Minimizing Downtime During Cloud Migration

How Netflix Migrated Critical Traffic of 100M+ Users with Zero Downtime Netflix undertook a major system migration, upgrading their backend infrastructure while ensuring a seamless streaming experience for over 100 million users. Here’s a breakdown of how they achieved this with zero downtime. ↳ Replay Traffic Testing - Netflix cloned live production traffic and redirected it to new systems, testing in parallel without affecting user experience. - Real traffic was used to simulate rare edge cases and validate system reliability. - This method stress-tested scalability, validated latency, and ensured operational insights were accurate before migration. ↳ Sticky Canaries - Netflix created small "canary" clusters—one running the old service, one the new version. A small portion of traffic was redirected to both. - Devices were consistently routed to the same canary for extended testing, capturing system-wide health beyond the individual service. - This approach validated overall stability, ensuring new changes didn’t negatively impact end-to-end user experience. ↳ A/B Testing for Backend Changes - Users were divided into groups with controlled exposure to the new backend, mitigating risk. - A/B tests ran for weeks/months to assess impacts on Quality of Experience (QoE) and key business metrics. - Specific exposure based on geography or device type allowed Netflix to limit risk and gauge effects on different segments. ↳ Traffic Dialing for Rollout - Gradual traffic increases were implemented, dialing up production traffic to the new systems in steps. - Metrics were monitored at each step, allowing instant rollback if issues emerged, ensuring minimal risk during final rollout. - Traffic dialed across data centers ensured metrics were compared effectively, catching potential deviations early. ↳ Dedicated Replay Service for Testing - Replay testing was isolated into a dedicated service, removing the risk of device or server-side interference. - This separation ensured replay logic did not impact core production, minimizing coupling risks. ↳ Dual-Writes for Stateful System Migration - Initial data loads to the new datastore were conducted via ETL, followed by dual-write operations for consistency. - Reads were verified between the old and new stores to maintain data accuracy before cutover. - Migration was scaled using dials, allowing incremental steps with proper monitoring to avoid performance issues. ↳ Cleanup After Migration - All temporary migration components, such as traffic dials, A/B tests, and replay integrations, were removed once migration was validated. - Netflix focused on eliminating tech debt, ensuring the system remained streamlined and efficient for future changes.

𝗛𝗼𝘄 𝗦𝘁𝗿𝗶𝗽𝗲’𝘀 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝟵𝟵.𝟵𝟵𝟵% 𝘂𝗽𝘁𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝘇𝗲𝗿𝗼-𝗱𝗼𝘄𝗻𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗦𝘁𝗿𝗶𝗽𝗲’𝘀 𝗱𝗼𝗲𝘀 𝗶𝘁 𝟭. 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗚𝗼𝗮𝗹? Scale effortlessly: Migrate data between shards as Stripe grows. Data accuracy: Every bit of data stays consistent and complete. Horizontal Scalability: As Stripe grows, data needs to be redistributed across shards. Zero Downtime: Millions of businesses rely on Stripe 24/7, so prolonged outages during migrations are unacceptable. 𝟮. 𝗛𝗼𝘄 𝗦𝘁𝗿𝗶𝗽𝗲 𝗺𝗮𝗱𝗲 𝗶𝘁 𝗵𝗮𝗽𝗽𝗲𝗻 𝗦𝘁𝗲𝗽 𝟭: 𝗣𝗹𝗮𝗻 𝘁𝗵𝗲 𝗠𝗼𝘃𝗲 Register the migration in Stripe’s system. Build indexes on the new (target) shard to prepare it for the incoming data. The chunk metadata service acts as the source of truth for mapping data chunks to shards. It ensures proxy servers know where to route traffic after the migration. Think of this as booking movers and ensuring the new house is ready before moving your stuff. 𝗦𝘁𝗲𝗽 𝟮: 𝗠𝗼𝘃𝗲 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮 Copy a snapshot of the existing data at a specific time from the old (source) shard to the new shard. Optimize how data is written to the new shard for speed. Optimizing insertion order ensures proximity of writes within the DocDB shard’s B-tree data structure, dramatically improving performance. This is like carefully packing and transporting your belongings to the new house. 𝗦𝘁𝗲𝗽 𝟯: 𝗦𝘆𝗻𝗰 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 While the historical data is being moved, the system tracks any new changes (like edits or additions) on the old shard. These changes are continuously synced to the new shard in near real-time. The system supports bidirectional replication (source ↔ target) with tagging to prevent cyclical replication. It is also resilient to temporary target shard outages and allows pausing/resuming from checkpoints. It’s like ensuring that any last-minute items packed in your old home also make it to the new one. 𝗦𝘁𝗲𝗽 𝟰: 𝗗𝗼𝘂𝗯𝗹𝗲-𝗖𝗵𝗲𝗰𝗸 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Run a thorough check to ensure all the data in the new shard matches exactly with what’s in the old one. Validate that everything is complete and accurate. This step is performed offline to avoid impacting shard throughput during live operations. Imagine double-checking that every item from your moving checklist has arrived in the new house. 𝗦𝘁𝗲𝗽 𝟱: 𝗥𝗲𝗱𝗶𝗿𝗲𝗰𝘁 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 Temporarily pause traffic to the old shard. Update routes so all reads and writes now go to the new shard. Restart traffic in under two seconds, ensuring smooth operations. Stripe uses a custom patch to MongoDB to enforce version token gating, ensuring traffic only reaches the correct shard. The entire process takes less than two seconds. This is like forwarding your mail to your new address.

