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𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 - 𝐀 𝐝𝐞𝐞𝐩 𝐝𝐢𝐯𝐞 💡 Deployment strategies offer a unique way to perform application updates with minimal downtime within a #kubernetes cluster. Let's break them down with pros & cons 👉 𝗥𝗲𝗰𝗿𝗲𝗮𝘁𝗲 - All existing instances are terminated at once and new instances with the updated version are created 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Totally renewed applicate state is achieved 𝑪𝒐𝒏𝒔 ❗ High impact on availability, downtime during shutdown and booting process 👉 𝗥𝗼𝗹𝗹𝗶𝗻𝗴 𝗨𝗽𝗱𝗮𝘁𝗲 - Application instances are updated one by one, ensuring high availability during the process 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Slow release of new version across instances ✅ Ideal for stateful applications which handle rebalancing of the data 𝑪𝒐𝒏𝒔 ❗ Support to multiple APIs is tough. 👉 𝗦𝗵𝗮𝗱𝗼𝘄 - A copy of the live traffic is redirected to the new version for testing without affecting production users. This is the most complex deployment strategy and involves establishing mock services to interact with the new version of the deployment 𝑷𝒓𝒐𝒔 ✅ Testing application performance in production. ✅ User is not aware of the impact. ✅ No rollout is done until performance and stability of new version is not established or met. 𝑪𝒐𝒏𝒔 ❗ Expensive to maintain dual resources ❗ Complex in setup 👉 𝗖𝗮𝗻𝗮𝗿𝘆 - The new version is released to a subset of users or servers for testing before broader deployment. Canary deployment requires two identical ReplicaSets one to roll out new features to a small group of users and another for all active users. Progressively the new version is pushed to the entire infrastructure and until canary version becomes the new version all live traffic is directed to canaries 𝑷𝒓𝒐𝒔 ✅ Version is released for a group of users. ✅ Ideal for performance monitoring and load. ✅ Rollback is fast. 𝑪𝒐𝒏𝒔 ❗Rollout is slow 👉 𝗕𝗹𝘂𝗲-𝗚𝗿𝗲𝗲𝗻 - - Two identical environments are maintained: one with the current version (blue) and the other with the updated version (green) - Traffic starts with blue, then switches to the prepared green environment for the updated version 𝑷𝒓𝒐𝒔 ✅ Instant rollback possible. ✅ Entire application state changes in one go hence avoids versioning issue. 𝑪𝒐𝒏𝒔 ❗Expensive as both versions required to be maintained ❗Testing of entire platform is required before production release ❗Stateful applications are hard to handle in this strategy 👉 𝗔/𝗕 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 - Multiple versions are concurrently tested on different users to compare performance or user experience 𝑷𝒓𝒐𝒔 ✅ Parallel running of several versions. ✅ Traffic distribution is fully controlled. 𝑪𝒐𝒏𝒔 ❗Intelligent load balancer is required. ❗Tough to troubleshoot errors in session, distributed tracing is required. #deployment #planning #devops #devsecops #infra #platform #paas #architect #consulting #solution #developer

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 - 𝐀 𝐝𝐞𝐞𝐩 𝐝𝐢𝐯𝐞 💡 Deployment strategies offer a unique way to perform application updates with minimal downtime within a #kubernetes cluster. Let's break them down with pros & cons 👉 𝗥𝗲𝗰𝗿𝗲𝗮𝘁𝗲 - All existing instances are terminated at once and new instances with the updated version are created 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Totally renewed applicate state is achieved 𝑪𝒐𝒏𝒔 ❗ High impact on availability, downtime during shutdown and booting process 👉 𝗥𝗼𝗹𝗹𝗶𝗻𝗴 𝗨𝗽𝗱𝗮𝘁𝗲 - Application instances are updated one by one, ensuring high availability during the process 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Slow release of new version across instances ✅ Ideal for stateful applications which handle rebalancing of the data 𝑪𝒐𝒏𝒔 ❗ Support to multiple APIs is tough. 