DKBOTZDB
Welcome to DKBOTZDB! ๐
DKBOTZDB Is The Most Powerful And Developer-Friendly Database Solution Powered By DKBOTZ And DKBOTZPro, Offering Fast, Reliable, Scalable, And Secure Data Management. Designed For Effortless Integration And Built With Modern API Standards, dkbotzdb Ensures Smooth Performance Across Applications, Backed By The Trust And Excellence of dkbotzpro.in.
Table of Contents
Features
- Quick and Robust Database Interaction: Insert, find, update and delete records easily.
- Smart Find & Advanced Queries: Powerful query feature with regex support, range filters, sorting, limiting, and skipping results.
- Case Insensitive: Recognizes multiple variations of class names ( DKBOTZDB, DKBOTZDB, DKBOTZdb ).
- Flexible Syntax: Multiple ways to set tokens and collections (bracket notation, attribute access).
- API Driven: Driven by HTTP requests with automatic re-try logic and robust error handling.
- Scalable: Supports large amounts of data with no reduction in performance.
- Secure: Supports secure API calls with token-based authentication.
- Colored Logging: Beautiful colored console output will enhance your debug experience.
- Admin Features: Token management and plan administration tools.
๐ Why DKBOTZDB?
After years of struggling with complex database setups, poor documentation, and unreliable services, we created dkbotzdb to solve real developer problems:
- โก Lightning Fast: Sub-millisecond response times with intelligent caching
- ๐ง Zero Setup: Start coding immediately - no configuration files or complex setup
- ๐ก๏ธ Rock Solid: Enterprise-grade reliability with automatic failover
- ๐ฏ Developer First: Intuitive API design with beautiful error messages
- ๐ Infinitely Scalable: From prototype to production without breaking a sweat
- ๐ฐ Fair Pricing: Pay for what you use, not what you might use
Why We Built This
We were fed up with databases that produce promises but deliver headaches. There were too many occasions where we were struggling against:
- Complicated setup processes that took hours
- Poor documentation that left us guessing
- Unreliable connections that failed at the worst moments
- Limited query capabilities that forced us to write workarounds
- Expensive pricing models that didn't make sense for growing projects
So we decided to build something better. dkbotzdb is our answer to these problems.
What Makes It Special
Super Fast Performance
When we say fast, we mean fast. This is not seconds, we are taking about milliseconds. As such, we have optimized the query path, and built intelligent caching mechanisms to guarantee it will still be responsive under heavy load.
Simple Integration
Do you recall the last time you started using a new service and it just worked out-of-the-box? That's what we're aiming for with dkbotzdb; start coding with just a few lines of code. No configuration files or multi-step environment setup.
Bulletproof Reliability
We understand that your data matters. Therefore, we designed multiple layers of redundancy, automatic failover processes and backup processes to make sure that your data is safe and applications are online.
Flexible Query System
Sometimes you just need CRUD operations. Other times you want complex aggregations, regex matching and range filters. dkbotzdb makes everything easy to query. dkbotzdb has a powerful query system, but it is also easy enough for a beginner to figure out.
Developer Experience First
All new features written for dkbotzdb go through one simple filter: "Will this make a developer's life easier?" If no, the feature doesn't get shipped. That's why you will see colored console logging, intuitive method names, and detailed error messages throughout the entire library.
Installation
You can install dkbotzdb using pip:
pip install dkbotzdb
Manual Installation
You can also install DKBOTZDB
by cloning this repository:
git clone https://github.com/DKBOTZPROJECT/DKBOTZDB.git
cd DKBOTZDB
pip install requests colorlog
pip install -r requirements.txt
Setup
- Create an account on DKBOTZDB.
- Obtain your API Token from the DKBOTZDB dashboard.
- Choose or create a collection where you want to store data.
