Powerful python library like Mongoose

PyDbSchema Features

The library enable one to create Schema validates the Schema.

The Library will also insert the Default values when one provided and it in schema but didn't provide during insertation.

The library is very hand and it's objects are directly Pymongo collection Object do one configuration the all of the.

Schema can access pymongo Object method the Shema are Collection Level, Let Bring the Mongoose Flavour.

Requirements || dependecies

pymongo >= 3.0

Usage

Configuration

Create a folder with the database configuration then pass database name and url or name only

    # import the module
    import PyDbSchema
    PyDbSchema.connect(<DatabaseName: str>)
    # Test if the database if connected
    if PyDbSchema.connection.connected:
        print("Connected")

So by this one folder all the database Configuration are ready Note: This code mostly should be at the top so as to enable Schema to access conection

Models Creation

Create a folder and insert all of your model Rules here

    # For Example here
    import PyDbSchema
    User = {
    'Attributes':[{ "Name": {
    "dtype": str,
    "req": True,
    "default": None
    }},
    { "email": {
    "dtype": str,
    "req": True,
    "default": None
    }},
    }

    ## Creating the Schema
    User = PyDbSchema.Schema(<Skelrton:dict>, <NameSchema: str>)
    # The Schema object is Collection Object by nature and pymongo methods can be used in itds

Using the Schema Object Method

The Schema Object perform all of the methods like how pymongo Object will perform but Crud( Create Read Update Delete) operation are easier as follows

Creating

Note: Advantages of PyDbSchema is that it's Schema are validated and default value are passed if not there

Methods

Model.insertOne(data) -> # Insert One object passed into the collection Used

Model.bulkInsert(data) -> # Insert list of object passed into the collection Used

    Example
    # Inserting Document
    # Create a folder and database configuration
    # Create the Models  Folder
    # Inside creates Schema Objects
    Example 
    from Models.User import User # We were having a folder with py file having User Object of the class of Schema
    mydata = {
    "Name": "Jack",
    'email': '[email protected]',
    }
    response = User.insertOne(credentials)
    print(response) #Return pymongo result object

    # Inserting Many Objects 
    ListOfData [
    {
            "Name": "Jack",
            'email': '[email protected]'
    },
    {
            "Name": "Onisa",
            'email': '[email protected]'
    }      
    ]

    response = User.bulkInsert(credentials)

Finding

Note: The Schema skeleton will be used for all of this methods

Methods

Model.find(credential) -> # find the Documents required and return list according to the credential passed

Model.findOne(data) -> # Find and return only one document if credentials passed

    # Find One Result
    # Import the Schema Objects
    # Example 
    from Models.User import User # We were having a folder with py file having User Object of the class of Schema
    credentials = {"Name": 'Yosia'}
    response = User.findOne(credentials)
    print(response) # Check the Response

    # To find many Use the findMany
    credentials = {"Name": 'Yosia'}
    response = User.find(credentials) # Return multiple of the instance found

Updating

Note: The Schema skeleton will be used for all of this methods

Methods

Model.updateOne(credentialForSearching, newdata,dict>) -> # update one according to credentil passed and new data to be inserted

Model.updateMany(credentialForSearching, newdata,dict>) -> # update Many according to credentil passed and new data to be inserted

    # Find One Result
    # Import the Schema Objects
    Example 
    
    from Models.User import User # We were having a folder with py file having User Object of the class of Schema
    myquery = { "Name": "Yosia" }
    newvalues = {"Name": "Shadrack" }
    response = User.updateOne(myquery, newvalues)
    print(response) # Check the Response

    # To updateMany  Use the <Model>.updateMany(dataForSearching<dict>, newData<dict>)
    response = User.updateMany(dataForSearching<dict>, newData<dic>)
    # check response like how pymongo insert method does like
    print(response.matched_count)

Deleting

Note: The Schema skeleton will be used for all of this methods

Methods

Model.deleteOne(credential) -> # delete one according to credentil passed

Model.deleteMany(credential) -> # delete Many according to credentil passed

    # Find One Result
    # Import the Schema Objects
    Example 
    
    from Models.User import User # We were having a folder with py file having User Object of the class of Schema
    response = User.deleteMany(myquery, newvalues)
    print(response) # Check the Response
    credentials = {"Name": 'Yosia'}
    # To updateMany  Use the <Model>.deleteMany(credentials:dict)
    credentials = {"Name": 'Yosia'}
    response = User.deleteOne(credentials<dict>)
    # check response like how pymongo insert method does like

FindById

Just pass the id and model will give you the back whole document

    # Example
    from Models.User import User # We were having a folder with py file having User Object of the class of Schema
    response = User.findById(id)
    print(response) # Check the Response

Using Schema as Object as Pymongo Collection

Just Use the attribute inside the Schema the collection attribute example

    # Example
    from Models.User import User # We were having a folder with py file having User Object of the class of Schema
    # collection
    collection = User.collection
    type(User.collection) # Return <class 'pymongo.collection.Collection'>
    # so all of the pymongo collection methods can be used
    # like
    collection.create_index,  next, watch

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