👉 𝗦𝗵𝗮𝗱𝗼𝘄 - A copy of the live traffic is redirected to the new version for testing without affecting production users. This is the most complex deployment strategy and involves establishing mock services to interact with the new version of the deployment 𝑷𝒓𝒐𝒔 ✅ Testing application performance in production. ✅ User is not aware of the impact. ✅ No rollout is done until performance and stability of new version is not established or met. 𝑪𝒐𝒏𝒔 ❗ Expensive to maintain dual resources ❗ Complex in setup 👉 𝗖𝗮𝗻𝗮𝗿𝘆 - The new version is released to a subset of users or servers for testing before broader deployment. Canary deployment requires two identical ReplicaSets one to roll out new features to a small group of users and another for all active users. Progressively the new version is pushed to the entire infrastructure and until canary version becomes the new version all live traffic is directed to canaries 𝑷𝒓𝒐𝒔 ✅ Version is released for a group of users. ✅ Ideal for performance monitoring and load. ✅ Rollback is fast. 𝑪𝒐𝒏𝒔 ❗Rollout is slow 👉 𝗕𝗹𝘂𝗲-𝗚𝗿𝗲𝗲𝗻 - - Two identical environments are maintained: one with the current version (blue) and the other with the updated version (green) - Traffic starts with blue, then switches to the prepared green environment for the updated version 𝑷𝒓𝒐𝒔 ✅ Instant rollback possible. ✅ Entire application state changes in one go hence avoids versioning issue. 𝑪𝒐𝒏𝒔 ❗Expensive as both versions required to be maintained ❗Testing of entire platform is required before production release ❗Stateful applications are hard to handle in this strategy 👉 𝗔/𝗕 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 - Multiple versions are concurrently tested on different users to compare performance or user experience 𝑷𝒓𝒐𝒔 ✅ Parallel running of several versions. ✅ Traffic distribution is fully controlled. 𝑪𝒐𝒏𝒔 ❗Intelligent load balancer is required. ❗Tough to troubleshoot errors in session, distributed tracing is required.

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 - 𝐀 𝐜𝐨𝐦𝐩𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 🤔 💡 Deployment strategies offer a unique way to perform application updates with minimal downtime within a #kubernetes cluster. 𝑳𝒆𝒕'𝒔 𝒃𝒓𝒆𝒂𝒌 𝒕𝒉𝒆𝒎 𝒅𝒐𝒘𝒏 𝒘𝒊𝒕𝒉 𝒑𝒓𝒐𝒔 & 𝒄𝒐𝒏𝒔: 👉 𝗥𝗲𝗰𝗿𝗲𝗮𝘁𝗲 - All existing instances are terminated at once and new instances with the updated version are created 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Totally renewed applicate state is achieved 𝑪𝒐𝒏𝒔 ❗ High impact on availability, downtime during shutdown and booting process 👉 𝗥𝗼𝗹𝗹𝗶𝗻𝗴 𝗨𝗽𝗱𝗮𝘁𝗲 - Application instances are updated one by one, ensuring high availability during the process 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Slow release of new version across instances ✅ Ideal for stateful applications which handle rebalancing of the data 𝑪𝒐𝒏𝒔 ❗ Support to multiple APIs is tough. 👉 𝗦𝗵𝗮𝗱𝗼𝘄 - A copy of the live traffic is redirected to the new version for testing without affecting production users. This is the most complex deployment strategy and involves establishing mock services to interact with the new version of the deployment 𝑷𝒓𝒐𝒔 ✅ Testing application performance in production. ✅ User is not aware of the impact. ✅ No rollout is done until performance and stability of new version is not established or met. 