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. ...

Prerequisite: Read Part 2 — Migrating Magento to Microservices: When & Why to understand monolithic database bottlenecks. Composable E-Commerce Migration: Overcoming Tech Debt with MACH Architecture Answer-first: Composable MACH architecture decomposes monolithic e-commerce platforms into modular, independently scalable services across three primary functional tiers: Core Transactional Domains (Catalog, Pricing, Cart, Checkout, Order), Supporting Engagement Domains (Customer, Reviews, Wishlist, Promotions), and Generic Utility Domains (Notifications, Audit, Search, Analytics). Implementing strict Domain-Driven Design (DDD) bounded contexts with gRPC Protobuf contracts eliminates monolithic coupling, elevates deployment velocity by 4x, and bounds P99 API response times below 45ms. ...

← 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. ...

Prerequisite: Read Part 3 — Composable E-Commerce Migration to understand domain bounded context mapping. Zero-Downtime Blueprint: Moving from Magento to Microservices via Strangler Fig Answer-first: Zero-downtime migration from a Magento monolith to Go microservices is executed via a 3-phase Strangler Fig pattern: Phase 1 (Interception) deploys Envoy Gateway 1.30+ to route live traffic and inject W3C traceparent headers; Phase 2 (Dual-Run & Shadowing) mirrors 100% of production traffic to newly extracted Go services while synchronizing state bidirectionally via Debezium 3.0+ CDC; and Phase 3 (Canary Cutover & Decommission) shifts traffic incrementally (1% -> 10% -> 100%) before retiring the PHP monolith after a 30-day hot-standby period. ...

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. ...

Prerequisite: Read Part 5 — Exporting Magento 2 Data: Flatten EAV with SQL & Node for data unpivoting fundamentals. Magento Database Migration: Shared DB, CDC, or Event Bus? Answer-first: While connecting new microservices directly to the existing Magento database (Shared Database pattern) appears tempting as a quick win, it introduces severe schema coupling, cross-service deadlock hazards, and violates core microservice boundaries. The 2027 production standard uses Debezium 3.0+ Change Data Capture (CDC) streaming row changes via Redpanda/Kafka into independent domain databases. This decouples schemas, guarantees sub-50ms data synchronization latency, and maintains dual-write integrity via the Transactional Outbox pattern. ...

← Previous Chapter: Part 6: Phase 1 — Strangler Fig | Series Hub | Next Chapter: Part 8: Phase 3 — Full Cutover → Answer-first: Dual-writing at the application layer creates race conditions and split-brain states. Instead, Phase 2 implements Change Data Capture (CDC) via Debezium reading the MySQL binlog directly, streaming event deltas through Apache Kafka to populate PostgreSQL microservice databases asynchronously. flowchart LR MagentoAdmin["Magento Admin Update"] --> MySQL["Magento MySQL"] MySQL -->|"Binlog Stream"| Debezium["Debezium CDC Connector"] Debezium -->|"JSON Event Deltas"| Kafka["Kafka Topic: magento.catalog.products"] Kafka -->|"Consumer Group"| GoSync["Go Catalog Sync Worker"] GoSync -->|"Upsert JSONB"| Postgres["Target PostgreSQL"]

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. ...