Data Storage

Marcelle provides a flexible interface for creating data stores that provide a unified interface for storing data either on the client side (in memory or using web storage) or on a remote server. Some components rely on the definition of a data store – for instance, the Dataset component that needs to store instances, – however data collections can be created on the fly to store custom information when relevant. This is particularly useful to store some of the state of the application (for instance the model's parameters), a history of changes to the application, or custom session logs recording some of the user's interactions.

We use the Feathersopen in new window framework to facilitate the creation of collections of heterogeneous data. When data is stored on the client side, no configuration is necessary. For remote data persistence, a server application can be generated in minutes using Feather’s command-line interface, with a large range of database systems available. The flexible selection of the data store's location is advantageous for rapid prototyping, where data is stored on the client side or using a local server during development.

DataStore

The following factory function creates and returns a Marcelle data store:

dataStore(location: string): DataStore

The location argument can either be:

'memory' (default): in this case the data is stored in memory, and does not persist after page refresh
'localStorage': in this case the data is stored using the browser's web storage. It will persist after page refresh, but there is a limitation on the quantity of data that can be stored.
a URL indicating the location of the server. The server needs to be programmed with Feathers, as described below.

.connect()

async connect(): Promise<User>

Connect to the data store backend. If using a remote backend, the server must be running, otherwise an exception will be thrown. If the backend is configured with user authentication, this method will require the user to log in.

This method is automatically called by dependent components such as datasets and models.

async login(email: string, password: string): Promise<User>;

.logout()

async logout(): Promise<void>

.service()

service(name: string): Service<unknown>

Get the Feathers service instance with the given name. If the service does not exist yet, it will be automatically created. Note that the name of the service determines the name of the collection in the data store. It is important to choose name to avoid potential conflicts between collections.

The method returnsa Feathers Service instance, which API is documented on Feathers' wesiteopen in new window. The interface exposes find, get, create, update, patch and remove methods for manipulating the data.

.uploadAsset()

async uploadAsset(blob: Blob, filename = ''): Promise<string>

Upload an asset to a remote backend from a Blob, with an optional filename. The method returns the path of the file on the backend server.

async signup(email: string, password: string): Promise<User>

Service

Data Services are instances of Feathers Services. For details, refer to Feather's documentationopen in new window. From Feathers:

Service methods are pre-defined CRUDopen in new window methods that your service object can implement (or that have already been implemented by one of the database adapters). Below is an example of a Feathers service using async/awaitopen in new window as a JavaScript classopen in new window:

class MyService {
  async find(params) {
    return [];
  }
  async get(id, params) {}
  async create(data, params) {}
  async update(id, data, params) {}
  async patch(id, data, params) {}
  async remove(id, params) {}
  setup(app, path) {}
}

app.use('/my-service', new MyService());

Service methods must use async/awaitopen in new window or return a Promiseopen in new window and have the following parameters:

id — The identifier for the resource. A resource is the data identified by a unique id.
data — The resource data.
params - Additional parameters for the method call (see Feathers Docsopen in new window)

.find()

Service<T>.find(params: Params): Promise<Paginated<T>>

Retrieves a list of all resources from the service. params.query can be used to filter and limit the returned data.

.get(id, params)

Service<T>.get(id: string, params: Params): Promise<T>

Retrieves a single resource with the given id from the service.

.create()

Service<T>.create(data: T, params: Params): Promise<T>

Creates a new resource with data. The method should return with the newly created data. data may also be an array.

.update()

Service<T>.update(id: string, data: T, params: Params): Promise<T>

Replaces the resource identified by id with data. The method should return with the complete, updated resource data. id can also be null when updating multiple records, with params.query containing the query criteria.

.patch()

Service<T>.patch(id: string, data: Partial<T>, params: Params): Promise<T>

Merges the existing data of the resource identified by id with the new data. id can also be null indicating that multiple resources should be patched with params.query containing the query criteria.

.remove()

Service<T>.remove(id: string, params: Params): Promise<T>

Removes the resource with id. The method should return with the removed data. id can also be null, which indicates the deletion of multiple resources, with params.query containing the query criteria.

