enzyme-db v1.0.3

EnzymeDB

Enzyme is an abstraction layer for the localStorage Web API provided by the browser, allowing you to define models and schemas for storing documents locally while also simplifying interaction with localStorage through intuitive and expressive easy-to-use methods.

# Installation (NodeJS v18.0.0)
npm i enzyme-db

Table of Contents

• Enzyme DB
• When or why should you use it?
• Built by developers, for developers!
• Basic Usage
  • Initialization
  • Document creation
  • Document search
  • Document update
  • Document deletion
• Rules defined within the schema
• Middlewares
  • Registering a middleware
• References between models
• Better practices with Enzyme
• Security and Data Privacy
• Performance Considerations
• Exploring the class
  • constructor(Name, Schema, Options)
  • Model.DropCollection()
  • GetCollection()
  • Overwrite(Collection)
  • Save(...Documents)
  • IsFieldInvalid(Field, Value)
  • Exists(Query)
  • CountDocuments(...Queries)
  • Find(...Queries)
  • FindOne(Query)
  • FindAll()
  • UpdateMany(Query, Update)
  • FindByIdAndDelete
  • FindByIdAndUpdate(Id, Update)
  • FindOneAndUpdate(Query, Update)
  • FindOneAndDelete(Query)
  • UpdateOne(Query, Update)
  • DeleteOne(Query)
  • DeleteMany(...Queries)
  • CreateMany(...Documents)
  • Create(Document)
• Examples and Use Cases
• Enzyme under MIT license

When or why should you use it?

• Data persistence: If your web application needs certain data to persist between the different sessions that a user may have.

• User Experience Optimization: Storing certain data locally can significantly improve the user experience within your web application by reducing the need to make requests to the server each time that data is needed, allowing you to have greater speed loading and printing by the client.

• Offline Web Apps: When you have a Progressive Web App (PWA) that works without an Internet connection, it can be useful to use Enzyme to store modeled data needed for the PWA to work seamlessly when the user is offline.

Built by developers, for developers!

• Data Modeling: Enzyme allows you to define models to store the data locally in the browser, defining in the model the structure of the document, the fields and the types of data guaranteeing the coherence of the data that a document will have within the collection.

• Validations: Likewise, the library provides you with a validation system, where it allows you to define custom rules in addition to the native ones in order to validate the data before it is stored in the collection, helping you maintain the integrity of the data and ensure that they comply with the established criteria.

• Queries and Operations: It facilitates the consultation of documents through the use of expressive, intuitive and easy-to-use methods.

• Middlewares: Enzyme allows you to register middlewares which are executed before or after certain operations, such as save, update, delete, validate, among others. Allowing you to execute custom logic at different stages of a document's lifecycle in the execution context.

• References: Although localStorage is not a database, much less relational, Enzyme allows you to create relationships with other models and perform different types of operations between them according to your documents.

• Schemas: Enzyme allows you to create schemas for the data, helping you to establish a predefined structure for the documents, useful to control the evolution of the data over time.

Basic Usage

Initialization

To create a model, you must provide the name of the model along with a schema and custom options (optionally).

const BlogSchema = {
    Title: {
        Type: String,
        MaxLength: 32,
        MinLength: 4,
        Slugify: true,
        LowerCase: true,
        Required: true
    },
    Description: {
        Type: String,
        MaxLength: 255,
        MinLength: 32,
        Default: 'Default Description'
    }
};

const Options = {};

const BlogModel = new Enzyme('Blog', BlogSchema, Options);

Consider that among the available options are:

Document creation

You can create a document using the methods: Create() or CreateMany()

const NewPost = {
    Title: 'This is an example',
    Description: 'Enzyme is elegant abstraction for the localStorage Web API'
};

const Result = BlogModel.Create(NewPost);

Document search

Using the Find(), FindOne(), FindById() methods, you can find documents within a given collection stored in a model.

const Query = { Title: 'this-is-an-example' };
const SearchResult = BlogModel.Find(Query);

Document update

With the help of the UpdateOne(), UpdateMany() or FindByIdAndUpdate() methods you can update documents.

const UpdateQuery = { Title: 'this-is-an-example' };
const UpdateData = { Description: 'This is an new description for the documentation example' };
const UpdateResult = BlogModel.UpdateOne(UpdateQuery, UpdateData);

Document deletion

Using the DeleteOne(), DeleteMany() and FindByIdAndDelete methods you can delete documents within the collection.

const DeleteQuery = { Title: 'this-is-an-example' };
const DeleteResult = BlogModel.DeleteOne(DeleteQuery);

Rules defined within the schema

The rules that are defined within a field in the schema of an Enzyme model allow establishing certain criteria in order to verify and validate the input data that will be stored in the collection of said model. Each rule will apply to the corresponding field value and these can help ensure the consistency and integrity of the data that will be stored in the collection. You will then be presented with the list of available rules.

