Distributed Data Patterns

Introduction

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

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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

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Orchestration-Based Saga

Event Sourcing

Core Concepts

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Event Store

Snapshots for Performance

CQRS (Command Query Responsibility Segregation)

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Outbox Pattern

Key Takeaways

  1. Sagas for distributed transactions - Choreography for simple flows, orchestration for complex ones

  2. Event sourcing for audit - Store events, derive state

  3. CQRS for optimization - Separate read and write models

  4. Eventual consistency is normal - Design for it from the start

  5. 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.


This article is part of the Microservice Architecture 101 series.

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