Dataset

dataset<T extends Instance>(name: string, store?: DataStore): Dataset<T>

A Dataset component allowing for capturing instances from a stream, storing them in a local or remote data-store. Items of the datasets are called instances, and are JavaScript objects with arbitrary shape, although by convention the fields x, y and thumbnail are commonly used. When using TypeScript, it is possible to extend the specification of the Instance interface:

export interface Instance {
  id?: ObjectId; // Object identifier in the database
  x: any; // Typically, input data
  y: any; // Typically, output data (for supervised learning)
  thumbnail?: string; // Thumbnail used for display in components such as datasetBrowser
  [key: string]: any;
}

Example:

const store = dataStore('localStorage');
const trainingSet = dataset('TrainingSet', store);

$instances.subscribe(trainingSet.create);

Parameters

Option	Type	Description	Required
name	string	The dataset name	✓
store	DataStore	The dataStore used to store the instances of the dataset.	✓

Properties

Name	Type	Description	Hold
$count	Stream<number>	Total number of instances in the dataset
$changes	Stream<DatasetChange[]>	Stream of changes applied to the dataset. Changes can concern a number of modifications (creation, update, deletion, ...) at various levels (dataset, class, instance). The interface is described below

Where dataset changes have the following interface:

interface DatasetChange {
  level: 'instance' | 'dataset';
  type: 'created' | 'updated' | 'removed' | 'renamed';
  data?: unknown;
}

.clear()

async clear(): Promise<void>

Clear the dataset, removing all instances.

.create()

async create(instance: Instance<InputType, OutputType>, params?: FeathersParams): Promise<Instance<InputType, OutputType>>

Create an instance in the dataset

Option	Type	Description	Required
instance	Instance<InputType, OutputType>	The instance data	✓
params	FeathersParams	Feathers Query parameters. See Feathers docsopen in new window.

.download()

async download(): Promise<void>

Download the dataset as a unique json file.

.find()

async find(params?: FeathersParams): Promise<Paginated<Instance<InputType, OutputType>>>

Get instances from the dataset, optionally passing Feathers parameters. Results are paginated, using the same format as services.

Option	Type	Description	Required
params	FeathersParams	Feathers Query parameters. See Feathers docsopen in new window.

.get()

async get(id: ObjectId, params?: FeathersParams): Promise<Instance<InputType, OutputType>>

Get an instance from the dataset by ID, optionally passing Feathers parameters.

Option	Type	Description	Required
id	ObjectId	The instance's unique ID	✓
params	FeathersParams	Feathers Query parameters. See Feathers docsopen in new window.

.items()

items(): ServiceIterable<Instance<InputType, OutputType>>

Get a lazy service iterable to iterate over the dataset.

TODO

Document lazy iterables and service iterables in data stores?

Example:

const instances = await dataset
  .items() // get iterable
  .query({ label: 'A' }) // query instances with label 'A'
  .select(['id', 'thumbnail']) // select the fields to return
  .toArray(); // convert to array

.patch()

patch(id: ObjectId, changes: Partial<Instance>, params?: FeathersParams): Promise<Instance>

Patch an instance in the dataset

Option	Type	Description	Required
id	ObjectId	The instance's unique ID	✓
changes	Partial<Instance>	The instance data	✓
params	FeathersParams	Feathers Query parameters. See Feathers docsopen in new window.

.remove()

remove(id: ObjectId, params?: FeathersParams): Promise<Instance>

Remove an instance from the dataset

Option	Type	Description	Required
id	ObjectId	The instance's unique ID	✓
params	FeathersParams	Feathers Query parameters. See Feathers docsopen in new window.

.sift()

sift(query: Query = {}): void

Filter the contents of the dataset from a Feathers Queryopen in new window. Sifting a dataset enforces that instances respect a given query. This affects all interactions with the dataset and dependent components. Note that it is possible to create several instances of datasets with different sift filters, that point to the same data store service (effectively creating different views on a given data collection).

