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133 posts about Migrations. Every summary links to the original.
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Amazon EVS runs VMware Cloud Foundation on AWS bare metal, so the design question becomes where each workload's data lives. This layered diagram shows how vSAN, FSx for ONTAP, and cross-Region DR divide the work.
A side-by-side comparison of legacy Tomcat defaults and the strict defaults in Tomcat 11, showing which behaviors change and what to verify before you upgrade a Java web tier.
Learn the step-by-step process for deploying .NET 10 applications to Heroku, from buildpack detection to SDK pinning, and understand the lifecycle of .NET LTS releases.
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At the MongoDB.local NYC 2025 event, MongoDB unveiled several platform updates aimed at modernizing enterprise database architectures and supporting emerging agentic artificial intelligence workloads. The newly released MongoDB 8.2 version provides enhanced performance alongside expanded developer features across the broader document database ecosystem. The company also integrated Voyage AI embedding and reranking models to improve context retrieval precision and efficiency when building production-grade AI systems. Furthermore, dedicated Search and Vector Search capabilities have reached general availability across both MongoDB Community Edition and Enterprise Server environments. To streamline infrastructure transitions, the newly introduced Application Modernization Platform leverages AI-powered tooling and specialized techniques to migrate legacy database systems two to three times faster.
At MongoDB.local NYC, new product capabilities were unveiled to support modern enterprise workloads and artificial intelligence applications. The release of MongoDB 8.2 delivers improved performance alongside embedding and reranking models from Voyage AI for AI application development. Additionally, search and vector search capabilities have entered public preview for both MongoDB Community Edition and Enterprise Server across customer runtime environments. To address the high operational costs of legacy database systems, MongoDB introduced the Application Modernization Platform to accelerate migration and automated code rewriting using AI-driven tooling. These capabilities position the JSON-based document database as a foundational data layer providing persistent state, contextual retrieval, and memory for agentic AI systems.
MongoDB presented multiple updates at MongoDB.local NYC 2025 aimed at supporting modern AI workloads and enterprise modernization. The event detailed MongoDB 8.2 alongside Voyage AI embedding models and rerankers designed to enhance accuracy and efficiency in AI applications. Search and vector search capabilities have expanded into public preview for MongoDB Community Edition and Enterprise Server. Furthermore, the company launched the MongoDB Application Modernization Platform, which combines AI-driven tools and expert workflows to migrate legacy systems to MongoDB two to three times faster. MongoDB positions its JSON document model as a foundation providing memory, facts, and state continuity for agentic AI applications.
MongoDB unveiled several core database updates aimed at supporting artificial intelligence workloads during MongoDB.local NYC 2025. The release of MongoDB 8.2 introduces performance enhancements alongside Voyage AI embedding models and rerankers for AI applications. Furthermore, Search and Vector Search are now available in public preview for both MongoDB Community Edition and MongoDB Enterprise Server. To address the costs and operational friction of legacy systems, MongoDB launched the Application Modernization Platform (AMP), which combines AI-powered tools with specialized talent to accelerate database migrations. These capabilities position the JSON-based document model to supply the persistent state, memory, and retrieval mechanisms required by emerging agentic AI architectures.
MongoDB announced several product releases and platform updates aimed at supporting artificial intelligence and agentic workflows at MongoDB.local NYC. The release of MongoDB 8.2 arrives alongside integrations with Voyage AI embedding and reranker models designed to enhance data retrieval precision. In addition, MongoDB launched Search and Vector Search in public preview for both Community Edition and Enterprise Server deployments, bringing vector capabilities to self-managed environments. To assist organizations transitioning away from rigid legacy database systems, MongoDB also introduced the Application Modernization Platform, which combines AI-driven tooling and specialized migration expertise. Early benchmarks from the modernization platform demonstrate legacy migrations running two to three times faster while accelerating code rewriting tasks by an order of magnitude.
Grab’s decade-old Go monorepo grew to 12.7 million commits, 73GB of Git-tree metadata, 176GB of blobs, 12 million references, and 444,000 files, causing replication lag, slow developer workflows, and GitLab operational bottlenecks. A staging proof of concept compared full and single-commit histories under production-like load: replication delay fell from 160–240 seconds to 1–2.5 seconds, nearly a 100x improvement. Because custom migration tooling processed about 100 commits per hour, the team retained commits reachable from more than 2,000 critical tags, one month of recent history, and simplified merge commits, while archiving older history. The resulting prune reduced commits by 99.9%, improved replication performance by 99%, preserved code and tags without data loss, and left teams with minor migration adjustments.
