Saga Pattern

When a customer places an order on the Composable Commerce Platform, seven events need to happen in sequence across four independent services: Order created → Payment authorized → Stock reserved → Fulfillment triggered → Notification sent → Loyalty points awarded → Shipping label generated. Any of these can fail. The network can fail. The database can fail. A third-party payment gateway can time out. Without a reliability mechanism, a 2% failure rate on any step means 2% of all orders are stuck in an inconsistent state, requiring manual intervention. ...

Prerequisite: Part 8 of the System Design Masterclass. Read Part 7: Idempotent API Design first — compensating transactions in Saga must be idempotent. Answer-first: The Saga Pattern coordinates distributed transactions across microservices by decomposing a large transaction into a sequence of local transactions. If any step fails, the system automatically executes compensating transactions in reverse order to undo completed steps. Each local transaction must be idempotent. What Are the Problems with 2PC in Microservices? Answer-first: Two-Phase Commit (2PC) is a blocking protocol with a coordinator single point of failure. If the coordinator crashes between the Prepare and Commit phases, all participants are blocked indefinitely with locks held — a catastrophic failure mode in microservices. These are the same core banking distributed transaction challenges seen in legacy systems. ...

Answer-first: Composable banking replaces rigid legacy cores with modular Go microservices. The transition uses the Strangler Fig pattern to decouple domains, while distributed Sagas manage eventual consistency across transaction engines, and NewSQL databases provide horizontal scaling without sacrificing ACID compliance. What You’ll Learn That AI Won’t Tell You Strangler fig patterns for core banking systems that prevent data corruption. How to bridge legacy COBOL records into dynamic JSON streams using Go middleware. Legacy core banking systems were designed in a different era. Temenos T24, Finacle, and Flexcube shared one defining assumption: the bank’s entire product catalogue — deposits, lending, payments, trade finance — would live inside a single, tightly coupled application and a single, shared database. That assumption held when banking moved at human speed. It breaks completely when product releases need to go from months to days, when a single fraud engine update must not risk a payments outage, and when engineers on a COBOL codebase are retiring faster than they can be replaced. ...

Answer-first: A modern banking microservices architecture replaces legacy monolithic ledgers (like T24 or Flexcube) using Go for high-throughput transaction routing. The system achieves distributed consistency without two-phase commit (2PC) by combining Event Sourcing (immutable ledger streams), Saga Orchestration (using Temporal or Dapr), the Transactional Outbox pattern, and PostgreSQL unique constraints for API idempotency. What You’ll Learn That AI Won’t Tell You How to implement transactional outbox pattern to guarantee eventual consistency. Saga Orchestration patterns that handle transient payment gateway timeouts gracefully. 1. Introduction: Deconstructing the Legacy Core For decades, banks relied on monolithic core systems like Temenos T24 or Oracle FLEXCUBE. While robust, these systems present severe bottlenecks for modern digital banking. They were designed for overnight batch processing, not real-time, API-first global transactions. ...

Answer-first: We decompose the monolith into 21 microservices using Domain-Driven Design (DDD) to isolate business boundaries. Implementing the Kratos framework in Go enables strong structural subtyping for clean layer segregation, while Dapr Workflows handle distributed transactions asynchronously via the Saga pattern to avoid race conditions. What You’ll Learn That AI Won’t Tell You The exact performance overhead of using Go’s structural subtyping versus manual dependency injection in high-throughput microservices. Why scoping database transactions to a single Aggregate root is critical, and how we resolved out-of-order event delivery using Kafka partition keys. Scaling an e-commerce platform past 10,000+ orders per day containing multiple SKUs across dynamic warehouses is where naive architecture breaks down. Hardware scaling ceases to be a magic bullet when distributed transactions, race conditions, and eventual consistency are involved. ...