When each service owns its database, you lose ACID transactions across service boundaries. From working on distributed systems, I've found that traditional two-phase commit doesn't scale. Instead, we need patterns like Sagas for distributed transactions, Event Sourcing for audit trails, and CQRS for read/write optimization.
This article covers practical implementations of these patterns with Python.
The Distributed Transaction Problem
Why 2PC Doesn't Work
Issue
Description
Blocking
All participants lock resources until coordinator decides
Single point of failure
Coordinator failure blocks all transactions
Network partitions
Participants may be unable to reach coordinator
Latency
Multiple round trips increase response time
Availability
All services must be available
Saga Pattern
Choreography-Based Saga
Orchestration-Based Saga
Event Sourcing
Core Concepts
Event Store
Snapshots for Performance
CQRS (Command Query Responsibility Segregation)
Outbox Pattern
Key Takeaways
Sagas for distributed transactions - Choreography for simple flows, orchestration for complex ones
Event sourcing for audit - Store events, derive state
CQRS for optimization - Separate read and write models
Eventual consistency is normal - Design for it from the start
Outbox for reliability - Ensure events are published
What's Next?
Distributed systems fail. In Article 9: Resilience Patterns, we'll cover circuit breakers, retries, bulkheads, and fallback strategies to build fault-tolerant services.