In addition to the validation rules established in the schema that a field must have, Enzyme also allows you to define rules that will modify the value of a field prior to being stored locally, allowing you to perform transformations on the original value to ensure that it complies with the requirements before storage, then you will be presented with the list of rules available for it:

These rules are useful for normalizing the data and making sure it follows the standards before persisting. Note that these parameters apply only if the field is valid.

Middlewares

Middlewares act as intermediaries between different parts of your application, allowing the execution of additional actions before or after an operation within the model. The latter allow the library to be more flexible regarding the implementation of functionalities without the need to modify or alter the main code.

Registering a middleware

MyModel.Before(Middleware, Callback);

Consider that the second parameter, Callback, must be expressed with the JavaScript function keyword and not as an arrow function, although it will work, from the abstraction layer the this object is binded, allowing you to access it directly from function(){} to the internal functionalities of the model, for example this.GetCollection().

Example using the Creation Middleware.

Model.Before('Create', function(){
    console.log('[BEFORE]: Create.');
});

Model.After('Create', function(){
    console.log('[AFTER]: Create.');
});

Model.Create({ Foo: 'Bar' });

Where Model.Before is the equivalent of schema.pre in mongoose and Model.After is schema.post.

References between models.

In Enzyme, it is possible to establish relationships between models through references, allowing the linking of documents in one model with documents belonging to another model.

For example, suppose we have two models: Author and Book, and we want to establish a relationship between them, where a Book has an Author in its schema.

Creating the Author model.

const AuthorSchema = {
  Name: {
    Type: String
  }
};

const Author = new Enzyme('Author', AuthorSchema);

Creating the Book model and referencing the Author model.

const BookSchema = {
  Title: {
    Type: String
  },
  Author: {
    // Set the reference to the Author model here
    Reference: Author,
    // // The field in the Book model that will contain the reference to the author
    Field: 'Id' 
  }
};

const Book = new Enzyme('Book', BookSchema);

Creating documents in the models and establishing the relationship.

// Create an author in the Author model
const { Document: AuthorDocument } = Author.Create({ Name: 'Rodolfo Herrera' });

// Create a book in the Book model and set the reference to the author
const { Document: BookDocument } = Book.Create({
  Title: 'Javascript Complete Course',
  // Set reference to author id
  Author: AuthorDocument.Id
});

console.log(Book.GetCollection());
/*
[
  {
    Title: 'Javascript Complete Course',
    Author: {
      Name: 'Rodolfo Herrera',
      Id: '19ed1dfa-b289-4376-9b10-fe4d049f11f9',
      CreatedAt: '2023-08-20T05:20:22.664Z'
    },
    Id: '02d35791-7883-4b05-9adb-c00e0af2dd26',
    CreatedAt: '2023-08-20T05:20:22.665Z'
  }
]
*/

In the example presented, a reference to the Author model has been created within the Book model schema. When creating a new book, we set the reference to the author's Id in the Author field, which is the field we've selected for the reference in the schema. In this way, we have established a relationship between the Author and Book models.

At the time that references are used, it is possible to access the author's details directly from the book document, which allows queries and related operations to be carried out in a much more efficient and structured way.

Better practices with Enzyme

When implementing Enzyme into your application and defining models, schemas, and operations, it's important to follow best practices to ensure that your implementation's source code is organized, maintainable, and efficient. Following are some of the best practices for building your models, schematics, and operations with Enzyme.

• Divide and Conquer: Divide your models and schematics into separate files for each entity or document type, allowing you to keep your code organized while also making it easy to navigate when looking for specific definitions.

• Reusable Models and Schemes: If you have entities that share similar structures, you can consider creating reusable schemas so that they can be shared by two or more models, thus avoiding code duplication and simplifying maintenance.

• Clear and Meaningful Names: Use descriptive model, field, and method names. Make your code readable and understandable to both you and other developers.

• Validations and Rules: Use the validation rules that are natively provided by Enzyme in order to guarantee the security and integrity of the stored data.

• Use of Middleware: You may consider using middlewares for the purpose of encapsulating common logic or actions that need to be performed before or after certain operations allowing you to centralize the logic and maintain a clear flow in your code.

• Avoid Complex Logic in Schemas: Avoid including complex business logic within schemas, keep the latter out of schema definitions.

Security and Data Privacy

The purpose of Enzyme is focused on simplifying the interaction with local storage by providing methods for data management. The security and privacy of information stored by the library will largely depend on how the library is implemented and used in conjunction with other security measures.