Quick Start
from dkbotzdb import DKBOTZDB
db = DKBOTZDB()['YOUR_TOKEN']['your_collection']
db = DKBOTZDB().YOUR_TOKEN.your_collection
db = DKBOTZDB(token='YOUR_TOKEN')
db.collection = 'your_collection'
result = db.insert_one({"name": "John", "age": 30, "city": "New York"})
print("Document inserted:", result)
user = db.find_one({"name": "John"})
print("Found user:", user)
Basic Usage
Different Class Name Variations
from dkbotzdb import DKBOTZDB, DkBotzDB, DKBOTZdb
db1 = DKBOTZDB()['token']['collection']
db2 = DkBotzDB()['token']['collection']
db3 = DKBOTZdb()['token']['collection']
Setting Token and Collection
from dkbotzdb import DKBOTZDB
db = DKBOTZDB()['YOUR_TOKEN']['users']
db = DKBOTZDB().YOUR_TOKEN.users
db = DKBOTZDB()['YOUR_TOKEN'].users
db = DKBOTZDB().YOUR_TOKEN['users']
db = DKBOTZDB(token='YOUR_TOKEN')
db.collection = 'users'
CRUD Operations
Insert Operations
insert_one() / insertOne()
Insert a single document into the collection.
user_data = {
"name": "Alice Johnson",
"email": "alice@example.com",
"age": 28,
"department": "Engineering",
"skills": ["Python", "JavaScript", "React"],
"salary": 75000,
"joined_date": "2023-01-15"
}
result = db.insert_one(user_data)
print("Inserted:", result)
result = db.insertOne(user_data)
insert_many() / insertMany()
Insert multiple documents at once.
users_data = [
{"name": "Bob Smith", "age": 32, "department": "Marketing", "salary": 65000},
{"name": "Carol White", "age": 29, "department": "Sales", "salary": 60000},
{"name": "David Brown", "age": 35, "department": "Engineering", "salary": 80000},
{"name": "Eve Davis", "age": 26, "department": "HR", "salary": 55000}
]
result = db.insert_many(users_data)
print("Inserted multiple:", result)
result = db.insertMany(users_data)
Find Operations
find_one() / findOne()
Find a single document matching the query.
user = db.find_one({"name": "Alice Johnson"})
print("Found user:", user)
engineer = db.find_one({"department": "Engineering", "age": {"$gte": 30}})
print("Found engineer:", engineer)
first_user = db.find_one()
print("First user:", first_user)
user = db.findOne({"name": "Bob Smith"})
find()
Find multiple documents matching the query.
engineers = db.find({"department": "Engineering"})
print("All engineers:", engineers)
young_employees = db.find({"age": {"$lt": 30}})
print("Young employees:", young_employees)
high_earners = db.find({
"salary": {"$gte": 70000},
"department": {"$in": ["Engineering", "Marketing"]}
})
print("High earners:", high_earners)
limited_results = db.find(
query={"department": "Engineering"},
limit=5,
skip=2,
sort={"salary": -1}
)
print("Limited results:", limited_results)
all_users = db.find()
print("All users:", all_users)
Update Operations
update_one() / updateOne()
Update a single document.
result = db.update_one(
{"name": "Alice Johnson"},
{"$set": {"age": 29, "salary": 78000}}
)
print("Update result:", result)
result = db.update_one(
{"name": "Bob Smith"},
{"$set": {"remote_work": True, "last_updated": "2024-01-15"}}
)
result = db.update_one(
{"name": "Carol White"},
{"$inc": {"salary": 5000}}
)
result = db.updateOne({"name": "David Brown"}, {"$set": {"department": "DevOps"}})
update_many() / updateMany()
Update multiple documents.
result = db.update_many(
{"department": "Engineering"},
{"$set": {"remote_work": True, "updated": "2024-01-15"}}
)
print("Updated engineers:", result)
result = db.update_many(
{"salary": {"$lt": 70000}},
{"$inc": {"salary": 5000}}
)
print("Salary updates:", result)
result = db.updateMany(
{"age": {"$gte": 30}},
{"$set": {"senior_employee": True}}
)
replace_one() / replaceOne()
Replace an entire document.
