DynaBridge

DynaBridge is a Python library that simplifies interactions with Amazon DynamoDB, a fast and flexible NoSQL database service provided by AWS. This library provides an easy-to-use interface for creating, querying, and managing tables and items in DynamoDB.

Table of Contents

• Installation
• Getting Started
• DynamoTable
• DynamoModel
• DynamoSchema and DynamoAttribute
• Examples
• Contributing
• License

Installation

You can install DynaBridge using pip:

pip install dynabridge

Getting Started

Before you can start using DynaBridge, you'll need to set up your AWS credentials. You can do this by configuring your AWS credentials using the AWS Command Line Interface (CLI) or by setting the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. Make sure that your IAM user or role has the necessary permissions to create and manage DynamoDB tables.

Here's a simple example of how to get started with DynaBridge:

from dynabridge import DynamoTable, DynamoModel

# Initialize DynamoTable with your AWS resource and table details
dynamo_table = DynamoTable(
    table_name='MyTable',
    db_resource=your_db_resource,  # Replace with your DynamoDB resource
    primary_key='your_primary_key',
    sort_key='your_sort_key'  # Optional
)

# Set the DynamoTable instance for your DynamoModel
DynamoModel.set_dynamo_table(dynamo_table)

# Create a DynamoModel instance and save it to the table
model_instance = DynamoModel()
model_instance.your_attribute = 'your_value'
model_instance.create()

Now let's dive into the details of DynamoTable and DynamoModel.

DynamoTable

The DynamoTable class provides methods to interact with DynamoDB tables. It includes functions for creating, retrieving, updating, and deleting items. Here are some of the key methods and their descriptions:

• _create_table: Creates a new DynamoDB table.
• create_table_if_not_exists: Checks if a table exists and creates it if not.
• save: Saves an item to the table.
• get: Retrieves an item from the table.
• scan: Retrieves items based on a filter expression.
• delete: Deletes an item from the table.

You can also use DynamoTable to handle versioning, schema validation, and transactional updates in DynamoDB.

DynamoModel

The DynamoModel class is designed to simplify the interaction with DynamoDB tables using a model-like approach. You can use DynamoModel to define and manipulate items within a table. Here are some of the key methods and their descriptions:

• create: Creates and saves an item to the associated table.
• get: Retrieves an item from the table based on the primary key.
• get_all: Retrieves all items from the associated table.
• delete_by_primary_key: Deletes an item based on the primary key.
• get_all_by_attribute: Retrieves items based on a specific attribute and its value.
• transactional_lock: Provides a mechanism for transactional operations on items in the table.

DynamoModel is a convenient way to work with DynamoDB tables, allowing you to define models that correspond to your data and perform operations on them with ease.

DynamoSchema and DynamoAttribute

DynaBridge also provides the DynamoSchema and DynamoAttribute classes for more fine-grained control over attribute management within DynamoDB schemas. Here's how you can use them:

Creating a DynamoSchema and DynamoAttribute

from DynaBridge.attribute import DynamoAttribute
from DynaBridge.exceptions import DynamoAttributeError, DynamoTypeError, DynamoValueError

# Create a DynamoAttribute
attribute = DynamoAttribute(
    attribute_name='example_attribute',
    data_type='str',
    attribute_default='default_value',
    attribute_required=True,
    attribute_immutable=False,
    attribute_validator=None
)

# Create a DynamoSchema
schema = DynamoSchema()
schema.add_attributes([attribute])

• DynamoAttribute allows you to specify attributes with detailed information, including name, data type, default value, required flag, immutability flag, and a validation function.

• DynamoSchema is used to store and manage attributes within a DynamoDB schema.

Using DynamoSchema to Validate and Fill Defaults

# Validate an item against the schema
item = {'example_attribute': 'item_value'}
schema.validate(item)

# Fill in defaults for an item
item = {}
item = schema.fill_defaults(item)

DynamoSchema can validate whether an item contains all required attributes, whether attribute types match expected types, and whether attribute values pass validation functions. It can also fill in missing attributes with their default values.