𝑪𝒐𝒏𝒔 ❗ Expensive to maintain dual resources ❗ Complex in setup 👉 𝗖𝗮𝗻𝗮𝗿𝘆 - The new version is released to a subset of users or servers for testing before broader deployment. Canary deployment requires two identical ReplicaSets one to roll out new features to a small group of users and another for all active users. Progressively the new version is pushed to the entire infrastructure and until canary version becomes the new version all live traffic is directed to canaries 𝑷𝒓𝒐𝒔 ✅ Version is released for a group of users. ✅ Ideal for performance monitoring and load. ✅ Rollback is fast. 𝑪𝒐𝒏𝒔 ❗Rollout is slow 👉 𝗕𝗹𝘂𝗲-𝗚𝗿𝗲𝗲𝗻 - - Two identical environments are maintained: one with the current version (blue) and the other with the updated version (green) - Traffic starts with blue, then switches to the prepared green environment for the updated version 𝑷𝒓𝒐𝒔 ✅ Instant rollback possible. ✅ Entire application state changes in one go hence avoids versioning issue. 𝑪𝒐𝒏𝒔 ❗Expensive as both versions required to be maintained ❗Testing of entire platform is required before production release ❗Stateful applications are hard to handle in this strategy 👉 𝗔/𝗕 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 - Multiple versions are concurrently tested on different users to compare performance or user experience 𝑷𝒓𝒐𝒔 ✅ Parallel running of several versions. ✅ Traffic distribution is fully controlled. 𝑪𝒐𝒏𝒔 ❗Intelligent load balancer is required. ❗Tough to troubleshoot Which one do you prefer ❓ #deployment #strategy #devops #sysops #platform #engineering #cloudnative #solutionengineer #k8s

𝐓𝐨𝐩 6 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 : 𝐀 𝐜𝐨𝐦𝐩𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 🤔 ☸️ Deployment strategies offer a unique way to perform application updates with minimal downtime in a #kubernetes cluster. 👉 𝗥𝗲𝗰𝗿𝗲𝗮𝘁𝗲 - All existing instances are terminated at once and new instances with the updated version are created 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Totally renewed applicate state is achieved 𝑪𝒐𝒏𝒔 ❗ High impact on availability, downtime during shutdown and booting process 👉 𝗥𝗼𝗹𝗹𝗶𝗻𝗴 𝗨𝗽𝗱𝗮𝘁𝗲 - Application instances are updated one by one, ensuring high availability during the process 𝑷𝒓𝒐𝒔 ✅ Ease of setup. ✅ Slow release of new version across instances ✅ Ideal for stateful applications which handle rebalancing of the data 𝑪𝒐𝒏𝒔 ❗ Support to multiple APIs is tough. 👉 𝗦𝗵𝗮𝗱𝗼𝘄 - A copy of the live traffic is redirected to the new version for testing without affecting production users. This is the most complex deployment strategy and involves establishing mock services to interact with the new version of the deployment 𝑷𝒓𝒐𝒔 ✅ Testing application performance in production. ✅ User is not aware of the impact. ✅ No rollout is done until performance and stability of new version is not established or met. 𝑪𝒐𝒏𝒔 ❗ Expensive to maintain dual resources ❗ Complex in setup 👉 𝗖𝗮𝗻𝗮𝗿𝘆 - The new version is released to a subset of users or servers for testing before broader deployment. Canary deployment requires two identical ReplicaSets one to roll out new features to a small group of users and another for all active users. Progressively the new version is pushed to the entire infrastructure and until canary version becomes the new version all live traffic is directed to canaries 𝑷𝒓𝒐𝒔 ✅ Version is released for a group of users. ✅ Ideal for performance monitoring and load. ✅ Rollback is fast. 𝑪𝒐𝒏𝒔 ❗Rollout is slow 👉 𝗕𝗹𝘂𝗲-𝗚𝗿𝗲𝗲𝗻 - - Two identical environments are maintained: one with the current version (blue) and the other with the updated version (green) - Traffic starts with blue, then switches to the prepared green environment for the updated version 𝑷𝒓𝒐𝒔 ✅ Instant rollback possible. ✅ Entire application state changes in one go hence avoids versioning issue. 