BQN: Modern APL-Like Language

BQN: An APL-Like Programming Language

BQN (pronounced “BQN”) is a modern programming language inspired by APL (A Programming Language), designed with simplicity and high expressiveness in mind. Created by Marshall Lochbaum in 2020, BQN stands as a self-hosted language that maintains the powerful array manipulation capabilities of APL while focusing on clarity and readability in its syntax. This article explores the BQN language, its features, design philosophy, and its place within the broader landscape of programming languages.

Background of BQN

BQN was developed to address the complexities and limitations found in traditional APL implementations. APL itself is renowned for its concise syntax, heavily relying on unique symbols to express operations on arrays. However, while powerful, APL’s symbol-heavy syntax can be challenging to read and maintain, especially for those new to the language.

Marshall Lochbaum, the creator of BQN, sought to retain APL’s powerful array-processing features while improving the language’s usability, readability, and extensibility. The result is a language that is both expressive and easy to use, striking a balance between brevity and clarity.

Key Features of BQN

1. Array-Oriented Programming

Like APL, BQN is designed to work efficiently with arrays. It allows for operations on arrays of any shape or dimension, providing a simple and intuitive way to perform complex manipulations on large datasets. Array operations in BQN are straightforward, enabling users to perform element-wise calculations, reductions, and transformations without needing to write complicated loops.

2. Self-Hosting

One of the distinguishing features of BQN is that it is self-hosted. This means that the BQN interpreter is written in BQN itself. Self-hosting is a significant achievement because it demonstrates that BQN can be used to create its own tools and compilers, making the language more self-sufficient and reducing reliance on external languages or frameworks.

This approach aligns with BQN’s philosophy of simplicity and minimalism. By using the language to build itself, BQN’s development is both streamlined and transparent. The self-hosted nature of the language also allows developers to customize and extend it as needed, providing a great deal of flexibility.

3. Concise Syntax

BQN uses a concise syntax, similar to APL but with an emphasis on readability. The language supports a small set of symbols to represent common operations, allowing users to write powerful expressions in a compact form. Unlike APL, BQN avoids the use of overly cryptic symbols, opting for a smaller and more approachable set of operators.

For example, in BQN, array manipulations can be done using standard mathematical symbols like +, -, ×, ÷, as well as specialized operators for more advanced operations. This helps users write more readable code without sacrificing the language’s expressive power.

4. Functional Programming Paradigm

BQN is designed around a functional programming paradigm. It supports high-order functions, where functions can be passed as arguments, returned as values, and composed together to build more complex operations. This functional approach makes it easy to define concise and reusable components, promoting code modularity and simplicity.

BQN also emphasizes immutability, meaning that once an array or value is created, it cannot be altered. This approach prevents side effects and makes code more predictable and easier to reason about, a key benefit for both debugging and maintaining code.

5. Rich Set of Built-In Functions

BQN includes a broad set of built-in functions to facilitate array manipulation and functional programming. These functions cover a wide range of operations, from basic arithmetic to more advanced mathematical and statistical computations. Some of the core functions include:

• Arithmetic and Logical Operations: BQN supports standard arithmetic operations like addition, subtraction, multiplication, and division, as well as logical operations such as and, or, and not.
• Reduction and Scan: BQN provides operators for reducing arrays to single values (such as summing the elements of an array) and scanning arrays to accumulate intermediate results.
• Sorting and Searching: BQN includes functions to sort and search arrays, including algorithms for binary search and sorting by custom criteria.
• Matrix and Linear Algebra Operations: For more advanced mathematical work, BQN includes a set of functions for working with matrices and performing linear algebra operations like matrix multiplication, inversion, and eigenvalue decomposition.

6. Minimalistic Design

BQN follows the principles of minimalism, aiming to provide just enough functionality to allow users to write powerful and expressive programs without being overwhelmed by unnecessary complexity. Its design minimizes the number of concepts and symbols users need to learn, ensuring that the language remains approachable and easy to use.

This minimalism is reflected in BQN’s compact syntax and small standard library. The language focuses on providing a small but powerful set of operations, which can be combined in different ways to achieve a wide variety of tasks.

7. Extensibility

Though minimalist, BQN is designed with extensibility in mind. The language is open to being expanded through custom functions and operators, allowing users to add their own functionality. Additionally, the fact that BQN is self-hosted means that advanced users can directly modify or extend the language’s core.

BQN in the Context of APL-Like Languages

BQN is part of a tradition of APL-like languages, which are known for their powerful array processing capabilities. APL was revolutionary in its approach to working with arrays, providing a compact, mathematical syntax for array manipulation. However, the language’s reliance on cryptic symbols and complex syntax has made it difficult to learn and use for many programmers.

Languages like J and K followed in APL’s footsteps, offering similar array-oriented features but with varying degrees of readability and expressiveness. BQN takes the lessons from these languages and aims to offer an alternative that is more user-friendly while retaining the expressive power of APL.

The primary difference between BQN and other APL-inspired languages lies in its syntax. While APL, J, and K all rely heavily on unique symbols, BQN adopts a more intuitive approach with a reduced set of symbols, making it easier for newcomers to grasp. At the same time, BQN does not sacrifice the power that comes from array-based programming, ensuring that experienced users can still perform complex operations with minimal code.

Practical Applications of BQN

BQN’s power lies in its ability to handle complex array manipulations with minimal code. This makes it particularly well-suited for tasks involving large datasets, such as data analysis, scientific computing, and machine learning. Some of the key use cases for BQN include:

1. Data Analysis: With its support for array operations and mathematical functions, BQN is an ideal language for processing and analyzing large datasets. Whether working with time series data, matrices, or multidimensional arrays, BQN’s array-oriented operations make it easy to perform complex transformations and statistical analyses.

2. Numerical Simulations: BQN’s rich set of mathematical functions makes it a powerful tool for simulating physical systems, solving differential equations, and performing other numerical computations. The language’s minimalistic syntax allows scientists and engineers to focus on the underlying problem rather than the details of the programming language.

3. Machine Learning: Although BQN is not specifically designed for machine learning, its array manipulation capabilities and support for functional programming make it suitable for implementing machine learning algorithms. For example, BQN can be used to implement neural network layers, gradient descent optimization, and matrix factorization techniques.

4. Algorithm Prototyping: BQN’s compact syntax and rich set of built-in functions make it a great language for rapidly prototyping algorithms. Whether working on signal processing, optimization, or data transformations, BQN allows developers to express ideas quickly and concisely.

Community and Resources

Although BQN is a relatively new language, it has already begun to build a small but active community. The BQN GitHub repository contains the source code for the language, along with documentation and examples to help new users get started. Users can also contribute to the development of the language by reporting issues or suggesting improvements.

For those looking to learn BQN, there are various online resources available, including tutorials, documentation, and forums where users can share ideas and solutions. The language’s website, BQN, provides an introduction to the language, along with links to additional resources and community discussions.

Conclusion

BQN is a modern, self-hosted programming language that combines the power of APL-like array processing with a more approachable syntax. By focusing on readability, simplicity, and extensibility, BQN makes it easier for developers to perform complex array manipulations while still retaining the expressive power of traditional APL languages.

Whether used for data analysis, numerical simulations, or machine learning, BQN offers a versatile and efficient toolset for solving a wide range of problems. As its community continues to grow and evolve, BQN is poised to become an essential language for those who need to work with arrays and large datasets in a concise and readable way.