Zero downtime during migration? Yes, it’s possible. When Foxtrot, Ukraine’s largest electronics retailer, came to us, they faced: ❌ Frequent slowdowns and downtime ❌ Infrastructure that couldn’t scale ❌ Inefficient AWS spending Here’s how we helped them turn things around: 🔹 Built a new AWS infrastructure from scratch (ECS, RDS on Graviton, Redis, Terraform, CI/CD pipelines) 🔹 Enabled full autoscaling for ECS tasks and EC2 instances 🔹 Optimized costs with Savings Plans, Reserved & Spot Instances, and scheduled shutdowns 🔹 Created dynamic environments on Amazon EKS for developers — cutting costs while boosting productivity The results:   - Migration completed with zero downtime   - 46% reduction in monthly AWS costs   - 99.99% uptime and improved fault tolerance   - 100% scalability for peak loads like Black Friday This shows that cloud migration doesn’t have to be disruptive. With the right approach, it can deliver stability, savings, and growth. More details on the case here: https://lnkd.in/e7PrJTbn 👉 Curious how to make your migration seamless? Let’s talk. #CloudMigration #AWS #DevOps #DigitalTransformation #RetailTech

If you work on a service that has non zero customers, chances are your projects are somewhat invovling migrating old to new, while keep the service running. The Strangler Migration pattern is a common model used to gradually migrate an existing service to a new system or technology stack. The key idea is to "strangle" the old system by incrementally replacing its functionality with the new system, similar to how a strangler fig plant grows around and eventually takes over an existing tree. This approach allows the migration to happen in a controlled and iterative manner, minimizing disruption to the existing application and its users. It involves creating a facade or proxy layer that routes requests to either the old or new system, gradually shifting more traffic to the new system over time. The Strangler Migration pattern is often used when the existing service is large, complex, or tightly coupled, service downtime is unacceptable or must be minimized, making a big-bang migration risky or impractical. It allows the new system to be developed and tested in parallel, while the old system continues to operate. Here are the key steps of the Strangler Migration process, specifically tailed for online services: 1. Prevention of New Dependencies * Stop new services from integrating with the legacy system * Ensure all new development connects to the new system * Establish clear guidelines for new development teams 2. Incremental Migration with Fallback * Gradually move existing dependencies from old to new system * Implement "kill switch" mechanism for safety * Allow quick rollback to old system if issues arise * Test each migration phase thoroughly * Monitor system behavior during transition 3. Complete Transition with Shadow Mode * Switch all use cases to the new system * Keep old system running in parallel (shadow mode) * Verify all functionality works correctly in new system * Compare outputs between old and new systems * Ensure no regression in business processes 4. Legacy System Decommissioning * Confirm all functionalities are working in new system * Verify no remaining dependencies on old system * Plan and execute resource cleanup * Document system retirement * Remove old system infrastructure If you are philosophy junkies like me, here is a bonus note: The Ship of Theseus paradox and the Strangler Fig Pattern are closely related concepts that deal with gradual replacement and identity. The Ship of Theseus is an ancient philosophical paradox about whether an object remains the same after all its components are gradually replaced. The paradox comes from a ship that had all its parts replaced over time, raising the question of whether it remained the same ship. Philosopher Thomas Hobbes asked - which ship would be the "original" if someone collected all the old parts and built another ship? Regardless what your answer is, migration is the only thing constant!

