Short answer
When designing scalable cloud applications, consider implementing CQRS and Event Sourcing to achieve independent scaling of read and write operations, leveraging reactive principles.
- Field
- Modelling
- Source
- IEEE Software (2017)
- Method
- Experimental performance evaluation
- Evidence
- Strong effect
Implementing Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) within a reactive architecture can achieve independent horizontal scalability for read and write operations in cloud applications. This modelling research insight is drawn from a 2017 study published in IEEE Software. Using Experimental performance evaluation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing scalable cloud applications, consider implementing CQRS and Event Sourcing to achieve independent scaling of read and write operations, leveraging reactive principles.
CQRS and Event Sourcing Architecture Scales Horizontally in Cloud Environments
Implementing Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) within a reactive architecture can achieve independent horizontal scalability for read and write operations in cloud applications.
IEEE Software · 2017
Key Findings
- 01The CQRS and Event Sourcing patterns demonstrate independent horizontal scalability for the application's read and write models.
- 02The proposed architecture can be effectively implemented using technologies like Akka, Cassandra, Kafka, and Neo4J.
- 03The architecture provides significant advantages without compromising performance.
Application
Design takeaway
When designing scalable cloud applications, consider implementing CQRS and Event Sourcing to achieve independent scaling of read and write operations, leveraging reactive principles.
How to apply
When building new cloud services or refactoring existing ones that experience imbalanced read/write loads, model and implement a CQRS and Event Sourcing architecture.
Project actions
- 01When designing a system that needs to handle many users or a lot of data, think about separating the parts that read information from the parts that write information.
- 02Consider using Event Sourcing to keep a full history of all changes, which can be useful for debugging and auditing.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Provides empirical evidence for the scalability of CQRS and Event Sourcing in a real-world context.
- +Offers practical guidance on assembling such an architecture with specific technologies.
- +Includes an open-source reference implementation.
Limitations
The complexity of implementing CQRS and Event Sourcing can be a barrier. The initial setup and learning curve for these patterns might be significant.
Reliability & validity
The study's validity is supported by experimental performance evaluation in a cloud environment. Reliability is enhanced by the use of specific technologies and the availability of a reference implementation, allowing for potential replication.
Think critically
While CQRS and Event Sourcing offer scalability benefits, what are the potential trade-offs in terms of system complexity and development overhead for smaller or less data-intensive applications?
Design Principles
"Decouple read and write operations to enable independent scaling and optimization."
This architectural pattern allows for distinct scaling strategies for data retrieval and data modification, optimizing resource utilization and performance in dynamic cloud environments. It provides a robust framework for building complex, high-throughput applications that can adapt to fluctuating demands.
What This Means for Your Design
This research shows that by separating how an application reads data from how it writes data (CQRS) and by storing all changes as a sequence of events (Event Sourcing), you can make the application much better at handling lots of users and data in the cloud.
How to use in your project
- 1.Reference this study when discussing architectural choices for scalable cloud applications in your design project.
- 2.Use the findings to justify the selection of CQRS and Event Sourcing patterns for your system's read and write models.
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Quick Cite
Paragraph starter
The research by Debski et al. (2017) demonstrates that adopting a reactive architecture incorporating Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) can lead to significant improvements in horizontal scalability for cloud applications. Their experimental evaluation of a flight-scheduling prototype showed that read and write operations could be scaled independently, offering a robust solution for systems with imbalanced data traffic.
Source
Questions About This Research
- What does the research say about cqrs and event sourcing architecture scales horizontally in cloud environments?
- When designing scalable cloud applications, consider implementing CQRS and Event Sourcing to achieve independent scaling of read and write operations, leveraging reactive principles. Evidence: IEEE Software (2017).
- Why does "CQRS and Event Sourcing Architecture Scales Horizontally in Cloud Environments" matter for design?
- This architectural pattern allows for distinct scaling strategies for data retrieval and data modification, optimizing resource utilization and performance in dynamic cloud environments. It provides a robust framework for building complex, high-throughput applications that can adapt to fluctuating demands.
- How can designers apply this research?
- When designing scalable cloud applications, consider implementing CQRS and Event Sourcing to achieve independent scaling of read and write operations, leveraging reactive principles.
- What were the main findings?
- The CQRS and Event Sourcing patterns demonstrate independent horizontal scalability for the application's read and write models.. The proposed architecture can be effectively implemented using technologies like Akka, Cassandra, Kafka, and Neo4J.. The architecture provides significant advantages without compromising performance.
- What research method was used?
- Experimental performance evaluation.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2017 journal from IEEE Software.
- What should I do differently in my next project?
- When building new cloud services or refactoring existing ones that experience imbalanced read/write loads, model and implement a CQRS and Event Sourcing architecture.
- What are the limitations?
- The study focused on a specific flight-scheduling application; results may vary for different application domains. Performance was evaluated on a fixed cloud infrastructure configuration.