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Farhad Rahimi Klie
Farhad Rahimi Klie

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How We Built Our Own Database Engine šŸš€

#database #mongodb #developing #programming

Building a database engine from scratch might sound intimidatingā€”but itā€™s one of the most rewarding ways to truly understand how data systems work under the hood. In this article, Iā€™ll walk you through the journey of creating a simple custom database engine, the challenges we faced, and what we learned along the way.

šŸ§  Why Build a Database Engine?

At first, it might seem unnecessaryā€”after all, we already have powerful databases like MySQL, PostgreSQL, and MongoDB. But building your own gives you:

  • A deep understanding of data storage and retrieval
  • Insight into performance optimization
  • Hands-on experience with memory management
  • Better problem-solving skills as a developer

This project isnā€™t about replacing existing databasesā€”itā€™s about learning how they work.

āš™ļø Core Concepts We Needed

Before writing any code, we had to understand the fundamentals:

1. Storage Engine

This is how data is physically stored. We chose a simple file-based system where records are written directly to disk.

2. Data Serialization

We needed a way to convert structured data into a format that can be stored and retrieved efficiently.

3. Indexing

Without indexing, searching would be painfully slow. We implemented a basic indexing mechanism to speed up lookups.

4. Query Processing

Even a minimal database needs to interpret commands like:

  • INSERT
  • SELECT
  • DELETE

šŸ—ļø Architecture Overview

Our database engine consists of several key components:

+---------------------+
|   Query Interface   |
+---------------------+
           |
+---------------------+
|   Query Parser      |
+---------------------+
           |
+---------------------+
| Execution Engine    |
+---------------------+
           |
+---------------------+
| Storage Manager     |
+---------------------+
           |
+---------------------+
| File System         |
+---------------------+
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Each layer has a specific responsibility, making the system modular and easier to debug.

šŸ’¾ Step 1: Designing the Storage Layer

We started by implementing a simple storage mechanism:

  • Data is stored in binary files
  • Each record has a fixed size
  • File offsets are used for quick access

Example idea in C:

typedef struct {
    int id;
    char name[50];
} Record;
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We used file operations like fopen, fwrite, and fread to manage data.

šŸ” Step 2: Implementing Indexing

To avoid scanning the entire file every time, we added a basic index:

  • Key ā†’ File offset mapping
  • Stored in memory for fast lookup

This allowed us to jump directly to the record location instead of reading everything.

šŸ§¾ Step 3: Query Parser

We created a simple parser that understands commands like:

INSERT 1 Farhad
SELECT 1
DELETE 1
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The parser splits input into tokens and maps them to operations.

āš” Step 4: Execution Engine

The execution engine is the brain of the system:

  • Receives parsed queries
  • Calls appropriate functions
  • Interacts with storage and index

For example:

if (command == INSERT) {
    insert_record(...);
}
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šŸ§¹ Step 5: Memory Management

Since we were working in C, memory handling was critical:

  • Manual allocation (malloc)
  • Deallocation (free)
  • Avoiding leaks and fragmentation

We also implemented simple block management to reuse freed space.

šŸš§ Challenges We Faced

1. Data Corruption

A small mistake in file handling could corrupt the entire database.

2. Synchronization

Ensuring data consistency between memory and disk was tricky.

3. Performance

Naive implementations were slowā€”indexing made a huge difference.

šŸ“ˆ What We Learned

  • How databases manage data internally
  • Importance of abstraction and modular design
  • Low-level memory and file system operations
  • Trade-offs between simplicity and performance

šŸš€ Future Improvements

If we were to take this further:

  • Add B-Tree indexing
  • Support complex queries (WHERE, JOIN)
  • Implement transactions (ACID properties)
  • Add concurrency control

šŸŽÆ Final Thoughts

Building your own database engine isnā€™t just a projectā€”itā€™s an experience that transforms how you think about data systems. Even a simple implementation teaches concepts that are used in real-world databases at scale.

If you're a developer who enjoys digging deep into systems, this is one of the best projects you can take on.

šŸ’” Bonus Tip

If youā€™re planning to share your project on GitHub, include:

  • Clear documentation
  • Example commands
  • Screenshots or demos

It makes your project much more attractive and understandable.

Happy coding! šŸ”„

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