On prem to Cloud migration Step-by-Step AWS Cloud Migration Process 1. Plan the Migration Assessment: Identify the current environment (servers, databases, dependencies, and configurations). Inventory: Document application components and dependencies. Sizing: Determine AWS resources (EC2 instance types, RDS configurations, etc.) based on current usage. Network Design: Plan VPC setup, subnets, security groups, and connectivity. Backup Plan: Create a fallback plan for any issues during migration. 2. Prepare the AWS Environment VPC Setup: Create a VPC with subnets across multiple Availability Zones (AZs). Security: Configure security groups, IAM roles, and policies. Database Configuration: Set up an Amazon RDS instance or EC2-based database for the migration. AD Server: Use AWS Managed Microsoft AD or deploy your AD on EC2. Application Server: Launch EC2 instances and configure the operating system and required dependencies. 3. Migrate Database Backup: Create a backup of the current database. Export/Import: Use database migration tools (e.g., AWS DMS or native database tools) to migrate data to the AWS database. Replication: Set up database replication for real-time sync with the on-prem database. Validation: Verify data consistency and integrity post-migration. 4. Migrate Application Server Packaging: Package the application (e.g., as Docker containers, AMIs, or simple binaries). Deployment: Deploy the application on AWS EC2 instances or use AWS Elastic Beanstalk. DNS Configuration: Update DNS records to point to the AWS environment. 5. Migrate Active Directory (AD) Replication: Create a replica of the on-prem AD in AWS using the AD Trust setup. DNS Sync: Sync DNS entries between on-prem and AWS environments. Validation: Test authentication and resource access. 6. Test and Validate End-to-End Testing: Validate the complete environment (application, database, and AD). Performance Check: Monitor performance using CloudWatch and address any issues. Failover Testing: Simulate failure scenarios to ensure HA/DR readiness. 7. Cutover and Go Live Schedule Downtime: Coordinate with stakeholders and users for a minimal downtime window. Final Sync: Perform a final sync of the database and switch traffic to AWS. DNS Propagation: Update DNS settings to route traffic to the AWS environment (may take up to 24 hours). Monitoring: Continuously monitor AWS resources and performance post-migration. 8. Post-Migration Optimization Scaling: Implement auto-scaling policies for the application. Security: Regularly review and improve security configurations. Cost Optimization: Use AWS Cost Explorer to analyze and optimize resource usage. Downtime Considerations Database Migration: Plan a maintenance window of 2–4 hours for the final database sync and cutover. DNS Propagation: Approx. 15 minutes to 24 hours, depending on TTL settings. Use short TTLs during migration to minimize delays. #AWSMigration #CloudMigration #MinimalDowntime #DatabaseToAWS #ApplicationToAWS #ADToAWS

Considering a move off your Object Storage service to Tigris for lower costs and higher performance? Data migrations can be complex and risky, especially when you have multiple services relying on object storage. Tigris "Shadow Buckets" feature simplifies the process by allowing your existing S3 buckets to serve as a real-time fallback while you transition your services. Here’s how it works: Dual-write capability: When you write to a Shadow Bucket, data is simultaneously written to both S3 and Tigris. On-demand reads: If an object isn’t present in Tigris yet, reads are automatically fetched from S3 and written in Tigris for future requests. Zero downtime, no data loss: Your applications continue operating uninterrupted, with the old and new storage layers seamlessly coexisting until you’re fully migrated. This approach minimizes operational risk and lets you move at your own pace. Learn more about "Shadow Buckets" and see a step-by-step migration example in our latest blog post: https://lnkd.in/eZyatxWH

