Debezium

Prerequisite: Review Deconstructing the Ecosystem: Service Details by Domain for background on domain boundaries before reading this migration guide. Composable E-Commerce Migration: Overcoming Tech Debt Answer-first: Migrating legacy e-commerce platforms to composable microservices requires incremental API facade routing, domain context decoupling, and zero-downtime Strangler Fig data synchronization. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. This design guarantees sub-50ms P99 latency bounds and zero-allocation memory pooling. ...

Prerequisite: Familiarity with the concepts introduced in Part 5 — Eav Schema Migration. Review it first if the terminology in this part is unfamiliar. Phase 1 is the safest phase of the migration — by design. No write operation touches the new microservices. Magento remains the source of truth for all data modifications. The only thing Phase 1 does is prove that your microservices can serve reads faster and more reliably than Magento. ...

Prerequisite: Review Magento Development in Vietnam: Cost, Hiring & Upgrade for overall team and cost strategy. Why Migrate Magento to Microservices: Zero-Downtime Blueprint Answer-first: Migrating from Magento monoliths to Go microservices replaces monolithic DB locks with independent schemas, asynchronous event streams, and Strangler Fig API routing for zero downtime. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. ...

Prerequisite: Review Why Migrate Magento to Microservices: Zero-Downtime Guide for initial architecture context. Magento Migration: Shared DB, CDC, or Event Bus? Answer-first: Implementing the Strangler Fig pattern with a shared database enables gradual monolith-to-microservice migration, using CDC event capture and API gateway proxies to decouple services safely. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. ...

The Saga Pattern coordinates distributed transactions across microservices by decomposing a large transaction into a sequence of local transactions. If any step fails, the system automatically executes compensating transactions in reverse order to undo completed steps. Each local transaction must be idempotent. Prerequisite: Part 8 of the System Design Masterclass. Read Part 7: Idempotent API Design first — compensating transactions in Saga must be idempotent. What You’ll Learn Temporal Workflow Determinism: How Temporal’s event sourcing workflow engine replays Go code, and why random functions or time sleeps crash workers. Debezium EventRouter Tuning: The exact JSON configuration keys needed to customize Kafka routing keys and prevent partition ordering issues. Pivot State Analysis: Identifying the “point of no return” in a distributed saga where compensations are no longer allowed. What Are the Problems with 2PC in Microservices? Answer-first: Orchestrating distributed transactions in Go uses the Saga pattern with Temporal workflows or Debezium CDC outbox streaming to execute multi-service steps and compensating rollbacks safely. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. ...

Real-Time Inventory Topology: CDC, Kafka, and Redis Answer-first: Real-time e-commerce inventory management uses Debezium CDC event streams, Kafka topic partitioning, and Redis memory caches to prevent stock over-selling during peak flash sales. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. This design guarantees sub-50ms P99 latency bounds and zero-allocation memory pooling. Real-time inventory synchronization is the process of propagating stock count changes from the system of record (database) to all sales channels — web storefront, mobile app, WMS, ERP — in sub-second time. Instead of batch ETL jobs that run every hour, a CDC + Kafka pipeline streams every committed stock change as an event, eliminating overselling and stale stock displays. ...