CDC

Multi-Language Edition: This chapter is also available in Vietnamese at 📖 Bản tiếng Việt (Vietnamese Edition). Previous Chapter: Part 1 — Microservices & GitOps Blueprint | Series Hub | Next Chapter: Part 3 — Data Infrastructure: From Aurora to TiDB Answer-First: Handling sudden promotional payment spikes of thousands of TPS requires complete decoupling of synchronous ingress requests from asynchronous ledger persistence. PayPay implements an Event-Driven Architecture centered on Apache Kafka. To guarantee zero financial discrepancies between the database and event streams, PayPay utilizes the Transactional Outbox Pattern with Debezium CDC, avoiding dual-write race conditions. Downstream consumer microservices enforce strict idempotency via Redis distributed locks and UUIDv7 idempotency keys, paired with isolated Dead Letter Queues (DLQ) to prevent poisoned payloads from blocking partition processing. ...

← 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. ...

Multi-Language Edition: This chapter is also available in Vietnamese at 📖 Bản tiếng Việt (Vietnamese Edition). Previous: Chapter 3 — Distributed Rate Limiting with Redis & GCRA | Series Hub | Next: Chapter 5 — Optimizing Golang Database Connection Pools Answer-First: Updating a relational database and publishing a message to Apache Kafka sequentially without a distributed two-phase commit protocol is mathematically guaranteed to suffer from the Dual-Write Problem. Network timeouts, process crashes, or broker rebalances inevitably leave the database and the message broker in inconsistent states. The definitive, cloud-native standard is the Transactional Outbox Pattern powered by Log-based Change Data Capture (CDC): business records and event payloads are committed atomically into an outbox table within the same database transaction, and a background CDC engine (Debezium, TiCDC, or pgoutput) streams events directly from the DB Write-Ahead Log (WAL) to Kafka with zero query polling overhead. ...

📖 Bản tiếng Việt (Vietnamese Edition) Prerequisite: Familiarity with the concepts introduced in Part 3 — Late Chunking Semantic Caching. Review it first if the terminology in this part is unfamiliar. Part 4 — Real-time Streaming CDC & Federated GraphRAG Architecture In mission-critical enterprise environments—such as financial trading desks, e-commerce order management, and medical health record platforms—data changes continuously. A product price adjustment, a contract terms revision, or a inventory status update occurs thousands of times per minute. ...

📖 Bản tiếng Việt (Vietnamese Edition) 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"]