Comparing Fluid-Framework to Signal-R: Understanding the Distinguishing Factors

Question

Comparing Fluid-Framework to Signal-R: Understanding the Distinguishing Factors

Query: Recently, Microsoft unveiled the Fluid-Framework on GitHub. What are the steps to replace Signal-R with Microsoft's Fluid-Framework and what are the main differences?

Scenario: It appears that Fluid supports collaboration and data synchronization for tools like Mermaid diagrams and Flowcharts, which is a key use case. However, when attempting to switch from Signal-R to Fluid-Framework, the lack of documentation and guidance became evident.

Some points of confusion that require clarification:

The absence of clear details on the client-server connection is puzzling. For instance, configuring
```
data sync frequency, fallback storage, custom claims within the container, port information, attribute binding
```
in the Action/API signature is challenging without strongly typed examples.
```
import { DataObject, DataObjectFactory } from "@fluidframework/aqueduct";
```
Requesting more robust server-side wiring examples.
Fluid Container: Obtaining Transition or Query information for the active nodes' state and next nodes from the mermaid chart is unclear. How can this be achieved?

Difficulties due to lack of documentation https://i.sstatic.net/a83Hz.png

javascript typescript asp.net-core signalr fluid-framework

Answer 1

Answer №1

SignalR is a powerful tool for facilitating communication between clients. One could consider the possibility of creating a custom driver to replace the existing WebSocket implementation, such as socket.io, within the Fluid Framework.

However, Fluid Framework is not a direct substitute for WebSockets, Socket.IO, or SignalR. It may require the application to transition more of the client state into Fluid distributed data structures.

Developers familiar with SignalR and WebSockets often rely on Last Write Wins data structures, where a message sent via WebSocket can update the client-side data. In certain cases, the message might serve as a simple notification, like receiving an alert.

While Fluid does incorporate Last Write Wins data structures, it focuses more on state management than message transmission. Some data structures within Fluid may necessitate more intricate state management, as not all data, like strings and sequences, follow a Last Write Wins approach.

Consider a scenario where two users concurrently edit a string using a Last Write Wins algorithm:

Starting state: "Hello World";
Alice adds "!" at the end;
Bob adds "?" at the end;

This could result in either "Hello World?" or "Hello World!" depending on the order of operations.

Fluid processes Bob's and Alice's changes through the service and applies them in a reproducible manner using a merge tree data structure. Ultimately, the string would display as "Hello World?!".

While simple examples, like Last Write Wins, may be straightforward to implement, handling more complex scenarios can be challenging. Operational Transformation (OT) and Conflict-free Replicated Data Types (CRDTs) are existing methods for managing state replication. Fluid's primary value lies in managing intricate state on behalf of the user.

Answer 2

SignalR is a powerful tool for facilitating communication between clients. One could consider the possibility of creating a custom driver to replace the existing WebSocket implementation, such as socket.io, within the Fluid Framework.

However, Fluid Framework is not a direct substitute for WebSockets, Socket.IO, or SignalR. It may require the application to transition more of the client state into Fluid distributed data structures.

Developers familiar with SignalR and WebSockets often rely on Last Write Wins data structures, where a message sent via WebSocket can update the client-side data. In certain cases, the message might serve as a simple notification, like receiving an alert.

While Fluid does incorporate Last Write Wins data structures, it focuses more on state management than message transmission. Some data structures within Fluid may necessitate more intricate state management, as not all data, like strings and sequences, follow a Last Write Wins approach.

Consider a scenario where two users concurrently edit a string using a Last Write Wins algorithm:

Starting state: "Hello World";
Alice adds "!" at the end;
Bob adds "?" at the end;

This could result in either "Hello World?" or "Hello World!" depending on the order of operations.

Fluid processes Bob's and Alice's changes through the service and applies them in a reproducible manner using a merge tree data structure. Ultimately, the string would display as "Hello World?!".

While simple examples, like Last Write Wins, may be straightforward to implement, handling more complex scenarios can be challenging. Operational Transformation (OT) and Conflict-free Replicated Data Types (CRDTs) are existing methods for managing state replication. Fluid's primary value lies in managing intricate state on behalf of the user.

Comparing Fluid-Framework to Signal-R: Understanding the Distinguishing Factors

Answer №1

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