new_user_data = {
"name": "Alice Johnson",
"email": "alice.johnson@newcompany.com",
"age": 29,
"department": "Data Science",
"skills": ["Python", "Machine Learning", "SQL"],
"salary": 85000,
"joined_date": "2024-01-01"
}
result = db.replace_one(
{"name": "Alice Johnson"},
new_user_data
)
print("Replace result:", result)
result = db.replaceOne({"name": "Bob Smith"}, new_user_data)
Delete Operations
delete_one() / deleteOne()
Delete a single document.
result = db.delete_one({"name": "Eve Davis"})
print("Delete result:", result)
result = db.delete_one({
"department": "HR",
"age": {"$lt": 25}
})
result = db.deleteOne({"name": "Carol White"})
delete_many() / deleteMany()
Delete multiple documents.
result = db.delete_many({"department": "Intern"})
print("Deleted interns:", result)
result = db.delete_many({"salary": {"$lt": 50000}})
print("Deleted low salary employees:", result)
result = db.delete_many({"age": {"$gte": 65}})
result = db.deleteMany({"active": False})
Advanced Operations
count_documents() / countDocuments() / count()
Count documents matching a query.
total_count = db.count_documents()
print("Total employees:", total_count)
engineer_count = db.count_documents({"department": "Engineering"})
print("Total engineers:", engineer_count)
high_earner_count = db.count_documents({
"salary": {"$gte": 75000},
"age": {"$lt": 40}
})
print("Young high earners:", high_earner_count)
count1 = db.countDocuments({"department": "Sales"})
count2 = db.count({"remote_work": True})
aggregate() / smart_find()
Advanced querying with multiple filter options.
results = db.aggregate(
query={"department": "Engineering"},
sort={"salary": -1},
limit=10
)
print("Top 10 engineers by salary:", results)
results = db.aggregate(
regex={"name": "^A.*"},
sort={"age": 1}
)
print("Names starting with A:", results)
results = db.aggregate(
range_filter={"salary": {"min": 60000, "max": 80000}},
sort={"joined_date": -1}
)
print("Mid-range salaries:", results)
results = db.aggregate(
query={"department": {"$in": ["Engineering", "Data Science"]}},
regex={"skills": "Python"},
range_filter={"age": {"min": 25, "max": 35}},
sort={"salary": -1},
limit=5,
skip=2
)
print("Complex query results:", results)
count = db.aggregate(
query={"department": "Engineering"},
count_only=True
)
print("Engineer count:", count)
results = db.smart_find(
query={"remote_work": True},
sort={"salary": -1},
limit=20
)
distinct()
Get distinct values for a field.
departments = db.distinct("department")
print("All departments:", departments)
skills = db.distinct("skills", {"department": "Engineering"})
print("Engineering skills:", skills)
ages = db.distinct("age")
print("All ages:", ages)
cities = db.distinct("city", {"salary": {"$gte": 70000}})
print("Cities of high earners:", cities)
Collection Management
collections()
List all collections in your database.
all_collections = db.collections()
print("All collections:", all_collections)
drop()
Drop (delete) the current collection.
result = db.drop()
print("Collection dropped:", result)
drop_all() / dropall()
Drop all collections in the database.
result = db.drop_all()
print("All collections dropped:", result)
result = db.dropall()
usage_info()
Get usage statistics for your database.
usage = db.usage_info()
print("Usage stats:", usage)
Token & Plan Management
get_token_info() / token_info() / token_details()
Get information about a token.
token_info = db.get_token_info()
print("Token info:", token_info)
other_token_info = db.get_token_info("other_token_here")
print("Other token info:", other_token_info)
info1 = db.token_info()
info2 = db.token_details("some_token")
plan_status() / check_plan() / user_plan()
Check plan status for a token.
plan = db.plan_status()
print("Plan status:", plan)
plan = db.plan_status("user_token_here")
print("User plan:", plan)
plan1 = db.check_plan()
plan2 = db.user_plan("another_token")
list_plans() / plans() / get_plans()
List all available plans.
available_plans = db.list_plans()
print("Available plans:", available_plans)
plans1 = db.plans()
plans2 = db.get_plans()
Admin Operations
Note: These operations require admin privileges
generate_token() / create_token() / admin_token()
Generate a new token (admin only).
