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. ...

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. ...