Stripe’s Organizations customer and payment method sharing addresses the difficulty of giving customers a unified checkout when a business uses separate accounts for business lines or countries. After access is enabled, all new and existing customers and their saved card payment methods can be shared between selected accounts, using the same IDs, so a saved card can be charged in another account without recollection. Only selected customer fields—such as name, email, phone, tax IDs, and preferred locales—are shared; metadata, shipping, and other fields remain account-specific, while non-card payment methods cannot be charged cross-account. The guide demonstrates testing in an organization sandbox, creating Checkout Sessions with the shared customer ID, viewing organization-wide transactions, and using a single organization webhook; sharing is in preview and requires customer consent.
Tinder modernized its recommendation system by migrating its core Elasticsearch infrastructure from an end-of-life Elasticsearch 6 deployment to Elasticsearch 8 on Kubernetes. The legacy setup relied on manual EC2 provisioning and custom scripts, preventing the engineering team from utilizing modern vector search capabilities while incurring substantial operational overhead. Tinder implemented Elastic Cloud on Kubernetes integrated with its in-house Infrastructure as Code framework, Scaffold, while refactoring custom Java ranking plugins to handle Elasticsearch 8 breaking changes like prohibited negative scoring. The migration was validated through offline benchmarking, online verification, and a three-wave A/B testing rollout. This transition completed with zero outages and under 0.2% data discrepancy, reducing p99 search latency by 12% to 56% and cutting annual infrastructure costs by over one million dollars.
Grab migrated its stream-processing (write) functionality from an existing service into a separate service while handling 20,000 reads per second from 16 Kafka streams and writing to output streams and DynamoDB tables. The migration required gradual stream-by-stream rollout, production validation, and a no-downtime switchover; feature flags were rejected because deployment timing could cause loss, duplication, or inconsistent values. The team moved processing into shared /commons code, created temporary validation sinks, and scheduled each switchover with a shared map[string]time.Time, allowing both services to change writers at the same time. During each cutover, both services consumed streams, the old service stopped writing to resources, and monitoring continued before old consumption was removed; the result was zero data loss or duplication, no downtime, and completion in about three weeks. It was temporary and tested, but rolling back either service could create inconsistency if systems fell out of sync.
Upgrading a third-party API can bring new features and fixes but may also introduce breaking changes, so applications should be evaluated before production changes. Stripe Sandboxes provide isolated test accounts with separate API keys and access controls, optionally copying production configuration without copying products, customers, or transaction data. The workflow uses Dashboard Workbench to inspect and upgrade the sandbox API version, then runs the application with sandbox credentials and forwards webhook events through the Stripe CLI. In the example, Stripe.net 41.0.0 rejected an event because it expected API version 2022-11-15 while the sandbox delivered 2024-09-30.acacia; upgrading the SDK to 46.0.0 resolved the mismatch.
Two Sigma's large computing footprint drives significant energy consumption and carbon emissions, particularly across live trading applications that require continuous real-time market data caching. To address this overhead without sacrificing performance, engineering teams used a routine hardware refresh to transition from legacy single-process machines to denser multi-core server configurations. By replacing roughly 60 legacy hosts with 28-core processor hardware, the team distributed baseline power draw over more cores and eliminated underutilized compute capacity. This architectural consolidation reduced absolute power consumption across production hosts by 66%, dropping electricity usage from 27 MWh in January 2023 to 10 MWh in January 2024. The initiative subsequently established an annual sustainability rationalization practice for hardware budgeting across the organization.
Grab's self-hosted GitLab instance fell approximately 14 months behind official releases, serving thousands of engineers and monorepos reaching up to 39TB in total footprint. To address security updates and system stability, the team established a structured upgrade routine using Terraform, Packer, and Ansible across GitLab's 5,000-user reference architecture. They implemented staged deployments, upgraded stateful Gitaly nodes via in-place rotation, and resolved primary node clustering bottlenecks under Praefect. By utilizing official GitLab Upgrade Paths and parallelizing component deployments, the team caught up on 24 months of releases in 11 months and reduced release lag from 396 days down to 35 days.
Shop app’s hockey-stick growth pushed its Ruby on Rails backend and MySQL primary datastore toward capacity, with multi-terabyte storage, week-long schema migrations, and throttled background jobs. After splitting databases and scaling surrounding systems, the team adopted Vitess to shard and re-shard MySQL while retaining SQL compatibility and self-managed primary datastores. They organized user-owned data into a sharded “users” keyspace using user_id, kept other data in an unsharded “global” keyspace, and built application-layer query verifiers to validate routing, distribution, and transaction correctness before migration. The move made schema migrations take hours instead of weeks, removed the reported capacity-driven throttling, and enabled further scaling by adding shards, while introducing concepts such as Sequences, Vindexes, and VSchemas that developers must learn.
