@fluidframework/azure-client
Advanced tools
A tool to enable creation and loading of Fluid containers using the Azure Fluid Relay service
The azure-client package provides a simple and powerful way to consume collaborative Fluid data with the Azure Fluid Relay service.
When taking a dependency on a Fluid Framework library's public APIs, we recommend using a ^ (caret) version range, such as ^1.3.4.
While Fluid Framework libraries may use different ranges with interdependencies between other Fluid Framework libraries,
library consumers should always prefer ^.
If using any of Fluid Framework's unstable APIs (for example, its beta APIs), we recommend using a more constrained version range, such as ~.
To get started, install the package by running the following command:
npm i @fluidframework/azure-client
This package leverages package.json exports to separate its APIs by support level. For more information on the related support guarantees, see API Support Levels.
To access the public (SemVer) APIs, import via @fluidframework/azure-client like normal.
To access the legacy APIs, import via @fluidframework/azure-client/legacy.
API documentation for @fluidframework/azure-client is available at https://fluidframework.com/docs/apis/azure-client.
The azure-client package has a AzureClient class that allows you to interact with Fluid.
import { AzureClient } from "@fluidframework/azure-client";
Fluid requires a backing service to enable collaborative communication. The AzureClient supports both instantiating against a deployed Azure Fluid Relay service instance for production scenarios, as well as against a local, in-memory service instance from the @fluidframework/azure-local-service library, for development purposes.
NOTE: You can use one instance of the AzureClient to create/fetch multiple containers from the same Azure Fluid Relay service instance.
In the example below we will walk through both connecting to a a live Azure Fluid Relay service instance by providing the tenant ID and key that is uniquely generated for us when onboarding to the service, as well as an example of running our application against the local service. We make use of AzureFunctionTokenProvider for token generation while running against a live Azure Fluid Relay instance and InsecureTokenProvider, from the @fluidframework/test-client-utils package, to authenticate a given user for access to the service locally. The AzureFunctionTokenProvider is an implementation that fulfills the ITokenProvider interface without exposing the tenant key secret in client-side code.
To run the local Azure Fluid Relay service with the default values of localhost:7070, enter the following command into a terminal window:
npx @fluidframework/azure-local-service@latest
Now, with our local service running in the background, we need to connect the application to it. For this, we first need to create our ITokenProvider instance to authenticate the current user to the service. For this, we can use the InsecureTokenProvider where we can pass anything into the key (since we are running locally) and an object identifying the current user. Our endpoint URL will point to the domain and port that our local Azure Fluid Relay service instance is running at. Lastly, to differentiate local mode from remote mode, we set the type to "local" or "remote" respectively.
import { AzureClient, AzureConnectionConfig } from "@fluidframework/azure-client";
import { InsecureTokenProvider } from "@fluidframework/test-client-utils";
const clientProps = {
connection: {
type: "local",
tokenProvider: new InsecureTokenProvider("fooBar", { id: "123", name: "Test User" }),
endpoint: "http://localhost:7070",
},
};
const azureClient = new AzureClient(clientProps);
When running against a live Azure Fluid Relay instance, we can use the same interface as we do locally but instead using the tenant ID, orderer, and storage URLs that were provided as part of the Azure Fluid Relay onboarding process. To ensure that the secret doesn't get exposed, it is passed to a secure, backend Azure function from which the token is fetched. We pass the Azure Function URL appended by /api/GetAzureToken along with the current user object to AzureFunctionTokenProvider. Later on, in AzureFunctionTokenProvider we make an axios GET request call to the Azure function by passing in the tenantID, documentId and id/name as optional parameters. Azure function is responsible for mapping between the tenant ID to a tenant key secret to generate and sign the token such that the service will accept it.
import { AzureClient, AzureConnectionConfig } from "@fluidframework/azure-client";
const clientProps = {
connection: {
type: "remote",
tenantId: "YOUR-TENANT-ID-HERE",
tokenProvider: new AzureFunctionTokenProvider("AZURE-FUNCTION-URL" + "/api/GetAzureToken", {
id: "test-user",
name: "Test User",
}),
endpoint: "ENTER-SERVICE-DISCOVERY-URL-HERE",
},
};
const azureClient = new AzureClient(clientProps);
AzureClient supports the ability to instantiate with experimental features enabled.
These features are experimental in nature and should NOT be used in production applications.
To learn more, see Experimental Features.
A Container instance is a organizational unit within Fluid. Each Container instance has a connection to the defined Fluid Service and contains a collection of collaborative objects.
Containers are created and identified by unique IDs. Management and storage of these IDs are the responsibility of the developer.
Fluid Containers are defined by a schema. The schema includes initial properties of the Container as well as what collaborative objects can be dynamically created.
