Build distributed, highly scalable applications in .NET using Microsoft Orleans
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The document discusses building distributed, highly scalable applications using Microsoft Orleans, which leverages the actor model to manage grains, the atomic units of the Orleans programming model. It covers grain lifecycle, persistence options, cluster management, streaming capabilities, and core concepts like timers and reentrancy. Additionally, it provides resources for further information and implementation details.
![Reentrancy
Grain activations are single-threaded and,
by default, process each request from
beginning to completion before the next
request can begin being processing.
In some circumstances, it may be desirable
for an activation to process other requests
while one request is waiting for an
asynchronous operation to complete
• The grain class is marked as [Reentrant]
• The interface method is marked as
[AlwaysInterleave]
• The requests within the same call chain
• The grain's MayInterleave predicate returns true](https://image.slidesharecdn.com/winwiremicrosoftorleansv2-190731171200/85/Build-distributed-highly-scalable-applications-in-NET-using-Microsoft-Orleans-12-320.jpg)
Build distributed, highly scalable applications in .NET using Microsoft Orleans
- 1. Build distributed, highly scalable applications in .NET using Microsoft Orleans
- 2. Agenda Introduction – Microsoft Orleans1 Grains and Grain Persistence2 Cluster Management3 Streaming, Observers, Reentrancy4 Q & A5
- 3. Implementation is based on the Actor Model Actor Model Orleans grains are virtual actors Orleans Grains Physical instantiations of grains are completely abstracted away and are automatically managed by the Orleans runtime Physical Instantiations Virtual Actor Model - High- scale dynamic workloads like cloud services Virtual Actor Model Build distributed high-scale computing applications without the need to learn and apply complex concurrency or other scaling patterns Introduction
- 4. Introduction Typical stateless n-tier model Orleans as a Stateful middle tier
- 5. Stateful 3-tier Architecture State Event Stream System WEB hosts with Orleans clients Orleans cluster
- 6. Grains What is a grain Grain Identity • Primitives of the Orleans programming model. • Building blocks of an Orleans application, they are atomic units of isolation, distribution, and persistence. • Objects that represent application entities. • An individual grain is a uniquely addressable instance of a grain type (class). • Each grain has a unique identity, also referred to as a grain key, within its type. • Grain identity within its type can be a long integer, a GUID, a string, or a combination of a long+string or GUID+string
- 8. Grains Active in Memory Deactivating Persisted Activating Grain Lifecycle Activation • Constructor of the grain is executed leveraging Dependency Injection, if applicable • If Declarative Persistence is used, the grain state is read from storage • If overridden, OnActivateAsync is called Deactivation • The grain remains idle for some time • Silo runtime decides to deactivate the grain • Silo runtime calls OnDeactivateAsync, if overridden • Silo runtime removes the grain from memory
- 9. Grain Persistence Orleans provides different type of storage providers for persisting the grain state AzureTableGrainStorage AzureBlobGrainStorage DynamoDBGrainStorage MemoryGrainStorage AdoNetGrainStorage SQL Server MySQL/MariaDB PostgreSQL Oracle
- 10. Cluster Management Orleans provides cluster management via a built-in membership protocol, which we sometimes refer to as Silo Membership. The goal of this protocol is for all silos (Orleans servers) to agree on the set of currently alive silos, detect failed silos, and allow new silos to join the cluster. Azure Table Storage Apache Zookeeper Consul IO AWS Dynamo DB In memory emulation for development setup Membership Table Implementations
- 11. Streaming Streaming extensions provide a set of abstractions and APIs that make thinking about and working with streams simpler and more robust Makes the programming model compatible with and portable across a wide range of existing queuing technologies, such as Event Hubs, ServiceBus, Azure Queues, and Apache Kafka No need to write special code or run dedicated processes to interact with such queues For receiving data from streams implicit/explicit subscriptions can be used
- 12. Reentrancy Grain activations are single-threaded and, by default, process each request from beginning to completion before the next request can begin being processing. In some circumstances, it may be desirable for an activation to process other requests while one request is waiting for an asynchronous operation to complete • The grain class is marked as [Reentrant] • The interface method is marked as [AlwaysInterleave] • The requests within the same call chain • The grain's MayInterleave predicate returns true
- 13. Other Core Concepts 1 Timers and Reminders 2 Request Context 3 Event Sourcing 4 Grain Services 5 Interceptors 6 External Tasks and Grains 7 Stateless Worker Grains
- 14. Demo
- 15. Q & A • Website: www.winwire.com • Email: marketing@winwire.com • Blog: http://www.winwire.com/blog/ • Twitter: @winwire