Option	Type	Description	Required
query	Query	Feathers Query parameters. See Feathers docsopen in new window.

.update()

update(id: ObjectId, instance: Instance<InputType, OutputType>, params?: FeathersParams): Promise<Instance>

Update an instance in the dataset

Option	Type	Description	Required
id	ObjectId	The instance's unique ID	✓
instance	Instance<InputType, OutputType>	The instance data	✓
params	FeathersParams	Feathers Query parameters. See Feathers docsopen in new window.

.upload()

async upload(files: File[]): Promise<void>

Upload a dataset from files.

Option	Type	Description	Required
files	File[]	Array of files of type File	✓

Server-Side Storage

Marcelle provides a dedicated package for server-side data storage: @marcellejs/backendopen in new window. It can easily be integrated into existing Marcelle applications using the CLI, and only require minimal configuration for local use.

Marcelle backends are FeathersJSopen in new window applications, that provide persistent data storage with either NeDb or MongoDb.

Disclaimer

The backend package is under active development and is not yet stable. It is not production-ready.

Adding a backend to an existing application

A backend can be added to a Marcelle application using the CLI:

npx marcelle

yarn marcelle

pnpx marcelle

Select 'Manage the backend', then 'Configure a backend'. this will install @marcellejs/backend as a dependency to your project and create configuration files.

Two database systems are currently available for storing data:

NeDbopen in new window - an embedded datastore with a MongoDB like API. NeDB can store data in-memory or on the filesystem which makes it useful as a persistent storage without a separate database server.
MongoDbopen in new window

The CLI will install @marcellejs/backend and store configuration files in backend/config.

To run the backend locally:

npm run backend

The backend API will be available on http://localhost:3030open in new window. From a Marcelle application, interacting with this backend can be done through data stores, by instanciating them with the server URL as location parameter:

const store = dataStore('http://localhost:3030');

Configuration

Backends can be configured through two JSON files located in the backend/config directory, for development of production. Please refer to Feather's documentationopen in new window for general information about Feathers configuration. In this section, we detail Marcelle-specific configuration only.

name	type	default	description
host	string	localhost	Host Name for development.
port	number	3030	Port
database	nedb \| mongodb	nedb	The type of database to use. This is pre-configured when generated with the CLI.
nedb	path	"../data"	The local path to the folder where NeDb data should be stored
uploads	path	"../uploads"	The local path to the folder where file uploads should be stored
mongodb	url	"mongodb://localhost:27017/marcelle_backend"	The URL of the MongoDB database used for development
gridfs	boolean	true	Whether or not to upload files and assets to GridFS instead of the file system
whitelist.services	string[] \| "*"	"*"	The list of services that are allowed on the backend. "*" acts as a wildcard, allowing any service to be created from Marcelle applications
whitelist.assets	string[] \| "*"	["jpg", "jpeg", "png", "wav"]	The types of assets (file extensions) allowed for file upload on the server
paginate.default	number	100	The default number of items per page for all requests
paginate.max	number	1000	The maximum number of items per page for all requests
authentication.enabled	boolean	false	Whether or not to enable authentication

# Data Storage

# DataStore

# .connect()

# .login()

# .logout()

# .service()

# .uploadAsset()

# .signup()

# Service

# .find()

# .get(id, params)

# .create()

# .update()

# .patch()

# .remove()

# Dataset

# Parameters

# Properties

# .clear()

# .create()

# .download()

# .find()

# .get()

# .items()

# .patch()

# .remove()

# .sift()

# .update()

# .upload()

# Server-Side Storage

# Adding a backend to an existing application

# Configuration

Data Storage

DataStore

.connect()

.login()

.logout()

.service()

.uploadAsset()

.signup()

Service

.find()

.get(id, params)

.create()

.update()

.patch()

.remove()

Dataset

Parameters

Properties

.clear()

.create()

.download()

.find()

.get()

.items()

.patch()

.remove()

.sift()

.update()

.upload()

Server-Side Storage

Adding a backend to an existing application

Configuration