Chapter 7: Data Management and Consistency Patterns

In this section

• 7.1 Database per Service Pattern
  • Decentralized Data Management in Microservices
    Explore the concept of decentralized data management in microservices, its benefits, challenges, and best practices for ensuring data autonomy and consistency.
  • Ensuring Data Ownership in Microservices Architecture
    Explore the principles and practices of ensuring data ownership in microservices, focusing on autonomy, access control, and encapsulation to build scalable systems.
  • Data Synchronization in Microservices: Ensuring Consistency Across Decentralized Databases
    Explore data synchronization strategies in microservices, including event-driven synchronization, Change Data Capture, conflict resolution, and best practices for maintaining data consistency.
• 7.2 Saga Pattern
  • Orchestration-Based Sagas: Managing Distributed Transactions in Microservices
    Explore orchestration-based sagas, a pattern for managing distributed transactions in microservices, ensuring data consistency and reliability across services.
  • Choreography-Based Sagas: Event-Driven Coordination in Microservices
    Explore choreography-based sagas in microservices, focusing on event-driven coordination, implementation of event handlers, and best practices for managing distributed transactions.
  • Implementing Sagas Effectively: A Guide to Microservices Transaction Management
    Learn how to implement sagas effectively in microservices architecture, focusing on orchestration and choreography approaches, reliable messaging, idempotency, state management, monitoring, and testing.
• 7.3 Command Query Responsibility Segregation (CQRS)
  • Separating Reads and Writes in CQRS: Optimizing Microservices for Scalability and Performance
    Explore the Command Query Responsibility Segregation (CQRS) pattern, focusing on separating reads and writes to enhance scalability and performance in microservices architecture.
  • Scalability Benefits of CQRS in Microservices
    Explore the scalability benefits of Command Query Responsibility Segregation (CQRS) in microservices, focusing on resource optimization, load balancing, elastic scaling, and fault isolation.
  • Implementing CQRS: A Comprehensive Guide to Command Query Responsibility Segregation
    Explore the implementation of CQRS in microservices architecture, focusing on separating command and query responsibilities for enhanced scalability and performance.
• 7.4 Event Sourcing
  • Event Sourcing: Storing State as Events in Microservices
    Explore the event sourcing pattern in microservices, focusing on storing state changes as immutable events, ensuring durability, and integrating with read models.
  • Rebuilding State in Event Sourcing: Techniques and Best Practices
    Explore the intricacies of rebuilding state in event-sourced systems, including replay mechanisms, snapshotting, and ensuring consistency.
  • Combining Event Sourcing with CQRS for Enhanced Data Management
    Explore the integration of Event Sourcing and CQRS to improve data management, scalability, and performance in microservices architectures.
• 7.5 Data Consistency and Transactions
  • Two-Phase Commit (2PC) in Distributed Microservices
    Explore the Two-Phase Commit (2PC) protocol for ensuring atomicity and consistency in distributed microservices transactions, with practical Java examples and best practices.
  • Eventual Consistency in Microservices: Balancing Availability and Consistency
    Explore the concept of eventual consistency in microservices, its implementation, and best practices for managing data consistency across distributed systems.
  • CAP Theorem Implications: Navigating Consistency, Availability, and Partition Tolerance in Microservices
    Explore the implications of the CAP Theorem in microservices architecture, understanding trade-offs between consistency, availability, and partition tolerance, and how to implement effective consistency models.
• 7.6 Data Partitioning and Sharding
  • Global vs. Local Transactions in Microservices: Balancing Consistency and Performance
    Explore the intricacies of global and local transactions in microservices, understanding their characteristics, use cases, and implementation strategies for optimal data consistency and performance.
  • Data Partitioning Strategies for Scalable Microservices
    Explore effective data partitioning strategies to enhance scalability and performance in microservices architectures, including partition key selection, range-based and hash-based partitioning, and handling data skew.
  • Managing Distributed Data in Microservices: Strategies and Best Practices
    Explore strategies for managing distributed data in microservices, including replication, consistency, transactions, synchronization, access control, and monitoring.