Grab's streaming data platform team migrated the Kafka Control Plane from Role-Based Access Control to Attribute-Based Access Control to eliminate operational bottlenecks and manual permission management. The previous model required defining hundreds of roles, permissions, and group mappings in an internal IAM service, leading to approval delays and stale memberships. Under the new architecture, user attributes sync from the HRMS and token payloads, while resource attributes are tagged upon creation or backfilled to reflect department and team ownership. Open Policy Agent evaluates access requests defined in Rego via middleware by comparing user attributes with resource metadata. This transition eliminated over 200 roles, 200 permissions, and roughly 3,000 unused IAM resources while automating access provisioning for new joiners.
HappyTeams, a bootstrapped startup, needed its database to support thousands of users and continued feature development without consuming scarce technical resources. Its Heroku Postgres deployment had developed scaling problems, including sluggish or uneven performance and limits on concurrent connections, prompting a database change during an application rebuild in early 2022. The company migrated to Supabase in less than an hour using pg_dump and pg_restore, and adopted PgBouncer for connection pooling; it selected the service for performance, connection flexibility, support and documentation, onboarding, and lower costs. The source reports that Supabase removed the performance issue, made pooling effortless, and helped HappyTeams release features faster, including a comments feature completed in two days, while the company considered expanding into Edge Functions and the API client.
As Tinder scaled over a decade, maintaining visual consistency across its client applications became unsustainable and increased engineering overhead. To address this, the UI Platform team developed Obsidian, a unified design system combining design tokens, reusable components, and documentation. Designers define base and context tokens in Figma using the Tokens Studio plugin, which are then transformed by Style Dictionary into native artifacts for iOS, Android, and Web. Web developers integrate these tokens using Atomizer to generate static stylesheets, while a Zeroheight documentation portal and a custom token lookup tool assist legacy migrations. The ongoing migration has streamlined collaboration between designers and engineers, resolved styling ambiguities, and improved color contrast accessibility across platforms.
Shopify’s largest mobile app, Shopify Mobile, had 300 screens per platform and more than six years of development, making a full rewrite impractical because it could take at least three years and halt feature work. The team first adopted React Native incrementally, but migration was projected to take four to five years while requiring three design-system implementations, three architectures, and costly native interoperability. It then switched to “Iterative Porting”: engineers were trained through the five-milestone RN Accelerator, surfaces were inventoried and prioritized with a RICE-based score, and eligible features were migrated behind flags, tested, monitored for two weeks, then completed by removing the native version. The effort also introduced Native Routes, modal deeplink handling on iOS, and component-level Safe Area fixes; with root screens ported and infrastructure in place, the team reports that ports are accelerating, although substantial work remains.
PostgreSQL 15, released as a stable version in October 2022, adds security, performance, SQL, replication, logging, compression, and string-processing changes. CREATE permission is revoked from all users except the database owner, views can use caller permissions with security_invoker=on, and sorting is reported up to four times faster for in-memory and on-disk workloads; window functions also improve. The release introduces MERGE, supports two-phase commits and selective data in logical replication, allows conflicting transactions to be skipped or subscriptions disabled, and enables parallel workers for SELECT DISTINCT. It also adds JSON logging, Zstandard and ZL4 compression for WAL and pg_basebackup, and POSIX regexp functions such as regexp_substr() and regexp_count(). The post recommends upgrading for bug fixes and stability, while noting that migrated databases may need manual permission changes and upgrades should be checked for rare manual actions.
Supabase released a new version of its documentation, rebuilding the site with Next.js after outgrowing Docusaurus. The earlier setup became harder to maintain as client-library versioning expanded, contributions grew more difficult, and the docs remained separate from Supabase’s Next.js and Turborepo monorepo. To reduce migration risk, the team first created a one-week Next.js MVP that copied Docusaurus, then spent a month developing the new release with two full-time developers. It introduces a new design, a single sidebar for broader discoverability, and reference pages that group all functions in a library while improving TypeDoc-based generation. Future work described includes unified authentication, project-specific documentation, runnable Postgres WASM examples, interactive tutorials, improved Postgres full-text search, and balancing sparse and overly verbose content.
Rails caching relied heavily on Marshal, but cached Ruby-specific objects could break after code changes when old payloads were loaded by code without the former classes. MessagePack provided a generic binary format whose core types exclude Ruby-specific Object and instance-variable encodings, while extension types allowed production-specific serializers such as the compact Date representation. During a roughly six-month migration, the team ran both formats, prefixed MessagePack payloads with a version byte, and used encoding failures to identify unsupported cache values. To scale beyond 128 extension types, they added an Object catchall based on as_pack and from_pack, plus Struct and T::Struct modules whose attribute digests detect refactors and turn stale data into cache misses. After logs showed that Marshal was no longer used, the core monolith moved exclusively to MessagePack, with the Paquito gem extracting much of the migration work.
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