🧠 MiniDB – SQL-like Database Engine in C++

A lightweight SQL-like database engine built from scratch in C++. This project demonstrates core database internals including parsing, execution, and persistent storage.

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🚀 Features

• CREATE TABLE support
• INSERT INTO queries
• SELECT queries with WHERE filtering
• UPDATE queries
• DELETE queries
• File-based persistence (data stored on disk)
• In-memory execution (fast query performance)
• Modular architecture (Parser, Executor, Storage)

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🏗️ Architecture

User Input (SQL)
        ↓
Parser (SQL → Command Struct)
        ↓
Executor (Decision Logic)
        ↓
Storage Engine
   ↙           ↘
Memory (Fast)   Disk (Persistent)

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🧩 Component Breakdown

🔹 Parser

• Converts SQL-like input into structured commands
• Handles keywords: CREATE, INSERT, SELECT, UPDATE, DELETE
• Cleans tokens (removes ; , ( ))

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🔹 Executor

• Interprets parsed commands
• Routes to appropriate storage operations

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🔹 Storage Engine

• Maintains in-memory database using:

  std::map<std::string, std::vector<std::vector<std::string>>>

• Handles file persistence (data/*.table)

• Loads data at startup

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📂 Project Structure

mini-db-engine-cpp/
 ├── src/
 │   ├── main.cpp
 │   ├── parser.cpp
 │   ├── executor.cpp
 │   ├── storage.cpp
 ├── include/
 │   ├── command.h
 │   ├── parser.h
 │   ├── executor.h
 │   ├── storage.h
 ├── data/
 ├── Makefile
 └── README.md

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⚙️ Build & Run

🔧 Compile

make

▶️ Run

./db

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🧪 Example Queries

Create Table

CREATE TABLE users;

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Insert Data

INSERT INTO users VALUES (1, Adarsh);
INSERT INTO users VALUES (2, Rahul);

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Select Data

SELECT * FROM users;

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Select with WHERE

SELECT * FROM users WHERE id = 1;

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Update Data

UPDATE users SET name = Mohit WHERE id = 1;

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Delete Data

DELETE FROM users WHERE id = 2;

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📁 Storage Format

Each table is stored as a file:

data/users.table

Example:

1 Adarsh
2 Rahul

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⚡ Key Design Decisions

• In-memory execution for fast queries
• File persistence for durability
• Modular architecture for scalability
• Simple parser instead of full SQL grammar

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⚠️ Limitations

• No schema validation (columns are assumed)
• Fixed column mapping (id, name)
• Limited SQL support (simplified syntax)
• No indexing or query optimization

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🚀 Future Improvements

• Dynamic schema support
• Indexing (B-Tree / Hash Index)
• Query optimization
• Transaction support (ACID properties)
• Full SQL parser

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🧠 Learning Outcomes

• Built a query parser from scratch
• Designed execution engine
• Implemented file-based storage
• Learned database internals
• Practiced modular system design in C++

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📌 Author

Adarsh Kumar

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⭐ Why This Project Matters

This project demonstrates understanding of:

• Database internals
• System design
• Data storage mechanisms
• Query processing

It goes beyond typical CRUD apps and showcases low-level engineering skills.

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