• Physical Access to the Device: Well, the data stored locally is subject to the physical security in which the application is executed. If the device is compromised or is accessible by unauthorized third parties, then the stored data could be in eventual risk.

• Data Encryption: Considering that Enzyme does not provide any data encryption by default, it is recommended to implement additional encryption measures in order to protect data stored locally. This could include encrypting the data before storing it using strong and secure methods.

• Data Validation and Sanitization: You must ensure that you sanitize and validate the data before storing it. You can prevent injection attacks and other types of malicious data manipulation.

• Regulatory Compliance: If the application in which you are integrating Enzyme handles personal or sensitive data, it is really important that you make sure that you fully comply with the existing privacy and data protection regulations that apply within your region. Considering regulations such as the General Data Protection Regulation (GDPR) in the European Union.

• Privacy Policy and Consent: Users of your application should be aware of how their data is stored and used locally by Enzyme if this stored data contains personal information or potentially sensitive data.

By combining Enzyme with good security and privacy practices, you can guarantee your users a safe and reliable experience within your application.

Performance Considerations

Initially in the introduction to the documentation, we mentioned that Enzyme is an abstraction layer for local storage in the browser, using the localStorage API. You should consider that local storage in the browser has limitations, which in short include the amount of data it can handle efficiently. Although there is no absolute limit in terms of the amount of data that Enzyme allows you to store in your application, there are practical limitations that you should be aware of when handling large amounts of data.

• Browser Storage Capacity: Local storage in the browser, including localStorage, generally has a storage limit which may vary by browser and device. In some browsers this limit is around 5-10 MB per domain, in the case of Enzyme, per model. If you exceed this limit, the data may not be stored correctly or errors may be generated in between.

• Perfomance: As the amount of data stored increases, performance in terms of reading or accessing the data may become poor. Read and write operations may become slower as the amount of data increases.

• Efficiency: As much as Enzyme takes care of working as quickly and efficiently as possible, localStorage isn't exactly designed to handle large data sets efficiently.

Enzyme uses localStorage as storage, the above limitations and considerations largely apply to how Enzyme handles large amounts of data. If you have a need to handle large data sets or if your query and storage requirements are complex, you might consider other server-side database solutions or cloud storage services.

Enzyme may be suitable and work optimally for applications with more modest local storage needs, it is important to consider the practical limitations of localStorage and assess whether it is the best option for your application.

Exploring the class

constructor(Name, Schema, Options) In order to initialize an instance of the Enzyme class where the parameters are:

• Name (String): The name you want to give to the model you are creating, for example 'Blog'.
• Schema (Object): Object that describes the schema that the model documents should comply with.
• Options (Object, Optional): Custom options for the model.

Model.DropCollection()

It allows to eliminate all the existing documents that a certain collection houses based on an instance (model).

• Return value: None.

GetCollection()

Allows you to retrieve all documents from the collection in the model within localStorage.

• Return value: Document array.

Overwrite(Collection)

Allows you to replace the current collection in the model with a new provided collection, which will be written to the localStorage.

• Parameters:
  • Collection (Array): It must be the new collection of documents.
• Return value: None.

Save(...Documents)

It allows adding new documents to the existing collection within the model, which will be stored in localStorage.

• Parameters:
  • Documents (Objects): One or more documents that are passed as a parameter to the function to add them into the collection.
• Return value: None.

IsFieldInvalid(Field, Value)

Checks if a field delivered in the document is valid according to the rules defined in the model schema.

• Parameters:
  • Field (String): The name of the field that you want to validate.
  • Value (Any Kind): The value of the field that you want to validate.
• Return value (Object): The return will only be when a field does not comply with the validations.
  • Rule (Object): In this context, Rule refers to the specific rule being evaluated (for example, MaxLength, MinLength, Required, etc.). A Rule object is created containing two properties:
    • Name (String): The name of the rule that is being evaluated (for example, "MaxLength").
    • Value (String): The specific value defined in the schema for that rule in the field in question (for example, the maximum length value allowed for MaxLength).
  • Field (Object): Similarly, a Field object is created that contains information about the field being evaluated:
    • Name (String): The name of the field being evaluated (for example, "Title").
    • Value (Any Kind): The current value of the field that failed validation.
    • Length (Number if Value is String else Undefined): The length of the field value, in the case of text type fields (for example, length of a character string).

Exists(Query)

Checks if there is a document that meets the query delivered as a parameter.

• Parameters:
  • Query (Object): Check that the document must comply.
• Return value (Object): If the document is found, it is returned.

CountDocuments(...Queries)

Counts the number of documents that match the queries delivered as a parameter.

• Parameters:
  • Queries (Objects): Queries to apply the filter to the document search.
• Return value (Object):
  • Documents (Array of Objects): Documents available in the collection.
  • Length (Number): Number of documents available in the collection.

