What if you could build your own database engine?

During my college days, I built a simple invoice management system using C, linked lists, and doubly linked lists. It allowed users to add, delete, update, and retrieve invoice records.

At the time, implementing these basic data operations from scratch was an exciting engineering challenge. I had to think about how records were organized, how memory was managed, and how different operations interacted with the underlying data structures.

Looking back, that project sparked my curiosity about how database systems work internally.

Fast-forward to today.

We now have AI coding assistants capable of generating code, explaining complex algorithms, helping design software architectures, and accelerating development in ways that were difficult to imagine during my college days.

And that got me thinking:

Could I build my own relational database engine?

Not another application running on PostgreSQL or SQLite.

An actual database engine that manages its own storage, processes SQL queries, maintains indexes, handles transactions, and recovers from failures.

That’s the engineering challenge I want to explore through aiDeeva.

01. What Does It Really Take to Build a Database Engine?

When most developers think about a database, they think about SQL.

Consider a simple example:

CREATE TABLE customers (
    customer_id INTEGER PRIMARY KEY,
    customer_name VARCHAR(100),
    email VARCHAR(255)
);

INSERT INTO customers
VALUES (
    1,
    'Jugal',
    'jugal@example.com'
);

SELECT *
FROM customers
WHERE customer_id = 1;

Three simple SQL statements.

But behind those statements is an entire execution infrastructure.

When you execute an INSERT, the database must validate the statement, locate the table, identify the appropriate storage pages, write the record, update indexes, and maintain transactional consistency.

When you execute a SELECT, the database must parse the SQL, resolve table and column references, determine an execution strategy, retrieve data, and return the result.

And when the system crashes, the database must recover without losing acknowledged committed transactions.

These are not simply programming tasks. They are architectural responsibilities.

Our goal is to build these components incrementally, using AI to accelerate implementation while retaining architectural control.

02. Introducing aiDeevaDB: Our Database Engineering Experiment

Before writing code, we need to define what we are actually building.

For this engineering experiment, let’s call our database aiDeevaDB.

Why start with a single-node database?

Because distribution introduces an entirely different category of engineering challenges.

Before solving consensus, replication, and distributed transaction coordination, we need to demonstrate that our database can reliably store and retrieve information on one machine.

Our initial success criterion is straightforward:

Then progressively introduce indexing, concurrent transactions, recovery, and query optimization.

03. The Engineering Roadmap: Seven Stages to a Relational Database

Building a database should be treated as a sequence of engineering milestones, not a single enormous AI prompt.

The roadmap describes our learning and development sequence, not seven completely independent components.

Storage, transactions, indexing, and recovery must eventually operate together through well-defined interfaces and protocols.

04. Where AI Changes the Development Process

Traditionally, building a database engine requires engineers to implement and validate each subsystem manually.

AI coding agents introduce a different development model.

An architect can define the subsystem, establish its interfaces, specify its invariants, and ask an AI agent to propose an implementation.

AI can generate code, write tests, explain design alternatives, and help investigate failures.

However, AI-generated code is not automatically correct, particularly in low-level systems where subtle concurrency or durability defects may only appear under unusual conditions.

The architect remains responsible for the system’s guarantees.

An Example: Building the Buffer Pool Manager

A generic prompt might say:

An architectural prompt should go further.

The difference is not simply a longer prompt.

It is the explicit definition of system behavior, failure conditions, and correctness requirements.

That is where architectural expertise becomes essential.

05. The Real Challenge: A Database Must Be Correct When Things Go Wrong

A database that successfully executes SQL under normal conditions is only the beginning.

What happens if the application crashes halfway through a transaction?

What happens when two transactions update the same record?

What happens when an index node splits while another operation is reading the index?

What happens when a storage write fails?

These questions separate a database demonstration from a reliable database system.

These guarantees require coordination across multiple database subsystems.

Write-ahead logging alone does not guarantee ACID. Neither does using a memory-safe programming language.

The complete transaction, storage, concurrency, and recovery protocols must work together.

For aiDeevaDB, every major engineering milestone should include tests that deliberately attempt to violate the guarantees the system claims to provide.

06. What Would a Working aiDeevaDB Actually Look Like?

Imagine opening a terminal and connecting to the database engine we have built.

The following is an illustrative target for our future implementation, not a demonstration of an already completed database.

aideevadb> CREATE TABLE products (
    id INTEGER PRIMARY KEY,
    name VARCHAR(100),
    price DECIMAL(10,2)
);

OK. Table created.

aideevadb> INSERT INTO products
VALUES (101, 'AI Server', 2499.00);

OK. 1 row inserted.

aideevadb> SELECT id, name, price
FROM products
WHERE id = 101;

+-----+-----------+---------+
| id  | name      | price   |
+-----+-----------+---------+
| 101 | AI Server | 2499.00 |
+-----+-----------+---------+

1 row returned.

The interface may look simple, but the system behind it is not.

The SQL parser recognizes the statement. The catalog resolves the table and columns. The planner selects an access path. The executor retrieves the relevant record through the storage and indexing subsystems.

The transaction manager and recovery infrastructure coordinate to maintain the database’s defined consistency and durability guarantees.

At this point, we have more than an AI-generated script.

We have the foundation of a relational database engine.

07. Where This Journey Could Lead: AI-Native Database Engineering

Building a database from scratch is an engineering challenge in its own right.

But the longer-term opportunity is exploring how AI can improve the way database systems are developed, operated, and optimized.

Imagine an AI-assisted database engineering platform that can examine query workloads, analyze execution plans, recommend indexing strategies, investigate performance regressions, and propose storage optimizations.

This is an important distinction.

An LLM should not be responsible for deciding whether a transaction is committed or whether a storage page is durable.

Those decisions belong to deterministic, verifiable database protocols.

AI can instead assist with higher-level analysis, engineering recommendations, and optimization workflows.

That separation allows us to explore AI-native database capabilities without abandoning the reliability principles that database systems depend on.

08. Can You Really Build a Database Engine With Vibe Coding?

Yes, AI-assisted development can help you build an educational database engine from scratch.

But there is a significant difference between generating a functional prototype and engineering a database that can safely support production workloads.

A prototype can demonstrate storage, SQL execution, and indexing with a limited feature set.

A production database requires considerably more work: comprehensive failure handling, concurrency correctness, recovery validation, operational tooling, security, performance engineering, and sustained testing.

AI can accelerate parts of that work.

It does not eliminate the need for database engineering expertise.

Final Thoughts

We are entering a period where AI-assisted development makes complex systems engineering more accessible.

Database engines, compilers, operating systems, and distributed platforms are no longer projects that engineers must approach entirely through manual implementation.

But accessibility should not be confused with simplicity.

The real engineering challenge is not getting AI to generate thousands of lines of code.

It is designing a system whose components work together correctly, defining the guarantees it must uphold, and validating those guarantees under realistic operating conditions.

That is the journey I want to explore through aiDeevaDB.

From the first persistent storage page to a functioning SQL engine, and eventually toward AI-assisted database optimization.