𝑪𝒐𝒏𝒔 ❗Expensive as both versions required to be maintained ❗Testing of entire platform is required before production release ❗Stateful applications are hard to handle in this strategy 👉 𝗔/𝗕 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 - Multiple versions are concurrently tested on different users to compare performance or user experience 𝑷𝒓𝒐𝒔 ✅ Parallel running of several versions. ✅ Traffic distribution is fully controlled. 𝑪𝒐𝒏𝒔 ❗Intelligent load balancer is required. ❗Tough to troubleshoot #infrastructure #paas #cloudnative #microservices #strategy #devops #architect #platform

Canary Deployments: A Safer Way to Roll Out Updates 🐦 Definition — Canary deployment is a strategy where a new software version is released to a small subset of users before a full rollout. This allows for testing in a real-world environment with minimal risk. 🔄 Process — The deployment involves running two versions of the application simultaneously. A small percentage of users access the new version (canary), while the majority continue using the stable version. 📊 Monitoring — During a canary deployment, developers monitor user interactions, system performance, and error logs to assess the new version's impact. 🔙 Rollback — If issues are detected, the deployment can be rolled back to the stable version, minimizing disruption. 🛠️ Benefits — This approach reduces the risk of widespread issues, allows for real-time feedback, and facilitates a smoother transition to new software versions. Deployment Strategies 🔵 Blue-Green Deployment — This involves maintaining two identical environments. One is life (blue), and the other is idle (green). Updates are made to the idle environment, and traffic is switched once testing is done. 🔄 Rolling Deployment — Updates are applied incrementally across servers, allowing some users to access the new version while others remain on the old version. ⚙️ Feature Flags — Used to control the exposure of new features to users, enabling developers to toggle features on or off without deploying new code. 📈 A/B Testing — A/B testing involves comparing two versions of a feature to determine which performs better. Benefits of Canary Deployment 🛡️ Risk Mitigation — By limiting the initial exposure of new features, canary deployments reduce the risk of widespread issues affecting all users. 📊 Real-Time Feedback — Developers can gather valuable user feedback and performance data, allowing for informed decisions on whether to proceed with the full rollout. 🔄 Easy Rollback — If problems arise, the deployment can be quickly rolled back to the stable version, minimizing user impact. 🚀 Faster Iteration — It enables faster iteration and testing of new features, accelerating the development cycle. 👥 User Segmentation — Allows targeting specific user groups, such as tech-savvy users, for initial testing. Challenges and Considerations ⚠️ Infrastructure Requirements — This may require additional infrastructure to support running multiple versions simultaneously. 🔍 Monitoring Complexity — Effective monitoring is crucial to detect issues early, which can add complexity to the deployment process. 🕒 Timing — Determining the appropriate duration for the canary phase can be challenging, as it depends on the application's complexity and user feedback. 👥 User Selection — Choosing the right subset of users for the canary phase is critical to obtaining meaningful feedback. 🔄 Rollback Strategy — A clear rollback plan must be in place to ensure a smooth transition back to the stable version if needed.

