CDC

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. ...

← 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. ...

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. ...

Series Hub | Previous Chapter: Part 3 — Late Chunking & Semantic Caching | Next Chapter: Part 5 — Enterprise Security & Data Poisoning Answer-first: Batch ETL pipelines introduce hours of data staleness and context drift, causing AI agents to retrieve obsolete enterprise records. Event-driven Change Data Capture using Debezium and Redpanda streams PostgreSQL WAL mutations directly into LanceDB and Apache Iceberg v3 lakehouses, guaranteeing sub-second vector index updates and zero ghost-context leaks across federated domain data meshes. ...

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"]