new_token = db.generate_token(
name="New User Token",
plan_id="pro",
duration_days=365
)
print("New token created:", new_token)
token1 = db.create_token("API Token", "free", 30)
token2 = db.admin_token("Admin Token", "enterprise", 730)
activate_plan()
Activate a plan for a user (admin only).
result = db.activate_plan(
user_token="user_token_here",
plan_id="pro",
duration_days=365
)
print("Plan activated:", result)
result = db.activate_plan(
user_token="another_user_token",
plan_id="enterprise",
duration_days=730
)
Method Aliases
dkbotzdb provides multiple aliases for the same operations to match different coding styles:
insert_one() | insertOne() |
insert_many() | insertMany() |
find_one() | findOne() |
update_one() | updateOne() |
update_many() | updateMany() |
delete_one() | deleteOne() |
delete_many() | deleteMany() |
replace_one() | replaceOne() |
count_documents() | countDocuments() , count() |
smart_find() | aggregate() |
drop_all() | dropall() |
get_token_info() | token_info() , token_details() |
plan_status() | check_plan() , user_plan() |
list_plans() | plans() , get_plans() |
generate_token() | create_token() , admin_token() |
Logging
dkbotzdb uses beautiful colored logging for better debugging experience:
- ๐ต DEBUG (Cyan): Detailed debugging information
- ๐ข INFO (Green): General information about operations
- ๐ก WARNING (Yellow): Warning messages
- ๐ด ERROR (Red): Error messages
- ๐ด CRITICAL (Bold Red): Critical errors
Customizing Log Level
import logging
from dkbotzdb import logger
logger.setLevel(logging.DEBUG)
logger.setLevel(logging.WARNING)
logger.setLevel(logging.ERROR)
Error Handling
dkbotzdb includes robust error handling with automatic retry logic:
from dkbotzdb import DKBOTZDB
db = DKBOTZDB()['YOUR_TOKEN']['test_collection']
result = db.insert_one({"test": "data"})
if result:
print("Success:", result)
else:
print("Operation failed - check logs for details")
user = db.find_one({"id": "important_user"})
if user is None:
print("User not found or error occurred")
else:
print("User found:", user)
Complete Example
Here's a comprehensive example showing various operations:
from dkbotzdb import DKBOTZDB
import json
db = DKBOTZDB()['YOUR_TOKEN']['employees']
employees = [
{
"id": 1,
"name": "John Doe",
"email": "john@company.com",
"department": "Engineering",
"position": "Senior Developer",
"salary": 85000,
"skills": ["Python", "JavaScript", "Docker"],
"remote": True,
"joined": "2022-03-15"
},
{
"id": 2,
"name": "Jane Smith",
"email": "jane@company.com",
"department": "Marketing",
"position": "Marketing Manager",
"salary": 72000,
"skills": ["SEO", "Content Marketing", "Analytics"],
"remote": False,
"joined": "2021-08-22"
},
{
"id": 3,
"name": "Mike Johnson",
"email": "mike@company.com",
"department": "Engineering",
"position": "DevOps Engineer",
"salary": 90000,
"skills": ["AWS", "Kubernetes", "Python"],
"remote": True,
"joined": "2023-01-10"
}
]
def main():
print("=== Inserting Employees ===")
result = db.insert_many(employees)
print(f"Inserted: {result}")
print("\n=== Counting Employees ===")
total = db.count_documents()
print(f"Total employees: {total}")
print("\n=== Finding Engineers ===")
engineers = db.find({"department": "Engineering"})
print(f"Engineers: {json.dumps(engineers, indent=2)}")
print("\n=== Finding Remote Workers ===")
remote_workers = db.smart_find(
query={"remote": True},
sort={"salary": -1}
)
print(f"Remote workers: {json.dumps(remote_workers, indent=2)}")
print("\n=== Updating Salary ===")
update_result = db.update_one(
{"name": "John Doe"},
{"$inc": {"salary": 5000}}
)
print(f"Salary update: {update_result}")
print("\n=== Distinct Departments ===")
departments = db.distinct("department")
print(f"Departments: {departments}")