Before you move a single SAP system, you need to answer 5 questions. Miss even one and your migration might fail before it starts. Most teams skip this part. They jump straight into provisioning cloud resources, copying environments, and trying to meet a go-live deadline. But that’s like building a train schedule without knowing how many trains you’ve got, or where they’re going. Back when I consulted for large SAP migrations - from Colgate to Fortune 100 manufacturers - we never started with tooling. We started with assessment. Because without a clear understanding of what you’re moving, how it’s connected, and what it impacts - you're flying blind. These are the 5 things I always map before touching a single system: 1. System inventory — what exists, and what’s connected You’d be surprised how many environments have orphaned or undocumented dependencies. Miss one? That’s your failure point. 2. Business criticality — what can’t go down, even for a minute Not all systems are equal. Some run background jobs. Others run revenue. You migrate those differently. 3. Resource constraints — who’s available, when, and for how long Most IT teams are already overloaded. You need to know what talent you have before committing to timelines. 4. Downtime thresholds — what’s the business actually willing to tolerate? I’ve seen 80-hour migration estimates get crammed into 24-hour windows. You don’t negotiate after you start. You plan ahead. 5. Migration sequencing — what moves first, and what moves in parallel Dependencies aren’t just technical — they’re operational. Order matters. Or everything stalls. Assessment isn’t overhead. It’s insurance. And the cost of skipping it? Blown deadlines. Missed shipments. Angry execs. And a team stuck in recovery mode for weeks. Every successful migration I’ve ever led had this phase built in from the start. And every failed one I’ve seen? Didn’t.

𝗧𝗵𝗲 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗔𝗪𝗦 𝘁𝗵𝗮𝘁 𝗮𝗹𝗺𝗼𝘀𝘁 𝗸𝗶𝗹𝗹𝗲𝗱 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 Your CTO announces a cloud migration. Everyone’s excited. AWS promises scalability, cost savings, and modern infrastructure. After six months of planning, you kick off the project. Eighteen months later, you’re spending triple the estimate, half the systems are still on-prem, and the team is ready to walk. 𝗪𝗵𝘆 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗴𝗼 𝘀𝗶𝗱𝗲𝘄𝗮𝘆𝘀: Leadership treats cloud migration as a tech upgrade. It’s not. It changes how you operate, architect, and manage costs. Teams plan for the tech shift but ignore the operating model shift. Companies that survive treat migrations as business transformations. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘁𝗿𝗮𝗽𝘀:  • Lift and shift first, optimize later. You just moved data center problems into AWS with higher costs.  • Six-month timeline. Missed the undocumented services and dependencies that derail cutovers.  • Assumed cost savings. No controls meant engineers spun up resources freely until the first $200K bill.  • Minimal process change. On-call, deployment, and monitoring all had to be redesigned. 𝗪𝗵𝗮𝘁 𝗯𝗿𝗼𝗸𝗲:  • Network latency. Cross-AZ hops slowed monolithic calls by seconds.  • Database licensing. Oracle on RDS turned a $40K annual license into $15K a month.  • Egress costs. Chatty microservices added $30K in data transfer fees.  • Security model mismatch. Public IPs and default passwords appeared when perimeter security failed.  • Skills gap. VMware experts struggled with AWS. Progress slowed drastically. 𝗪𝗵𝗮𝘁 𝘀𝗮𝘃𝗲𝗱 𝗶𝘁: Leadership paused, admitted the failure, and brought in AWS architects to coach and embed with teams. 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝗲𝗱:  • Adopted hybrid for 18 months to build in-house expertise.  • Rearchitected apps into containers and moved to managed databases.  • Implemented FinOps early with tagging, alerts, and ownership.  • Formed a dedicated migration team so product velocity didn’t stall.  • Used phased cutovers with rollback options to de-risk each step. If you’re planning a migration, double your timeline and triple your budget. Not from pessimism, but experience, most companies underestimate both. The ones that don’t are the ones that make it. What was the most expensive surprise in your cloud migration? #AWS #awscommunity #kubernetes #CloudNative #DevOps #Containers #TechLeadership