SimDB - A Lightweight C++ Database System 🚀

SimDB is a lightweight file-based database system written in C++, supporting basic SQL operations like CREATE TABLE, INSERT, SELECT, and DELETE. It utilizes binary file storage and indexing with B-Trees (future feature) for efficient data retrieval.

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📌 Features

✔️ Basic SQL Operations (CREATE, INSERT, SELECT, DELETE)
✔️ File-Based Storage (Data stored in binary files)
✔️ Indexing with Hash Index (Current Indexing)
✔️ Indexing with B-Trees (Upcoming feature)
✔️ SQL Query Parser
✔️ Docker Support for easy deployment

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🛠️ Project Structure

    SimpleDB/
    │── src/
    │   ├── main.cpp          # Entry point
    │   ├── storage.cpp       # Handles file-based storage
    │   ├── btree.cpp         # B-Tree indexing implementation
    │   ├── parser.cpp        # SQL query parsing
    │   ├── executor.cpp      # Executes parsed queries
    │── include/
    │   ├── storage.h
    │   ├── btree.h
    │   ├── parser.h
    │   ├── executor.h
    │── data/                 # Stores database files
    │── tests/                # Unit tests
    │── CMakeLists.txt        # Build configuration
    │── README.md             # Project documentation

🔧 Installation & Setup

📌 Prerequisites

• C++ (GCC/Clang/MSVC)
• CMake
• Docker (Optional for containerized execution)

🔹 Build the Project

Run the following commands to build and compile:

mkdir build && cd build
cmake ..
make
./SimDB

🚀 Running with Docker

🔹 Build the Docker Image

docker build -t simdb .

Run the Container

docker run -it simdb

🔹 Inside the Container shell

cd build

./SimDB

🛠️ How It Works

1️⃣ Query Parsing (parser.cpp)

• Parses SQL queries into structured commands.
• Example:

  INSERT INTO employees VALUES (John, 25, 5000)

  → { Table: employees, Values: [John, 25, 5000] }

2️⃣ Query Execution (executor.cpp)

• Calls Storage functions based on parsed queries.
• Example:

  parseInsert(query) → executeInsert(tableName, values)

3️⃣ Storage Engine (storage.cpp)

• Stores records in binary files.
• Uses indexed offsets for fast retrieval.

4️⃣ Indexing (btree.cpp - Future Work)

• Uses indexed offsets for fast retrieval as HashMap Index.
• Will improve search performance using a B-Tree index.

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💡 Challenges Faced & Solutions

1️⃣ Handling Dynamic SQL Parsing

• Problem: Parsing SQL queries dynamically while supporting different formats.
• Solution: Implemented a string tokenizer & condition parser to extract and process queries efficiently.

2️⃣ Efficient Storage & Retrieval

• Problem: Storing data in a binary file while allowing fast lookups.
• Solution: Used an index file to store offsets for quick access.

3️⃣ Data Consistency in Deletions

• Problem: Deleting records without breaking indexing order.
• Solution: Implemented a record swapping mechanism to maintain sequential integrity.