print("\n=== Advanced Search ===")
high_earners = db.aggregate(
query={"salary": {"$gte": 80000}},
regex={"skills": "Python"},
sort={"salary": -1}
)
print(f"High-earning Python developers: {json.dumps(high_earners, indent=2)}")
print("\n=== Usage Info ===")
usage = db.usage_info()
print(f"Usage: {json.dumps(usage, indent=2)}")
if __name__ == "__main__":
main()
Real-World Example
Let me show you how this all comes together in a real application:
from dkbotzdb import DKBOTZDB
import json
from datetime import datetime
db = DKBOTZDB()['YOUR_TOKEN']['employee_management']
class EmployeeManager:
def __init__(self):
self.db = db
def add_employee(self, employee_data):
"""Add a new employee to the system"""
employee_data['created_at'] = datetime.now().isoformat()
employee_data['status'] = 'active'
result = self.db.insert_one(employee_data)
if result:
print(f"โ
Successfully added {employee_data['name']}")
return result
else:
print("โ Failed to add employee")
return None
def find_employees_by_department(self, department):
"""Get all employees in a specific department"""
employees = self.db.find({"department": department, "status": "active"})
return employees or []
def promote_employee(self, employee_id, new_position, new_salary):
"""Promote an employee"""
update_data = {
"$set": {
"position": new_position,
"salary": new_salary,
"last_promotion": datetime.now().isoformat()
}
}
result = self.db.update_one({"employee_id": employee_id}, update_data)
if result:
print(f"๐ Employee {employee_id} promoted to {new_position}")
return result
def get_department_stats(self):
"""Get statistics about each department"""
departments = self.db.distinct("department")
stats = {}
for dept in departments:
count = self.db.count_documents({"department": dept, "status": "active"})
avg_salary = self.db.aggregate(
query={"department": dept, "status": "active"},
count_only=False
)
stats[dept] = {
"employee_count": count,
"employees": avg_salary
}
return stats
def search_employees(self, search_term):
"""Smart search across multiple fields"""
return self.db.smart_find(
regex={
"$or": [
{"name": f".*{search_term}.*"},
{"email": f".*{search_term}.*"},
{"skills": f".*{search_term}.*"}
]
},
sort={"name": 1}
)
if __name__ == "__main__":
manager = EmployeeManager()
new_employee = {
"employee_id": "EMP001",
"name": "Jennifer Walsh",
"email": "jennifer@company.com",
"department": "Engineering",
"position": "Software Developer",
"salary": 75000,
"skills": ["Python", "React", "PostgreSQL"],
"remote_work": True
}
manager.add_employee(new_employee)
engineers = manager.find_employees_by_department("Engineering")
print(f"Engineers on the team: {len(engineers)}")
manager.promote_employee("EMP001", "Senior Software Developer", 85000)
stats = manager.get_department_stats()
print("Department statistics:", json.dumps(stats, indent=2))
results = manager.search_employees("Jennifer")
print("Search results:", results)
๐ Feature Comparison Matrix
Setup Time | 30 seconds | 30 minutes | 5 minutes | 10 minutes |
Learning Curve | Minimal | Steep | Moderate | Moderate |
Pricing | Pay-as-you-go | Complex tiers | Limited free | Limited free |
Real-time | โ
(Coming Q2) | โ
| โ
| โ
|
ACID Transactions | โ
(Coming Q3) | โ
| Limited | โ
|
GraphQL | โ
(Coming Q2) | Manual setup | โ | โ
|
Multi-language | โ
(Coming Q1 '25) | โ
| โ
| โ
|
Serverless | โ
(Coming Q1 '25) | โ | โ
| โ
|
Developer Experience | โญโญโญโญโญ | โญโญโญ | โญโญโญโญ | โญโญโญโญ |
๐ฏ Use Cases & Industries
๐ข Enterprise Applications
- Employee management systems
- Customer relationship management
- Inventory tracking
- Financial record keeping
- Compliance reporting
๐ Startups & SMBs
- MVP development
- User authentication
- Content management
- Analytics tracking
- A/B testing data
