Show HN: I Implemented A Neural Network In SQL

A developer has successfully built a neural network entirely within SQL, demonstrating that complex machine learning models can be implemented without traditional programming languages. This achievement challenges the conventional separation between databases and AI development, highlighting potential for in-database AI processing.

The project was shared on Show HN by an individual who detailed their implementation of a neural network using only SQL queries and functions. The developer emphasized that their approach does not rely on external libraries or languages, but leverages advanced SQL features such as recursive queries, window functions, and user-defined functions to simulate neural network operations.

While the implementation is primarily a proof of concept, it demonstrates that fundamental neural network computations—such as matrix multiplications, activation functions, and backpropagation—can be approximated within SQL. The developer noted that this method is not optimized for performance but serves as a demonstration of SQL’s expressive power for complex algorithms.

Implications for In-Database AI and Data Processing

This development is significant because it suggests that machine learning models could be integrated directly into database systems, reducing data movement and latency. In scenarios where data resides in SQL databases, running models without exporting data could streamline workflows and improve efficiency. It also opens questions about the potential for other AI algorithms to be implemented within SQL, leveraging existing database infrastructure.

Evolution of Machine Learning and Database Integration

Traditionally, machine learning models are developed using specialized frameworks like TensorFlow or PyTorch outside of databases. Data is then exported or connected via APIs for inference. Recent efforts have explored in-database machine learning, but implementing neural networks directly in SQL remains rare. The developer’s project builds on this trend, pushing the boundaries of what can be achieved within relational databases.

Two weeks prior to the project sharing, the developer was in Corfu, Greece, overseeing a GSoC intern working on their database library, Xarray-SQL. The intern added features like `to_dataset()`, indicating ongoing development focus on expanding SQL capabilities for data analysis and processing.

“Building a neural network entirely in SQL is a proof of concept that demonstrates the expressive power of modern SQL features.”

— Developer (via Show HN post)

Limitations and Practicality of SQL-Based Neural Networks

It is not yet clear how scalable or efficient this SQL implementation is for real-world applications. The project is primarily a proof of concept, and performance constraints likely limit its use outside experimental or educational contexts. Further testing and optimization are needed to evaluate its viability for production environments.

Additionally, it remains uncertain whether this approach can be extended to more complex neural networks or other machine learning models, or whether it can be integrated into existing database systems at scale.

Future Directions for In-Database AI Development

The next steps involve testing the implementation’s performance and exploring ways to optimize SQL-based neural networks. Developers and researchers may investigate hybrid approaches, combining SQL with external machine learning frameworks, or further pushing SQL’s capabilities for AI tasks. Community discussions and peer reviews could also influence future developments in this area.

It is also possible that database vendors might explore native support for AI operations, inspired by projects like this, to facilitate in-database machine learning workflows.

Key Questions

Is this implementation practical for real-world use?

Currently, the implementation is primarily a proof of concept. Its practicality for production depends on further optimization and scalability testing.

How does building a neural network in SQL compare to traditional frameworks?

Traditional frameworks like TensorFlow or PyTorch are optimized for performance and flexibility, while SQL-based implementations are more experimental and limited in efficiency but demonstrate SQL’s expressive power.

Could this approach be used for large-scale machine learning tasks?

It is unlikely at this stage, as SQL implementations are not optimized for large-scale computations. The focus is on demonstrating feasibility rather than practical deployment.

What are the benefits of integrating AI directly into databases?

In-database AI could reduce data movement, lower latency, and streamline workflows by enabling models to run where data resides, but practical implementations are still in early stages.

Source: hn