Database AutoComplete

Database autocomplete for SQL, NoSQL & Cloud databases. Upgrade to PRO for AI powered intelligence for data science.

Features

Free Version

• SQL Databases: MySQL, PostgreSQL, SQL Server, Oracle, SQLite
• NoSQL Databases: MongoDB, Redis, Firebase, Cassandra
• Cloud Databases: AWS DynamoDB, Google Firestore, Azure Cosmos
• Smart autocomplete for tables, columns, collections, and queries
• Cross-file analysis of your database code

PRO Version ($20/year)

• AI-powered SQL suggestions
• Query performance analysis
• Schema intelligence
• Advanced optimization tips
• Data science insights

Supported Databases

SQL Databases

• MySQL - Tables, columns, functions, variables
• PostgreSQL - Advanced types, functions, variables
• SQL Server - T-SQL, stored procedures
• Oracle - PL/SQL, packages
• SQLite - Lightweight database support

NoSQL Databases

• MongoDB - Collections, aggregation pipelines
• Redis - Keys, data structures, commands
• Firebase/Firestore - Collections, documents, queries
• Cassandra - Tables, CQL commands

Cloud Databases

• AWS - DynamoDB, RDS
• Google Cloud - Firestore, BigQuery
• Azure - Cosmos DB, SQL Database

Installation

1. Open VS Code
2. Go to Extensions (Ctrl+Shift+X)
3. Search "Database AutoComplete"
4. Click Install

PRO Features

Use commands:

1. Database AutoComplete: Analyze Query Performance
2. Database AutoComplete: Upgrade to PRO ($20/year)
3. Visit: https://henrymbise.gumroad.com/l/database-autocomplete-pro
4. Purchase the PRO version
5. Check your email for the license key (Gumroad format: XXXX-XXXX-XXXX-XXXX)
6. Activate in VS Code:
7. Press Ctrl+Shift+P
8. Run: Database AutoComplete: Activate Premium Features
9. Enter your license key

Configuration Add your PRO license key in VS Code settings:

json { "databaseAutocomplete.licenseKey": "YOUR_GUMROAD_LICENSE_KEY" }

Note: License keys follow Gumroad's format (e.g., XXXX-XXXX-XXXX-XXXX)

License MIT License - See LICENSE file for details

Free Version - Powerful Basics:

sql -- Smart table/column detection SELECT name, email FROM users WHERE active = 1 -- ↑ Suggests: users.name, users.email, users.active

-- Cross-file intelligence -- If you have multiple SQL files, it learns all your tables!

PRO Version - AI-Powered Intelligence ($20/year):

sql -- AI Performance Suggestions SELECT * FROM large_table -- PRO suggests: "Add WHERE clause + INDEX for better performance"

-- Query Analysis SELECT * FROM users WHERE name LIKE '%john%' -- PRO warns: "Leading % prevents index usage - consider full-text search"

-- Schema Intelligence SELECT u.name, o.total FROM users u JOIN orders o ON u.id = o. -- PRO completes: o.user_id (understands relationships)

Real-World Examples

Example 1: E-Commerce Database sql -- FREE: Basic autocomplete SELECT product_name, price FROM products WHERE category = 'electronics'

-- PRO: AI-powered enhancements -- "Consider adding INDEX on category + price for faster filtering" -- "Join with inventory table to check stock levels" -- "Add LIMIT for pagination on large datasets"

Example 2: User Analytics javascript // FREE: MongoDB basics db.users.find({ country: 'US', age: { $gt: 21 } })

// PRO: Advanced suggestions // "Add compound index on country + age for better performance" // "Consider $project to only return needed fields" // "Use $sample for random user sampling instead of full scan"

Example 3: Multi-Table Queries sql -- FREE: Table/column suggestions SELECT u.name, o.order_date, p.product_name FROM users u JOIN orders o ON u.id = o.user_id JOIN order_items oi ON o.id = oi.order_id JOIN products p ON oi.product_id = p.id

-- PRO: Relationship intelligence -- "Detected: users.id → orders.user_id (foreign key)" -- "Performance: Add indexes on join columns" -- "Optimization: Use WHERE before JOINs to reduce data"

Support For issues and feature requests, visit the GitHub repository: https://github.com/henry17-dev/my_code