Find(...Queries)

Find all documents matching the given queries.

• Parameters:
  • ...Queries (Objects): One or more queries that will be applied as a filter to find comments.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Documents (Array of Objects): Contains the documents that matches.

FindOne(Query)

Allows you to find a document that matches the given query, if there are multiple documents, the first one found will be returned.

• Parameters:
  • Query (Object): Query to apply as a filter for the search.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Current collection of documents.
  • Document (Object): Contains the found document.

FindAll()

Gets all the documents that the collection of a respective model contains.

• Return value (Object):
  • Documents (Array of Objects): Documents available in the collection.
  • Length (Number): Number of documents available in the collection.

UpdateMany(Query, Update)

Update one or more documents that match the query received.

• Parameters:
  • Query (Object): Query to apply as a filter for the selection of documents.
  • Update (Object): Update data.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • UpdatedDocuments (Array of Objects): Updated documents.
  • Collection (Array of Objects): Collection of already updated documents.

FindByIdAndDelete

It finds a document within the collection according to its Id and proceeds to delete it after.

• Parameters:
  • Id (String): Identifier of the document to delete.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Contains the deleted document.

FindByIdAndUpdate(Id, Update)

Finds a specific document based on the Identifier provided as a parameter and updates it with the second provided parameter.

• Parameters:
  • Id (String): Identifier of the document to be updated.
  • Update (Object): Update data.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Original document before applying the update.
  • UpdatedDocument (Object): Contains the already updated document.

FindOneAndUpdate(Query, Update)

Finds a document that matches the provided query and updates it with the provided data. If there are multiple documents that match the search, the first of these is updated.

• Parameters:
  • Query (Object): Query that will be applied as a filter for the selection of the document.
  • Update (Object): Update data.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Original document before applying the update.
  • UpdatedDocument (Object): Contains the already updated document.

FindOneAndDelete(Query)

Finds a document that matches the given query and then deletes it.

• Parameters:
  • Query (Object): Query that will be applied for the selection of the document.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Contains the deleted document.

UpdateOne(Query, Update)

Updates a document that matches the supplied query.

• Parameters:
  • Query (Object): Query that will be applied for the selection of the document, if there are multiple coincidences, the first one will be chosen.
  • Update (Object): Update data.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Original document before applying the update.
  • UpdatedDocument (Object): Contains the already updated document.

DeleteOne(Query)

Deletes a document that matches the provided query.

• Parameters:
  • Query (Object): Query that will be applied for the selection of the document.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Contains the deleted document.

DeleteMany(...Queries)

Deletes one or more documents that match the queries provided as a parameter.

• Parameters:
  • ...Queries (Objects): One or more queries for the selection of documents.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • DeletedDocuments (Array of Objects): Contains the deleted documents.

CreateMany(...Documents)

Allows the creation of one or more documents within the collection.

• Parameters:
  • ...Documents (Objects): One or more documents to create..
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • CreatedDocuments (Array of Objects): Contains the created documents.

Create(Document)

Create a document in the collection.

• Parameters:
  • Document (Object): Document to create.
• Return value (Object):
  • Status (String): Indicates the status of the operation.
  • Collection (Array of Objects): Collection of already updated documents.
  • Document (Object): Document created.

Examples and Use Cases

• PWA with Dynamic Data: In the context of a Progressive Web App (PWA), Enzyme can be useful for locally storing data necessary for the PWA to function without an internet connection. Where you could include product information, catalogs, schedules, etc.

• Notes management: You can use Enzyme to create a model which represents the notes in a notes application. Where each note could have a title, a content and a date. Users could create, update and delete notes locally using Enzyme, allowing for a better user experience by allowing you to access your notes even when offline.

• Task lists in a productivity application: If you have or want to create a to-do list application, you can use Enzyme for administration or management of a user's to-do list. Each task could contain a title, description and a status (whether it was completed or pending). Users could add new tasks, mark as completed, and delete them using Enzyme.

You can use Enzyme in any scenario where your application needs to store data locally in order to improve the user experience when using the latter. Whether it is to maintain personal information, manage content, track progress or ensure the availability of offline data. With Enzyme, you can greatly simplify interaction with local storage and help optimize your web application or PWA.

Enzyme under MIT license

The MIT license (Massachusetts Institute of Technology License) is a permissive open source software license. It allows developers to use, modify, distribute and sublicense the software without material restrictions. The MIT license is characterized by its simplicity and does not impose many legal restrictions on users.

• Rights Of Use: You might consider mentioning that Enzyme is available under the MIT license, so users have the right to use it in their projects at no cost.

• Modification and Distribution: Enzyme's source code can be modified and adapted to the specific needs of an application and distribute its modified versions.

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