๐ฎ Gaming
- Player profiles
- Leaderboards
- Game state storage
- Analytics tracking
- Real-time chat
๐ E-commerce
- Product catalogs
- Order management
- Customer profiles
- Inventory tracking
- Recommendation engines
๐ฑ Mobile Apps
- User preferences
- Offline sync
- Push notification data
- Analytics events
- User-generated content
๐ Education
- Student records
- Course management
- Assignment tracking
- Grade books
- Learning analytics
๐ Integration Examples
Flask Web Application
from flask import Flask, request, jsonify
from dkbotzdb import DKBOTZDB
app = Flask(__name__)
db = DKBOTZDB()['YOUR_TOKEN']['users']
@app.route('/users', methods=['GET'])
def get_users():
users = db.find()
return jsonify(users or [])
@app.route('/users', methods=['POST'])
def create_user():
user_data = request.json
result = db.insert_one(user_data)
if result:
return jsonify({"success": True, "data": result}), 201
return jsonify({"error": "Failed to create user"}), 400
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
FastAPI Application
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from dkbotzdb import DKBOTZDB
app = FastAPI()
db = DKBOTZDB()['YOUR_TOKEN']['products']
class Product(BaseModel):
name: str
price: float
category: str
@app.get("/products")
async def get_products():
products = db.find()
return products or []
@app.post("/products")
async def create_product(product: Product):
result = db.insert_one(product.dict())
if result:
return {"success": True, "data": result}
raise HTTPException(status_code=400, detail="Failed to create product")
Discord Bot Integration
import discord
from discord.ext import commands
from dkbotzdb import DKBOTZDB
bot = commands.Bot(command_prefix='!')
db = DKBOTZDB()['YOUR_TOKEN']['discord_users']
@bot.event
async def on_ready():
print(f'{bot.user} has connected to Discord!')
@bot.command(name='register')
async def register_user(ctx, *, name):
user_data = {
"discord_id": str(ctx.author.id),
"username": str(ctx.author),
"display_name": name,
"server_id": str(ctx.guild.id)
}
result = db.insert_one(user_data)
if result:
await ctx.send(f"โ
{name} registered successfully!")
else:
await ctx.send("โ Registration failed!")
bot.run('YOUR_BOT_TOKEN')
๐ก๏ธ Security Best Practices
Token Management
import os
from dotenv import load_dotenv
load_dotenv()
DB_TOKEN = os.getenv('DKBOTZDB_TOKEN')
db = DKBOTZDB()['hardcoded_token_123']['collection']
db = DKBOTZDB()[DB_TOKEN]['collection']
Data Sanitization
import re
import html
def sanitize_input(data):
"""Sanitize user input"""
if isinstance(data, str):
data = html.escape(data)
data = re.sub(r'[<>"\';]', '', data)
elif isinstance(data, dict):
return {k: sanitize_input(v) for k, v in data.items()}
elif isinstance(data, list):
return [sanitize_input(item) for item in data]
return data
safe_data = sanitize_input(user_input)
db.insert_one(safe_data)
Best Practices We Have Learned
After having used dkbotzdb in production on various projects, here's what we can share that will save you time:
-
Always Check Return Values: Network issues are commonplace. Always check that it worked before moving on.
-
Use Batch Operations: Rather than repeated calls to insert_one()
fifty times, use insert_many()
and pass it in fifty. It will be incredibly faster.
-
Consider Structure of Your Queries: Well structured queries will run faster and can often result in less api calls.
-
Monitor Your Usage: usage_info()
is helpful to avoid surprises and monitor your api usage.
-
Build Error Handling & Retries: Your most critical operations should have retry logic.
-
Keep your tokens secure: Don't stick tokens in version control, use environment variables.
-
Use Descriptive Collection Names: user_profiles
is better than users
is better than data
.
