Event Stream Processing is a popular paradigm for building robust and performant systems in many different domains, from IoT to fraud detection to high-frequency trading. Because of the wide range of scenarios and requirements, it is difficult to conceptualize a unified programming model that would be equally applicable to all of them. Another tough challenge is how to build streaming systems with cardinalities of topics ranging from hundreds to billions while delivering good performance and scalability. In this session, Sergey Bykov will talk about the journey of building Orleans Streams that originated in gaming and monitoring scenarios, and quickly expanded beyond them. He will cover the programming model of virtual streams that emerged as a natural extension of the virtual actor model of Orleans, the architecture of the underlying runtime system, the compromises and hard choices made in the process. Sergey will share the lessons learned from the experience of running the system in production, and future ideas and opportunities that remain to be explored.

1. Drinking
from the
Firehose
With Virtual Streams & Virtual Actors
Sergey Bykov
Microsoft, @sergeybykov

3. Event Streaming

4. Compute
Storage
Typical Workloads

5. User X
Event
User X
State
Updated
User X
State
User X
Event
User X
Event
Processing an Event

6. Challenges
Y X X Z X Z Y Z
C A C B B B A C
D E F F DD D E
B A C E F D Z A
Z X X Z X Z Y X
A A B C C C B A
E D E F F F E D
A Y Z C F D B A
Storage
High volume of events
▪Need multiple servers
Each event requires context
▪Load state from storage
▪Write results to storage
Uncoordinated IO is expensive
▪Caching
▪Write conflicts
Servers may go down
▪Clustering / coordination needed

7. User X
Event
User X
Event
Processing an Event
User X
Event
User X
State
Updated
User X
State

8. User X
Event
User X
State
Updated
User X
State
Benefits of Single Processor
User X
Event
User X
Event
User Y
Event
User Y
Event
User Y
Event
User Z
Event
User Z
Event
User Z
Event

9. Orleans Architecture
Frontends Orleans Cluster Storage
Grain
Persisted
Activating
Active
in memory
Deactivating

10. Challenges
Y X X Z X Z Y Z
C A C B B B A C
D E F F DD D E
B A C E F D Z A
Z X X Z X Z Y X
A A B C C C B A
E D E F F F E D
A Y Z C F D B A
Storage
High volume of events
▪Need multiple servers
Each event requires context
▪Load state from storage
▪Write results to storage
Uncoordinated IO is expensive
▪Caching
▪Write conflicts
Servers may go down
▪Clustering / coordination needed

11. PA PA PA PA
Messaging
Cluster Membership
Grain Directory
PA PA PA PA
Messaging
Cluster Membership
Grain Directory
Pulling Agents
Y X X Z X Z Y Z
C A C B B B A C
D E F F DD D E
B A C E F D Z A
Z X X Z X Z Y X
A A B C C C B A
E D E F F F E D
A Y Z C F D B A

12. Virtual Streams
A A A A A A A A
B B B B B B B B
C C C C C C C C
A A A A A A A A
B B B B B B B B
C C C C C C C C
A B B C B C A A A B B C A B B B C A A C A B B A
DD E F E E D F D E F F DD E E F F E F D E DD
X Y Y Z Y Z X X X Y Y Z X Y Y Y Z X X Z X Y Y X
A
B
C
D
D
Logical (Virtual) Streams
1000s to Ms to Bs
Physical Persistent Queues
10s to 1000s

13. Programming Model
public class UserGrain : Grain, IAsyncObserver<GameEvent>
{
public async Task Initialize()
{
var provider = GetStreamProvider(“EventHub Provider”);
var stream = provider.GetStream<GameEvent>(userId, “PlayerEvents”);
await stream.Subscribe(this);
}
public async Task OnNext(GameEvent event)
{
// Processing logic
...
await AwardAchievement(Achievements.MasterChief);
}
...
public async Task AwardAchievement(Achievements achievement)
{
var stream = provider.GetStream<Achievements>(streamId, “Achievements”);
await stream.OnNext(achievement);
}
}
// Rx-like interface to implement
public interface IAsyncObserver<T>
{
Task OnNext(T event);
Task OnComplete();
Task OnError();
}

14. Event Streaming is a powerful paradigm
Routing events to affected entity’s state is much better
▪ Reduces read and write load on storage
▪ Can serve RPC calls from same in-memory state
Virtual Actors are natural fit for processing events
▪ Cluster objects with stable identities
▪ Automatic resource management
Virtual Streams provide a simple but powerful model
▪ Orleans code is open source (MIT license)
▪ Runs on Windows, Linux, MacOS (via .NET Core)
Takeaways

16. Muchas Gracias!
Sergey Bykov
Microsoft, @sergeybykov