirroring

Not all traffic needs to be mirrored to the shadow environment. Selective traffic mirroring involves choosing specific types of traffic or requests that are most relevant to the changes being tested. This approach can reduce the load on the shadow system and focus on the most critical or high-risk areas. 

For example, if an update pertains to a checkout process, only mirroring traffic related to transactions may be necessary.

4. Fail-Safe Mechanisms

To prevent any accidental impact on the live system, fail-safe mechanisms should be an integral part of shadow deployments. These mechanisms include the ability to quickly divert or stop the mirrored traffic, automatic rollback capabilities if issues are detected, and real-time monitoring with alerts sent to relevant staff. 

5. Iterative Testing

Shadow deployment should be an ongoing process, enabling iterative testing. This involves deploying changes incrementally and continuously observing their effects in the shadow environment. 

The feedback and data collected from each iteration are used to improve the update, leading to a more stable and reliable deployment when the changes are finally released to the live environment. The iterative approach also allows for a more manageable workload for the development team and a smoother transition to production.

Advanced Progressive Delivery in Kubernetes with Argo Rollouts and Codefresh

Codefresh offers advanced progressive delivery methods, including blue green and canary deployment, by leveraging Argo Rollouts, a project specifically designed for gradual deployments to Kubernetes.

Through Argo Rollouts, Codefresh can perform advanced canary deployments that support:

  • Declarative configuration – all aspects of the blue/green deployment are defined in code and checked into a Git repository, supporting a GitOps process.
  • Pausing and resuming – pausing a deployment and resuming it after user-defined tests have succeeded.
  • Advanced traffic switching – leveraging methods that take advantage of service meshes available on the Kubernetes cluster.
  • Verifying new version – creating a preview service that can be used to verify the new release (i.e smoke testing before the traffic switch takes place).
  • Improved utilization – leveraging anti-affinity rules for better cluster utilization to avoid wasted resources in a canary deployment.
  • Easy management of the rollout – view status and manage the deployment via the new Applications Dashboard.

Learn more about the Codefresh platform

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