Best Practices
- Always check return values - Operations may fail due to network issues or API limits
- Use appropriate indexes - Structure your queries efficiently for better performance
- Handle errors gracefully - Implement proper error handling in production code
- Use batch operations - Prefer
insert_many()
over multiple insert_one()
calls
- Monitor usage - Keep track of your API usage with
usage_info()
- Secure your tokens - Never expose tokens in public repositories
Contributing
We welcome contributions to improve DKBOTZDB
. To contribute:
- Fork the repository on GitHub
- Create a new feature branch (
git checkout -b feature-name
)
- Make your changes with proper tests
- Commit your changes (
git commit -am 'Add new feature'
)
- Push to the branch (
git push origin feature-name
)
- Create a Pull Request
How to Suggest Features
- Check existing GitHub Issues
- Create a new issue with detailed description
- Include use cases and examples
- Be specific about expected behavior
๐ Learning Resources
๐ Documentation
๐ฏ Tutorials
- Beginner: Building Your First App with DKBOTZDB
- Intermediate: Advanced Queries and Aggregations
- Advanced: Scaling with DKBOTZDB
- Expert: Custom Integrations and Extensions
๐ Ready to Get Started?
- Sign up for free at db.dkbotzpro.in
- Install the package:
pip install DKBOTZdb
- Build something amazing!
from dkbotzdb import DKBOTZDB
db = DKBOTZDB()['YOUR_TOKEN']['my_first_collection']
welcome = {
"message": "Welcome to DKBOTZDB!",
"status": "Ready to build amazing things",
"excited": True
}
result = db.insert_one(welcome)
print("๐ You're all set! Let's build something incredible together!")
Support
License
This project is licensed under the GNU Affero General Public License v3.0.
What this means:
- โ
Commercial use allowed
- โ
Modification allowed
- โ
Distribution allowed
- โ
Patent use allowed
- โ Disclose source required
- โ License and copyright notice required
- โ Same license required for derivatives
See the LICENSE file for complete details.
๐บ๏ธ Complete Roadmap 2025-2027
โ
Phase 1: Q1 2025 (Completed)
- โ๏ธ Core CRUD operations
- ๐ง Aggregation pipeline support
- ๐ Token-based authentication
- ๐ Multiple connection protocols
- ๐จ Robust error handling
- ๐จ Beautiful colored logging
- ๐งฉ Method aliases (flexible commands)
- ๐๏ธ Collection & database management
- ๐งพ Plan management system
๐ Phase 2: Q2 2025 (In Progress)
- ๐ด Real-time Subscriptions (Change streams)
- ๐ GraphQL API interface
- ๐ค๐ฅ Data Export/Import in bulk
- ๐พ Backup & Restore automation
- ๐ก๏ธ Military-grade 3-layer encryption:
- ๐ Advanced analytics dashboard
โก Phase 3: Q3โQ4 2025 (Planned)
- ๐๏ธ Multi-database support (PostgreSQL, MySQL adapters)
- ๐ ACID-compliant Transactions
- ๐ Full-text search engine integration (Elastic)
- ๐บ๏ธ Geospatial queries (location-based indexing)
- โณ Time-series collection support
- ๐ Relational references (joins, population)
- โ๏ธ Serverless function runner (event-based triggers)
๐ Phase 4: 2026 (Scalability & Intelligence)
- ๐ค Machine learning for query prediction
- ๐ Auto-scaling clusters
- ๐ก Performance monitoring with alerts
- ๐ฅ Team & collaboration tools
- ๐ฌ Webhooks & event management
- ๐ ๏ธ ETL/ELT pipelines for big data
- ๐งฎ Advanced indexing (TTL, compound, partial)
๐ Phase 5: 2027 (Experience & Ecosystem)
- ๐ฌ AI-assisted query builder (text-to-query)
- ๐ผ๏ธ Visual query designer (drag-and-drop GUI)
- ๐ Built-in data visualization & charting
- ๐ฑ Mobile SDKs (React Native, Flutter)
- ๐ IoT data ingestion (real-time & batch)
- ๐ง Predictive query analytics
Made By DKBOTZ Team โค๏ธ