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.

Step 1 of 7

14%

Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

Multiple separate tools for different environments

Multiple separate tools for different monitoring and management functions

Single consolidated dashboard

Custom built monitoring and management solution

No full monitoring and management solution

Don’t know

How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

Extremely valuable

Moderately valuable

Somewhat valuable

Slightly valuable

Not valuable

How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

Don’t know

Other

What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

Automated cost monitoring tools

Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

Native financial operations tools provided by the cloud provider

No formal controls in place

Don’t know

Other

Please specify:

Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

Automated scanning tools

Manual assessments

Vendor notifications

Threat modeling (for early detection)

None of these

Don’t know

Other

Please specify:

What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

Based on CVSS scores

Risk-based analysis of multiple factors

First-in-first-out approach

No formal prioritization process

Don’t know

How do you typically handle system updates and security patches?(Required)

Automated updates and patches

Manually review and apply updates and patches on a regular schedule

A combination of automation and manual review

Apply updates and patches on an as-needed basis.

Outsourced update and patch management

No defined process

Don’t know

Other

Δ

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.

Benefits of Shadow Deployment

Risk Mitigation

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.

Real-World Testing

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.

Efficient Scaling

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.

Use Cases for Shadow Deployment

Performance Testing

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.

Application Migration

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.

Service Dependency Validation

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.

Testing New Plugins or Extensions

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.

Shadow Deployment Vs. Other Deployment Strategies

Let’s compare the practice of shadow deployment with some of the other common deployment strategies used in the industry.

Blue-Green Deployment

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

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

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.

Best Practices for Shadow Deployment

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.

Isolate Real and Shadow Systems

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.

Set Clear Duration and End Criteria

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.

Set Up Traffic Duplication Efficiently

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.

Prioritize Feedback Loops

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.

Conclusion

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.