Hello Folks, 🧑🤝🧑 📍𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 Kubernetes deployment strategies are methods used to update applications running in a Kubernetes cluster. They ensure that new versions of applications are deployed smoothly and with minimal disruption to users. Here are some common Kubernetes deployment strategies let's get to understand: [1] 𝐑𝐞𝐜𝐫𝐞𝐚𝐭𝐞 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 The recreate deployment strategy is the simplest and most straightforward approach. In this strategy, all existing pods are terminated before new ones are created. - Use Cases: Applications where downtime is acceptable. - Pros: Simple and ensures a clean slate. - Cons: Causes downtime. [2] 𝐑𝐨𝐥𝐥𝐢𝐧𝐠 𝐔𝐩𝐝𝐚𝐭𝐞 The rolling update strategy is one of the most commonly used deployment strategies. It updates pods incrementally, ensuring that the application remains available during the update process. - Use Cases: High-availability applications needing updates without downtime. - Pros: Zero downtime and allows monitoring. - Cons: More complex and can be slower. [3] 𝐁𝐥𝐮𝐞-𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Blue-green deployment involves running two separate environments: one for the current version (blue) and one for the new version (green). Traffic is switched between these environments to deploy updates. - Use Cases: Critical applications requiring seamless updates. - Pros: Zero downtime and easy rollback. - Cons: Resource-intensive and complex. [4] 𝐂𝐚𝐧𝐚𝐫𝐲 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Canary deployment involves gradually rolling out the new version to a small subset of users before deploying it to the entire user base. This allows for monitoring and validating the new version with minimal impact. How It Work - Use Cases: Applications needing risk minimization and user feedback. - Pros: Limits impact and early issue detection. - Cons: Complex and slower rollout. [5] 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 A/B testing deployment is similar to canary deployment but focuses on delivering different versions of the application to different segments of users simultaneously. This allows for performance comparison and user experience analysis. - Use Cases: Optimizing user experience and validating features. - Pros: Data-driven decisions and controlled rollout. - Cons: Complex and resource-intensive. Based on this point we can conclude that: Kubernetes offers various deployment strategies to ensure efficient application updates. Understanding their benefits and use cases helps make informed decisions to meet your application and business needs. #k8s #deploymentstrategies #bluegreendeployment #rollingupdates #devopsengineerhiring

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 Kubernetes deployment strategies are methods used to update applications running in a Kubernetes cluster. They ensure that new versions of applications are deployed smoothly and with minimal disruption to users. Here are some common Kubernetes deployment strategies: 1. 𝐑𝐞𝐜𝐫𝐞𝐚𝐭𝐞 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 The recreate deployment strategy is the simplest and most straightforward approach. In this strategy, all existing pods are terminated before new ones are created. - Use Cases: Applications where downtime is acceptable. - Pros: Simple and ensures a clean slate. - Cons: Causes downtime. 2. 𝐑𝐨𝐥𝐥𝐢𝐧𝐠 𝐔𝐩𝐝𝐚𝐭𝐞 The rolling update strategy is one of the most commonly used deployment strategies. It updates pods incrementally, ensuring that the application remains available during the update process. - Use Cases: High-availability applications needing updates without downtime. - Pros: Zero downtime and allows monitoring. - Cons: More complex and can be slower. 3. 𝐁𝐥𝐮𝐞-𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Blue-green deployment involves running two separate environments: one for the current version (blue) and one for the new version (green). Traffic is switched between these environments to deploy updates. - Use Cases: Critical applications requiring seamless updates. - Pros: Zero downtime and easy rollback. - Cons: Resource-intensive and complex. 4. 𝐂𝐚𝐧𝐚𝐫𝐲 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Canary deployment involves gradually rolling out the new version to a small subset of users before deploying it to the entire user base. This allows for monitoring and validating the new version with minimal impact. How It Work - Use Cases: Applications needing risk minimization and user feedback. - Pros: Limits impact and early issue detection. - Cons: Complex and slower rollout. 5. 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 A/B testing deployment is similar to canary deployment but focuses on delivering different versions of the application to different segments of users simultaneously. This allows for performance comparison and user experience analysis. - Use Cases: Optimizing user experience and validating features. - Pros: Data-driven decisions and controlled rollout. - Cons: Complex and resource-intensive. 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 - Recreate: When downtime is acceptable. - Rolling Update: For high availability without downtime. - Blue-Green: For seamless transition and quick rollback. - Canary: For minimizing risk and gathering feedback. - A/B Testing: For performance comparison and user experience analysis. 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧 Kubernetes offers various deployment strategies to ensure efficient application updates. Understanding their benefits and use cases helps make informed decisions to meet your application and business needs.