Examples

Here are some examples of how to use DynaBridge for common DynamoDB operations:

Creating a DynamoTable and Saving an Item

from dynabridge import DynamoTable, DynamoModel

# Initialize DynamoTable
dynamo_table = DynamoTable(
    table_name='MyTable',
    db_resource=your_db_resource,
    primary_key='your_primary_key',
    sort_key='your_sort_key'  # Optional
)

# Set the DynamoTable instance for your DynamoModel
DynamoModel.set_dynamo_table(dynamo_table)

# Create a DynamoModel instance and save it to the table
model_instance = DynamoModel()
model_instance.your_attribute = 'your_value'
model_instance.create()

Retrieving an Item by Primary Key

# Get an item by its primary key
key = {'your_primary_key': 'key_value'}
retrieved_item = DynamoModel.get_by_primary_key(key)

Retrieving All Items

# Get all items from the table
all_items = DynamoModel.get_all()

Deleting an Item by Primary Key

# Delete an item by its primary key
key = {'your_primary_key': 'key_value'}
DynamoModel.delete_by_primary_key(key)

Sample App

Here is an example app that defines a User model using DynamoModel and performs basic operations on it using DynamoBridge:

from DynaBridge.table import DynamoTable
from DynaBridge.models import DynamoModel
from DynaBridge.exceptions import *
from DynaBridge.attribute import *
from DynaBridge.schema import *
import boto3

# Create a DynamoTable
UserTable = DynamoTable(table_name='User', db_resource=boto3.resource('dynamodb'), primary_key='UserId')

# Define a DynamoSchema with attributes
UserSchema = DynamoSchema().add_attributes([
    DynamoAttribute(attribute_name='UserId', data_type='uuid', attribute_required=True),
    DynamoAttribute(attribute_name='Name', data_type='str', attribute_required=True),
    DynamoAttribute(attribute_name='Country', data_type='str', attribute_required=True, attribute_default='India'),
    DynamoAttribute(attribute_name='Email', data_type='str', attribute_required=False,
                    attribute_validator=lambda x: '@' in x, attribute_default="[email protected]"),
    DynamoAttribute(attribute_name='Status', data_type='str', attribute_required=True)
])

# Register the schema with the table
UserTable.register_schema(UserSchema)

# Define a User model class
class User(DynamoModel):
    def __init__(self, **kwargs):
        super().__init__()
        if "UserId" in kwargs:
            self.UserId = kwargs['UserId']
        if "Name" in kwargs:
            self.Name = kwargs['Name']
        if "Country" in kwargs:
            self.Country = kwargs['Country']
        if "Email" in kwargs:
            self.Email = kwargs['Email']
        if "Status" in kwargs:
            self.Status = kwargs['Status']

    def update_status(self, new_status):
        self.Status = new_status
        self.save()

# Associate the User model with the UserTable
User.set_dynamo_table(UserTable)

# Create and save a user
user = User(Name='John', Country='India', Email='[email protected]', Status='Active').save()
print(f"User created: {user.get_self_json()}")

# Retrieve the user by primary key
retrieved_user = User.get_by_primary_key(key={'UserId': user.UserId})
print(f"Retrieved user: {retrieved_user.get_self_json()}")

# Retrieve all users with a specific attribute value
users_in_india = User.get_all_by_attribute(attribute='Country', value='India')
print("Users in India:")
for user in users_in_india:
    print(user.get_self_json())

# Update the user
user.Name = 'Updated John'
user.save()
print(f"User updated: {user.get_self_json()}")

user.update_status(new_status='Inactive')

# Additional feature: Conditional updates
user.Status = 'Suspended'
user.save_if(lambda obj: obj.Status == 'Active')
print(f"User updated with condition: {user.get_self_json()}")

# Delete the user
user.delete()
print("User deleted")

# Check if the user exists
if User.exists(key={'UserId': user.UserId}):
    print("User exists")
else:
    print("User does not exist")

Limitations

While DynaBridge is a powerful library for DynamoDB interactions, it has some limitations:

• Complex Queries: Complex queries and joins are not supported out of the box, as DynamoDB is designed for high-performance and scalability rather than complex querying.

• Scaling and Throughput: You need to carefully manage and provision your DynamoDB throughput to ensure optimal performance.

• No Support for Nested Data Structures: DynaBridge does not support nested dictionaries and lists as custom attributes or within schemas. DynamoDB is optimized for simple data structures, so storing complex nested data may require flattening or other strategies.

• No Support for Enums in Custom Attributes: While you can specify data types like 'str' or 'int' for custom attributes using DynamoAttribute, there's no built-in support for enums. You would need to handle enum-like behavior at the application level.

Please be aware of these limitations when designing your DynamoDB schemas and working with custom attributes in DynaBridge.

Contributing

If you'd like to contribute to DynaBridge, please see our contributing guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.