See ContainerSchema in ./src/types/ts for details about the specific properties.
const schema = {
initialObjects: {
/* ... */
},
dynamicObjectTypes: [
/*...*/
],
};
const azureClient = new AzureClient(props);
const { container, services } = await azureClient.createContainer(schema, "2" /* compatibilityMode */);
// Set any default data on the container's `initialObjects` before attaching
// Returned ID can be used to fetch the container via `getContainer` below
const id = await container.attach();
Using the AzureClient object the developer can create and get Fluid containers. Because Fluid needs to be connected to a server, containers need to be created and retrieved asynchronously.
import { AzureClient } from "@fluidframework/azure-client";
const azureClient = new AzureClient(props);
const { container, services } = await azureClient.getContainer("_unique-id_", schema, "2" /* compatibilityMode */);
Note: When using the AzureClient with tenantId set to "local", all containers that have been created will be deleted when the instance of the local Azure Fluid Relay service (not client) that was run from the terminal window is closed. However, any containers created when running against a remote Azure Fluid Relay service will be persisted. Container IDs cannot be reused between local and remote Azure Fluid Relay services to fetch back the same container.
The most common way to use Fluid is through initial collaborative objects that are created when the Container is created.DistributedDataStructures and DataObjects are both supported types of collaborative objects.
initialObjects are loaded into memory when the Container is loaded and the developer can access them via the Container's initialObjects property. The initialObjects property has the same signature as the Container schema.
// Define the keys and types of the initial list of collaborative objects.
// Here, we are using a SharedMap DDS on key "map1" and a SharedString on key "text1".
const schema = {
initialObjects: {
map1: SharedMap,
text1: SharedString,
},
};
// Fetch back the container that had been created earlier with the same ID and schema
const { container, services } = await azureClient.getContainer("_unique-id_", schema, "2" /* compatibilityMode */);
// Get our list of initial objects that we had defined in the schema. initialObjects here will have the same signature
const initialObjects = container.initialObjects;
// Use the keys that we had set in the schema to load the individual objects
const map1 = initialObjects.map1;
const text1 = initialObjects["text1"];
LoadableObjects can also be created dynamically during runtime. Dynamic object types need to be defined in the dynamicObjectTypes property of the ContainerSchema.
The Container has a create method that will create a new instance of the provided type. This instance will be local to the user until attached to another LoadableObject. Dynamic objects created this way should be stored in initialObjects, which are attached when the Container is created. When storing a LoadableObject you must store a reference to the object and not the object itself. To do this use the handle property on the LoadableObject.
Dynamic objects are loaded on-demand to optimize for data virtualization. To get the LoadableObject, first get the stored handle then resolve that handle.
const schema = {
initialObjects: {
map1: SharedMap,
},
dynamicObjectTypes: [SharedString],
};
const { container, services } = await azureClient.getContainer("_unique-id_", schema, "2" /* compatibilityMode */);
const map1 = container.initialObjects.map1;
const text1 = await container.create(SharedString);
map1.set("text1-unique-id", text1.handle);
// ...
const text1Handle = map1.get("text1-unique-id"); // Get the handle
const text1 = await map1.get(); // Resolve the handle to get the object
// or
const text1 = await map1.get("text1-unique-id").get();
These are the platform requirements for the current version of Fluid Framework Client Packages. These requirements err on the side of being too strict since within a major version they can be relaxed over time, but not made stricter. For Long Term Support (LTS) versions this can require supporting these platforms for several years.
It is likely that other configurations will work, but they are not supported: if they stop working, we do not consider that a bug. If you would benefit from support for something not listed here, file an issue and the product team will evaluate your request. When making such a request please include if the configuration already works (and thus the request is just that it becomes officially supported), or if changes are required to get it working.
strict options are supported.strictNullChecks is required.exactOptionalPropertyTypes is currently not fully supported.
If used, narrowing members of Fluid Framework types types using in, Reflect.has, Object.hasOwn or Object.prototype.hasOwnProperty should be avoided as they may incorrectly exclude undefined from the possible values in some cases.Node16, NodeNext, or Bundler resolution should be used with TypeScript compilerOptions to follow the Node.js v12+ ESM Resolution and Loading algorithm.
Node10 resolution is not supported as it does not support Fluid Framework's API structuring pattern that is used to distinguish stable APIs from those that are in development.
ES Modules: ES Modules are the preferred way to consume our client packages (including in NodeJs) and consuming our client packages from ES Modules is fully supported.
CommonJs: Consuming our client packages as CommonJs is supported only in NodeJS and only for the cases listed below. This is done to accommodate some workflows without good ES Module support. If you have a workflow you would like included in this list, file an issue. Once this list of workflows motivating CommonJS support is empty, we may drop support for CommonJS one year after notice of the change is posted here.
There are many ways to contribute to Fluid.
Detailed instructions for working in the repo can be found in the Wiki.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project may contain Microsoft trademarks or logos for Microsoft projects, products, or services. Use of these trademarks or logos must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Not finding what you're looking for in this README? Check out fluidframework.com.
Still not finding what you're looking for? Please file an issue.
Thank you!
This project may contain Microsoft trademarks or logos for Microsoft projects, products, or services.
Use of these trademarks or logos must follow Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
FAQs
A tool to enable creation and loading of Fluid containers using the Azure Fluid Relay service
The npm package @fluidframework/azure-client receives a total of 5,366 weekly downloads. As such, @fluidframework/azure-client popularity was classified as popular.
We found that @fluidframework/azure-client demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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