🚀 Mastering Deployment Strategies is key to delivering reliable, user-centric applications. From Canary Releases 🐤 to Blue/Green Deployments 💙💚, each strategy ensures smoother rollouts, minimal risk, and enhanced user experience. Which one do you prefer? 🤔 1. Canary Releases 🐤 What’s It: Gradual release of a new version to a small subset of users, allowing monitoring before a full rollout. Use Case: 🧪 Testing new features in production with minimal risk. 🔍 Ensuring system stability for critical updates. Benefits: ✅ Early detection of issues with minimal user impact. 🔄 Simplified rollback process. 📊 Real-time monitoring and feedback. 2. Rolling Deployment 🔄 What’s It: Incrementally replacing instances of the old version with the new one, ensuring no downtime. Use Case: 🛠 Updating microservices or cloud-native systems. 🕒 Maintaining service availability during updates. Benefits: ⚙️ Ensures continuous system uptime. 🛡 Gradual updates reduce deployment risks. 🔙 Easier rollback for faulty updates. 3. A/B Testing 🔀 What’s It: Running two versions of an application simultaneously to compare performance and user preference. Use Case: 🎨 Comparing UI designs or features. 📈 Optimizing user engagement and conversions. Benefits: 📊 Data-driven decision-making. 🤝 Improves user experience through experimentation. 4. Feature Toggles 🎛️ What’s It: Using configurations to enable or disable features without deploying new code. Use Case: 🔧 Testing incomplete features safely. 🎯 Releasing features to specific user groups (e.g., beta testers). Benefits: ⏱ Decouples deployment from feature release. 🧪 Enables experimentation with quick feedback. 🚀 Quick rollback by toggling off features. 5. Phased Rollout 📦 What’s It: Gradually releasing updates to users based on region, user type, or device type. Use Case: 🌎 Launching updates for global audiences in a controlled manner. ⚖️ Managing infrastructure load during updates. Benefits: 📉 Minimizes risk by isolating potential issues. 🔧 Allows targeted issue resolution. 6. Blue/Green Deployment 💙💚 What’s It: Maintaining two environments (Blue for current production, Green for the new version) and switching traffic after validation. Use Case: 🛑 Systems requiring zero downtime during deployment. 🔄 Disaster recovery scenarios. Benefits: 🚦 Zero downtime during deployment. 🔙 Easy rollback by switching back to Blue. 🔍 Full validation of the new version pre-launch. 7. Dark Launches 🌑 What’s It: Releasing a feature in production but keeping it hidden from users to test its backend performance. Use Case: 🧪 Validating backend functionality before enabling the UI. 🔍 Testing system capacity for new features. Benefits: 🔧 Identifies performance bottlenecks early. 🛡 Reduces risks before full exposure. #DeploymentStrategies #TechJobs #JobSeekers #Freshers #CareerInTech #DevOpsCareers #CloudComputing #CI_CD #TechLearning #SoftwareEngineering #EntryLevelJobs #ITCareers #ContinuousDelivery

💙💚 Were you told that you must install additional tools in order to perform a blue–green deployment in Kubernetes? That’s not actually required! You can do it using only Kubernetes Primitives! ⬇️ Here it’s how: ⬇️ First some background: Blue–green deployment refers to running two application environments in parallel in a production cluster. The first environment (blue) is running the stable application version and the second environment (green) is running the new version. By default, Kubernetes performs a rolling update of a deployment. The old version is replaced by the new one during the rollout. However, in case of some applications we want to keep the old version “on stand-by” for a while after the new rollout. Luckily, it is possible to perform blue–green deployments using only Kubernetes primitives! Here I show you how to do it in five easy steps: 1️⃣ 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚 𝐁𝐥𝐮𝐞 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 That’s our stable version. In this example we use a deployment with 3 replicas of our application. Notice two labels, app and track, their values and the image tag – v1. 2️⃣ 𝐄𝐱𝐩𝐨𝐬𝐞 𝐢𝐭 𝐰𝐢𝐭𝐡 𝐚 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 We use a service to expose our application. Service selector uses both labels – app and track. Therefore, it precisely matches the blue deployment! 