GoMongoDB: File-Based Database System in Go

Introduction

GoMongoDB is a lightweight, file-based database system written in Go. It provides an easy-to-use interface for performing CRUD (Create, Read, Update, Delete) operations on data collections. Inspired by MongoDB, it supports a range of query operators for searching and filtering data.

Features

• Simple CRUD Operations: Perform basic Create, Read, Update, and Delete operations on data collections.
• Flexible Querying: Utilize MongoDB-like query operators for advanced search and filtering capabilities.
• Concurrency Safety: Ensures data integrity and prevents race conditions using mutexes.
• File-Based Storage: Stores data persistently in files, ensuring durability and portability.

Data Integrity and Concurrency Management

GoMongoDB employs mutexes to ensure data integrity and manage concurrent access safely. Each collection has its own mutex, allowing for concurrent operations without compromising data consistency.

Getting Started

To get started with GoMongoDB, follow these steps:

1. Clone the repository:

   git clone https://github.com/Sakthe-Balan/GoMongoDB.git

2. Run the main.go file:

   go run main.go

Alternatively, you can use the Dockerfile to build and run the application:

# Build the Docker image
docker build -t go-mongodb-app .

# Run the Docker container
docker run -p 6942:6942 go-mongodb-app

Collections and Resources

In GoMongoDB, data is organized into collections, which act as containers for storing related documents. Each document within a collection is a JSON-like object representing a single record or entity.

• Collections: Collections are analogous to tables in relational databases or folders in a file system. They provide a way to logically group similar data together.

• Resources: Resources refer to individual documents within a collection. Each resource contains fields with corresponding values, similar to attributes in a table row or properties in an object.

When performing CRUD (Create, Read, Update, Delete) operations, you specify both the collection and the resource. This allows for efficient management and retrieval of data within GoMongoDB.

Instructions to Use Dashboard

To interact with the database through the dashboard, follow these steps:

1. Navigate to the dashboard directory in your terminal.

2. Put your Streamlit UI file (let's call it ui.py) in this directory.

3. Run the Streamlit app using the following command:

    streamlit run ui.py
   

This command will start the Streamlit server and launch your dashboard UI in a web browser. You can then use the dashboard to interact with the database, perform CRUD operations, and visualize data as needed.

Endpoints

1. Create Resource

Endpoint: /write

Method: POST

Description: Adds a new resource to a specified collection.

Parameters:

• collection: The name of the collection.
• resource: The name of the resource.

Example Usage:

curl -X POST "http://localhost:6942/write?collection=<Collection>&resource=<resource>" \
-H "Content-Type: application/json" \
-d '{"name":"John Doe","age":35,"city":"New York"}'

Example Usage (Python)

import requests

url = 'http://localhost:6942/write?collection=&resource='
headers = {'Content-Type': 'application/json'}
data = {
    "name": "John Doe",
    "age": 35,
    "city": "New York",
   
}

response = requests.post(url, json=data, headers=headers)
print(response)

2. Read Resource

Endpoint: /read

Method: GET

Description: Retrieves a specific resource from a collection.

Parameters:

• collection: The name of the collection.
• resource: The name of the resource.

Example Usage:

curl -X GET "http://localhost:6942/read?collection=<Collection>&resource=<resource>"

Example Usage (Python)

import requests

url = 'http://localhost:6942/read?collection=&resource='
headers = {'Content-Type': 'application/json'}


response = requests.get(url, headers=headers)
print(response.json())

3. Read All Resources

Endpoint: /readAll

Method: GET

Description: Retrieves all resources from a collection.

Parameters:

• collection: The name of the collection.

Example Usage:

curl -X GET "http://localhost:6942/readall?collection=<Collection>"

Example Usage (Python)

import requests

url = 'http://localhost:6942/readall?collection='
headers = {'Content-Type': 'application/json'}


response = requests.get(url, headers=headers)
print(response.json())

4. Delete Resource

Endpoint: /delete

Method: DELETE

Description: Removes a specific resource from a collection.

Parameters:

• collection: The name of the collection.
• resource: The name of the resource.

Example Usage:

curl -X DELETE "http://localhost:6942/delete?collection=<Collection>&resource=<resource>"

Example Usage (Python)

import requests

url = 'http://localhost:6942/delete?collection=&resource='
headers = {'Content-Type': 'application/json'}


response = requests.delete(url, headers=headers)
print(response)

5. Delete All Resources

Endpoint: /deleteAll

Method: DELETE

Description: Removes all resources from a collection.

Parameters:

• collection: The name of the collection.

Example Usage:

curl -X DELETE "http://localhost:6942/deleteall?collection=<Collection>"

Example Usage (Python)

import requests

url = 'http://localhost:6942/deleteall?collection='
headers = {'Content-Type': 'application/json'}


response = requests.delete(url, headers=headers)
print(response)

6. Search

Endpoint: /search

Method: POST

Description: Search for resources using MongoDB-like query operators.

Parameters:

• collection: The name of the collection.
• query: MongoDB-like query to filter resources.

Example Usage:

curl -X POST "http://localhost:6942/search?collection=<Collection>" \
-H "Content-Type: application/json" \
-d '{"age":{"$lte":30}}'

Example Usage (Python)

import requests


url = 'http://localhost:6942/search?collection='
headers = {'Content-Type': 'application/json'}

data = {"age": {"$lte": 35}}

response = requests.post(url, json=data, headers=headers)
print(response.text)

7. RegexSearch

Endpoint: /regexsearch

Method: POST

Description: Search for resources using regular expressions to match fields.

Parameters:

• collection: The name of the collection.
• query: A map of field names to regex patterns.

Example Usage:

curl -X POST "http://localhost:6942/regexsearch?collection=<Collection>" \
-H "Content-Type: application/json" \
-d '{"name": "^John", "email": ".*@example\\.com$"}'

Example Usage (Python)

import requests

url = 'http://localhost:6942/regexsearch?collection=<Collection>'
headers = {'Content-Type': 'application/json'}

data = {
    "name": "^John",
    "email": ".*@example\\.com$"
}

response = requests.post(url, json=data, headers=headers)
print(response.text)

MongoDB-like Query Operators:

The search endpoint supports various MongoDB-like query operators such as $eq, $ne, $gt, $gte, $lt, $lte, $in, etc. Refer to the MongoDB documentation for more details on query operators.

Features to be Added

• Regex and Other Useful Functions: Enhance querying capabilities by adding support for regular expressions and other useful functions.
• Distributed Framework for Handling Large Data: Implement a distributed framework to handle large datasets, ensuring scalability and performance.
• Framework for Database Access: Develop a framework to simplify and streamline access to the database, making integration and development more efficient.
• Replication and Sharding Logic: Integrate replication and sharding logic to improve data availability, reliability, and distribution across multiple nodes.
• Dashboard for Handling Data:

Contributing

Contributions are welcome! Feel free to open issues or pull requests on the GitHub repository.