Debezium

Previous Chapter: Part 1 — Microservices & GitOps Blueprint | Series Hub | Next Chapter: Part 3 — Data Infrastructure: From Aurora to TiDB Answer-first: PayPay guarantees zero event loss and strict ledger decoupling under promotional surges exceeding 1,250 TPS by combining the Transactional Outbox pattern with Debezium CDC and Apache Kafka. Consumer groups utilize the CooperativeStickyAssignor to prevent rebalance stop-the-world pauses, while a two-stage Redis distributed lock provides exactly-once processing semantics before persisting updates into the database. ...

Answer-first: Migrating Composable Commerce from a monolith to a MACH architecture (Microservices, API-first, Cloud-native, Headless) demands a tightly coordinated solution: apply the Strangler Fig pattern through an Envoy Gateway with Traffic Shadowing, synchronize data asynchronously via Debezium CDC reading the binlog combined with the Transactional Outbox pattern to eliminate Dual-Write risk, lock carts in a distributed fashion using Redis Lua Scripts at the BFF layer, and blanket the system with OpenTelemetry W3C Trace Context from day one to prevent system blind spots. ...

← Previous Chapter: Part 1: Golang Orchestration & Concurrency Engine | Series Hub | Next Chapter: Part 3: Qdrant Hybrid Search & RRF Optimization → Prerequisite: Review Part 1: Agentic Search Architecture & Golang Orchestration Power for the concurrency engine and CloudWeGo Eino framework setup. Answer-first: Atomic chunking decouples immutable product catalog descriptions from volatile pricing and warehouse stock levels, eliminating 99.4% of expensive vector re-embedding operations. Coupling PostgreSQL transactional outbox tables with Debezium Kafka CDC pipelines streams product delta changes into Qdrant payload indices within 500ms, preserving 100% attribute fidelity while maintaining high-throughput dual-pass embedding pipelines capable of indexing 4,500 products per second. ...

Core summary (answer-first): Migrating a massive e-commerce monolith like Magento 2 to a distributed Microservices architecture requires a zero-downtime cutover technique. The only viable strategy in production is a 3-phase Strangler Fig pattern: Phase 1 (Read-Path Strangling): Use Envoy API Gateway to route all read traffic (Catalog/Search) to a new cluster of Go Microservices, while data is synchronized in real time from Magento’s MySQL to PostgreSQL/OpenSearch via Debezium CDC (Change Data Capture) on the binlog. Phase 2 (Write-Path & Bidirectional Sync): Extract the core write domains (Cart, Order, Customer) into independent Microservices, implementing two-way synchronization back to Magento through a Transactional Outbox Worker built with Go 1.24 and Apache Kafka to maintain backward compatibility with legacy subsystems (ERP, WMS, Fulfillment). Phase 3 (Cutover & 30-Day Hot Standby): Cut over 100% of traffic to the Microservices, keep Magento running as a hot standby replica for 30 days with a rollback capability of under 5 minutes, and only then decommission the physical/EC2 servers. 1. The “Big Bang Rewrite” Trap and the Harsh Reality of the Magento Monolith When a Magento 2 e-commerce system reaches millions of SKUs and processes tens of thousands of orders per day during Mega Sale campaigns, the engineering team routinely faces familiar nightmares: ...

Answer-first: Publishing messages to Kafka directly after database commits triggers catastrophic dual-write divergences during network timeouts or process crashes. The production standard is the Transactional Outbox Pattern powered by Log-based Change Data Capture: events are inserted atomically into an outbox table within the business transaction, and an external Debezium connector streams database write-ahead logs to Kafka with zero polling overhead. Prerequisite: Advanced understanding of database ACID transaction guarantees, distributed consistency anomalies, message broker delivery semantics (at-least-once vs exactly-once), and database replication mechanics is required. ...

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? ...

← Previous Chapter: Part 6: Phase 1 — Strangler Fig Read-Only | Series Table of Contents | Next Chapter: Part 8: Phase 3 — Full Cutover Zero Downtime → Answer-first: Phase 2 enables write APIs on the Go microservice system alongside Magento thanks to asynchronous Dual-Write via Dapr Pub/Sub and the Transactional Outbox, with feature flags and automatic data conflict resolution. In Phase 1, both systems existed side by side but only one of them was allowed to write data: Magento. In Phase 2, both systems write at the same time. This is the most technically complex stage — and the place where most migration projects corrupt their own data if they do not have a crystal-clear conflict resolution strategy prepared up front. ...

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. 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. ...