How to handle errors in Rubico functional pipelines?

Use the `tryCatch` operator to wrap functions and catch exceptions, returning default values or handling errors within compositions. It works seamlessly with async functions, integrating error management into pipelines without breaking flow. Check the docs for examples with promise-based operations.

Rubico vs Ramda: which is better for asynchronous code?

Rubico is specifically designed for async-agnostic functional programming, handling promises transparently out of the box, while Ramda requires manual promise handling or libraries like Ramda Fantasy. However, Ramda has a larger ecosystem and more community resources. Choose Rubico if seamless async integration is critical.

How to control concurrency when mapping over async functions in Rubico?

Use property operators like `map.pool` to limit concurrent executions or `map.series` for serial execution. These provide fine-grained control over async operations, preventing resource overload. Examples are provided in the README's advanced features section.

Does Rubico work with TypeScript?

Rubico is written in JavaScript and lacks official TypeScript definitions, though community-contributed types might exist. You may need to create custom declarations or check the GitHub issues for support. This can add overhead for TypeScript projects.

What are the browser requirements for Rubico?

Rubico requires modern browsers with support for ES6 and promises: minimum versions are Chrome 63, Firefox 57, Edge 79, and Safari 11.1. Ensure your target environment meets these to avoid compatibility issues.

How to reduce bundle size when using Rubico?

Import specific operators individually via paths like `require('rubico/pipe')` instead of the full library to minimize bundle impact. The core is ~8.3kB min+gzip, and selective imports can further reduce size for performance-critical applications.

rubico — Async Functional Programming Library

What is rubico?

Rubico is a JavaScript library for asynchronous functional programming that enables developers to write composable, declarative code while seamlessly handling promises. It solves the problem of entangled asynchronous code by providing operators that treat promises as values, allowing functions like `map` and `filter` to work with async data transparently.

Target Audience

JavaScript developers building data-intensive applications, especially those working with asynchronous APIs, data pipelines, or seeking to adopt functional programming patterns without async complexity.

Value Proposition

Developers choose Rubico for its async-agnostic design, which eliminates boilerplate promise handling, and its versatile operators that work across multiple data types, enabling clean, composable transformations in both Node.js and browser environments.

[A]synchronous Functional Programming

Use Cases

Best For

Transforming asynchronous data streams with functional pipelines
Building declarative data processing workflows that mix sync and async operations
Simplifying complex promise chains into readable functional compositions
Creating reusable transformation logic that works across arrays, objects, and Maps
Implementing efficient data processing with transducer patterns
Writing maintainable code where asynchronous behavior is transparent

Not Ideal For

Projects with exclusively synchronous data flows that don't benefit from async-agnostic features
Teams already deeply integrated with alternative functional libraries like Ramda or Lodash-FP
Environments with extreme bundle size constraints where every kilobyte counts
Developers preferring imperative or class-based code over functional composition patterns

Pros & Cons

Pros

Transparent Async Handling

Functions like `map` and `filter` automatically resolve promises in arguments and return values, allowing seamless mixing of sync and async operations without boilerplate, as shown in the example with `asyncSquare` in the README.

Dual API Design

Supports both eager (immediate execution) and lazy (function-returning) invocation styles, enabling flexible composition patterns, demonstrated in the code snippet where `map` is used both ways.

Versatile Data Structure Support

Operators work uniformly across arrays, objects, Maps, and other JavaScript types, making it adaptable for various transformation tasks, illustrated in the Map iteration example from the README.

Advanced Concurrency Control

Provides specialized operators like `map.pool` and `map.series` for managing asynchronous execution with fine-grained concurrency limits, addressing complex async scenarios mentioned in the features.

Cons

Separate Extended Module

Many utilities are housed in the `rubico/x` module, requiring additional imports and potentially increasing setup complexity for accessing full functionality, as noted in the README.

Limited Community and Ecosystem

As a niche library, it has fewer third-party integrations, plugins, and community support compared to established alternatives like Ramda or Lodash, which can hinder adoption and troubleshooting.

Functional Programming Barrier

Requires familiarity with functional concepts such as composition and transducers, which can be a steep learning curve for developers accustomed to imperative or object-oriented paradigms.

Frequently Asked Questions

What is rubico?

Target Audience

Value Proposition

Use Cases

Best For

Transforming asynchronous data streams with functional pipelines
Building declarative data processing workflows that mix sync and async operations
Simplifying complex promise chains into readable functional compositions
Creating reusable transformation logic that works across arrays, objects, and Maps
Implementing efficient data processing with transducer patterns
Writing maintainable code where asynchronous behavior is transparent

Not Ideal For

Projects with exclusively synchronous data flows that don't benefit from async-agnostic features
Teams already deeply integrated with alternative functional libraries like Ramda or Lodash-FP
Environments with extreme bundle size constraints where every kilobyte counts
Developers preferring imperative or class-based code over functional composition patterns

Pros & Cons

Pros

Transparent Async Handling

Dual API Design

Supports both eager (immediate execution) and lazy (function-returning) invocation styles, enabling flexible composition patterns, demonstrated in the code snippet where `map` is used both ways.

Versatile Data Structure Support

Operators work uniformly across arrays, objects, Maps, and other JavaScript types, making it adaptable for various transformation tasks, illustrated in the Map iteration example from the README.

Advanced Concurrency Control

Provides specialized operators like `map.pool` and `map.series` for managing asynchronous execution with fine-grained concurrency limits, addressing complex async scenarios mentioned in the features.

Cons

Separate Extended Module

Many utilities are housed in the `rubico/x` module, requiring additional imports and potentially increasing setup complexity for accessing full functionality, as noted in the README.

Limited Community and Ecosystem

As a niche library, it has fewer third-party integrations, plugins, and community support compared to established alternatives like Ramda or Lodash, which can hinder adoption and troubleshooting.

Functional Programming Barrier

Requires familiarity with functional concepts such as composition and transducers, which can be a steep learning curve for developers accustomed to imperative or object-oriented paradigms.

Frequently Asked Questions

rubico

What is rubico?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

rubico

What is rubico?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?