Rolling deployment vs smoke testing: Choosing a deployment strategy

Rolling deployment

Rolling deployment is a gradual deployment strategy where new application versions are released incrementally across server instances or containers. Instead of updating all servers simultaneously, the deployment process systematically replaces old instances with new ones, typically updating a small percentage at a time. This approach maintains service availability throughout the deployment process since some instances continue running the previous version while others are being updated.

The rolling deployment strategy provides built-in rollback capabilities and reduces deployment risk by limiting the blast radius of potential issues. If problems arise during deployment, the process can be halted and rolled back before affecting all users. This method is particularly effective for applications with load balancers that can automatically route traffic away from instances being updated, ensuring seamless user experience during deployments.

Smoke test

Smoke testing is a deployment validation strategy that involves running a limited set of critical tests immediately after deployment to verify basic functionality. These tests focus on core application features and system health rather than comprehensive functionality, providing quick feedback about whether the deployment was successful. The term “smoke test” originates from hardware testing, where powering on a device and checking for smoke indicated basic operational status.

In deployment contexts, smoke tests serve as an early warning system to catch major issues before they impact users significantly. These tests typically include health checks, basic API functionality verification, database connectivity tests, and critical user journey validation. If smoke tests fail, automated systems can trigger immediate rollbacks or alerts, preventing faulty deployments from remaining in production environments.

Comparison

Deployment approach

• Rolling Deployment: Gradually replaces instances over time to maintain availability

• Smoke Test: Validates deployment quality through immediate post-deployment testing

Risk management

• Rolling Deployment: Limits risk by exposing only a subset of users to changes at any time

• Smoke Test: Identifies critical issues quickly before they affect large user populations

Rollback strategy

• Rolling Deployment: Can halt and reverse the deployment process mid-way through execution

• Smoke Test: Triggers immediate rollback when critical functionality failures are detected

Implementation complexity

• Rolling Deployment: Requires orchestration tools and load balancer configuration for seamless traffic management

• Smoke Test: Needs automated test suites and monitoring systems integrated with deployment pipelines

Time to complete

• Rolling Deployment: Takes longer due to gradual instance replacement across infrastructure

• Smoke Test: Completes quickly with focused testing of essential application functions

Feature flags integration

Feature flags complement rolling deployments by providing an additional layer of control over feature exposure during gradual rollouts. As new instances are deployed with updated code, feature flags can selectively enable or disable specific functionality for different user segments, allowing teams to decouple deployment from feature release. This combination enables more granular control over the user experience, where new features can be deployed to all instances but only activated for specific user groups or regions, reducing risk even further than rolling deployment alone.

When combined with smoke testing, feature flags serve as a safety mechanism that allows teams to quickly disable problematic features without requiring full rollbacks. If smoke tests detect issues with specific functionality after deployment, feature flags can immediately turn off the affected features while keeping the rest of the application operational. This approach maintains system stability while providing development teams time to investigate and fix issues, making the deployment process more resilient and recovery more surgical than traditional all-or-nothing rollback approaches.

Rolling Deployment offers the advantage of zero downtime by gradually updating instances while maintaining service availability, making it ideal for production environments where continuous operation is critical. It provides natural rollback capabilities and allows for real-time monitoring of the deployment’s impact across different server instances. However, rolling deployments can be complex to manage, especially when database schema changes are involved, and they may result in temporary inconsistencies as different versions run simultaneously. The deployment process is also slower since updates happen incrementally, and troubleshooting issues becomes more challenging when multiple versions coexist.

Smoke Test Deployment excels in risk mitigation by deploying to a small subset of production traffic first, allowing teams to validate critical functionality with real user data before full rollout. This strategy provides excellent visibility into potential issues and enables quick rollback decisions based on actual performance metrics. However, it requires sophisticated traffic routing mechanisms and monitoring infrastructure to be effective. The approach also introduces complexity in managing multiple environments simultaneously and may delay full feature availability to all users. Choose rolling deployment when you need guaranteed uptime and have confidence in your testing, while smoke test deployment is preferable when you’re introducing significant changes or working with critical systems where early issue detection is paramount.

What is an example of a rolling deployment strategy?

A rolling deployment strategy involves gradually updating server instances or containers one at a time or in small batches while maintaining service availability. For example, if you have 10 application servers, you might update 2 servers at a time while the remaining 8 continue serving traffic. The load balancer automatically routes traffic away from the instances being updated to those still running the previous version. This process continues systematically until all instances have been updated to the new version, ensuring zero downtime throughout the deployment.

How does rolling deployment compare to canary deployment?

Rolling deployment updates instances gradually across your entire infrastructure, replacing old versions with new ones systematically until all servers are updated. Canary deployment, on the other hand, releases the new version to only a small percentage of users or servers initially, monitoring performance and user feedback before deciding whether to proceed with a full rollout. While rolling deployment focuses on maintaining availability during the update process, canary deployment emphasizes risk validation by testing with limited exposure before broader release.

What are the differences between rolling deployment and blue-green deployment?

Rolling deployment gradually replaces instances over time, meaning both old and new versions run simultaneously during the deployment process. Blue-green deployment maintains two identical production environments – when deploying, traffic is switched entirely from the current environment (blue) to the new environment (green) all at once. Rolling deployment offers continuous availability with incremental updates but can have temporary version inconsistencies, while blue-green deployment provides instant rollback capabilities and version consistency but requires double the infrastructure resources.

How do rolling deployment, blue-green, and canary deployment strategies differ?

These three strategies differ primarily in their approach to risk management and traffic distribution. Rolling deployment gradually updates all instances while maintaining service availability, suitable for applications requiring zero downtime. Blue-green deployment switches traffic between two complete environments instantly, providing quick rollback but requiring duplicate infrastructure. Canary deployment releases to a small subset of users first for validation before broader rollout, offering excellent risk mitigation but potentially slower full deployment. Rolling focuses on availability, blue-green on instant switching, and canary on gradual risk validation.

What is the recreate deployment strategy in software deployment?

The recreate deployment strategy involves completely shutting down the existing application version before deploying the new version. This approach terminates all running instances of the current version, deploys the new version, and then starts the updated application. While this strategy is simple to implement and avoids version compatibility issues since only one version runs at a time, it results in downtime during the deployment process. Recreate deployment is typically used for applications where downtime is acceptable or in development environments where simplicity is preferred over availability.