Deployment Rollback Strategies

We reviewed 13 CI/CD pipelines. 11 had ZERO rollback strategy. Let’s be blunt. That’s not CI/CD. That’s gambling with production. In the last 6 months, my team at Infra360.io reviewed 13 production-grade pipelines. 🚫 No versioned artifacts. 🚫 No traffic shifting. 🚫 No automated rollback triggers. 🚫 No database rollback plans. Just blind confidence that every release would “somehow” work. Here are the real gaps nobody talks about: 1. 𝐀𝐫𝐭𝐢𝐟𝐚𝐜𝐭𝐬 𝐀𝐫𝐞 𝐍𝐎𝐓 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞   → Teams rebuild during rollback, introducing new variables. Pro Tip: Use artifact repositories like Artifactory or ECR. Your rollback should be a redeploy, not a rebuild. 2. 𝐙𝐞𝐫𝐨 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐏𝐥𝐚𝐜𝐞 → One bad deploy and 100% of traffic hits it. Pro Tip: Implement blue/green or canary rollouts with Argo Rollouts or Flagger. Control exposure like a pro. 3. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 𝐀𝐫𝐞 𝐎𝐧𝐞-𝐖𝐚𝐲 𝐓𝐫𝐢𝐩𝐬 → Code rollback is useless if schema changes can’t roll back. Pro Tip: Integrate Flyway or Liquibase for proper schema versioning and rollback scripts. 4. 𝐍𝐨 𝐂𝐨𝐧𝐟𝐢𝐠 𝐚𝐧𝐝 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠 → Rollback happens but config stays broken. Pro Tip: Use GitOps to version everything—including configs and secrets. 5. 𝐑𝐨𝐥𝐥𝐛𝐚𝐜𝐤 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐬 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐞𝐧𝐭𝐢𝐨𝐧 → And that usually happens at 2 AM, under pressure. Pro Tip: Automate rollback triggers based on SLO breaches, error rates, and health checks. If you can’t undo a deployment in under 60 seconds, your pipeline isn’t fast. It’s dangerous. Fast delivery means nothing without fast recovery. Would you trust your last deploy to auto-recover? ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐨 𝐎𝐭𝐡𝐞𝐫𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧.

This morning, much of the world woke up to the dreaded BSOD (Blue Screen of Death), causing a global outage of IT systems due to a single content update from CrowdStrike. Having worked with deployment strategies in the past at large organizations like Mercedes and even within our startup, I've always ensured we utilized one of these rollout strategies: Canary Releases: Select a subset of users as "canaries" and deploy the update to them. Monitor KPIs, errors, and performance for any issues. If the canaries do not encounter problems, gradually move into a general availability (GA) release. In some cases, a canary release can be turned into a phased rollout strategy for extremely risky deployments. Rolling Deployments (Phased Rollouts): This is the one I've always favored since it's easier to automate. You gradually and incrementally replace older versions of your application. You can follow a linear, exponential, or logarithmic release path. You still reap some of the benefits of the canary process through a phased approach, buying you lead time to catch and fix errors. Blue-Green Deployments: This is the strategy we use here at Tara AI. We maintain two identical environments. All users are routed to the blue environment. The new version goes to green, where it undergoes thorough testing. Once we have the all-clear, traffic is switched over to the green environment, and the blue is archived. There is zero downtime and granular rollback capability. Some other steps we would take during any updates to our customers: - There was always a documented rollback plan. We documented everything from the version to the estimated recovery time and probable SLA impact. - We listed known and unknown risks right before deploying to customers. Often, organizations are fully aware of what they're doing; someone just forgets to communicate key information. - We used multi-stage CI/CD pipelines with fail-safes that checked core vitals. This slowed our releases but ensured data integrity, customer experience, and performance. - We over-communicated rollout updates. During rollouts, communication was constant with key stakeholders.

My first two instincts to mitigate an incident were wrong! During my on-call, I saw someone posting in our slack channel saying they are not able to submit the form, and they also don’t see any errors in UI. I looked at the recording, reproduced the issue on my browser and then jumped into solving it..that was my first mistake. As a developer we are always eager to fix the bugs as soon as we see them. We immediately start thinking where it went wrong, what file and what function is culprit. I knew it would be just a single line that needs a change, and I can fix the issue in few minutes. In just a moment I realized that I shouldn’t fix the issue, but rather find a working commit and deploy to production as soon as possible. Why? Mitigation takes precedence over resolution. The first focus should be - how can I bring my system back to normal? And that doesn’t always mean fixing the root cause, but could just be deploying a commit that’s working normally. I saw a recent commit which I hypothesized to be culprit. So I took a commit before that and verified that UI form works as expected. I immediately reached out to deployment team and asked them to deploy that “working” commit. That was my second mistake. While I was reporting my steps in slack, my skip manager asked me - “why not rollback the latest release?”. I realized this would be faster mitigation than making a hot fix. I learned that frontend deployments could be rolled back to earlier version with just a runtime change. And with that, it took us just 60 seconds to rollback our production version to previous release. Lesson? Your first line of defense should always be rollback, if possible. Roughly 50% of incidents are attributed to bad deployment, and rollback is the fastest mitigation step. When rollback doesn’t work? - What if the latest release had a fix to a larger incident? When you rollback that release, you are bringing back that larger incident. - Some infrastructure deployments are not that easy to rollback. Rollback might not bring your system to exact state as it was before the forward deployment.

