G.O.D Framework

Introduction

The ai_mongodb_dridfs_module_storage.py script is a robust storage utility designed for working with MongoDB and GridFS. It enables efficient storage, retrieval, and management of large files such as datasets, model files, logs, and configurations in a distributed MongoDB environment. The module is optimized to support scalable AI workflows with a focus on secure and organized data storage.

Purpose

The primary goals of ai_mongodb_dridfs_module_storage.py include:

• Providing a storage layer for AI artifacts such as models, datasets, logs, and metrics using MongoDB.
• Optimizing large file handling with the MongoDB GridFS API.
• Ensuring secure and efficient data saves and retrievals for usage in distributed environments.
• Supporting the AI pipeline in scenarios that require high data availability and durability.

Key Features

• GridFS Integration: Allows seamless working with large files using MongoDB GridFS for storage.
• File Metadata Management: Supports tagging and searching files based on metadata for efficient retrieval.
• Encryption Support: Securely stores files by leveraging optional encryption layers.
• Versioning System: Automatically manages file versions for easy rollback and upgrade processes.
• Fault Tolerance: Ensures support for distributed deployment over multiple nodes in the MongoDB cluster.

Logic and Implementation

This script predominantly uses the pymongo library for MongoDB interactions and the gridfs package to interact with large files. Files are uploaded to and downloaded from MongoDB collections in chunks using GridFS, enabling efficient storage of data too large to fit in MongoDB's 16MB document size limit. Each file is stored with metadata to help identify, version, and search for it later.

            import pymongo
            import gridfs

            class MongoGridFSHandler:
                """
                MongoDB GridFS handler for efficiently storing and retrieving large files.
                """

                def __init__(self, uri="mongodb://localhost:27017", db_name="ai_storage"):
                    self.client = pymongo.MongoClient(uri)
                    self.db = self.client[db_name]
                    self.fs = gridfs.GridFS(self.db)

                def upload_file(self, file_path, metadata=None):
                    """
                    Upload a file to MongoDB GridFS.
                    """
                    with open(file_path, "rb") as f:
                        file_id = self.fs.put(f, filename=file_path, metadata=metadata)
                        print(f"File {file_path} uploaded with ID: {file_id}")
                        return file_id

                def download_file(self, file_id, output_path):
                    """
                    Download a file from MongoDB GridFS.
                    """
                    file_data = self.fs.get(file_id)
                    with open(output_path, "wb") as f:
                        f.write(file_data.read())
                    print(f"File downloaded to {output_path}")

                def delete_file(self, file_id):
                    """
                    Delete a file from GridFS storage.
                    """
                    self.fs.delete(file_id)
                    print(f"File ID {file_id} deleted from storage.")

                def find_files(self, query=None):
                    """
                    Find files in GridFS based on metadata or other attributes.
                    """
                    query = query or {}
                    return list(self.fs.find(query))

            # Example Usage
            if __name__ == "__main__":
                handler = MongoGridFSHandler(uri="mongodb://localhost:27017", db_name="ai_data")
                file_id = handler.upload_file("example_model.pkl", metadata={"type": "model", "version": "v1"})
                handler.download_file(file_id, "downloaded_model.pkl")
                files = handler.find_files({"metadata.type": "model"})
                print(f"Found {len(files)} file(s) with type 'model'.")
            

Dependencies

• pymongo: MongoDB Python client for connecting to databases.
• gridfs: Utility in pymongo for handling large files.
• os: Python standard library for file handling.

Usage

The ai_mongodb_dridfs_module_storage.py provides a straightforward API for uploading, retrieving, and managing large files in AI workflows. Below is an example of how to use this script:

            # Initialize the MongoGridFSHandler
            handler = MongoGridFSHandler(uri="mongodb://your-mongodb-uri:27017", db_name="ai_assets")

            # Upload a file
            file_id = handler.upload_file("path/to/your/large_file.csv", metadata={"type": "dataset"})

            # Retrieve and download the file
            handler.download_file(file_id, "path/to/downloaded_large_file.csv")

            # Delete the file
            handler.delete_file(file_id)
            

System Integration

ai_mongodb_dridfs_module_storage.py can integrate seamlessly with other modules within the G.O.D Framework. Key use cases include:

• Data Pipelines: Store and retrieve datasets required by the ai_automated_data_pipeline.py module.
• Model Storage: Use with model training and deployment scripts (e.g., ai_model_export.py) to persist trained models securely.
• Logs and Monitoring: Archive monitoring results from ai_monitoring.py for historical analysis.

Future Enhancements

• Introduce advanced search features for metadata querying.
• Support automated file encryption using services like AWS KMS.
• Integration with cloud storage services for hybrid storage solutions.
• Optimize file versioning for rollback operations in CI/CD pipelines.