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Distributed .NET with Microsoft Orleans

You're reading from   Distributed .NET with Microsoft Orleans Build robust and highly scalable distributed applications without worrying about complex programming patterns

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781801818971
Length 262 pages
Edition 1st Edition
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Authors (2):
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Bhupesh Guptha Muthiyalu Bhupesh Guptha Muthiyalu
Author Profile Icon Bhupesh Guptha Muthiyalu
Bhupesh Guptha Muthiyalu
Suneel Kumar Kunani Suneel Kumar Kunani
Author Profile Icon Suneel Kumar Kunani
Suneel Kumar Kunani
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Table of Contents (17) Chapters Close

Preface 1. Section 1 - Distributed Applications Architecture
2. Chapter 1: An Introduction to Distributed Applications FREE CHAPTER 3. Chapter 2: Cloud Architecture and Patterns for Distributed Applications 4. Section 2 - Working with Microsoft Orleans
5. Chapter 3: Introduction to Microsoft Orleans 6. Chapter 4: Understanding Grains and Silos 7. Chapter 5: Persistence in Grains 8. Chapter 6: Scheduling and Notifying in Orleans 9. Chapter 7: Engineering Fundamentals in Orleans 10. Section 3 - Building Patterns in Orleans
11. Chapter 8: Advanced Concepts in Orleans 12. Chapter 9: Design Patterns in Orleans 13. Section 4 - Hosting and Deploying Orleans Applications to Azure
14. Chapter 10: Deploying an Orleans Application in Azure Kubernetes 15. Chapter 11: Deploying an Orleans Application to Azure App Service 16. Other Books You May Enjoy

Understanding the actor model for distributed systems

In this section, we will learn about the motivation behind the building of Microsoft Orleans and the different trade-offs that Microsoft Research took in defining its features.

In a traditional three-tier system, shown in the following figure, we will have a stateless presentation tier and a stateless middle tier or service layer (business tier). The storage layer (or the third tier) presents a bottleneck for scalability. In this system, the services can be scaled horizontally, so there is a possibility that several clients will attempt to access shared resources concurrently, for example, a record in a database table. In Chapter 1, An Introduction to Distributed Applications, we saw that we can scale the database horizontally too. With the horizontal scaling of databases, we need to deal with the challenges of maintaining data consistency across data centers. Pessimistic locking is an approach we can take to ensure data consistency...

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