Strangler Fig

Answer-first: Migrating from Magento Enterprise to Composable Commerce using a 3-phase Strangler Fig model combined with Go 1.25, Kratos v2, and Dapr Pub/Sub eliminates the $200k/year license fee entirely, splits the domain into 21 DDD bounded contexts, guarantees zero downtime, and prepares the platform for Agentic Commerce. Welcome to the complete handbook for Composable Commerce Migration — how to surgically dismantle a Magento 2 monolith into a production-grade microservices platform without dropping a single order during the transition, ready for the Agentic Commerce trend (commerce optimized for both human users and AI agents). ...

Series Hub | Next Chapter: Part 1: Decomposing Magento into 21 Services with DDD → Answer-first: Migrating from a monolithic Magento deployment to a Composable Commerce platform with 21 Go microservices eliminates the $200k/year license fee entirely, boosts flash-sale capacity by 10x, and removes the risk of single-vendor lock-in. Answer-first: Start with a Modular Monolith mindset, then progressively shift to Composable Commerce built on 21 Go microservices, Kratos v2, and Dapr PubSub. This is the definitive answer to replacing Magento Enterprise. It delivers extreme commerce capability (multi-warehouse, payment sagas, real-time search) at a license cost of $0, while also meeting the API-first requirements of Agentic Commerce in the 2026 AI ecosystem. ...

Answer-first: Migrating Magento to Golang offers 3 strategies: Shared DB delivers an immediate compute speed-up but accumulates technical debt; CDC (Debezium) + Outbox lets Go own a flat schema without touching a single line of PHP code; a Full Event Bus (Kafka) provides complete isolation but requires the PHP team to maintain a rock-solid event publisher. What you will learn that AI will not tell you Why Go running on Magento’s MySQL is faster at the compute layer but still hits a bottleneck at the EAV query layer — and what the real solution is. The single deciding factor between CDC (Option B) and Event Bus (Option C): who owns the Magento PHP source code. The Strangler Fig Dilemma: Compute vs. Data When converting from the Magento monolith to a Golang backend, architects face a decision that is anything but obvious: should we migrate the API layer and the database at the same time? ...

Answer-first: Consolidating fragmented microservices back into a modular monolith utilizes the Reverse Strangler Fig pattern with dual-writing and zero-downtime database schema mergers. Merging database schemas using logical schema separation (PostgreSQL schemas) preserves strict module autonomy while eliminating distributed transaction complexity. Adopting this pattern guarantees sub-50ms P99 latency bounds, zero-allocation memory optimization, and fault-tolerant event-driven state synchronization across production systems. Prerequisite: Before reading this part, please review Part 5: Observability in Memory. ...

← Previous Chapter: Part 5: Migrating the Magento EAV Schema | Series Table of Contents | Next Chapter: Part 7: Phase 2 — Dual-Write with Dapr PubSub → Answer-first: Phase 1 of the Strangler Fig model migrates only the read/query path (Catalog, Search) to Go microservices, using Change Data Capture (Debezium CDC) to sync data from Magento’s MySQL in near real time without disrupting the business write path. Phase 1 is the safest stage of the entire migration — and that is by design. No write operation ever touches the new microservice system. Magento remains the single source of truth for every data modification. The only thing Phase 1 has to prove is that your pile of microservices can serve read operations faster and more reliably than Magento. ...

← Part 6: E2E Testing & Edge Caching | Series Hub Prerequisite: Complete all preceding modules (Executive Summary through Part 6) before executing this migration playbook. Answer-first: Migrating enterprise applications from legacy chatbots to Generative UI follows a structured 4-phase Strangler Fig pattern that incrementally replaces text responses with interactive component widgets. Backed by OpenTelemetry streaming instrumentation, strict P99 latency SLOs (<50ms render duration), and canary feature flagging, this playbook mitigates deployment risk, guarantees backward compatibility, and accelerates enterprise user workflow completion rates by 3.2x. ...