3️⃣ 𝐀𝐝𝐝 𝐚𝐧 𝐈𝐧𝐠𝐫𝐞𝐬𝐬 To expose our service outside the cluster we use an ingress. 4️⃣ 𝐀𝐝𝐝 𝐚 𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Finally, we add the green deployment using a separate Kubernetes deployment resource. Notice different values of track label and image tag. The number of replicas is identical as in the blue deployment since the green deployment should take over the whole workload. We wait until the green deployment is ready before moving to the next step. 5️⃣ 𝐒𝐰𝐢𝐭𝐜𝐡 𝐭𝐨 𝐭𝐡𝐞 𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Now it’s time to direct the traffic to the green deployment! To make a switch, we modify the service resource. Notice a new value of the track service selector – green. Therefore, the service matches only the green deployment now! The switch however, did not affect the blue deployment, so it is still available in the cluster in case we need to revert the change. And voila, you've just created your blue–green deployment! ___ Do you utilize blue-green deployments in your projects? If so, do you use any automation tools for that or follow the steps I outlined in this post? ⬇️ Please comment below! ⬇️ ___ My name is Jakub Krzywda, PhD. I post about: #kubernetes, #cloudnative technologies and #devops practices. #K8sWithK5a Click my name + follow + 🔔 so you don’t miss my next posts! Looking for training in Kubernetes or other Cloud Native technologies? 🔝 Enrol today in the course that best suits your needs (link on the top of my profile)

Kubernetes Deployment Strategies: Ensuring Smooth and Secure Application Rollouts Tired of downtime and disruption during application deployments? Kubernetes offers several deployment strategies to help you roll out new features and updates seamlessly, minimizing impact on end users. Understanding Deployment Strategies: Kubernetes deployment strategies refer to the methods used to manage and update applications running on Kubernetes clusters. Choosing the right strategy depends on your specific needs and risk tolerance. Here are some common approaches: 1. Rolling Update (Default): · Gradually replaces old pods with new ones, one by one, ensuring continuous application availability. · Ideal for balancing risk and speed during updates, minimizing downtime. 2. Recreate Deployments: · Simplest approach, but involves downtime as all pods are terminated and replaced simultaneously. · Primarily used for non-critical applications or initial deployments where downtime is acceptable. 3. Blue/Green Deployments: · Creates two identical environments: "blue" (current) and "green" (new). · Routes traffic to the "green" environment after successful testing, then switches traffic seamlessly. · Suitable for high-risk deployments or major application changes, offering zero-downtime rollouts with easy rollback options. 4. Canary Deployments: · Deploys a new version to a small subset of pods initially, allowing for early monitoring and rollback if issues arise. · Ideal for testing new features and catching regressions before wider rollout, minimizing risk. 5. A/B Testing: · Runs multiple versions of an application simultaneously and routes different users to different versions to compare their performance or gather feedback. · Kubernetes can be used to manage the routing of traffic, enabling data-driven decision making for future deployments. 6. Shadow Deployment: · Deploys the new version alongside the existing version, but it doesn't receive live traffic. · Monitors the behavior of the application and its interactions with the environment, allowing for thorough testing in a production-like setting without affecting users. The best deployment strategy for your application depends on: · Downtime tolerance: Can your application handle brief periods of downtime during updates? · Risk tolerance: How comfortable are you with the potential for issues during rollout? · Complexity of changes: Are you making minor updates or substantial changes to the application? You can leverage the power of Kubernetes to select and implement the most suitable deployment strategy for your unique needs, ensuring smooth, efficient, and secure application rollouts! Reference: https://lnkd.in/dkfjb7Jv https://lnkd.in/dxYCwgfE #kubernetes ✌