Post 13: Real-Time Cloud & DevOps Scenario Scenario: Your organization hosts a critical application on AWS Elastic Beanstalk. Recently, the application experienced downtime due to an untested update that caused compatibility issues. The rollback process took longer than expected, resulting in customer complaints. As a DevOps engineer, your task is to implement a robust deployment strategy and minimize downtime for future updates. Step-by-Step Solution: Adopt Blue/Green Deployments: Deploy the updated version of the application to a separate environment while keeping the existing environment live. Once verified, switch traffic to the updated environment using Elastic Beanstalk Swap CNAMEs. Rollback becomes simple by reverting the CNAME to the previous environment. Implement Canary Deployments: Gradually route a small percentage of traffic to the new version using tools like AWS App Runner or AWS CodeDeploy. Monitor performance and rollback if issues are detected during the initial phase. Set Up Pre-Deployment Testing: Automate integration and smoke tests using AWS CodePipeline and CodeBuild to ensure updates pass all tests before deployment. Integrate tests into the Elastic Beanstalk deployment pipeline. Enable Application Health Monitoring: Configure Elastic Beanstalk’s health checks to detect and alert on degraded performance after deployment. Use CloudWatch Alarms to trigger notifications for anomalies. Use Immutable Deployments: Choose immutable updates in Elastic Beanstalk to deploy the new version on a fresh set of instances. This ensures the old version remains untouched during the update process. Leverage Deployment Policies: Configure deployment settings in Elastic Beanstalk: All at Once: Quick but risky; use only for non-critical updates. Rolling: Updates instances in batches, balancing risk and speed. Rolling with Additional Batch: Adds a new batch to minimize downtime. Immutable: Creates a completely new environment. Automate Rollbacks: Use AWS CodeDeploy Automatic Rollbacks for Elastic Beanstalk to revert to the previous version if deployment health checks fail. Define failure thresholds for automatic rollback triggers. Document and Train: Document the deployment process and conduct regular training sessions for the team to ensure smooth updates. Perform mock scenarios to practice rollbacks and disaster recovery. Outcome: Improved deployment reliability with minimal downtime and faster rollback mechanisms. Enhanced customer satisfaction through consistent application availability. 💬 What deployment strategies have worked best for your teams? Let’s exchange ideas in the comments! ✅ Follow Thiruppathi Ayyavoo for daily real-time scenarios in Cloud and DevOps. Let’s grow and innovate together! #DevOps #AWS #ElasticBeanstalk #CloudComputing #BlueGreenDeployment #CanaryDeployment #RealTimeScenarios #CloudEngineering #TechSolutions #LinkedInLearning #careerbytecode #thirucloud #linkedin #USA CareerByteCode

Title: "Implementing Blue/Green Deployments in Amazon ECS with Amazon CodeCatalyst: A High Level Overview" Blue/Green deployments are a method for releasing applications by shifting traffic between two identical environments that are running different versions of an application. In the context of Amazon ECS (Elastic Container Service) and Amazon CodeCatalyst, this process typically involves: 1. Setting up two environments (Blue and Green): These are usually two separate but identical setups of your application. The Blue environment might be running your current production version, while the Green environment is set up with the new version of the application. 2. Deploying the new version on the Green environment: Using Amazon ECS, you deploy the new version of your application in containers. This environment is a replica of your current production environment (Blue). 3. Testing the Green environment: Before making the new version live, it is thoroughly tested to ensure it meets all quality and performance benchmarks. 4. Switching traffic from Blue to Green: Once you are satisfied with the new version in the Green environment, you can switch the traffic from the Blue environment to the Green environment. This is often achieved through DNS routing or a load balancer. 5. Monitoring the Green environment: After the switch, it's crucial to monitor the new production environment for any unforeseen issues. 6. Rollback if needed: If something goes wrong, you can quickly revert to the Blue environment since it is still running the old, stable version of the application. 7. Cleanup: Once you're confident that the Green environment is stable, you can decommission the old Blue environment. Amazon CodeCatalyst, being a comprehensive suite for CI/CD, can automate much of this process. You can set up pipelines in CodeCatalyst to build, test, and deploy your applications, managing the complexity of Blue/Green deployments more efficiently. This automation ensures minimal downtime and provides a safer mechanism to roll out updates and new features.